{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":14774,"databundleVersionId":875431,"sourceType":"competition"},{"sourceId":556303,"sourceType":"datasetVersion","datasetId":266957},{"sourceId":556726,"sourceType":"datasetVersion","datasetId":267272},{"sourceId":8296392,"sourceType":"datasetVersion","datasetId":4928189}],"dockerImageVersionId":29188,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nimport sys\nimport cv2\nimport shutil\nimport random\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom tensorflow import set_random_seed\nfrom sklearn.utils import class_weight\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, cohen_kappa_score\nfrom keras import backend as K\nfrom keras.models import Model\nfrom keras.utils import to_categorical\nfrom keras import optimizers, applications\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.layers import Dense, Dropout, GlobalAveragePooling2D, Input\nfrom keras.callbacks import EarlyStopping, ReduceLROnPlateau, Callback, LearningRateScheduler\n\ndef seed_everything(seed=0):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n set_random_seed(0)\n\nseed = 0\nseed_everything(seed)\n%matplotlib inline\nsns.set(style=\"whitegrid\")\nwarnings.filterwarnings(\"ignore\")\nsys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))\nfrom efficientnet import *","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-output":true,"execution":{"iopub.status.busy":"2024-05-03T02:25:38.700647Z","iopub.execute_input":"2024-05-03T02:25:38.70097Z","iopub.status.idle":"2024-05-03T02:25:44.26676Z","shell.execute_reply.started":"2024-05-03T02:25:38.700907Z","shell.execute_reply":"2024-05-03T02:25:44.266012Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\nUsing TensorFlow backend.\n","output_type":"stream"}]},{"cell_type":"code","source":"fold_set = pd.read_csv('/kaggle/input/main-data/5-fold.csv')\nX_train = fold_set[fold_set['fold_0'] == 'train']\nX_val = fold_set[fold_set['fold_0'] == 'validation']\ntest = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')\nprint('Number of train samples: ', X_train.shape[0])\nprint('Number of validation samples: ', X_val.shape[0])\nprint('Number of test samples: ', test.shape[0])\n\n# Preprocecss data\nX_train[\"id_code\"] = X_train[\"id_code\"].apply(lambda x: x + \".png\")\nX_val[\"id_code\"] = X_val[\"id_code\"].apply(lambda x: x + \".png\")\ntest[\"id_code\"] = test[\"id_code\"].apply(lambda x: x + \".png\")\ndisplay(X_train.head())","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-05-03T02:25:44.269153Z","iopub.execute_input":"2024-05-03T02:25:44.269498Z","iopub.status.idle":"2024-05-03T02:25:44.527208Z","shell.execute_reply.started":"2024-05-03T02:25:44.269436Z","shell.execute_reply":"2024-05-03T02:25:44.526513Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Number of train samples: 2929\nNumber of validation samples: 733\nNumber of test samples: 1928\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" id_code diagnosis height width fold_0 fold_1 fold_2 \\\n0 000c1434d8d7.png 2 2136.0 3216.0 train train train \n1 001639a390f0.png 4 2136.0 3216.0 train train train \n3 002c21358ce6.png 0 1050.0 1050.0 train train train \n5 0083ee8054ee.png 4 2588.0 3388.0 train train validation \n8 00b74780d31d.png 2 1958.0 2588.0 train train train \n\n fold_3 fold_4 \n0 validation train \n1 train validation \n3 validation train \n5 train train \n8 train validation ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosisheightwidthfold_0fold_1fold_2fold_3fold_4
0000c1434d8d7.png22136.03216.0traintraintrainvalidationtrain
1001639a390f0.png42136.03216.0traintraintraintrainvalidation
3002c21358ce6.png01050.01050.0traintraintrainvalidationtrain
50083ee8054ee.png42588.03388.0traintrainvalidationtraintrain
800b74780d31d.png21958.02588.0traintraintraintrainvalidation
\n
"},"metadata":{}}]},{"cell_type":"code","source":"# Model parameters\nFACTOR = 4\nBATCH_SIZE = 8 * FACTOR\nEPOCHS = 20\nWARMUP_EPOCHS = 5\nLEARNING_RATE = 1e-4 * FACTOR\nWARMUP_LEARNING_RATE = 1e-3 * FACTOR\nHEIGHT = 224\nWIDTH = 224\nCHANNELS = 3\nES_PATIENCE = 5\nRLROP_PATIENCE = 3\nDECAY_DROP = 0.5\nLR_WARMUP_EPOCHS_1st = 2\nLR_WARMUP_EPOCHS_2nd = 5\nSTEP_SIZE = len(X_train) // BATCH_SIZE\nTOTAL_STEPS_1st = WARMUP_EPOCHS * STEP_SIZE\nTOTAL_STEPS_2nd = EPOCHS * STEP_SIZE\nWARMUP_STEPS_1st = LR_WARMUP_EPOCHS_1st * STEP_SIZE\nWARMUP_STEPS_2nd = LR_WARMUP_EPOCHS_2nd * STEP_SIZE","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-05-03T02:25:44.528701Z","iopub.execute_input":"2024-05-03T02:25:44.528946Z","iopub.status.idle":"2024-05-03T02:25:44.53626Z","shell.execute_reply.started":"2024-05-03T02:25:44.528898Z","shell.execute_reply":"2024-05-03T02:25:44.535257Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"train_base_path = '../input/aptos2019-blindness-detection/train_images/'\ntest_base_path = '../input/aptos2019-blindness-detection/test_images/'\ntrain_dest_path = 'base_dir/train_images/'\nvalidation_dest_path = 'base_dir/validation_images/'\ntest_dest_path = 'base_dir/test_images/'\n\nif os.path.exists(train_dest_path):\n shutil.rmtree(train_dest_path)\nif os.path.exists(validation_dest_path):\n shutil.rmtree(validation_dest_path)\nif os.path.exists(test_dest_path):\n shutil.rmtree(test_dest_path)\n \nos.makedirs(train_dest_path)\nos.makedirs(validation_dest_path)\nos.makedirs(test_dest_path)\n\ndef crop_image(img, tol=7):\n if img.ndim ==2:\n mask = img>tol\n return img[np.ix_(mask.any(1),mask.any(0))]\n elif img.ndim==3:\n gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n mask = gray_img>tol\n check_shape = img[:,:,0][np.ix_(mask.any(1),mask.any(0))].shape[0]\n if (check_shape == 0): # image is too dark so that we crop out everything,\n return img # return original image\n else:\n img1=img[:,:,0][np.ix_(mask.any(1),mask.any(0))]\n img2=img[:,:,1][np.ix_(mask.any(1),mask.any(0))]\n img3=img[:,:,2][np.ix_(mask.any(1),mask.any(0))]\n img = np.stack([img1,img2,img3],axis=-1)\n \n return img\n\ndef circle_crop(img):\n img = crop_image(img)\n\n height, width, depth = img.shape\n largest_side = np.max((height, width))\n img = cv2.resize(img, (largest_side, largest_side))\n\n height, width, depth = img.shape\n\n x = width//2\n y = height//2\n r = np.amin((x, y))\n\n circle_img = np.zeros((height, width), np.uint8)\n cv2.circle(circle_img, (x, y), int(r), 1, thickness=-1)\n img = cv2.bitwise_and(img, img, mask=circle_img)\n img = crop_image(img)\n\n return img\n \ndef preprocess_image(base_path, save_path, image_id, HEIGHT, WIDTH, sigmaX=10):\n image = cv2.imread(base_path + image_id)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image = circle_crop(image)\n image = cv2.resize(image, (HEIGHT, WIDTH))\n image = cv2.addWeighted(image, 4, cv2.GaussianBlur(image, (0,0), sigmaX), -4 , 128)\n cv2.imwrite(save_path + image_id, image)\n \n# Pre-procecss train set\nfor i, image_id in enumerate(X_train['id_code']):\n preprocess_image(train_base_path, train_dest_path, image_id, HEIGHT, WIDTH)\n \n# Pre-procecss validation set\nfor i, image_id in enumerate(X_val['id_code']):\n preprocess_image(train_base_path, validation_dest_path, image_id, HEIGHT, WIDTH)\n \n# Pre-procecss test set\nfor i, image_id in enumerate(test['id_code']):\n preprocess_image(test_base_path, test_dest_path, image_id, HEIGHT, WIDTH)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T02:25:44.538027Z","iopub.execute_input":"2024-05-03T02:25:44.538398Z","iopub.status.idle":"2024-05-03T02:49:36.698414Z","shell.execute_reply.started":"2024-05-03T02:25:44.538336Z","shell.execute_reply":"2024-05-03T02:49:36.697478Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"datagen=ImageDataGenerator(rescale=1./255, \n rotation_range=360,\n horizontal_flip=True,\n vertical_flip=True)\n\ntrain_generator=datagen.flow_from_dataframe(\n dataframe=X_train,\n directory=train_dest_path,\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n class_mode=\"raw\",\n batch_size=BATCH_SIZE,\n target_size=(HEIGHT, WIDTH),\n seed=seed)\n\nvalid_generator=datagen.flow_from_dataframe(\n dataframe=X_val,\n directory=validation_dest_path,\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n class_mode=\"raw\",\n batch_size=BATCH_SIZE,\n target_size=(HEIGHT, WIDTH),\n seed=seed)\n\ntest_generator=datagen.flow_from_dataframe( \n dataframe=test,\n directory=test_dest_path,\n x_col=\"id_code\",\n batch_size=1,\n class_mode=None,\n shuffle=False,\n target_size=(HEIGHT, WIDTH),\n seed=seed)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T02:49:36.701589Z","iopub.execute_input":"2024-05-03T02:49:36.701835Z","iopub.status.idle":"2024-05-03T02:49:36.783507Z","shell.execute_reply.started":"2024-05-03T02:49:36.701794Z","shell.execute_reply":"2024-05-03T02:49:36.782802Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"Found 2929 validated image filenames.\nFound 733 validated image filenames.\nFound 1928 validated image filenames.\n","output_type":"stream"}]},{"cell_type":"code","source":"def cosine_decay_with_warmup(global_step,\n learning_rate_base,\n total_steps,\n warmup_learning_rate=0.0,\n warmup_steps=0,\n hold_base_rate_steps=0):\n \n\n if total_steps < warmup_steps:\n raise ValueError('total_steps must be larger or equal to warmup_steps.')\n learning_rate = 0.5 * learning_rate_base * (1 + np.cos(\n np.pi *\n (global_step - warmup_steps - hold_base_rate_steps\n ) / float(total_steps - warmup_steps - hold_base_rate_steps)))\n if hold_base_rate_steps > 0:\n learning_rate = np.where(global_step > warmup_steps + hold_base_rate_steps,\n learning_rate, learning_rate_base)\n if warmup_steps > 0:\n if learning_rate_base < warmup_learning_rate:\n raise ValueError('learning_rate_base must be larger or equal to warmup_learning_rate.')\n slope = (learning_rate_base - warmup_learning_rate) / warmup_steps\n warmup_rate = slope * global_step + warmup_learning_rate\n learning_rate = np.where(global_step < warmup_steps, warmup_rate,\n learning_rate)\n return np.where(global_step > total_steps, 0.0, learning_rate)\n\n\nclass WarmUpCosineDecayScheduler(Callback):\n\n def __init__(self,\n learning_rate_base,\n total_steps,\n global_step_init=0,\n warmup_learning_rate=0.0,\n warmup_steps=0,\n hold_base_rate_steps=0,\n verbose=0):\n \n\n super(WarmUpCosineDecayScheduler, self).__init__()\n self.learning_rate_base = learning_rate_base\n self.total_steps = total_steps\n self.global_step = global_step_init\n self.warmup_learning_rate = warmup_learning_rate\n self.warmup_steps = warmup_steps\n self.hold_base_rate_steps = hold_base_rate_steps\n self.verbose = verbose\n self.learning_rates = []\n\n def on_batch_end(self, batch, logs=None):\n self.global_step = self.global_step + 1\n lr = K.get_value(self.model.optimizer.lr)\n self.learning_rates.append(lr)\n\n def on_batch_begin(self, batch, logs=None):\n lr = cosine_decay_with_warmup(global_step=self.global_step,\n learning_rate_base=self.learning_rate_base,\n total_steps=self.total_steps,\n warmup_learning_rate=self.warmup_learning_rate,\n warmup_steps=self.warmup_steps,\n hold_base_rate_steps=self.hold_base_rate_steps)\n K.set_value(self.model.optimizer.lr, lr)\n if self.verbose > 0:\n print('\\nBatch %02d: setting learning rate to %s.' % (self.global_step + 1, lr))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T02:49:36.78702Z","iopub.execute_input":"2024-05-03T02:49:36.787335Z","iopub.status.idle":"2024-05-03T02:49:36.805968Z","shell.execute_reply.started":"2024-05-03T02:49:36.787281Z","shell.execute_reply":"2024-05-03T02:49:36.804867Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"def create_model(input_shape):\n input_tensor = Input(shape=input_shape)\n base_model = EfficientNetB5(weights=None, \n include_top=False,\n input_tensor=input_tensor)\n base_model.load_weights('../input/efficientnet-keras-weights-b0b5/efficientnet-b5_imagenet_1000_notop.h5')\n\n x = GlobalAveragePooling2D()(base_model.output)\n final_output = Dense(1, activation='linear', name='final_output')(x)\n model = Model(input_tensor, final_output)\n \n return model","metadata":{"execution":{"iopub.status.busy":"2024-05-03T02:49:36.807645Z","iopub.execute_input":"2024-05-03T02:49:36.807996Z","iopub.status.idle":"2024-05-03T02:49:36.823812Z","shell.execute_reply.started":"2024-05-03T02:49:36.807944Z","shell.execute_reply":"2024-05-03T02:49:36.822991Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"model = create_model(input_shape=(HEIGHT, WIDTH, CHANNELS))\n\nfor layer in model.layers:\n layer.trainable = False\n\nfor i in range(-2, 0):\n model.layers[i].trainable = True\n\ncosine_lr_1st = WarmUpCosineDecayScheduler(learning_rate_base=WARMUP_LEARNING_RATE,\n total_steps=TOTAL_STEPS_1st,\n warmup_learning_rate=0.0,\n warmup_steps=WARMUP_STEPS_1st,\n hold_base_rate_steps=(2 * STEP_SIZE))\n\nmetric_list = [\"accuracy\"]\ncallback_list = [cosine_lr_1st]\noptimizer = optimizers.Adam(lr=WARMUP_LEARNING_RATE)\nmodel.compile(optimizer=optimizer, loss='mean_squared_error', metrics=metric_list)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T02:49:36.825018Z","iopub.execute_input":"2024-05-03T02:49:36.825268Z","iopub.status.idle":"2024-05-03T02:50:06.136012Z","shell.execute_reply.started":"2024-05-03T02:49:36.825221Z","shell.execute_reply":"2024-05-03T02:50:06.135271Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_1 (InputLayer) (None, 224, 224, 3) 0 \n__________________________________________________________________________________________________\nconv2d_1 (Conv2D) (None, 112, 112, 48) 1296 input_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_1 (BatchNor (None, 112, 112, 48) 192 conv2d_1[0][0] \n__________________________________________________________________________________________________\nswish_1 (Swish) (None, 112, 112, 48) 0 batch_normalization_1[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_1 (DepthwiseCo (None, 112, 112, 48) 432 swish_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_2 (BatchNor (None, 112, 112, 48) 192 depthwise_conv2d_1[0][0] \n__________________________________________________________________________________________________\nswish_2 (Swish) (None, 112, 112, 48) 0 batch_normalization_2[0][0] \n__________________________________________________________________________________________________\nlambda_1 (Lambda) (None, 1, 1, 48) 0 swish_2[0][0] \n__________________________________________________________________________________________________\nconv2d_2 (Conv2D) (None, 1, 1, 12) 588 lambda_1[0][0] \n__________________________________________________________________________________________________\nswish_3 (Swish) (None, 1, 1, 12) 0 conv2d_2[0][0] \n__________________________________________________________________________________________________\nconv2d_3 (Conv2D) (None, 1, 1, 48) 624 swish_3[0][0] \n__________________________________________________________________________________________________\nactivation_1 (Activation) (None, 1, 1, 48) 0 conv2d_3[0][0] \n__________________________________________________________________________________________________\nmultiply_1 (Multiply) (None, 112, 112, 48) 0 activation_1[0][0] \n swish_2[0][0] \n__________________________________________________________________________________________________\nconv2d_4 (Conv2D) (None, 112, 112, 24) 1152 multiply_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_3 (BatchNor (None, 112, 112, 24) 96 conv2d_4[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_2 (DepthwiseCo (None, 112, 112, 24) 216 batch_normalization_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_4 (BatchNor (None, 112, 112, 24) 96 depthwise_conv2d_2[0][0] \n__________________________________________________________________________________________________\nswish_4 (Swish) (None, 112, 112, 24) 0 batch_normalization_4[0][0] \n__________________________________________________________________________________________________\nlambda_2 (Lambda) (None, 1, 1, 24) 0 swish_4[0][0] \n__________________________________________________________________________________________________\nconv2d_5 (Conv2D) (None, 1, 1, 6) 150 lambda_2[0][0] \n__________________________________________________________________________________________________\nswish_5 (Swish) (None, 1, 1, 6) 0 conv2d_5[0][0] \n__________________________________________________________________________________________________\nconv2d_6 (Conv2D) (None, 1, 1, 24) 168 swish_5[0][0] \n__________________________________________________________________________________________________\nactivation_2 (Activation) (None, 1, 1, 24) 0 conv2d_6[0][0] \n__________________________________________________________________________________________________\nmultiply_2 (Multiply) (None, 112, 112, 24) 0 activation_2[0][0] \n swish_4[0][0] \n__________________________________________________________________________________________________\nconv2d_7 (Conv2D) (None, 112, 112, 24) 576 multiply_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_5 (BatchNor (None, 112, 112, 24) 96 conv2d_7[0][0] \n__________________________________________________________________________________________________\ndrop_connect_1 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_5[0][0] \n__________________________________________________________________________________________________\nadd_1 (Add) (None, 112, 112, 24) 0 drop_connect_1[0][0] \n batch_normalization_3[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_3 (DepthwiseCo (None, 112, 112, 24) 216 add_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_6 (BatchNor (None, 112, 112, 24) 96 depthwise_conv2d_3[0][0] \n__________________________________________________________________________________________________\nswish_6 (Swish) (None, 112, 112, 24) 0 batch_normalization_6[0][0] \n__________________________________________________________________________________________________\nlambda_3 (Lambda) (None, 1, 1, 24) 0 swish_6[0][0] \n__________________________________________________________________________________________________\nconv2d_8 (Conv2D) (None, 1, 1, 6) 150 lambda_3[0][0] \n__________________________________________________________________________________________________\nswish_7 (Swish) (None, 1, 1, 6) 0 conv2d_8[0][0] \n__________________________________________________________________________________________________\nconv2d_9 (Conv2D) (None, 1, 1, 24) 168 swish_7[0][0] \n__________________________________________________________________________________________________\nactivation_3 (Activation) (None, 1, 1, 24) 0 conv2d_9[0][0] \n__________________________________________________________________________________________________\nmultiply_3 (Multiply) (None, 112, 112, 24) 0 activation_3[0][0] \n swish_6[0][0] \n__________________________________________________________________________________________________\nconv2d_10 (Conv2D) (None, 112, 112, 24) 576 multiply_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_7 (BatchNor (None, 112, 112, 24) 96 conv2d_10[0][0] \n__________________________________________________________________________________________________\ndrop_connect_2 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_7[0][0] \n__________________________________________________________________________________________________\nadd_2 (Add) (None, 112, 112, 24) 0 drop_connect_2[0][0] \n add_1[0][0] \n__________________________________________________________________________________________________\nconv2d_11 (Conv2D) (None, 112, 112, 144 3456 add_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_8 (BatchNor (None, 112, 112, 144 576 conv2d_11[0][0] \n__________________________________________________________________________________________________\nswish_8 (Swish) (None, 112, 112, 144 0 batch_normalization_8[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_4 (DepthwiseCo (None, 56, 56, 144) 1296 swish_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_9 (BatchNor (None, 56, 56, 144) 576 depthwise_conv2d_4[0][0] \n__________________________________________________________________________________________________\nswish_9 (Swish) (None, 56, 56, 144) 0 batch_normalization_9[0][0] \n__________________________________________________________________________________________________\nlambda_4 (Lambda) (None, 1, 1, 144) 0 swish_9[0][0] \n__________________________________________________________________________________________________\nconv2d_12 (Conv2D) (None, 1, 1, 6) 870 lambda_4[0][0] \n__________________________________________________________________________________________________\nswish_10 (Swish) (None, 1, 1, 6) 0 conv2d_12[0][0] \n__________________________________________________________________________________________________\nconv2d_13 (Conv2D) (None, 1, 1, 144) 1008 swish_10[0][0] \n__________________________________________________________________________________________________\nactivation_4 (Activation) (None, 1, 1, 144) 0 conv2d_13[0][0] \n__________________________________________________________________________________________________\nmultiply_4 (Multiply) (None, 56, 56, 144) 0 activation_4[0][0] \n swish_9[0][0] \n__________________________________________________________________________________________________\nconv2d_14 (Conv2D) (None, 56, 56, 40) 5760 multiply_4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_10 (BatchNo (None, 56, 56, 40) 160 conv2d_14[0][0] \n__________________________________________________________________________________________________\nconv2d_15 (Conv2D) (None, 56, 56, 240) 9600 batch_normalization_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_11 (BatchNo (None, 56, 56, 240) 960 conv2d_15[0][0] \n__________________________________________________________________________________________________\nswish_11 (Swish) (None, 56, 56, 240) 0 batch_normalization_11[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_5 (DepthwiseCo (None, 56, 56, 240) 2160 swish_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_12 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_5[0][0] \n__________________________________________________________________________________________________\nswish_12 (Swish) (None, 56, 56, 240) 0 batch_normalization_12[0][0] \n__________________________________________________________________________________________________\nlambda_5 (Lambda) (None, 1, 1, 240) 0 swish_12[0][0] \n__________________________________________________________________________________________________\nconv2d_16 (Conv2D) (None, 1, 1, 10) 2410 lambda_5[0][0] \n__________________________________________________________________________________________________\nswish_13 (Swish) (None, 1, 1, 10) 0 conv2d_16[0][0] \n__________________________________________________________________________________________________\nconv2d_17 (Conv2D) (None, 1, 1, 240) 2640 swish_13[0][0] \n__________________________________________________________________________________________________\nactivation_5 (Activation) (None, 1, 1, 240) 0 conv2d_17[0][0] \n__________________________________________________________________________________________________\nmultiply_5 (Multiply) (None, 56, 56, 240) 0 activation_5[0][0] \n swish_12[0][0] \n__________________________________________________________________________________________________\nconv2d_18 (Conv2D) (None, 56, 56, 40) 9600 multiply_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_13 (BatchNo (None, 56, 56, 40) 160 conv2d_18[0][0] \n__________________________________________________________________________________________________\ndrop_connect_3 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_13[0][0] \n__________________________________________________________________________________________________\nadd_3 (Add) (None, 56, 56, 40) 0 drop_connect_3[0][0] \n batch_normalization_10[0][0] \n__________________________________________________________________________________________________\nconv2d_19 (Conv2D) (None, 56, 56, 240) 9600 add_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_14 (BatchNo (None, 56, 56, 240) 960 conv2d_19[0][0] \n__________________________________________________________________________________________________\nswish_14 (Swish) (None, 56, 56, 240) 0 batch_normalization_14[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_6 (DepthwiseCo (None, 56, 56, 240) 2160 swish_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_15 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_6[0][0] \n__________________________________________________________________________________________________\nswish_15 (Swish) (None, 56, 56, 240) 0 batch_normalization_15[0][0] \n__________________________________________________________________________________________________\nlambda_6 (Lambda) (None, 1, 1, 240) 0 swish_15[0][0] \n__________________________________________________________________________________________________\nconv2d_20 (Conv2D) (None, 1, 1, 10) 2410 lambda_6[0][0] \n__________________________________________________________________________________________________\nswish_16 (Swish) (None, 1, 1, 10) 0 conv2d_20[0][0] \n__________________________________________________________________________________________________\nconv2d_21 (Conv2D) (None, 1, 1, 240) 2640 swish_16[0][0] \n__________________________________________________________________________________________________\nactivation_6 (Activation) (None, 1, 1, 240) 0 conv2d_21[0][0] \n__________________________________________________________________________________________________\nmultiply_6 (Multiply) (None, 56, 56, 240) 0 activation_6[0][0] \n swish_15[0][0] \n__________________________________________________________________________________________________\nconv2d_22 (Conv2D) (None, 56, 56, 40) 9600 multiply_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_16 (BatchNo (None, 56, 56, 40) 160 conv2d_22[0][0] \n__________________________________________________________________________________________________\ndrop_connect_4 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_16[0][0] \n__________________________________________________________________________________________________\nadd_4 (Add) (None, 56, 56, 40) 0 drop_connect_4[0][0] \n add_3[0][0] \n__________________________________________________________________________________________________\nconv2d_23 (Conv2D) (None, 56, 56, 240) 9600 add_4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_17 (BatchNo (None, 56, 56, 240) 960 conv2d_23[0][0] \n__________________________________________________________________________________________________\nswish_17 (Swish) (None, 56, 56, 240) 0 batch_normalization_17[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_7 (DepthwiseCo (None, 56, 56, 240) 2160 swish_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_18 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_7[0][0] \n__________________________________________________________________________________________________\nswish_18 (Swish) (None, 56, 56, 240) 0 batch_normalization_18[0][0] \n__________________________________________________________________________________________________\nlambda_7 (Lambda) (None, 1, 1, 240) 0 swish_18[0][0] \n__________________________________________________________________________________________________\nconv2d_24 (Conv2D) (None, 1, 1, 10) 2410 lambda_7[0][0] \n__________________________________________________________________________________________________\nswish_19 (Swish) (None, 1, 1, 10) 0 conv2d_24[0][0] \n__________________________________________________________________________________________________\nconv2d_25 (Conv2D) (None, 1, 1, 240) 2640 swish_19[0][0] \n__________________________________________________________________________________________________\nactivation_7 (Activation) (None, 1, 1, 240) 0 conv2d_25[0][0] \n__________________________________________________________________________________________________\nmultiply_7 (Multiply) (None, 56, 56, 240) 0 activation_7[0][0] \n swish_18[0][0] \n__________________________________________________________________________________________________\nconv2d_26 (Conv2D) (None, 56, 56, 40) 9600 multiply_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_19 (BatchNo (None, 56, 56, 40) 160 conv2d_26[0][0] \n__________________________________________________________________________________________________\ndrop_connect_5 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_19[0][0] \n__________________________________________________________________________________________________\nadd_5 (Add) (None, 56, 56, 40) 0 drop_connect_5[0][0] \n add_4[0][0] \n__________________________________________________________________________________________________\nconv2d_27 (Conv2D) (None, 56, 56, 240) 9600 add_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_20 (BatchNo (None, 56, 56, 240) 960 conv2d_27[0][0] \n__________________________________________________________________________________________________\nswish_20 (Swish) (None, 56, 56, 240) 0 batch_normalization_20[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_8 (DepthwiseCo (None, 56, 56, 240) 2160 swish_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_21 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_8[0][0] \n__________________________________________________________________________________________________\nswish_21 (Swish) (None, 56, 56, 240) 0 batch_normalization_21[0][0] \n__________________________________________________________________________________________________\nlambda_8 (Lambda) (None, 1, 1, 240) 0 swish_21[0][0] \n__________________________________________________________________________________________________\nconv2d_28 (Conv2D) (None, 1, 1, 10) 2410 lambda_8[0][0] \n__________________________________________________________________________________________________\nswish_22 (Swish) (None, 1, 1, 10) 0 conv2d_28[0][0] \n__________________________________________________________________________________________________\nconv2d_29 (Conv2D) (None, 1, 1, 240) 2640 swish_22[0][0] \n__________________________________________________________________________________________________\nactivation_8 (Activation) (None, 1, 1, 240) 0 conv2d_29[0][0] \n__________________________________________________________________________________________________\nmultiply_8 (Multiply) (None, 56, 56, 240) 0 activation_8[0][0] \n swish_21[0][0] \n__________________________________________________________________________________________________\nconv2d_30 (Conv2D) (None, 56, 56, 40) 9600 multiply_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_22 (BatchNo (None, 56, 56, 40) 160 conv2d_30[0][0] \n__________________________________________________________________________________________________\ndrop_connect_6 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_22[0][0] \n__________________________________________________________________________________________________\nadd_6 (Add) (None, 56, 56, 40) 0 drop_connect_6[0][0] \n add_5[0][0] \n__________________________________________________________________________________________________\nconv2d_31 (Conv2D) (None, 56, 56, 240) 9600 add_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_23 (BatchNo (None, 56, 56, 240) 960 conv2d_31[0][0] \n__________________________________________________________________________________________________\nswish_23 (Swish) (None, 56, 56, 240) 0 batch_normalization_23[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_9 (DepthwiseCo (None, 28, 28, 240) 6000 swish_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_24 (BatchNo (None, 28, 28, 240) 960 depthwise_conv2d_9[0][0] \n__________________________________________________________________________________________________\nswish_24 (Swish) (None, 28, 28, 240) 0 batch_normalization_24[0][0] \n__________________________________________________________________________________________________\nlambda_9 (Lambda) (None, 1, 1, 240) 0 swish_24[0][0] \n__________________________________________________________________________________________________\nconv2d_32 (Conv2D) (None, 1, 1, 10) 2410 lambda_9[0][0] \n__________________________________________________________________________________________________\nswish_25 (Swish) (None, 1, 1, 10) 0 conv2d_32[0][0] \n__________________________________________________________________________________________________\nconv2d_33 (Conv2D) (None, 1, 1, 240) 2640 swish_25[0][0] \n__________________________________________________________________________________________________\nactivation_9 (Activation) (None, 1, 1, 240) 0 conv2d_33[0][0] \n__________________________________________________________________________________________________\nmultiply_9 (Multiply) (None, 28, 28, 240) 0 activation_9[0][0] \n swish_24[0][0] \n__________________________________________________________________________________________________\nconv2d_34 (Conv2D) (None, 28, 28, 64) 15360 multiply_9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_25 (BatchNo (None, 28, 28, 64) 256 conv2d_34[0][0] \n__________________________________________________________________________________________________\nconv2d_35 (Conv2D) (None, 28, 28, 384) 24576 batch_normalization_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_26 (BatchNo (None, 28, 28, 384) 1536 conv2d_35[0][0] \n__________________________________________________________________________________________________\nswish_26 (Swish) (None, 28, 28, 384) 0 batch_normalization_26[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_10 (DepthwiseC (None, 28, 28, 384) 9600 swish_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_27 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_10[0][0] \n__________________________________________________________________________________________________\nswish_27 (Swish) (None, 28, 28, 384) 0 batch_normalization_27[0][0] \n__________________________________________________________________________________________________\nlambda_10 (Lambda) (None, 1, 1, 384) 0 swish_27[0][0] \n__________________________________________________________________________________________________\nconv2d_36 (Conv2D) (None, 1, 1, 16) 6160 lambda_10[0][0] \n__________________________________________________________________________________________________\nswish_28 (Swish) (None, 1, 1, 16) 0 conv2d_36[0][0] \n__________________________________________________________________________________________________\nconv2d_37 (Conv2D) (None, 1, 1, 384) 6528 swish_28[0][0] \n__________________________________________________________________________________________________\nactivation_10 (Activation) (None, 1, 1, 384) 0 conv2d_37[0][0] \n__________________________________________________________________________________________________\nmultiply_10 (Multiply) (None, 28, 28, 384) 0 activation_10[0][0] \n swish_27[0][0] \n__________________________________________________________________________________________________\nconv2d_38 (Conv2D) (None, 28, 28, 64) 24576 multiply_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_28 (BatchNo (None, 28, 28, 64) 256 conv2d_38[0][0] \n__________________________________________________________________________________________________\ndrop_connect_7 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_28[0][0] \n__________________________________________________________________________________________________\nadd_7 (Add) (None, 28, 28, 64) 0 drop_connect_7[0][0] \n batch_normalization_25[0][0] \n__________________________________________________________________________________________________\nconv2d_39 (Conv2D) (None, 28, 28, 384) 24576 add_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_29 (BatchNo (None, 28, 28, 384) 1536 conv2d_39[0][0] \n__________________________________________________________________________________________________\nswish_29 (Swish) (None, 28, 28, 384) 0 batch_normalization_29[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_11 (DepthwiseC (None, 28, 28, 384) 9600 swish_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_30 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_11[0][0] \n__________________________________________________________________________________________________\nswish_30 (Swish) (None, 28, 28, 384) 0 batch_normalization_30[0][0] \n__________________________________________________________________________________________________\nlambda_11 (Lambda) (None, 1, 1, 384) 0 swish_30[0][0] \n__________________________________________________________________________________________________\nconv2d_40 (Conv2D) (None, 1, 1, 16) 6160 lambda_11[0][0] \n__________________________________________________________________________________________________\nswish_31 (Swish) (None, 1, 1, 16) 0 conv2d_40[0][0] \n__________________________________________________________________________________________________\nconv2d_41 (Conv2D) (None, 1, 1, 384) 6528 swish_31[0][0] \n__________________________________________________________________________________________________\nactivation_11 (Activation) (None, 1, 1, 384) 0 conv2d_41[0][0] \n__________________________________________________________________________________________________\nmultiply_11 (Multiply) (None, 28, 28, 384) 0 activation_11[0][0] \n swish_30[0][0] \n__________________________________________________________________________________________________\nconv2d_42 (Conv2D) (None, 28, 28, 64) 24576 multiply_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_31 (BatchNo (None, 28, 28, 64) 256 conv2d_42[0][0] \n__________________________________________________________________________________________________\ndrop_connect_8 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_31[0][0] \n__________________________________________________________________________________________________\nadd_8 (Add) (None, 28, 28, 64) 0 drop_connect_8[0][0] \n add_7[0][0] \n__________________________________________________________________________________________________\nconv2d_43 (Conv2D) (None, 28, 28, 384) 24576 add_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_32 (BatchNo (None, 28, 28, 384) 1536 conv2d_43[0][0] \n__________________________________________________________________________________________________\nswish_32 (Swish) (None, 28, 28, 384) 0 batch_normalization_32[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_12 (DepthwiseC (None, 28, 28, 384) 9600 swish_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_33 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_12[0][0] \n__________________________________________________________________________________________________\nswish_33 (Swish) (None, 28, 28, 384) 0 batch_normalization_33[0][0] \n__________________________________________________________________________________________________\nlambda_12 (Lambda) (None, 1, 1, 384) 0 swish_33[0][0] \n__________________________________________________________________________________________________\nconv2d_44 (Conv2D) (None, 1, 1, 16) 6160 lambda_12[0][0] \n__________________________________________________________________________________________________\nswish_34 (Swish) (None, 1, 1, 16) 0 conv2d_44[0][0] \n__________________________________________________________________________________________________\nconv2d_45 (Conv2D) (None, 1, 1, 384) 6528 swish_34[0][0] \n__________________________________________________________________________________________________\nactivation_12 (Activation) (None, 1, 1, 384) 0 conv2d_45[0][0] \n__________________________________________________________________________________________________\nmultiply_12 (Multiply) (None, 28, 28, 384) 0 activation_12[0][0] \n swish_33[0][0] \n__________________________________________________________________________________________________\nconv2d_46 (Conv2D) (None, 28, 28, 64) 24576 multiply_12[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_34 (BatchNo (None, 28, 28, 64) 256 conv2d_46[0][0] \n__________________________________________________________________________________________________\ndrop_connect_9 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_34[0][0] \n__________________________________________________________________________________________________\nadd_9 (Add) (None, 28, 28, 64) 0 drop_connect_9[0][0] \n add_8[0][0] \n__________________________________________________________________________________________________\nconv2d_47 (Conv2D) (None, 28, 28, 384) 24576 add_9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_35 (BatchNo (None, 28, 28, 384) 1536 conv2d_47[0][0] \n__________________________________________________________________________________________________\nswish_35 (Swish) (None, 28, 28, 384) 0 batch_normalization_35[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_13 (DepthwiseC (None, 28, 28, 384) 9600 swish_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_36 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_13[0][0] \n__________________________________________________________________________________________________\nswish_36 (Swish) (None, 28, 28, 384) 0 batch_normalization_36[0][0] \n__________________________________________________________________________________________________\nlambda_13 (Lambda) (None, 1, 1, 384) 0 swish_36[0][0] \n__________________________________________________________________________________________________\nconv2d_48 (Conv2D) (None, 1, 1, 16) 6160 lambda_13[0][0] \n__________________________________________________________________________________________________\nswish_37 (Swish) (None, 1, 1, 16) 0 conv2d_48[0][0] \n__________________________________________________________________________________________________\nconv2d_49 (Conv2D) (None, 1, 1, 384) 6528 swish_37[0][0] \n__________________________________________________________________________________________________\nactivation_13 (Activation) (None, 1, 1, 384) 0 conv2d_49[0][0] \n__________________________________________________________________________________________________\nmultiply_13 (Multiply) (None, 28, 28, 384) 0 activation_13[0][0] \n swish_36[0][0] \n__________________________________________________________________________________________________\nconv2d_50 (Conv2D) (None, 28, 28, 64) 24576 multiply_13[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_37 (BatchNo (None, 28, 28, 64) 256 conv2d_50[0][0] \n__________________________________________________________________________________________________\ndrop_connect_10 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_37[0][0] \n__________________________________________________________________________________________________\nadd_10 (Add) (None, 28, 28, 64) 0 drop_connect_10[0][0] \n add_9[0][0] \n__________________________________________________________________________________________________\nconv2d_51 (Conv2D) (None, 28, 28, 384) 24576 add_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_38 (BatchNo (None, 28, 28, 384) 1536 conv2d_51[0][0] \n__________________________________________________________________________________________________\nswish_38 (Swish) (None, 28, 28, 384) 0 batch_normalization_38[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_14 (DepthwiseC (None, 14, 14, 384) 3456 swish_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_39 (BatchNo (None, 14, 14, 384) 1536 depthwise_conv2d_14[0][0] \n__________________________________________________________________________________________________\nswish_39 (Swish) (None, 14, 14, 384) 0 batch_normalization_39[0][0] \n__________________________________________________________________________________________________\nlambda_14 (Lambda) (None, 1, 1, 384) 0 swish_39[0][0] \n__________________________________________________________________________________________________\nconv2d_52 (Conv2D) (None, 1, 1, 16) 6160 lambda_14[0][0] \n__________________________________________________________________________________________________\nswish_40 (Swish) (None, 1, 1, 16) 0 conv2d_52[0][0] \n__________________________________________________________________________________________________\nconv2d_53 (Conv2D) (None, 1, 1, 384) 6528 swish_40[0][0] \n__________________________________________________________________________________________________\nactivation_14 (Activation) (None, 1, 1, 384) 0 conv2d_53[0][0] \n__________________________________________________________________________________________________\nmultiply_14 (Multiply) (None, 14, 14, 384) 0 activation_14[0][0] \n swish_39[0][0] \n__________________________________________________________________________________________________\nconv2d_54 (Conv2D) (None, 14, 14, 128) 49152 multiply_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_40 (BatchNo (None, 14, 14, 128) 512 conv2d_54[0][0] \n__________________________________________________________________________________________________\nconv2d_55 (Conv2D) (None, 14, 14, 768) 98304 batch_normalization_40[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_41 (BatchNo (None, 14, 14, 768) 3072 conv2d_55[0][0] \n__________________________________________________________________________________________________\nswish_41 (Swish) (None, 14, 14, 768) 0 batch_normalization_41[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_15 (DepthwiseC (None, 14, 14, 768) 6912 swish_41[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_42 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_15[0][0] \n__________________________________________________________________________________________________\nswish_42 (Swish) (None, 14, 14, 768) 0 batch_normalization_42[0][0] \n__________________________________________________________________________________________________\nlambda_15 (Lambda) (None, 1, 1, 768) 0 swish_42[0][0] \n__________________________________________________________________________________________________\nconv2d_56 (Conv2D) (None, 1, 1, 32) 24608 lambda_15[0][0] \n__________________________________________________________________________________________________\nswish_43 (Swish) (None, 1, 1, 32) 0 conv2d_56[0][0] \n__________________________________________________________________________________________________\nconv2d_57 (Conv2D) (None, 1, 1, 768) 25344 swish_43[0][0] \n__________________________________________________________________________________________________\nactivation_15 (Activation) (None, 1, 1, 768) 0 conv2d_57[0][0] \n__________________________________________________________________________________________________\nmultiply_15 (Multiply) (None, 14, 14, 768) 0 activation_15[0][0] \n swish_42[0][0] \n__________________________________________________________________________________________________\nconv2d_58 (Conv2D) (None, 14, 14, 128) 98304 multiply_15[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_43 (BatchNo (None, 14, 14, 128) 512 conv2d_58[0][0] \n__________________________________________________________________________________________________\ndrop_connect_11 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_43[0][0] \n__________________________________________________________________________________________________\nadd_11 (Add) (None, 14, 14, 128) 0 drop_connect_11[0][0] \n batch_normalization_40[0][0] \n__________________________________________________________________________________________________\nconv2d_59 (Conv2D) (None, 14, 14, 768) 98304 add_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_44 (BatchNo (None, 14, 14, 768) 3072 conv2d_59[0][0] \n__________________________________________________________________________________________________\nswish_44 (Swish) (None, 14, 14, 768) 0 batch_normalization_44[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_16 (DepthwiseC (None, 14, 14, 768) 6912 swish_44[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_45 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_16[0][0] \n__________________________________________________________________________________________________\nswish_45 (Swish) (None, 14, 14, 768) 0 batch_normalization_45[0][0] \n__________________________________________________________________________________________________\nlambda_16 (Lambda) (None, 1, 1, 768) 0 swish_45[0][0] \n__________________________________________________________________________________________________\nconv2d_60 (Conv2D) (None, 1, 1, 32) 24608 lambda_16[0][0] \n__________________________________________________________________________________________________\nswish_46 (Swish) (None, 1, 1, 32) 0 conv2d_60[0][0] \n__________________________________________________________________________________________________\nconv2d_61 (Conv2D) (None, 1, 1, 768) 25344 swish_46[0][0] \n__________________________________________________________________________________________________\nactivation_16 (Activation) (None, 1, 1, 768) 0 conv2d_61[0][0] \n__________________________________________________________________________________________________\nmultiply_16 (Multiply) (None, 14, 14, 768) 0 activation_16[0][0] \n swish_45[0][0] \n__________________________________________________________________________________________________\nconv2d_62 (Conv2D) (None, 14, 14, 128) 98304 multiply_16[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_46 (BatchNo (None, 14, 14, 128) 512 conv2d_62[0][0] \n__________________________________________________________________________________________________\ndrop_connect_12 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_46[0][0] \n__________________________________________________________________________________________________\nadd_12 (Add) (None, 14, 14, 128) 0 drop_connect_12[0][0] \n add_11[0][0] \n__________________________________________________________________________________________________\nconv2d_63 (Conv2D) (None, 14, 14, 768) 98304 add_12[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_47 (BatchNo (None, 14, 14, 768) 3072 conv2d_63[0][0] \n__________________________________________________________________________________________________\nswish_47 (Swish) (None, 14, 14, 768) 0 batch_normalization_47[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_17 (DepthwiseC (None, 14, 14, 768) 6912 swish_47[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_48 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_17[0][0] \n__________________________________________________________________________________________________\nswish_48 (Swish) (None, 14, 14, 768) 0 batch_normalization_48[0][0] \n__________________________________________________________________________________________________\nlambda_17 (Lambda) (None, 1, 1, 768) 0 swish_48[0][0] \n__________________________________________________________________________________________________\nconv2d_64 (Conv2D) (None, 1, 1, 32) 24608 lambda_17[0][0] \n__________________________________________________________________________________________________\nswish_49 (Swish) (None, 1, 1, 32) 0 conv2d_64[0][0] \n__________________________________________________________________________________________________\nconv2d_65 (Conv2D) (None, 1, 1, 768) 25344 swish_49[0][0] \n__________________________________________________________________________________________________\nactivation_17 (Activation) (None, 1, 1, 768) 0 conv2d_65[0][0] \n__________________________________________________________________________________________________\nmultiply_17 (Multiply) (None, 14, 14, 768) 0 activation_17[0][0] \n swish_48[0][0] \n__________________________________________________________________________________________________\nconv2d_66 (Conv2D) (None, 14, 14, 128) 98304 multiply_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_49 (BatchNo (None, 14, 14, 128) 512 conv2d_66[0][0] \n__________________________________________________________________________________________________\ndrop_connect_13 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_49[0][0] \n__________________________________________________________________________________________________\nadd_13 (Add) (None, 14, 14, 128) 0 drop_connect_13[0][0] \n add_12[0][0] \n__________________________________________________________________________________________________\nconv2d_67 (Conv2D) (None, 14, 14, 768) 98304 add_13[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_50 (BatchNo (None, 14, 14, 768) 3072 conv2d_67[0][0] \n__________________________________________________________________________________________________\nswish_50 (Swish) (None, 14, 14, 768) 0 batch_normalization_50[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_18 (DepthwiseC (None, 14, 14, 768) 6912 swish_50[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_51 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_18[0][0] \n__________________________________________________________________________________________________\nswish_51 (Swish) (None, 14, 14, 768) 0 batch_normalization_51[0][0] \n__________________________________________________________________________________________________\nlambda_18 (Lambda) (None, 1, 1, 768) 0 swish_51[0][0] \n__________________________________________________________________________________________________\nconv2d_68 (Conv2D) (None, 1, 1, 32) 24608 lambda_18[0][0] \n__________________________________________________________________________________________________\nswish_52 (Swish) (None, 1, 1, 32) 0 conv2d_68[0][0] \n__________________________________________________________________________________________________\nconv2d_69 (Conv2D) (None, 1, 1, 768) 25344 swish_52[0][0] \n__________________________________________________________________________________________________\nactivation_18 (Activation) (None, 1, 1, 768) 0 conv2d_69[0][0] \n__________________________________________________________________________________________________\nmultiply_18 (Multiply) (None, 14, 14, 768) 0 activation_18[0][0] \n swish_51[0][0] \n__________________________________________________________________________________________________\nconv2d_70 (Conv2D) (None, 14, 14, 128) 98304 multiply_18[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_52 (BatchNo (None, 14, 14, 128) 512 conv2d_70[0][0] \n__________________________________________________________________________________________________\ndrop_connect_14 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_52[0][0] \n__________________________________________________________________________________________________\nadd_14 (Add) (None, 14, 14, 128) 0 drop_connect_14[0][0] \n add_13[0][0] \n__________________________________________________________________________________________________\nconv2d_71 (Conv2D) (None, 14, 14, 768) 98304 add_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_53 (BatchNo (None, 14, 14, 768) 3072 conv2d_71[0][0] \n__________________________________________________________________________________________________\nswish_53 (Swish) (None, 14, 14, 768) 0 batch_normalization_53[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_19 (DepthwiseC (None, 14, 14, 768) 6912 swish_53[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_54 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_19[0][0] \n__________________________________________________________________________________________________\nswish_54 (Swish) (None, 14, 14, 768) 0 batch_normalization_54[0][0] \n__________________________________________________________________________________________________\nlambda_19 (Lambda) (None, 1, 1, 768) 0 swish_54[0][0] \n__________________________________________________________________________________________________\nconv2d_72 (Conv2D) (None, 1, 1, 32) 24608 lambda_19[0][0] \n__________________________________________________________________________________________________\nswish_55 (Swish) (None, 1, 1, 32) 0 conv2d_72[0][0] \n__________________________________________________________________________________________________\nconv2d_73 (Conv2D) (None, 1, 1, 768) 25344 swish_55[0][0] \n__________________________________________________________________________________________________\nactivation_19 (Activation) (None, 1, 1, 768) 0 conv2d_73[0][0] \n__________________________________________________________________________________________________\nmultiply_19 (Multiply) (None, 14, 14, 768) 0 activation_19[0][0] \n swish_54[0][0] \n__________________________________________________________________________________________________\nconv2d_74 (Conv2D) (None, 14, 14, 128) 98304 multiply_19[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_55 (BatchNo (None, 14, 14, 128) 512 conv2d_74[0][0] \n__________________________________________________________________________________________________\ndrop_connect_15 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_55[0][0] \n__________________________________________________________________________________________________\nadd_15 (Add) (None, 14, 14, 128) 0 drop_connect_15[0][0] \n add_14[0][0] \n__________________________________________________________________________________________________\nconv2d_75 (Conv2D) (None, 14, 14, 768) 98304 add_15[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_56 (BatchNo (None, 14, 14, 768) 3072 conv2d_75[0][0] \n__________________________________________________________________________________________________\nswish_56 (Swish) (None, 14, 14, 768) 0 batch_normalization_56[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_20 (DepthwiseC (None, 14, 14, 768) 6912 swish_56[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_57 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_20[0][0] \n__________________________________________________________________________________________________\nswish_57 (Swish) (None, 14, 14, 768) 0 batch_normalization_57[0][0] \n__________________________________________________________________________________________________\nlambda_20 (Lambda) (None, 1, 1, 768) 0 swish_57[0][0] \n__________________________________________________________________________________________________\nconv2d_76 (Conv2D) (None, 1, 1, 32) 24608 lambda_20[0][0] \n__________________________________________________________________________________________________\nswish_58 (Swish) (None, 1, 1, 32) 0 conv2d_76[0][0] \n__________________________________________________________________________________________________\nconv2d_77 (Conv2D) (None, 1, 1, 768) 25344 swish_58[0][0] \n__________________________________________________________________________________________________\nactivation_20 (Activation) (None, 1, 1, 768) 0 conv2d_77[0][0] \n__________________________________________________________________________________________________\nmultiply_20 (Multiply) (None, 14, 14, 768) 0 activation_20[0][0] \n swish_57[0][0] \n__________________________________________________________________________________________________\nconv2d_78 (Conv2D) (None, 14, 14, 128) 98304 multiply_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_58 (BatchNo (None, 14, 14, 128) 512 conv2d_78[0][0] \n__________________________________________________________________________________________________\ndrop_connect_16 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_58[0][0] \n__________________________________________________________________________________________________\nadd_16 (Add) (None, 14, 14, 128) 0 drop_connect_16[0][0] \n add_15[0][0] \n__________________________________________________________________________________________________\nconv2d_79 (Conv2D) (None, 14, 14, 768) 98304 add_16[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_59 (BatchNo (None, 14, 14, 768) 3072 conv2d_79[0][0] \n__________________________________________________________________________________________________\nswish_59 (Swish) (None, 14, 14, 768) 0 batch_normalization_59[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_21 (DepthwiseC (None, 14, 14, 768) 19200 swish_59[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_60 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_21[0][0] \n__________________________________________________________________________________________________\nswish_60 (Swish) (None, 14, 14, 768) 0 batch_normalization_60[0][0] \n__________________________________________________________________________________________________\nlambda_21 (Lambda) (None, 1, 1, 768) 0 swish_60[0][0] \n__________________________________________________________________________________________________\nconv2d_80 (Conv2D) (None, 1, 1, 32) 24608 lambda_21[0][0] \n__________________________________________________________________________________________________\nswish_61 (Swish) (None, 1, 1, 32) 0 conv2d_80[0][0] \n__________________________________________________________________________________________________\nconv2d_81 (Conv2D) (None, 1, 1, 768) 25344 swish_61[0][0] \n__________________________________________________________________________________________________\nactivation_21 (Activation) (None, 1, 1, 768) 0 conv2d_81[0][0] \n__________________________________________________________________________________________________\nmultiply_21 (Multiply) (None, 14, 14, 768) 0 activation_21[0][0] \n swish_60[0][0] \n__________________________________________________________________________________________________\nconv2d_82 (Conv2D) (None, 14, 14, 176) 135168 multiply_21[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_61 (BatchNo (None, 14, 14, 176) 704 conv2d_82[0][0] \n__________________________________________________________________________________________________\nconv2d_83 (Conv2D) (None, 14, 14, 1056) 185856 batch_normalization_61[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_62 (BatchNo (None, 14, 14, 1056) 4224 conv2d_83[0][0] \n__________________________________________________________________________________________________\nswish_62 (Swish) (None, 14, 14, 1056) 0 batch_normalization_62[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_22 (DepthwiseC (None, 14, 14, 1056) 26400 swish_62[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_63 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_22[0][0] \n__________________________________________________________________________________________________\nswish_63 (Swish) (None, 14, 14, 1056) 0 batch_normalization_63[0][0] \n__________________________________________________________________________________________________\nlambda_22 (Lambda) (None, 1, 1, 1056) 0 swish_63[0][0] \n__________________________________________________________________________________________________\nconv2d_84 (Conv2D) (None, 1, 1, 44) 46508 lambda_22[0][0] \n__________________________________________________________________________________________________\nswish_64 (Swish) (None, 1, 1, 44) 0 conv2d_84[0][0] \n__________________________________________________________________________________________________\nconv2d_85 (Conv2D) (None, 1, 1, 1056) 47520 swish_64[0][0] \n__________________________________________________________________________________________________\nactivation_22 (Activation) (None, 1, 1, 1056) 0 conv2d_85[0][0] \n__________________________________________________________________________________________________\nmultiply_22 (Multiply) (None, 14, 14, 1056) 0 activation_22[0][0] \n swish_63[0][0] \n__________________________________________________________________________________________________\nconv2d_86 (Conv2D) (None, 14, 14, 176) 185856 multiply_22[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_64 (BatchNo (None, 14, 14, 176) 704 conv2d_86[0][0] \n__________________________________________________________________________________________________\ndrop_connect_17 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_64[0][0] \n__________________________________________________________________________________________________\nadd_17 (Add) (None, 14, 14, 176) 0 drop_connect_17[0][0] \n batch_normalization_61[0][0] \n__________________________________________________________________________________________________\nconv2d_87 (Conv2D) (None, 14, 14, 1056) 185856 add_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_65 (BatchNo (None, 14, 14, 1056) 4224 conv2d_87[0][0] \n__________________________________________________________________________________________________\nswish_65 (Swish) (None, 14, 14, 1056) 0 batch_normalization_65[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_23 (DepthwiseC (None, 14, 14, 1056) 26400 swish_65[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_66 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_23[0][0] \n__________________________________________________________________________________________________\nswish_66 (Swish) (None, 14, 14, 1056) 0 batch_normalization_66[0][0] \n__________________________________________________________________________________________________\nlambda_23 (Lambda) (None, 1, 1, 1056) 0 swish_66[0][0] \n__________________________________________________________________________________________________\nconv2d_88 (Conv2D) (None, 1, 1, 44) 46508 lambda_23[0][0] \n__________________________________________________________________________________________________\nswish_67 (Swish) (None, 1, 1, 44) 0 conv2d_88[0][0] \n__________________________________________________________________________________________________\nconv2d_89 (Conv2D) (None, 1, 1, 1056) 47520 swish_67[0][0] \n__________________________________________________________________________________________________\nactivation_23 (Activation) (None, 1, 1, 1056) 0 conv2d_89[0][0] \n__________________________________________________________________________________________________\nmultiply_23 (Multiply) (None, 14, 14, 1056) 0 activation_23[0][0] \n swish_66[0][0] \n__________________________________________________________________________________________________\nconv2d_90 (Conv2D) (None, 14, 14, 176) 185856 multiply_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_67 (BatchNo (None, 14, 14, 176) 704 conv2d_90[0][0] \n__________________________________________________________________________________________________\ndrop_connect_18 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_67[0][0] \n__________________________________________________________________________________________________\nadd_18 (Add) (None, 14, 14, 176) 0 drop_connect_18[0][0] \n add_17[0][0] \n__________________________________________________________________________________________________\nconv2d_91 (Conv2D) (None, 14, 14, 1056) 185856 add_18[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_68 (BatchNo (None, 14, 14, 1056) 4224 conv2d_91[0][0] \n__________________________________________________________________________________________________\nswish_68 (Swish) (None, 14, 14, 1056) 0 batch_normalization_68[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_24 (DepthwiseC (None, 14, 14, 1056) 26400 swish_68[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_69 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_24[0][0] \n__________________________________________________________________________________________________\nswish_69 (Swish) (None, 14, 14, 1056) 0 batch_normalization_69[0][0] \n__________________________________________________________________________________________________\nlambda_24 (Lambda) (None, 1, 1, 1056) 0 swish_69[0][0] \n__________________________________________________________________________________________________\nconv2d_92 (Conv2D) (None, 1, 1, 44) 46508 lambda_24[0][0] \n__________________________________________________________________________________________________\nswish_70 (Swish) (None, 1, 1, 44) 0 conv2d_92[0][0] \n__________________________________________________________________________________________________\nconv2d_93 (Conv2D) (None, 1, 1, 1056) 47520 swish_70[0][0] \n__________________________________________________________________________________________________\nactivation_24 (Activation) (None, 1, 1, 1056) 0 conv2d_93[0][0] \n__________________________________________________________________________________________________\nmultiply_24 (Multiply) (None, 14, 14, 1056) 0 activation_24[0][0] \n swish_69[0][0] \n__________________________________________________________________________________________________\nconv2d_94 (Conv2D) (None, 14, 14, 176) 185856 multiply_24[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_70 (BatchNo (None, 14, 14, 176) 704 conv2d_94[0][0] \n__________________________________________________________________________________________________\ndrop_connect_19 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_70[0][0] \n__________________________________________________________________________________________________\nadd_19 (Add) (None, 14, 14, 176) 0 drop_connect_19[0][0] \n add_18[0][0] \n__________________________________________________________________________________________________\nconv2d_95 (Conv2D) (None, 14, 14, 1056) 185856 add_19[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_71 (BatchNo (None, 14, 14, 1056) 4224 conv2d_95[0][0] \n__________________________________________________________________________________________________\nswish_71 (Swish) (None, 14, 14, 1056) 0 batch_normalization_71[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_25 (DepthwiseC (None, 14, 14, 1056) 26400 swish_71[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_72 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_25[0][0] \n__________________________________________________________________________________________________\nswish_72 (Swish) (None, 14, 14, 1056) 0 batch_normalization_72[0][0] \n__________________________________________________________________________________________________\nlambda_25 (Lambda) (None, 1, 1, 1056) 0 swish_72[0][0] \n__________________________________________________________________________________________________\nconv2d_96 (Conv2D) (None, 1, 1, 44) 46508 lambda_25[0][0] \n__________________________________________________________________________________________________\nswish_73 (Swish) (None, 1, 1, 44) 0 conv2d_96[0][0] \n__________________________________________________________________________________________________\nconv2d_97 (Conv2D) (None, 1, 1, 1056) 47520 swish_73[0][0] \n__________________________________________________________________________________________________\nactivation_25 (Activation) (None, 1, 1, 1056) 0 conv2d_97[0][0] \n__________________________________________________________________________________________________\nmultiply_25 (Multiply) (None, 14, 14, 1056) 0 activation_25[0][0] \n swish_72[0][0] \n__________________________________________________________________________________________________\nconv2d_98 (Conv2D) (None, 14, 14, 176) 185856 multiply_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_73 (BatchNo (None, 14, 14, 176) 704 conv2d_98[0][0] \n__________________________________________________________________________________________________\ndrop_connect_20 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_73[0][0] \n__________________________________________________________________________________________________\nadd_20 (Add) (None, 14, 14, 176) 0 drop_connect_20[0][0] \n add_19[0][0] \n__________________________________________________________________________________________________\nconv2d_99 (Conv2D) (None, 14, 14, 1056) 185856 add_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_74 (BatchNo (None, 14, 14, 1056) 4224 conv2d_99[0][0] \n__________________________________________________________________________________________________\nswish_74 (Swish) (None, 14, 14, 1056) 0 batch_normalization_74[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_26 (DepthwiseC (None, 14, 14, 1056) 26400 swish_74[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_75 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_26[0][0] \n__________________________________________________________________________________________________\nswish_75 (Swish) (None, 14, 14, 1056) 0 batch_normalization_75[0][0] \n__________________________________________________________________________________________________\nlambda_26 (Lambda) (None, 1, 1, 1056) 0 swish_75[0][0] \n__________________________________________________________________________________________________\nconv2d_100 (Conv2D) (None, 1, 1, 44) 46508 lambda_26[0][0] \n__________________________________________________________________________________________________\nswish_76 (Swish) (None, 1, 1, 44) 0 conv2d_100[0][0] \n__________________________________________________________________________________________________\nconv2d_101 (Conv2D) (None, 1, 1, 1056) 47520 swish_76[0][0] \n__________________________________________________________________________________________________\nactivation_26 (Activation) (None, 1, 1, 1056) 0 conv2d_101[0][0] \n__________________________________________________________________________________________________\nmultiply_26 (Multiply) (None, 14, 14, 1056) 0 activation_26[0][0] \n swish_75[0][0] \n__________________________________________________________________________________________________\nconv2d_102 (Conv2D) (None, 14, 14, 176) 185856 multiply_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_76 (BatchNo (None, 14, 14, 176) 704 conv2d_102[0][0] \n__________________________________________________________________________________________________\ndrop_connect_21 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_76[0][0] \n__________________________________________________________________________________________________\nadd_21 (Add) (None, 14, 14, 176) 0 drop_connect_21[0][0] \n add_20[0][0] \n__________________________________________________________________________________________________\nconv2d_103 (Conv2D) (None, 14, 14, 1056) 185856 add_21[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_77 (BatchNo (None, 14, 14, 1056) 4224 conv2d_103[0][0] \n__________________________________________________________________________________________________\nswish_77 (Swish) (None, 14, 14, 1056) 0 batch_normalization_77[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_27 (DepthwiseC (None, 14, 14, 1056) 26400 swish_77[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_78 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_27[0][0] \n__________________________________________________________________________________________________\nswish_78 (Swish) (None, 14, 14, 1056) 0 batch_normalization_78[0][0] \n__________________________________________________________________________________________________\nlambda_27 (Lambda) (None, 1, 1, 1056) 0 swish_78[0][0] \n__________________________________________________________________________________________________\nconv2d_104 (Conv2D) (None, 1, 1, 44) 46508 lambda_27[0][0] \n__________________________________________________________________________________________________\nswish_79 (Swish) (None, 1, 1, 44) 0 conv2d_104[0][0] \n__________________________________________________________________________________________________\nconv2d_105 (Conv2D) (None, 1, 1, 1056) 47520 swish_79[0][0] \n__________________________________________________________________________________________________\nactivation_27 (Activation) (None, 1, 1, 1056) 0 conv2d_105[0][0] \n__________________________________________________________________________________________________\nmultiply_27 (Multiply) (None, 14, 14, 1056) 0 activation_27[0][0] \n swish_78[0][0] \n__________________________________________________________________________________________________\nconv2d_106 (Conv2D) (None, 14, 14, 176) 185856 multiply_27[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_79 (BatchNo (None, 14, 14, 176) 704 conv2d_106[0][0] \n__________________________________________________________________________________________________\ndrop_connect_22 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_79[0][0] \n__________________________________________________________________________________________________\nadd_22 (Add) (None, 14, 14, 176) 0 drop_connect_22[0][0] \n add_21[0][0] \n__________________________________________________________________________________________________\nconv2d_107 (Conv2D) (None, 14, 14, 1056) 185856 add_22[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_80 (BatchNo (None, 14, 14, 1056) 4224 conv2d_107[0][0] \n__________________________________________________________________________________________________\nswish_80 (Swish) (None, 14, 14, 1056) 0 batch_normalization_80[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_28 (DepthwiseC (None, 7, 7, 1056) 26400 swish_80[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_81 (BatchNo (None, 7, 7, 1056) 4224 depthwise_conv2d_28[0][0] \n__________________________________________________________________________________________________\nswish_81 (Swish) (None, 7, 7, 1056) 0 batch_normalization_81[0][0] \n__________________________________________________________________________________________________\nlambda_28 (Lambda) (None, 1, 1, 1056) 0 swish_81[0][0] \n__________________________________________________________________________________________________\nconv2d_108 (Conv2D) (None, 1, 1, 44) 46508 lambda_28[0][0] \n__________________________________________________________________________________________________\nswish_82 (Swish) (None, 1, 1, 44) 0 conv2d_108[0][0] \n__________________________________________________________________________________________________\nconv2d_109 (Conv2D) (None, 1, 1, 1056) 47520 swish_82[0][0] \n__________________________________________________________________________________________________\nactivation_28 (Activation) (None, 1, 1, 1056) 0 conv2d_109[0][0] \n__________________________________________________________________________________________________\nmultiply_28 (Multiply) (None, 7, 7, 1056) 0 activation_28[0][0] \n swish_81[0][0] \n__________________________________________________________________________________________________\nconv2d_110 (Conv2D) (None, 7, 7, 304) 321024 multiply_28[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_82 (BatchNo (None, 7, 7, 304) 1216 conv2d_110[0][0] \n__________________________________________________________________________________________________\nconv2d_111 (Conv2D) (None, 7, 7, 1824) 554496 batch_normalization_82[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_83 (BatchNo (None, 7, 7, 1824) 7296 conv2d_111[0][0] \n__________________________________________________________________________________________________\nswish_83 (Swish) (None, 7, 7, 1824) 0 batch_normalization_83[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_29 (DepthwiseC (None, 7, 7, 1824) 45600 swish_83[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_84 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_29[0][0] \n__________________________________________________________________________________________________\nswish_84 (Swish) (None, 7, 7, 1824) 0 batch_normalization_84[0][0] \n__________________________________________________________________________________________________\nlambda_29 (Lambda) (None, 1, 1, 1824) 0 swish_84[0][0] \n__________________________________________________________________________________________________\nconv2d_112 (Conv2D) (None, 1, 1, 76) 138700 lambda_29[0][0] \n__________________________________________________________________________________________________\nswish_85 (Swish) (None, 1, 1, 76) 0 conv2d_112[0][0] \n__________________________________________________________________________________________________\nconv2d_113 (Conv2D) (None, 1, 1, 1824) 140448 swish_85[0][0] \n__________________________________________________________________________________________________\nactivation_29 (Activation) (None, 1, 1, 1824) 0 conv2d_113[0][0] \n__________________________________________________________________________________________________\nmultiply_29 (Multiply) (None, 7, 7, 1824) 0 activation_29[0][0] \n swish_84[0][0] \n__________________________________________________________________________________________________\nconv2d_114 (Conv2D) (None, 7, 7, 304) 554496 multiply_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_85 (BatchNo (None, 7, 7, 304) 1216 conv2d_114[0][0] \n__________________________________________________________________________________________________\ndrop_connect_23 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_85[0][0] \n__________________________________________________________________________________________________\nadd_23 (Add) (None, 7, 7, 304) 0 drop_connect_23[0][0] \n batch_normalization_82[0][0] \n__________________________________________________________________________________________________\nconv2d_115 (Conv2D) (None, 7, 7, 1824) 554496 add_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_86 (BatchNo (None, 7, 7, 1824) 7296 conv2d_115[0][0] \n__________________________________________________________________________________________________\nswish_86 (Swish) (None, 7, 7, 1824) 0 batch_normalization_86[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_30 (DepthwiseC (None, 7, 7, 1824) 45600 swish_86[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_87 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_30[0][0] \n__________________________________________________________________________________________________\nswish_87 (Swish) (None, 7, 7, 1824) 0 batch_normalization_87[0][0] \n__________________________________________________________________________________________________\nlambda_30 (Lambda) (None, 1, 1, 1824) 0 swish_87[0][0] \n__________________________________________________________________________________________________\nconv2d_116 (Conv2D) (None, 1, 1, 76) 138700 lambda_30[0][0] \n__________________________________________________________________________________________________\nswish_88 (Swish) (None, 1, 1, 76) 0 conv2d_116[0][0] \n__________________________________________________________________________________________________\nconv2d_117 (Conv2D) (None, 1, 1, 1824) 140448 swish_88[0][0] \n__________________________________________________________________________________________________\nactivation_30 (Activation) (None, 1, 1, 1824) 0 conv2d_117[0][0] \n__________________________________________________________________________________________________\nmultiply_30 (Multiply) (None, 7, 7, 1824) 0 activation_30[0][0] \n swish_87[0][0] \n__________________________________________________________________________________________________\nconv2d_118 (Conv2D) (None, 7, 7, 304) 554496 multiply_30[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_88 (BatchNo (None, 7, 7, 304) 1216 conv2d_118[0][0] \n__________________________________________________________________________________________________\ndrop_connect_24 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_88[0][0] \n__________________________________________________________________________________________________\nadd_24 (Add) (None, 7, 7, 304) 0 drop_connect_24[0][0] \n add_23[0][0] \n__________________________________________________________________________________________________\nconv2d_119 (Conv2D) (None, 7, 7, 1824) 554496 add_24[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_89 (BatchNo (None, 7, 7, 1824) 7296 conv2d_119[0][0] \n__________________________________________________________________________________________________\nswish_89 (Swish) (None, 7, 7, 1824) 0 batch_normalization_89[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_31 (DepthwiseC (None, 7, 7, 1824) 45600 swish_89[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_90 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_31[0][0] \n__________________________________________________________________________________________________\nswish_90 (Swish) (None, 7, 7, 1824) 0 batch_normalization_90[0][0] \n__________________________________________________________________________________________________\nlambda_31 (Lambda) (None, 1, 1, 1824) 0 swish_90[0][0] \n__________________________________________________________________________________________________\nconv2d_120 (Conv2D) (None, 1, 1, 76) 138700 lambda_31[0][0] \n__________________________________________________________________________________________________\nswish_91 (Swish) (None, 1, 1, 76) 0 conv2d_120[0][0] \n__________________________________________________________________________________________________\nconv2d_121 (Conv2D) (None, 1, 1, 1824) 140448 swish_91[0][0] \n__________________________________________________________________________________________________\nactivation_31 (Activation) (None, 1, 1, 1824) 0 conv2d_121[0][0] \n__________________________________________________________________________________________________\nmultiply_31 (Multiply) (None, 7, 7, 1824) 0 activation_31[0][0] \n swish_90[0][0] \n__________________________________________________________________________________________________\nconv2d_122 (Conv2D) (None, 7, 7, 304) 554496 multiply_31[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_91 (BatchNo (None, 7, 7, 304) 1216 conv2d_122[0][0] \n__________________________________________________________________________________________________\ndrop_connect_25 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_91[0][0] \n__________________________________________________________________________________________________\nadd_25 (Add) (None, 7, 7, 304) 0 drop_connect_25[0][0] \n add_24[0][0] \n__________________________________________________________________________________________________\nconv2d_123 (Conv2D) (None, 7, 7, 1824) 554496 add_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_92 (BatchNo (None, 7, 7, 1824) 7296 conv2d_123[0][0] \n__________________________________________________________________________________________________\nswish_92 (Swish) (None, 7, 7, 1824) 0 batch_normalization_92[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_32 (DepthwiseC (None, 7, 7, 1824) 45600 swish_92[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_93 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_32[0][0] \n__________________________________________________________________________________________________\nswish_93 (Swish) (None, 7, 7, 1824) 0 batch_normalization_93[0][0] \n__________________________________________________________________________________________________\nlambda_32 (Lambda) (None, 1, 1, 1824) 0 swish_93[0][0] \n__________________________________________________________________________________________________\nconv2d_124 (Conv2D) (None, 1, 1, 76) 138700 lambda_32[0][0] \n__________________________________________________________________________________________________\nswish_94 (Swish) (None, 1, 1, 76) 0 conv2d_124[0][0] \n__________________________________________________________________________________________________\nconv2d_125 (Conv2D) (None, 1, 1, 1824) 140448 swish_94[0][0] \n__________________________________________________________________________________________________\nactivation_32 (Activation) (None, 1, 1, 1824) 0 conv2d_125[0][0] \n__________________________________________________________________________________________________\nmultiply_32 (Multiply) (None, 7, 7, 1824) 0 activation_32[0][0] \n swish_93[0][0] \n__________________________________________________________________________________________________\nconv2d_126 (Conv2D) (None, 7, 7, 304) 554496 multiply_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_94 (BatchNo (None, 7, 7, 304) 1216 conv2d_126[0][0] \n__________________________________________________________________________________________________\ndrop_connect_26 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_94[0][0] \n__________________________________________________________________________________________________\nadd_26 (Add) (None, 7, 7, 304) 0 drop_connect_26[0][0] \n add_25[0][0] \n__________________________________________________________________________________________________\nconv2d_127 (Conv2D) (None, 7, 7, 1824) 554496 add_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_95 (BatchNo (None, 7, 7, 1824) 7296 conv2d_127[0][0] \n__________________________________________________________________________________________________\nswish_95 (Swish) (None, 7, 7, 1824) 0 batch_normalization_95[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_33 (DepthwiseC (None, 7, 7, 1824) 45600 swish_95[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_96 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_33[0][0] \n__________________________________________________________________________________________________\nswish_96 (Swish) (None, 7, 7, 1824) 0 batch_normalization_96[0][0] \n__________________________________________________________________________________________________\nlambda_33 (Lambda) (None, 1, 1, 1824) 0 swish_96[0][0] \n__________________________________________________________________________________________________\nconv2d_128 (Conv2D) (None, 1, 1, 76) 138700 lambda_33[0][0] \n__________________________________________________________________________________________________\nswish_97 (Swish) (None, 1, 1, 76) 0 conv2d_128[0][0] \n__________________________________________________________________________________________________\nconv2d_129 (Conv2D) (None, 1, 1, 1824) 140448 swish_97[0][0] \n__________________________________________________________________________________________________\nactivation_33 (Activation) (None, 1, 1, 1824) 0 conv2d_129[0][0] \n__________________________________________________________________________________________________\nmultiply_33 (Multiply) (None, 7, 7, 1824) 0 activation_33[0][0] \n swish_96[0][0] \n__________________________________________________________________________________________________\nconv2d_130 (Conv2D) (None, 7, 7, 304) 554496 multiply_33[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_97 (BatchNo (None, 7, 7, 304) 1216 conv2d_130[0][0] \n__________________________________________________________________________________________________\ndrop_connect_27 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_97[0][0] \n__________________________________________________________________________________________________\nadd_27 (Add) (None, 7, 7, 304) 0 drop_connect_27[0][0] \n add_26[0][0] \n__________________________________________________________________________________________________\nconv2d_131 (Conv2D) (None, 7, 7, 1824) 554496 add_27[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_98 (BatchNo (None, 7, 7, 1824) 7296 conv2d_131[0][0] \n__________________________________________________________________________________________________\nswish_98 (Swish) (None, 7, 7, 1824) 0 batch_normalization_98[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_34 (DepthwiseC (None, 7, 7, 1824) 45600 swish_98[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_99 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_34[0][0] \n__________________________________________________________________________________________________\nswish_99 (Swish) (None, 7, 7, 1824) 0 batch_normalization_99[0][0] \n__________________________________________________________________________________________________\nlambda_34 (Lambda) (None, 1, 1, 1824) 0 swish_99[0][0] \n__________________________________________________________________________________________________\nconv2d_132 (Conv2D) (None, 1, 1, 76) 138700 lambda_34[0][0] \n__________________________________________________________________________________________________\nswish_100 (Swish) (None, 1, 1, 76) 0 conv2d_132[0][0] \n__________________________________________________________________________________________________\nconv2d_133 (Conv2D) (None, 1, 1, 1824) 140448 swish_100[0][0] \n__________________________________________________________________________________________________\nactivation_34 (Activation) (None, 1, 1, 1824) 0 conv2d_133[0][0] \n__________________________________________________________________________________________________\nmultiply_34 (Multiply) (None, 7, 7, 1824) 0 activation_34[0][0] \n swish_99[0][0] \n__________________________________________________________________________________________________\nconv2d_134 (Conv2D) (None, 7, 7, 304) 554496 multiply_34[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_100 (BatchN (None, 7, 7, 304) 1216 conv2d_134[0][0] \n__________________________________________________________________________________________________\ndrop_connect_28 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_100[0][0] \n__________________________________________________________________________________________________\nadd_28 (Add) (None, 7, 7, 304) 0 drop_connect_28[0][0] \n add_27[0][0] \n__________________________________________________________________________________________________\nconv2d_135 (Conv2D) (None, 7, 7, 1824) 554496 add_28[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_101 (BatchN (None, 7, 7, 1824) 7296 conv2d_135[0][0] \n__________________________________________________________________________________________________\nswish_101 (Swish) (None, 7, 7, 1824) 0 batch_normalization_101[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_35 (DepthwiseC (None, 7, 7, 1824) 45600 swish_101[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_102 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_35[0][0] \n__________________________________________________________________________________________________\nswish_102 (Swish) (None, 7, 7, 1824) 0 batch_normalization_102[0][0] \n__________________________________________________________________________________________________\nlambda_35 (Lambda) (None, 1, 1, 1824) 0 swish_102[0][0] \n__________________________________________________________________________________________________\nconv2d_136 (Conv2D) (None, 1, 1, 76) 138700 lambda_35[0][0] \n__________________________________________________________________________________________________\nswish_103 (Swish) (None, 1, 1, 76) 0 conv2d_136[0][0] \n__________________________________________________________________________________________________\nconv2d_137 (Conv2D) (None, 1, 1, 1824) 140448 swish_103[0][0] \n__________________________________________________________________________________________________\nactivation_35 (Activation) (None, 1, 1, 1824) 0 conv2d_137[0][0] \n__________________________________________________________________________________________________\nmultiply_35 (Multiply) (None, 7, 7, 1824) 0 activation_35[0][0] \n swish_102[0][0] \n__________________________________________________________________________________________________\nconv2d_138 (Conv2D) (None, 7, 7, 304) 554496 multiply_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_103 (BatchN (None, 7, 7, 304) 1216 conv2d_138[0][0] \n__________________________________________________________________________________________________\ndrop_connect_29 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_103[0][0] \n__________________________________________________________________________________________________\nadd_29 (Add) (None, 7, 7, 304) 0 drop_connect_29[0][0] \n add_28[0][0] \n__________________________________________________________________________________________________\nconv2d_139 (Conv2D) (None, 7, 7, 1824) 554496 add_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_104 (BatchN (None, 7, 7, 1824) 7296 conv2d_139[0][0] \n__________________________________________________________________________________________________\nswish_104 (Swish) (None, 7, 7, 1824) 0 batch_normalization_104[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_36 (DepthwiseC (None, 7, 7, 1824) 45600 swish_104[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_105 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_36[0][0] \n__________________________________________________________________________________________________\nswish_105 (Swish) (None, 7, 7, 1824) 0 batch_normalization_105[0][0] \n__________________________________________________________________________________________________\nlambda_36 (Lambda) (None, 1, 1, 1824) 0 swish_105[0][0] \n__________________________________________________________________________________________________\nconv2d_140 (Conv2D) (None, 1, 1, 76) 138700 lambda_36[0][0] \n__________________________________________________________________________________________________\nswish_106 (Swish) (None, 1, 1, 76) 0 conv2d_140[0][0] \n__________________________________________________________________________________________________\nconv2d_141 (Conv2D) (None, 1, 1, 1824) 140448 swish_106[0][0] \n__________________________________________________________________________________________________\nactivation_36 (Activation) (None, 1, 1, 1824) 0 conv2d_141[0][0] \n__________________________________________________________________________________________________\nmultiply_36 (Multiply) (None, 7, 7, 1824) 0 activation_36[0][0] \n swish_105[0][0] \n__________________________________________________________________________________________________\nconv2d_142 (Conv2D) (None, 7, 7, 304) 554496 multiply_36[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_106 (BatchN (None, 7, 7, 304) 1216 conv2d_142[0][0] \n__________________________________________________________________________________________________\ndrop_connect_30 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_106[0][0] \n__________________________________________________________________________________________________\nadd_30 (Add) (None, 7, 7, 304) 0 drop_connect_30[0][0] \n add_29[0][0] \n__________________________________________________________________________________________________\nconv2d_143 (Conv2D) (None, 7, 7, 1824) 554496 add_30[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_107 (BatchN (None, 7, 7, 1824) 7296 conv2d_143[0][0] \n__________________________________________________________________________________________________\nswish_107 (Swish) (None, 7, 7, 1824) 0 batch_normalization_107[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_37 (DepthwiseC (None, 7, 7, 1824) 16416 swish_107[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_108 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_37[0][0] \n__________________________________________________________________________________________________\nswish_108 (Swish) (None, 7, 7, 1824) 0 batch_normalization_108[0][0] \n__________________________________________________________________________________________________\nlambda_37 (Lambda) (None, 1, 1, 1824) 0 swish_108[0][0] \n__________________________________________________________________________________________________\nconv2d_144 (Conv2D) (None, 1, 1, 76) 138700 lambda_37[0][0] \n__________________________________________________________________________________________________\nswish_109 (Swish) (None, 1, 1, 76) 0 conv2d_144[0][0] \n__________________________________________________________________________________________________\nconv2d_145 (Conv2D) (None, 1, 1, 1824) 140448 swish_109[0][0] \n__________________________________________________________________________________________________\nactivation_37 (Activation) (None, 1, 1, 1824) 0 conv2d_145[0][0] \n__________________________________________________________________________________________________\nmultiply_37 (Multiply) (None, 7, 7, 1824) 0 activation_37[0][0] \n swish_108[0][0] \n__________________________________________________________________________________________________\nconv2d_146 (Conv2D) (None, 7, 7, 512) 933888 multiply_37[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_109 (BatchN (None, 7, 7, 512) 2048 conv2d_146[0][0] \n__________________________________________________________________________________________________\nconv2d_147 (Conv2D) (None, 7, 7, 3072) 1572864 batch_normalization_109[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_110 (BatchN (None, 7, 7, 3072) 12288 conv2d_147[0][0] \n__________________________________________________________________________________________________\nswish_110 (Swish) (None, 7, 7, 3072) 0 batch_normalization_110[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_38 (DepthwiseC (None, 7, 7, 3072) 27648 swish_110[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_111 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_38[0][0] \n__________________________________________________________________________________________________\nswish_111 (Swish) (None, 7, 7, 3072) 0 batch_normalization_111[0][0] \n__________________________________________________________________________________________________\nlambda_38 (Lambda) (None, 1, 1, 3072) 0 swish_111[0][0] \n__________________________________________________________________________________________________\nconv2d_148 (Conv2D) (None, 1, 1, 128) 393344 lambda_38[0][0] \n__________________________________________________________________________________________________\nswish_112 (Swish) (None, 1, 1, 128) 0 conv2d_148[0][0] \n__________________________________________________________________________________________________\nconv2d_149 (Conv2D) (None, 1, 1, 3072) 396288 swish_112[0][0] \n__________________________________________________________________________________________________\nactivation_38 (Activation) (None, 1, 1, 3072) 0 conv2d_149[0][0] \n__________________________________________________________________________________________________\nmultiply_38 (Multiply) (None, 7, 7, 3072) 0 activation_38[0][0] \n swish_111[0][0] \n__________________________________________________________________________________________________\nconv2d_150 (Conv2D) (None, 7, 7, 512) 1572864 multiply_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_112 (BatchN (None, 7, 7, 512) 2048 conv2d_150[0][0] \n__________________________________________________________________________________________________\ndrop_connect_31 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_112[0][0] \n__________________________________________________________________________________________________\nadd_31 (Add) (None, 7, 7, 512) 0 drop_connect_31[0][0] \n batch_normalization_109[0][0] \n__________________________________________________________________________________________________\nconv2d_151 (Conv2D) (None, 7, 7, 3072) 1572864 add_31[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_113 (BatchN (None, 7, 7, 3072) 12288 conv2d_151[0][0] \n__________________________________________________________________________________________________\nswish_113 (Swish) (None, 7, 7, 3072) 0 batch_normalization_113[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_39 (DepthwiseC (None, 7, 7, 3072) 27648 swish_113[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_114 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_39[0][0] \n__________________________________________________________________________________________________\nswish_114 (Swish) (None, 7, 7, 3072) 0 batch_normalization_114[0][0] \n__________________________________________________________________________________________________\nlambda_39 (Lambda) (None, 1, 1, 3072) 0 swish_114[0][0] \n__________________________________________________________________________________________________\nconv2d_152 (Conv2D) (None, 1, 1, 128) 393344 lambda_39[0][0] \n__________________________________________________________________________________________________\nswish_115 (Swish) (None, 1, 1, 128) 0 conv2d_152[0][0] \n__________________________________________________________________________________________________\nconv2d_153 (Conv2D) (None, 1, 1, 3072) 396288 swish_115[0][0] \n__________________________________________________________________________________________________\nactivation_39 (Activation) (None, 1, 1, 3072) 0 conv2d_153[0][0] \n__________________________________________________________________________________________________\nmultiply_39 (Multiply) (None, 7, 7, 3072) 0 activation_39[0][0] \n swish_114[0][0] \n__________________________________________________________________________________________________\nconv2d_154 (Conv2D) (None, 7, 7, 512) 1572864 multiply_39[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_115 (BatchN (None, 7, 7, 512) 2048 conv2d_154[0][0] \n__________________________________________________________________________________________________\ndrop_connect_32 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_115[0][0] \n__________________________________________________________________________________________________\nadd_32 (Add) (None, 7, 7, 512) 0 drop_connect_32[0][0] \n add_31[0][0] \n__________________________________________________________________________________________________\nconv2d_155 (Conv2D) (None, 7, 7, 2048) 1048576 add_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_116 (BatchN (None, 7, 7, 2048) 8192 conv2d_155[0][0] \n__________________________________________________________________________________________________\nswish_116 (Swish) (None, 7, 7, 2048) 0 batch_normalization_116[0][0] \n__________________________________________________________________________________________________\nglobal_average_pooling2d_1 (Glo (None, 2048) 0 swish_116[0][0] \n__________________________________________________________________________________________________\nfinal_output (Dense) (None, 1) 2049 global_average_pooling2d_1[0][0] \n==================================================================================================\nTotal params: 28,515,569\nTrainable params: 2,049\nNon-trainable params: 28,513,520\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"STEP_SIZE_TRAIN = train_generator.n//train_generator.batch_size\nSTEP_SIZE_VALID = valid_generator.n//valid_generator.batch_size\n\nhistory_warmup = model.fit_generator(generator=train_generator,\n steps_per_epoch=STEP_SIZE_TRAIN,\n validation_data=valid_generator,\n validation_steps=STEP_SIZE_VALID,\n epochs=WARMUP_EPOCHS,\n callbacks=callback_list,\n verbose=2).history","metadata":{"execution":{"iopub.status.busy":"2024-05-03T02:50:06.137528Z","iopub.execute_input":"2024-05-03T02:50:06.137795Z","iopub.status.idle":"2024-05-03T02:53:42.095494Z","shell.execute_reply.started":"2024-05-03T02:50:06.137744Z","shell.execute_reply":"2024-05-03T02:53:42.09442Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"Epoch 1/5\n - 53s - loss: 2.1217 - acc: 0.4227 - val_loss: 1.4636 - val_acc: 0.2543\nEpoch 2/5\n - 41s - loss: 1.2114 - acc: 0.4339 - val_loss: 1.4940 - val_acc: 0.2668\nEpoch 3/5\n - 40s - loss: 0.9714 - acc: 0.4454 - val_loss: 1.5299 - val_acc: 0.2340\nEpoch 4/5\n - 40s - loss: 0.8861 - acc: 0.4529 - val_loss: 2.3635 - val_acc: 0.3752\nEpoch 5/5\n - 41s - loss: 0.8598 - acc: 0.4653 - val_loss: 1.6375 - val_acc: 0.3252\n","output_type":"stream"}]},{"cell_type":"code","source":"for layer in model.layers:\n layer.trainable = True\n\nes = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)\ncosine_lr_2nd = WarmUpCosineDecayScheduler(learning_rate_base=LEARNING_RATE,\n total_steps=TOTAL_STEPS_2nd,\n warmup_learning_rate=0.0,\n warmup_steps=WARMUP_STEPS_2nd,\n hold_base_rate_steps=(3 * STEP_SIZE))\n\ncallback_list = [es, cosine_lr_2nd]\noptimizer = optimizers.Adam(lr=LEARNING_RATE)\nmodel.compile(optimizer=optimizer, loss='mean_squared_error', metrics=metric_list)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T02:53:42.097221Z","iopub.execute_input":"2024-05-03T02:53:42.097536Z","iopub.status.idle":"2024-05-03T02:53:42.380298Z","shell.execute_reply.started":"2024-05-03T02:53:42.097482Z","shell.execute_reply":"2024-05-03T02:53:42.379469Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_1 (InputLayer) (None, 224, 224, 3) 0 \n__________________________________________________________________________________________________\nconv2d_1 (Conv2D) (None, 112, 112, 48) 1296 input_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_1 (BatchNor (None, 112, 112, 48) 192 conv2d_1[0][0] \n__________________________________________________________________________________________________\nswish_1 (Swish) (None, 112, 112, 48) 0 batch_normalization_1[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_1 (DepthwiseCo (None, 112, 112, 48) 432 swish_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_2 (BatchNor (None, 112, 112, 48) 192 depthwise_conv2d_1[0][0] \n__________________________________________________________________________________________________\nswish_2 (Swish) (None, 112, 112, 48) 0 batch_normalization_2[0][0] \n__________________________________________________________________________________________________\nlambda_1 (Lambda) (None, 1, 1, 48) 0 swish_2[0][0] \n__________________________________________________________________________________________________\nconv2d_2 (Conv2D) (None, 1, 1, 12) 588 lambda_1[0][0] \n__________________________________________________________________________________________________\nswish_3 (Swish) (None, 1, 1, 12) 0 conv2d_2[0][0] \n__________________________________________________________________________________________________\nconv2d_3 (Conv2D) (None, 1, 1, 48) 624 swish_3[0][0] \n__________________________________________________________________________________________________\nactivation_1 (Activation) (None, 1, 1, 48) 0 conv2d_3[0][0] \n__________________________________________________________________________________________________\nmultiply_1 (Multiply) (None, 112, 112, 48) 0 activation_1[0][0] \n swish_2[0][0] \n__________________________________________________________________________________________________\nconv2d_4 (Conv2D) (None, 112, 112, 24) 1152 multiply_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_3 (BatchNor (None, 112, 112, 24) 96 conv2d_4[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_2 (DepthwiseCo (None, 112, 112, 24) 216 batch_normalization_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_4 (BatchNor (None, 112, 112, 24) 96 depthwise_conv2d_2[0][0] \n__________________________________________________________________________________________________\nswish_4 (Swish) (None, 112, 112, 24) 0 batch_normalization_4[0][0] \n__________________________________________________________________________________________________\nlambda_2 (Lambda) (None, 1, 1, 24) 0 swish_4[0][0] \n__________________________________________________________________________________________________\nconv2d_5 (Conv2D) (None, 1, 1, 6) 150 lambda_2[0][0] \n__________________________________________________________________________________________________\nswish_5 (Swish) (None, 1, 1, 6) 0 conv2d_5[0][0] \n__________________________________________________________________________________________________\nconv2d_6 (Conv2D) (None, 1, 1, 24) 168 swish_5[0][0] \n__________________________________________________________________________________________________\nactivation_2 (Activation) (None, 1, 1, 24) 0 conv2d_6[0][0] \n__________________________________________________________________________________________________\nmultiply_2 (Multiply) (None, 112, 112, 24) 0 activation_2[0][0] \n swish_4[0][0] \n__________________________________________________________________________________________________\nconv2d_7 (Conv2D) (None, 112, 112, 24) 576 multiply_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_5 (BatchNor (None, 112, 112, 24) 96 conv2d_7[0][0] \n__________________________________________________________________________________________________\ndrop_connect_1 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_5[0][0] \n__________________________________________________________________________________________________\nadd_1 (Add) (None, 112, 112, 24) 0 drop_connect_1[0][0] \n batch_normalization_3[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_3 (DepthwiseCo (None, 112, 112, 24) 216 add_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_6 (BatchNor (None, 112, 112, 24) 96 depthwise_conv2d_3[0][0] \n__________________________________________________________________________________________________\nswish_6 (Swish) (None, 112, 112, 24) 0 batch_normalization_6[0][0] \n__________________________________________________________________________________________________\nlambda_3 (Lambda) (None, 1, 1, 24) 0 swish_6[0][0] \n__________________________________________________________________________________________________\nconv2d_8 (Conv2D) (None, 1, 1, 6) 150 lambda_3[0][0] \n__________________________________________________________________________________________________\nswish_7 (Swish) (None, 1, 1, 6) 0 conv2d_8[0][0] \n__________________________________________________________________________________________________\nconv2d_9 (Conv2D) (None, 1, 1, 24) 168 swish_7[0][0] \n__________________________________________________________________________________________________\nactivation_3 (Activation) (None, 1, 1, 24) 0 conv2d_9[0][0] \n__________________________________________________________________________________________________\nmultiply_3 (Multiply) (None, 112, 112, 24) 0 activation_3[0][0] \n swish_6[0][0] \n__________________________________________________________________________________________________\nconv2d_10 (Conv2D) (None, 112, 112, 24) 576 multiply_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_7 (BatchNor (None, 112, 112, 24) 96 conv2d_10[0][0] \n__________________________________________________________________________________________________\ndrop_connect_2 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_7[0][0] \n__________________________________________________________________________________________________\nadd_2 (Add) (None, 112, 112, 24) 0 drop_connect_2[0][0] \n add_1[0][0] \n__________________________________________________________________________________________________\nconv2d_11 (Conv2D) (None, 112, 112, 144 3456 add_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_8 (BatchNor (None, 112, 112, 144 576 conv2d_11[0][0] \n__________________________________________________________________________________________________\nswish_8 (Swish) (None, 112, 112, 144 0 batch_normalization_8[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_4 (DepthwiseCo (None, 56, 56, 144) 1296 swish_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_9 (BatchNor (None, 56, 56, 144) 576 depthwise_conv2d_4[0][0] \n__________________________________________________________________________________________________\nswish_9 (Swish) (None, 56, 56, 144) 0 batch_normalization_9[0][0] \n__________________________________________________________________________________________________\nlambda_4 (Lambda) (None, 1, 1, 144) 0 swish_9[0][0] \n__________________________________________________________________________________________________\nconv2d_12 (Conv2D) (None, 1, 1, 6) 870 lambda_4[0][0] \n__________________________________________________________________________________________________\nswish_10 (Swish) (None, 1, 1, 6) 0 conv2d_12[0][0] \n__________________________________________________________________________________________________\nconv2d_13 (Conv2D) (None, 1, 1, 144) 1008 swish_10[0][0] \n__________________________________________________________________________________________________\nactivation_4 (Activation) (None, 1, 1, 144) 0 conv2d_13[0][0] \n__________________________________________________________________________________________________\nmultiply_4 (Multiply) (None, 56, 56, 144) 0 activation_4[0][0] \n swish_9[0][0] \n__________________________________________________________________________________________________\nconv2d_14 (Conv2D) (None, 56, 56, 40) 5760 multiply_4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_10 (BatchNo (None, 56, 56, 40) 160 conv2d_14[0][0] \n__________________________________________________________________________________________________\nconv2d_15 (Conv2D) (None, 56, 56, 240) 9600 batch_normalization_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_11 (BatchNo (None, 56, 56, 240) 960 conv2d_15[0][0] \n__________________________________________________________________________________________________\nswish_11 (Swish) (None, 56, 56, 240) 0 batch_normalization_11[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_5 (DepthwiseCo (None, 56, 56, 240) 2160 swish_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_12 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_5[0][0] \n__________________________________________________________________________________________________\nswish_12 (Swish) (None, 56, 56, 240) 0 batch_normalization_12[0][0] \n__________________________________________________________________________________________________\nlambda_5 (Lambda) (None, 1, 1, 240) 0 swish_12[0][0] \n__________________________________________________________________________________________________\nconv2d_16 (Conv2D) (None, 1, 1, 10) 2410 lambda_5[0][0] \n__________________________________________________________________________________________________\nswish_13 (Swish) (None, 1, 1, 10) 0 conv2d_16[0][0] \n__________________________________________________________________________________________________\nconv2d_17 (Conv2D) (None, 1, 1, 240) 2640 swish_13[0][0] \n__________________________________________________________________________________________________\nactivation_5 (Activation) (None, 1, 1, 240) 0 conv2d_17[0][0] \n__________________________________________________________________________________________________\nmultiply_5 (Multiply) (None, 56, 56, 240) 0 activation_5[0][0] \n swish_12[0][0] \n__________________________________________________________________________________________________\nconv2d_18 (Conv2D) (None, 56, 56, 40) 9600 multiply_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_13 (BatchNo (None, 56, 56, 40) 160 conv2d_18[0][0] \n__________________________________________________________________________________________________\ndrop_connect_3 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_13[0][0] \n__________________________________________________________________________________________________\nadd_3 (Add) (None, 56, 56, 40) 0 drop_connect_3[0][0] \n batch_normalization_10[0][0] \n__________________________________________________________________________________________________\nconv2d_19 (Conv2D) (None, 56, 56, 240) 9600 add_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_14 (BatchNo (None, 56, 56, 240) 960 conv2d_19[0][0] \n__________________________________________________________________________________________________\nswish_14 (Swish) (None, 56, 56, 240) 0 batch_normalization_14[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_6 (DepthwiseCo (None, 56, 56, 240) 2160 swish_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_15 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_6[0][0] \n__________________________________________________________________________________________________\nswish_15 (Swish) (None, 56, 56, 240) 0 batch_normalization_15[0][0] \n__________________________________________________________________________________________________\nlambda_6 (Lambda) (None, 1, 1, 240) 0 swish_15[0][0] \n__________________________________________________________________________________________________\nconv2d_20 (Conv2D) (None, 1, 1, 10) 2410 lambda_6[0][0] \n__________________________________________________________________________________________________\nswish_16 (Swish) (None, 1, 1, 10) 0 conv2d_20[0][0] \n__________________________________________________________________________________________________\nconv2d_21 (Conv2D) (None, 1, 1, 240) 2640 swish_16[0][0] \n__________________________________________________________________________________________________\nactivation_6 (Activation) (None, 1, 1, 240) 0 conv2d_21[0][0] \n__________________________________________________________________________________________________\nmultiply_6 (Multiply) (None, 56, 56, 240) 0 activation_6[0][0] \n swish_15[0][0] \n__________________________________________________________________________________________________\nconv2d_22 (Conv2D) (None, 56, 56, 40) 9600 multiply_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_16 (BatchNo (None, 56, 56, 40) 160 conv2d_22[0][0] \n__________________________________________________________________________________________________\ndrop_connect_4 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_16[0][0] \n__________________________________________________________________________________________________\nadd_4 (Add) (None, 56, 56, 40) 0 drop_connect_4[0][0] \n add_3[0][0] \n__________________________________________________________________________________________________\nconv2d_23 (Conv2D) (None, 56, 56, 240) 9600 add_4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_17 (BatchNo (None, 56, 56, 240) 960 conv2d_23[0][0] \n__________________________________________________________________________________________________\nswish_17 (Swish) (None, 56, 56, 240) 0 batch_normalization_17[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_7 (DepthwiseCo (None, 56, 56, 240) 2160 swish_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_18 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_7[0][0] \n__________________________________________________________________________________________________\nswish_18 (Swish) (None, 56, 56, 240) 0 batch_normalization_18[0][0] \n__________________________________________________________________________________________________\nlambda_7 (Lambda) (None, 1, 1, 240) 0 swish_18[0][0] \n__________________________________________________________________________________________________\nconv2d_24 (Conv2D) (None, 1, 1, 10) 2410 lambda_7[0][0] \n__________________________________________________________________________________________________\nswish_19 (Swish) (None, 1, 1, 10) 0 conv2d_24[0][0] \n__________________________________________________________________________________________________\nconv2d_25 (Conv2D) (None, 1, 1, 240) 2640 swish_19[0][0] \n__________________________________________________________________________________________________\nactivation_7 (Activation) (None, 1, 1, 240) 0 conv2d_25[0][0] \n__________________________________________________________________________________________________\nmultiply_7 (Multiply) (None, 56, 56, 240) 0 activation_7[0][0] \n swish_18[0][0] \n__________________________________________________________________________________________________\nconv2d_26 (Conv2D) (None, 56, 56, 40) 9600 multiply_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_19 (BatchNo (None, 56, 56, 40) 160 conv2d_26[0][0] \n__________________________________________________________________________________________________\ndrop_connect_5 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_19[0][0] \n__________________________________________________________________________________________________\nadd_5 (Add) (None, 56, 56, 40) 0 drop_connect_5[0][0] \n add_4[0][0] \n__________________________________________________________________________________________________\nconv2d_27 (Conv2D) (None, 56, 56, 240) 9600 add_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_20 (BatchNo (None, 56, 56, 240) 960 conv2d_27[0][0] \n__________________________________________________________________________________________________\nswish_20 (Swish) (None, 56, 56, 240) 0 batch_normalization_20[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_8 (DepthwiseCo (None, 56, 56, 240) 2160 swish_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_21 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_8[0][0] \n__________________________________________________________________________________________________\nswish_21 (Swish) (None, 56, 56, 240) 0 batch_normalization_21[0][0] \n__________________________________________________________________________________________________\nlambda_8 (Lambda) (None, 1, 1, 240) 0 swish_21[0][0] \n__________________________________________________________________________________________________\nconv2d_28 (Conv2D) (None, 1, 1, 10) 2410 lambda_8[0][0] \n__________________________________________________________________________________________________\nswish_22 (Swish) (None, 1, 1, 10) 0 conv2d_28[0][0] \n__________________________________________________________________________________________________\nconv2d_29 (Conv2D) (None, 1, 1, 240) 2640 swish_22[0][0] \n__________________________________________________________________________________________________\nactivation_8 (Activation) (None, 1, 1, 240) 0 conv2d_29[0][0] \n__________________________________________________________________________________________________\nmultiply_8 (Multiply) (None, 56, 56, 240) 0 activation_8[0][0] \n swish_21[0][0] \n__________________________________________________________________________________________________\nconv2d_30 (Conv2D) (None, 56, 56, 40) 9600 multiply_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_22 (BatchNo (None, 56, 56, 40) 160 conv2d_30[0][0] \n__________________________________________________________________________________________________\ndrop_connect_6 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_22[0][0] \n__________________________________________________________________________________________________\nadd_6 (Add) (None, 56, 56, 40) 0 drop_connect_6[0][0] \n add_5[0][0] \n__________________________________________________________________________________________________\nconv2d_31 (Conv2D) (None, 56, 56, 240) 9600 add_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_23 (BatchNo (None, 56, 56, 240) 960 conv2d_31[0][0] \n__________________________________________________________________________________________________\nswish_23 (Swish) (None, 56, 56, 240) 0 batch_normalization_23[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_9 (DepthwiseCo (None, 28, 28, 240) 6000 swish_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_24 (BatchNo (None, 28, 28, 240) 960 depthwise_conv2d_9[0][0] \n__________________________________________________________________________________________________\nswish_24 (Swish) (None, 28, 28, 240) 0 batch_normalization_24[0][0] \n__________________________________________________________________________________________________\nlambda_9 (Lambda) (None, 1, 1, 240) 0 swish_24[0][0] \n__________________________________________________________________________________________________\nconv2d_32 (Conv2D) (None, 1, 1, 10) 2410 lambda_9[0][0] \n__________________________________________________________________________________________________\nswish_25 (Swish) (None, 1, 1, 10) 0 conv2d_32[0][0] \n__________________________________________________________________________________________________\nconv2d_33 (Conv2D) (None, 1, 1, 240) 2640 swish_25[0][0] \n__________________________________________________________________________________________________\nactivation_9 (Activation) (None, 1, 1, 240) 0 conv2d_33[0][0] \n__________________________________________________________________________________________________\nmultiply_9 (Multiply) (None, 28, 28, 240) 0 activation_9[0][0] \n swish_24[0][0] \n__________________________________________________________________________________________________\nconv2d_34 (Conv2D) (None, 28, 28, 64) 15360 multiply_9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_25 (BatchNo (None, 28, 28, 64) 256 conv2d_34[0][0] \n__________________________________________________________________________________________________\nconv2d_35 (Conv2D) (None, 28, 28, 384) 24576 batch_normalization_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_26 (BatchNo (None, 28, 28, 384) 1536 conv2d_35[0][0] \n__________________________________________________________________________________________________\nswish_26 (Swish) (None, 28, 28, 384) 0 batch_normalization_26[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_10 (DepthwiseC (None, 28, 28, 384) 9600 swish_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_27 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_10[0][0] \n__________________________________________________________________________________________________\nswish_27 (Swish) (None, 28, 28, 384) 0 batch_normalization_27[0][0] \n__________________________________________________________________________________________________\nlambda_10 (Lambda) (None, 1, 1, 384) 0 swish_27[0][0] \n__________________________________________________________________________________________________\nconv2d_36 (Conv2D) (None, 1, 1, 16) 6160 lambda_10[0][0] \n__________________________________________________________________________________________________\nswish_28 (Swish) (None, 1, 1, 16) 0 conv2d_36[0][0] \n__________________________________________________________________________________________________\nconv2d_37 (Conv2D) (None, 1, 1, 384) 6528 swish_28[0][0] \n__________________________________________________________________________________________________\nactivation_10 (Activation) (None, 1, 1, 384) 0 conv2d_37[0][0] \n__________________________________________________________________________________________________\nmultiply_10 (Multiply) (None, 28, 28, 384) 0 activation_10[0][0] \n swish_27[0][0] \n__________________________________________________________________________________________________\nconv2d_38 (Conv2D) (None, 28, 28, 64) 24576 multiply_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_28 (BatchNo (None, 28, 28, 64) 256 conv2d_38[0][0] \n__________________________________________________________________________________________________\ndrop_connect_7 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_28[0][0] \n__________________________________________________________________________________________________\nadd_7 (Add) (None, 28, 28, 64) 0 drop_connect_7[0][0] \n batch_normalization_25[0][0] \n__________________________________________________________________________________________________\nconv2d_39 (Conv2D) (None, 28, 28, 384) 24576 add_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_29 (BatchNo (None, 28, 28, 384) 1536 conv2d_39[0][0] \n__________________________________________________________________________________________________\nswish_29 (Swish) (None, 28, 28, 384) 0 batch_normalization_29[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_11 (DepthwiseC (None, 28, 28, 384) 9600 swish_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_30 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_11[0][0] \n__________________________________________________________________________________________________\nswish_30 (Swish) (None, 28, 28, 384) 0 batch_normalization_30[0][0] \n__________________________________________________________________________________________________\nlambda_11 (Lambda) (None, 1, 1, 384) 0 swish_30[0][0] \n__________________________________________________________________________________________________\nconv2d_40 (Conv2D) (None, 1, 1, 16) 6160 lambda_11[0][0] \n__________________________________________________________________________________________________\nswish_31 (Swish) (None, 1, 1, 16) 0 conv2d_40[0][0] \n__________________________________________________________________________________________________\nconv2d_41 (Conv2D) (None, 1, 1, 384) 6528 swish_31[0][0] \n__________________________________________________________________________________________________\nactivation_11 (Activation) (None, 1, 1, 384) 0 conv2d_41[0][0] \n__________________________________________________________________________________________________\nmultiply_11 (Multiply) (None, 28, 28, 384) 0 activation_11[0][0] \n swish_30[0][0] \n__________________________________________________________________________________________________\nconv2d_42 (Conv2D) (None, 28, 28, 64) 24576 multiply_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_31 (BatchNo (None, 28, 28, 64) 256 conv2d_42[0][0] \n__________________________________________________________________________________________________\ndrop_connect_8 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_31[0][0] \n__________________________________________________________________________________________________\nadd_8 (Add) (None, 28, 28, 64) 0 drop_connect_8[0][0] \n add_7[0][0] \n__________________________________________________________________________________________________\nconv2d_43 (Conv2D) (None, 28, 28, 384) 24576 add_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_32 (BatchNo (None, 28, 28, 384) 1536 conv2d_43[0][0] \n__________________________________________________________________________________________________\nswish_32 (Swish) (None, 28, 28, 384) 0 batch_normalization_32[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_12 (DepthwiseC (None, 28, 28, 384) 9600 swish_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_33 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_12[0][0] \n__________________________________________________________________________________________________\nswish_33 (Swish) (None, 28, 28, 384) 0 batch_normalization_33[0][0] \n__________________________________________________________________________________________________\nlambda_12 (Lambda) (None, 1, 1, 384) 0 swish_33[0][0] \n__________________________________________________________________________________________________\nconv2d_44 (Conv2D) (None, 1, 1, 16) 6160 lambda_12[0][0] \n__________________________________________________________________________________________________\nswish_34 (Swish) (None, 1, 1, 16) 0 conv2d_44[0][0] \n__________________________________________________________________________________________________\nconv2d_45 (Conv2D) (None, 1, 1, 384) 6528 swish_34[0][0] \n__________________________________________________________________________________________________\nactivation_12 (Activation) (None, 1, 1, 384) 0 conv2d_45[0][0] \n__________________________________________________________________________________________________\nmultiply_12 (Multiply) (None, 28, 28, 384) 0 activation_12[0][0] \n swish_33[0][0] \n__________________________________________________________________________________________________\nconv2d_46 (Conv2D) (None, 28, 28, 64) 24576 multiply_12[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_34 (BatchNo (None, 28, 28, 64) 256 conv2d_46[0][0] \n__________________________________________________________________________________________________\ndrop_connect_9 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_34[0][0] \n__________________________________________________________________________________________________\nadd_9 (Add) (None, 28, 28, 64) 0 drop_connect_9[0][0] \n add_8[0][0] \n__________________________________________________________________________________________________\nconv2d_47 (Conv2D) (None, 28, 28, 384) 24576 add_9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_35 (BatchNo (None, 28, 28, 384) 1536 conv2d_47[0][0] \n__________________________________________________________________________________________________\nswish_35 (Swish) (None, 28, 28, 384) 0 batch_normalization_35[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_13 (DepthwiseC (None, 28, 28, 384) 9600 swish_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_36 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_13[0][0] \n__________________________________________________________________________________________________\nswish_36 (Swish) (None, 28, 28, 384) 0 batch_normalization_36[0][0] \n__________________________________________________________________________________________________\nlambda_13 (Lambda) (None, 1, 1, 384) 0 swish_36[0][0] \n__________________________________________________________________________________________________\nconv2d_48 (Conv2D) (None, 1, 1, 16) 6160 lambda_13[0][0] \n__________________________________________________________________________________________________\nswish_37 (Swish) (None, 1, 1, 16) 0 conv2d_48[0][0] \n__________________________________________________________________________________________________\nconv2d_49 (Conv2D) (None, 1, 1, 384) 6528 swish_37[0][0] \n__________________________________________________________________________________________________\nactivation_13 (Activation) (None, 1, 1, 384) 0 conv2d_49[0][0] \n__________________________________________________________________________________________________\nmultiply_13 (Multiply) (None, 28, 28, 384) 0 activation_13[0][0] \n swish_36[0][0] \n__________________________________________________________________________________________________\nconv2d_50 (Conv2D) (None, 28, 28, 64) 24576 multiply_13[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_37 (BatchNo (None, 28, 28, 64) 256 conv2d_50[0][0] \n__________________________________________________________________________________________________\ndrop_connect_10 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_37[0][0] \n__________________________________________________________________________________________________\nadd_10 (Add) (None, 28, 28, 64) 0 drop_connect_10[0][0] \n add_9[0][0] \n__________________________________________________________________________________________________\nconv2d_51 (Conv2D) (None, 28, 28, 384) 24576 add_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_38 (BatchNo (None, 28, 28, 384) 1536 conv2d_51[0][0] \n__________________________________________________________________________________________________\nswish_38 (Swish) (None, 28, 28, 384) 0 batch_normalization_38[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_14 (DepthwiseC (None, 14, 14, 384) 3456 swish_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_39 (BatchNo (None, 14, 14, 384) 1536 depthwise_conv2d_14[0][0] \n__________________________________________________________________________________________________\nswish_39 (Swish) (None, 14, 14, 384) 0 batch_normalization_39[0][0] \n__________________________________________________________________________________________________\nlambda_14 (Lambda) (None, 1, 1, 384) 0 swish_39[0][0] \n__________________________________________________________________________________________________\nconv2d_52 (Conv2D) (None, 1, 1, 16) 6160 lambda_14[0][0] \n__________________________________________________________________________________________________\nswish_40 (Swish) (None, 1, 1, 16) 0 conv2d_52[0][0] \n__________________________________________________________________________________________________\nconv2d_53 (Conv2D) (None, 1, 1, 384) 6528 swish_40[0][0] \n__________________________________________________________________________________________________\nactivation_14 (Activation) (None, 1, 1, 384) 0 conv2d_53[0][0] \n__________________________________________________________________________________________________\nmultiply_14 (Multiply) (None, 14, 14, 384) 0 activation_14[0][0] \n swish_39[0][0] \n__________________________________________________________________________________________________\nconv2d_54 (Conv2D) (None, 14, 14, 128) 49152 multiply_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_40 (BatchNo (None, 14, 14, 128) 512 conv2d_54[0][0] \n__________________________________________________________________________________________________\nconv2d_55 (Conv2D) (None, 14, 14, 768) 98304 batch_normalization_40[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_41 (BatchNo (None, 14, 14, 768) 3072 conv2d_55[0][0] \n__________________________________________________________________________________________________\nswish_41 (Swish) (None, 14, 14, 768) 0 batch_normalization_41[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_15 (DepthwiseC (None, 14, 14, 768) 6912 swish_41[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_42 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_15[0][0] \n__________________________________________________________________________________________________\nswish_42 (Swish) (None, 14, 14, 768) 0 batch_normalization_42[0][0] \n__________________________________________________________________________________________________\nlambda_15 (Lambda) (None, 1, 1, 768) 0 swish_42[0][0] \n__________________________________________________________________________________________________\nconv2d_56 (Conv2D) (None, 1, 1, 32) 24608 lambda_15[0][0] \n__________________________________________________________________________________________________\nswish_43 (Swish) (None, 1, 1, 32) 0 conv2d_56[0][0] \n__________________________________________________________________________________________________\nconv2d_57 (Conv2D) (None, 1, 1, 768) 25344 swish_43[0][0] \n__________________________________________________________________________________________________\nactivation_15 (Activation) (None, 1, 1, 768) 0 conv2d_57[0][0] \n__________________________________________________________________________________________________\nmultiply_15 (Multiply) (None, 14, 14, 768) 0 activation_15[0][0] \n swish_42[0][0] \n__________________________________________________________________________________________________\nconv2d_58 (Conv2D) (None, 14, 14, 128) 98304 multiply_15[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_43 (BatchNo (None, 14, 14, 128) 512 conv2d_58[0][0] \n__________________________________________________________________________________________________\ndrop_connect_11 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_43[0][0] \n__________________________________________________________________________________________________\nadd_11 (Add) (None, 14, 14, 128) 0 drop_connect_11[0][0] \n batch_normalization_40[0][0] \n__________________________________________________________________________________________________\nconv2d_59 (Conv2D) (None, 14, 14, 768) 98304 add_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_44 (BatchNo (None, 14, 14, 768) 3072 conv2d_59[0][0] \n__________________________________________________________________________________________________\nswish_44 (Swish) (None, 14, 14, 768) 0 batch_normalization_44[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_16 (DepthwiseC (None, 14, 14, 768) 6912 swish_44[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_45 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_16[0][0] \n__________________________________________________________________________________________________\nswish_45 (Swish) (None, 14, 14, 768) 0 batch_normalization_45[0][0] \n__________________________________________________________________________________________________\nlambda_16 (Lambda) (None, 1, 1, 768) 0 swish_45[0][0] \n__________________________________________________________________________________________________\nconv2d_60 (Conv2D) (None, 1, 1, 32) 24608 lambda_16[0][0] \n__________________________________________________________________________________________________\nswish_46 (Swish) (None, 1, 1, 32) 0 conv2d_60[0][0] \n__________________________________________________________________________________________________\nconv2d_61 (Conv2D) (None, 1, 1, 768) 25344 swish_46[0][0] \n__________________________________________________________________________________________________\nactivation_16 (Activation) (None, 1, 1, 768) 0 conv2d_61[0][0] \n__________________________________________________________________________________________________\nmultiply_16 (Multiply) (None, 14, 14, 768) 0 activation_16[0][0] \n swish_45[0][0] \n__________________________________________________________________________________________________\nconv2d_62 (Conv2D) (None, 14, 14, 128) 98304 multiply_16[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_46 (BatchNo (None, 14, 14, 128) 512 conv2d_62[0][0] \n__________________________________________________________________________________________________\ndrop_connect_12 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_46[0][0] \n__________________________________________________________________________________________________\nadd_12 (Add) (None, 14, 14, 128) 0 drop_connect_12[0][0] \n add_11[0][0] \n__________________________________________________________________________________________________\nconv2d_63 (Conv2D) (None, 14, 14, 768) 98304 add_12[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_47 (BatchNo (None, 14, 14, 768) 3072 conv2d_63[0][0] \n__________________________________________________________________________________________________\nswish_47 (Swish) (None, 14, 14, 768) 0 batch_normalization_47[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_17 (DepthwiseC (None, 14, 14, 768) 6912 swish_47[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_48 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_17[0][0] \n__________________________________________________________________________________________________\nswish_48 (Swish) (None, 14, 14, 768) 0 batch_normalization_48[0][0] \n__________________________________________________________________________________________________\nlambda_17 (Lambda) (None, 1, 1, 768) 0 swish_48[0][0] \n__________________________________________________________________________________________________\nconv2d_64 (Conv2D) (None, 1, 1, 32) 24608 lambda_17[0][0] \n__________________________________________________________________________________________________\nswish_49 (Swish) (None, 1, 1, 32) 0 conv2d_64[0][0] \n__________________________________________________________________________________________________\nconv2d_65 (Conv2D) (None, 1, 1, 768) 25344 swish_49[0][0] \n__________________________________________________________________________________________________\nactivation_17 (Activation) (None, 1, 1, 768) 0 conv2d_65[0][0] \n__________________________________________________________________________________________________\nmultiply_17 (Multiply) (None, 14, 14, 768) 0 activation_17[0][0] \n swish_48[0][0] \n__________________________________________________________________________________________________\nconv2d_66 (Conv2D) (None, 14, 14, 128) 98304 multiply_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_49 (BatchNo (None, 14, 14, 128) 512 conv2d_66[0][0] \n__________________________________________________________________________________________________\ndrop_connect_13 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_49[0][0] \n__________________________________________________________________________________________________\nadd_13 (Add) (None, 14, 14, 128) 0 drop_connect_13[0][0] \n add_12[0][0] \n__________________________________________________________________________________________________\nconv2d_67 (Conv2D) (None, 14, 14, 768) 98304 add_13[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_50 (BatchNo (None, 14, 14, 768) 3072 conv2d_67[0][0] \n__________________________________________________________________________________________________\nswish_50 (Swish) (None, 14, 14, 768) 0 batch_normalization_50[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_18 (DepthwiseC (None, 14, 14, 768) 6912 swish_50[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_51 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_18[0][0] \n__________________________________________________________________________________________________\nswish_51 (Swish) (None, 14, 14, 768) 0 batch_normalization_51[0][0] \n__________________________________________________________________________________________________\nlambda_18 (Lambda) (None, 1, 1, 768) 0 swish_51[0][0] \n__________________________________________________________________________________________________\nconv2d_68 (Conv2D) (None, 1, 1, 32) 24608 lambda_18[0][0] \n__________________________________________________________________________________________________\nswish_52 (Swish) (None, 1, 1, 32) 0 conv2d_68[0][0] \n__________________________________________________________________________________________________\nconv2d_69 (Conv2D) (None, 1, 1, 768) 25344 swish_52[0][0] \n__________________________________________________________________________________________________\nactivation_18 (Activation) (None, 1, 1, 768) 0 conv2d_69[0][0] \n__________________________________________________________________________________________________\nmultiply_18 (Multiply) (None, 14, 14, 768) 0 activation_18[0][0] \n swish_51[0][0] \n__________________________________________________________________________________________________\nconv2d_70 (Conv2D) (None, 14, 14, 128) 98304 multiply_18[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_52 (BatchNo (None, 14, 14, 128) 512 conv2d_70[0][0] \n__________________________________________________________________________________________________\ndrop_connect_14 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_52[0][0] \n__________________________________________________________________________________________________\nadd_14 (Add) (None, 14, 14, 128) 0 drop_connect_14[0][0] \n add_13[0][0] \n__________________________________________________________________________________________________\nconv2d_71 (Conv2D) (None, 14, 14, 768) 98304 add_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_53 (BatchNo (None, 14, 14, 768) 3072 conv2d_71[0][0] \n__________________________________________________________________________________________________\nswish_53 (Swish) (None, 14, 14, 768) 0 batch_normalization_53[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_19 (DepthwiseC (None, 14, 14, 768) 6912 swish_53[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_54 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_19[0][0] \n__________________________________________________________________________________________________\nswish_54 (Swish) (None, 14, 14, 768) 0 batch_normalization_54[0][0] \n__________________________________________________________________________________________________\nlambda_19 (Lambda) (None, 1, 1, 768) 0 swish_54[0][0] \n__________________________________________________________________________________________________\nconv2d_72 (Conv2D) (None, 1, 1, 32) 24608 lambda_19[0][0] \n__________________________________________________________________________________________________\nswish_55 (Swish) (None, 1, 1, 32) 0 conv2d_72[0][0] \n__________________________________________________________________________________________________\nconv2d_73 (Conv2D) (None, 1, 1, 768) 25344 swish_55[0][0] \n__________________________________________________________________________________________________\nactivation_19 (Activation) (None, 1, 1, 768) 0 conv2d_73[0][0] \n__________________________________________________________________________________________________\nmultiply_19 (Multiply) (None, 14, 14, 768) 0 activation_19[0][0] \n swish_54[0][0] \n__________________________________________________________________________________________________\nconv2d_74 (Conv2D) (None, 14, 14, 128) 98304 multiply_19[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_55 (BatchNo (None, 14, 14, 128) 512 conv2d_74[0][0] \n__________________________________________________________________________________________________\ndrop_connect_15 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_55[0][0] \n__________________________________________________________________________________________________\nadd_15 (Add) (None, 14, 14, 128) 0 drop_connect_15[0][0] \n add_14[0][0] \n__________________________________________________________________________________________________\nconv2d_75 (Conv2D) (None, 14, 14, 768) 98304 add_15[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_56 (BatchNo (None, 14, 14, 768) 3072 conv2d_75[0][0] \n__________________________________________________________________________________________________\nswish_56 (Swish) (None, 14, 14, 768) 0 batch_normalization_56[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_20 (DepthwiseC (None, 14, 14, 768) 6912 swish_56[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_57 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_20[0][0] \n__________________________________________________________________________________________________\nswish_57 (Swish) (None, 14, 14, 768) 0 batch_normalization_57[0][0] \n__________________________________________________________________________________________________\nlambda_20 (Lambda) (None, 1, 1, 768) 0 swish_57[0][0] \n__________________________________________________________________________________________________\nconv2d_76 (Conv2D) (None, 1, 1, 32) 24608 lambda_20[0][0] \n__________________________________________________________________________________________________\nswish_58 (Swish) (None, 1, 1, 32) 0 conv2d_76[0][0] \n__________________________________________________________________________________________________\nconv2d_77 (Conv2D) (None, 1, 1, 768) 25344 swish_58[0][0] \n__________________________________________________________________________________________________\nactivation_20 (Activation) (None, 1, 1, 768) 0 conv2d_77[0][0] \n__________________________________________________________________________________________________\nmultiply_20 (Multiply) (None, 14, 14, 768) 0 activation_20[0][0] \n swish_57[0][0] \n__________________________________________________________________________________________________\nconv2d_78 (Conv2D) (None, 14, 14, 128) 98304 multiply_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_58 (BatchNo (None, 14, 14, 128) 512 conv2d_78[0][0] \n__________________________________________________________________________________________________\ndrop_connect_16 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_58[0][0] \n__________________________________________________________________________________________________\nadd_16 (Add) (None, 14, 14, 128) 0 drop_connect_16[0][0] \n add_15[0][0] \n__________________________________________________________________________________________________\nconv2d_79 (Conv2D) (None, 14, 14, 768) 98304 add_16[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_59 (BatchNo (None, 14, 14, 768) 3072 conv2d_79[0][0] \n__________________________________________________________________________________________________\nswish_59 (Swish) (None, 14, 14, 768) 0 batch_normalization_59[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_21 (DepthwiseC (None, 14, 14, 768) 19200 swish_59[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_60 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_21[0][0] \n__________________________________________________________________________________________________\nswish_60 (Swish) (None, 14, 14, 768) 0 batch_normalization_60[0][0] \n__________________________________________________________________________________________________\nlambda_21 (Lambda) (None, 1, 1, 768) 0 swish_60[0][0] \n__________________________________________________________________________________________________\nconv2d_80 (Conv2D) (None, 1, 1, 32) 24608 lambda_21[0][0] \n__________________________________________________________________________________________________\nswish_61 (Swish) (None, 1, 1, 32) 0 conv2d_80[0][0] \n__________________________________________________________________________________________________\nconv2d_81 (Conv2D) (None, 1, 1, 768) 25344 swish_61[0][0] \n__________________________________________________________________________________________________\nactivation_21 (Activation) (None, 1, 1, 768) 0 conv2d_81[0][0] \n__________________________________________________________________________________________________\nmultiply_21 (Multiply) (None, 14, 14, 768) 0 activation_21[0][0] \n swish_60[0][0] \n__________________________________________________________________________________________________\nconv2d_82 (Conv2D) (None, 14, 14, 176) 135168 multiply_21[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_61 (BatchNo (None, 14, 14, 176) 704 conv2d_82[0][0] \n__________________________________________________________________________________________________\nconv2d_83 (Conv2D) (None, 14, 14, 1056) 185856 batch_normalization_61[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_62 (BatchNo (None, 14, 14, 1056) 4224 conv2d_83[0][0] \n__________________________________________________________________________________________________\nswish_62 (Swish) (None, 14, 14, 1056) 0 batch_normalization_62[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_22 (DepthwiseC (None, 14, 14, 1056) 26400 swish_62[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_63 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_22[0][0] \n__________________________________________________________________________________________________\nswish_63 (Swish) (None, 14, 14, 1056) 0 batch_normalization_63[0][0] \n__________________________________________________________________________________________________\nlambda_22 (Lambda) (None, 1, 1, 1056) 0 swish_63[0][0] \n__________________________________________________________________________________________________\nconv2d_84 (Conv2D) (None, 1, 1, 44) 46508 lambda_22[0][0] \n__________________________________________________________________________________________________\nswish_64 (Swish) (None, 1, 1, 44) 0 conv2d_84[0][0] \n__________________________________________________________________________________________________\nconv2d_85 (Conv2D) (None, 1, 1, 1056) 47520 swish_64[0][0] \n__________________________________________________________________________________________________\nactivation_22 (Activation) (None, 1, 1, 1056) 0 conv2d_85[0][0] \n__________________________________________________________________________________________________\nmultiply_22 (Multiply) (None, 14, 14, 1056) 0 activation_22[0][0] \n swish_63[0][0] \n__________________________________________________________________________________________________\nconv2d_86 (Conv2D) (None, 14, 14, 176) 185856 multiply_22[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_64 (BatchNo (None, 14, 14, 176) 704 conv2d_86[0][0] \n__________________________________________________________________________________________________\ndrop_connect_17 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_64[0][0] \n__________________________________________________________________________________________________\nadd_17 (Add) (None, 14, 14, 176) 0 drop_connect_17[0][0] \n batch_normalization_61[0][0] \n__________________________________________________________________________________________________\nconv2d_87 (Conv2D) (None, 14, 14, 1056) 185856 add_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_65 (BatchNo (None, 14, 14, 1056) 4224 conv2d_87[0][0] \n__________________________________________________________________________________________________\nswish_65 (Swish) (None, 14, 14, 1056) 0 batch_normalization_65[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_23 (DepthwiseC (None, 14, 14, 1056) 26400 swish_65[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_66 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_23[0][0] \n__________________________________________________________________________________________________\nswish_66 (Swish) (None, 14, 14, 1056) 0 batch_normalization_66[0][0] \n__________________________________________________________________________________________________\nlambda_23 (Lambda) (None, 1, 1, 1056) 0 swish_66[0][0] \n__________________________________________________________________________________________________\nconv2d_88 (Conv2D) (None, 1, 1, 44) 46508 lambda_23[0][0] \n__________________________________________________________________________________________________\nswish_67 (Swish) (None, 1, 1, 44) 0 conv2d_88[0][0] \n__________________________________________________________________________________________________\nconv2d_89 (Conv2D) (None, 1, 1, 1056) 47520 swish_67[0][0] \n__________________________________________________________________________________________________\nactivation_23 (Activation) (None, 1, 1, 1056) 0 conv2d_89[0][0] \n__________________________________________________________________________________________________\nmultiply_23 (Multiply) (None, 14, 14, 1056) 0 activation_23[0][0] \n swish_66[0][0] \n__________________________________________________________________________________________________\nconv2d_90 (Conv2D) (None, 14, 14, 176) 185856 multiply_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_67 (BatchNo (None, 14, 14, 176) 704 conv2d_90[0][0] \n__________________________________________________________________________________________________\ndrop_connect_18 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_67[0][0] \n__________________________________________________________________________________________________\nadd_18 (Add) (None, 14, 14, 176) 0 drop_connect_18[0][0] \n add_17[0][0] \n__________________________________________________________________________________________________\nconv2d_91 (Conv2D) (None, 14, 14, 1056) 185856 add_18[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_68 (BatchNo (None, 14, 14, 1056) 4224 conv2d_91[0][0] \n__________________________________________________________________________________________________\nswish_68 (Swish) (None, 14, 14, 1056) 0 batch_normalization_68[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_24 (DepthwiseC (None, 14, 14, 1056) 26400 swish_68[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_69 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_24[0][0] \n__________________________________________________________________________________________________\nswish_69 (Swish) (None, 14, 14, 1056) 0 batch_normalization_69[0][0] \n__________________________________________________________________________________________________\nlambda_24 (Lambda) (None, 1, 1, 1056) 0 swish_69[0][0] \n__________________________________________________________________________________________________\nconv2d_92 (Conv2D) (None, 1, 1, 44) 46508 lambda_24[0][0] \n__________________________________________________________________________________________________\nswish_70 (Swish) (None, 1, 1, 44) 0 conv2d_92[0][0] \n__________________________________________________________________________________________________\nconv2d_93 (Conv2D) (None, 1, 1, 1056) 47520 swish_70[0][0] \n__________________________________________________________________________________________________\nactivation_24 (Activation) (None, 1, 1, 1056) 0 conv2d_93[0][0] \n__________________________________________________________________________________________________\nmultiply_24 (Multiply) (None, 14, 14, 1056) 0 activation_24[0][0] \n swish_69[0][0] \n__________________________________________________________________________________________________\nconv2d_94 (Conv2D) (None, 14, 14, 176) 185856 multiply_24[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_70 (BatchNo (None, 14, 14, 176) 704 conv2d_94[0][0] \n__________________________________________________________________________________________________\ndrop_connect_19 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_70[0][0] \n__________________________________________________________________________________________________\nadd_19 (Add) (None, 14, 14, 176) 0 drop_connect_19[0][0] \n add_18[0][0] \n__________________________________________________________________________________________________\nconv2d_95 (Conv2D) (None, 14, 14, 1056) 185856 add_19[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_71 (BatchNo (None, 14, 14, 1056) 4224 conv2d_95[0][0] \n__________________________________________________________________________________________________\nswish_71 (Swish) (None, 14, 14, 1056) 0 batch_normalization_71[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_25 (DepthwiseC (None, 14, 14, 1056) 26400 swish_71[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_72 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_25[0][0] \n__________________________________________________________________________________________________\nswish_72 (Swish) (None, 14, 14, 1056) 0 batch_normalization_72[0][0] \n__________________________________________________________________________________________________\nlambda_25 (Lambda) (None, 1, 1, 1056) 0 swish_72[0][0] \n__________________________________________________________________________________________________\nconv2d_96 (Conv2D) (None, 1, 1, 44) 46508 lambda_25[0][0] \n__________________________________________________________________________________________________\nswish_73 (Swish) (None, 1, 1, 44) 0 conv2d_96[0][0] \n__________________________________________________________________________________________________\nconv2d_97 (Conv2D) (None, 1, 1, 1056) 47520 swish_73[0][0] \n__________________________________________________________________________________________________\nactivation_25 (Activation) (None, 1, 1, 1056) 0 conv2d_97[0][0] \n__________________________________________________________________________________________________\nmultiply_25 (Multiply) (None, 14, 14, 1056) 0 activation_25[0][0] \n swish_72[0][0] \n__________________________________________________________________________________________________\nconv2d_98 (Conv2D) (None, 14, 14, 176) 185856 multiply_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_73 (BatchNo (None, 14, 14, 176) 704 conv2d_98[0][0] \n__________________________________________________________________________________________________\ndrop_connect_20 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_73[0][0] \n__________________________________________________________________________________________________\nadd_20 (Add) (None, 14, 14, 176) 0 drop_connect_20[0][0] \n add_19[0][0] \n__________________________________________________________________________________________________\nconv2d_99 (Conv2D) (None, 14, 14, 1056) 185856 add_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_74 (BatchNo (None, 14, 14, 1056) 4224 conv2d_99[0][0] \n__________________________________________________________________________________________________\nswish_74 (Swish) (None, 14, 14, 1056) 0 batch_normalization_74[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_26 (DepthwiseC (None, 14, 14, 1056) 26400 swish_74[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_75 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_26[0][0] \n__________________________________________________________________________________________________\nswish_75 (Swish) (None, 14, 14, 1056) 0 batch_normalization_75[0][0] \n__________________________________________________________________________________________________\nlambda_26 (Lambda) (None, 1, 1, 1056) 0 swish_75[0][0] \n__________________________________________________________________________________________________\nconv2d_100 (Conv2D) (None, 1, 1, 44) 46508 lambda_26[0][0] \n__________________________________________________________________________________________________\nswish_76 (Swish) (None, 1, 1, 44) 0 conv2d_100[0][0] \n__________________________________________________________________________________________________\nconv2d_101 (Conv2D) (None, 1, 1, 1056) 47520 swish_76[0][0] \n__________________________________________________________________________________________________\nactivation_26 (Activation) (None, 1, 1, 1056) 0 conv2d_101[0][0] \n__________________________________________________________________________________________________\nmultiply_26 (Multiply) (None, 14, 14, 1056) 0 activation_26[0][0] \n swish_75[0][0] \n__________________________________________________________________________________________________\nconv2d_102 (Conv2D) (None, 14, 14, 176) 185856 multiply_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_76 (BatchNo (None, 14, 14, 176) 704 conv2d_102[0][0] \n__________________________________________________________________________________________________\ndrop_connect_21 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_76[0][0] \n__________________________________________________________________________________________________\nadd_21 (Add) (None, 14, 14, 176) 0 drop_connect_21[0][0] \n add_20[0][0] \n__________________________________________________________________________________________________\nconv2d_103 (Conv2D) (None, 14, 14, 1056) 185856 add_21[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_77 (BatchNo (None, 14, 14, 1056) 4224 conv2d_103[0][0] \n__________________________________________________________________________________________________\nswish_77 (Swish) (None, 14, 14, 1056) 0 batch_normalization_77[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_27 (DepthwiseC (None, 14, 14, 1056) 26400 swish_77[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_78 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_27[0][0] \n__________________________________________________________________________________________________\nswish_78 (Swish) (None, 14, 14, 1056) 0 batch_normalization_78[0][0] \n__________________________________________________________________________________________________\nlambda_27 (Lambda) (None, 1, 1, 1056) 0 swish_78[0][0] \n__________________________________________________________________________________________________\nconv2d_104 (Conv2D) (None, 1, 1, 44) 46508 lambda_27[0][0] \n__________________________________________________________________________________________________\nswish_79 (Swish) (None, 1, 1, 44) 0 conv2d_104[0][0] \n__________________________________________________________________________________________________\nconv2d_105 (Conv2D) (None, 1, 1, 1056) 47520 swish_79[0][0] \n__________________________________________________________________________________________________\nactivation_27 (Activation) (None, 1, 1, 1056) 0 conv2d_105[0][0] \n__________________________________________________________________________________________________\nmultiply_27 (Multiply) (None, 14, 14, 1056) 0 activation_27[0][0] \n swish_78[0][0] \n__________________________________________________________________________________________________\nconv2d_106 (Conv2D) (None, 14, 14, 176) 185856 multiply_27[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_79 (BatchNo (None, 14, 14, 176) 704 conv2d_106[0][0] \n__________________________________________________________________________________________________\ndrop_connect_22 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_79[0][0] \n__________________________________________________________________________________________________\nadd_22 (Add) (None, 14, 14, 176) 0 drop_connect_22[0][0] \n add_21[0][0] \n__________________________________________________________________________________________________\nconv2d_107 (Conv2D) (None, 14, 14, 1056) 185856 add_22[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_80 (BatchNo (None, 14, 14, 1056) 4224 conv2d_107[0][0] \n__________________________________________________________________________________________________\nswish_80 (Swish) (None, 14, 14, 1056) 0 batch_normalization_80[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_28 (DepthwiseC (None, 7, 7, 1056) 26400 swish_80[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_81 (BatchNo (None, 7, 7, 1056) 4224 depthwise_conv2d_28[0][0] \n__________________________________________________________________________________________________\nswish_81 (Swish) (None, 7, 7, 1056) 0 batch_normalization_81[0][0] \n__________________________________________________________________________________________________\nlambda_28 (Lambda) (None, 1, 1, 1056) 0 swish_81[0][0] \n__________________________________________________________________________________________________\nconv2d_108 (Conv2D) (None, 1, 1, 44) 46508 lambda_28[0][0] \n__________________________________________________________________________________________________\nswish_82 (Swish) (None, 1, 1, 44) 0 conv2d_108[0][0] \n__________________________________________________________________________________________________\nconv2d_109 (Conv2D) (None, 1, 1, 1056) 47520 swish_82[0][0] \n__________________________________________________________________________________________________\nactivation_28 (Activation) (None, 1, 1, 1056) 0 conv2d_109[0][0] \n__________________________________________________________________________________________________\nmultiply_28 (Multiply) (None, 7, 7, 1056) 0 activation_28[0][0] \n swish_81[0][0] \n__________________________________________________________________________________________________\nconv2d_110 (Conv2D) (None, 7, 7, 304) 321024 multiply_28[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_82 (BatchNo (None, 7, 7, 304) 1216 conv2d_110[0][0] \n__________________________________________________________________________________________________\nconv2d_111 (Conv2D) (None, 7, 7, 1824) 554496 batch_normalization_82[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_83 (BatchNo (None, 7, 7, 1824) 7296 conv2d_111[0][0] \n__________________________________________________________________________________________________\nswish_83 (Swish) (None, 7, 7, 1824) 0 batch_normalization_83[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_29 (DepthwiseC (None, 7, 7, 1824) 45600 swish_83[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_84 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_29[0][0] \n__________________________________________________________________________________________________\nswish_84 (Swish) (None, 7, 7, 1824) 0 batch_normalization_84[0][0] \n__________________________________________________________________________________________________\nlambda_29 (Lambda) (None, 1, 1, 1824) 0 swish_84[0][0] \n__________________________________________________________________________________________________\nconv2d_112 (Conv2D) (None, 1, 1, 76) 138700 lambda_29[0][0] \n__________________________________________________________________________________________________\nswish_85 (Swish) (None, 1, 1, 76) 0 conv2d_112[0][0] \n__________________________________________________________________________________________________\nconv2d_113 (Conv2D) (None, 1, 1, 1824) 140448 swish_85[0][0] \n__________________________________________________________________________________________________\nactivation_29 (Activation) (None, 1, 1, 1824) 0 conv2d_113[0][0] \n__________________________________________________________________________________________________\nmultiply_29 (Multiply) (None, 7, 7, 1824) 0 activation_29[0][0] \n swish_84[0][0] \n__________________________________________________________________________________________________\nconv2d_114 (Conv2D) (None, 7, 7, 304) 554496 multiply_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_85 (BatchNo (None, 7, 7, 304) 1216 conv2d_114[0][0] \n__________________________________________________________________________________________________\ndrop_connect_23 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_85[0][0] \n__________________________________________________________________________________________________\nadd_23 (Add) (None, 7, 7, 304) 0 drop_connect_23[0][0] \n batch_normalization_82[0][0] \n__________________________________________________________________________________________________\nconv2d_115 (Conv2D) (None, 7, 7, 1824) 554496 add_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_86 (BatchNo (None, 7, 7, 1824) 7296 conv2d_115[0][0] \n__________________________________________________________________________________________________\nswish_86 (Swish) (None, 7, 7, 1824) 0 batch_normalization_86[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_30 (DepthwiseC (None, 7, 7, 1824) 45600 swish_86[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_87 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_30[0][0] \n__________________________________________________________________________________________________\nswish_87 (Swish) (None, 7, 7, 1824) 0 batch_normalization_87[0][0] \n__________________________________________________________________________________________________\nlambda_30 (Lambda) (None, 1, 1, 1824) 0 swish_87[0][0] \n__________________________________________________________________________________________________\nconv2d_116 (Conv2D) (None, 1, 1, 76) 138700 lambda_30[0][0] \n__________________________________________________________________________________________________\nswish_88 (Swish) (None, 1, 1, 76) 0 conv2d_116[0][0] \n__________________________________________________________________________________________________\nconv2d_117 (Conv2D) (None, 1, 1, 1824) 140448 swish_88[0][0] \n__________________________________________________________________________________________________\nactivation_30 (Activation) (None, 1, 1, 1824) 0 conv2d_117[0][0] \n__________________________________________________________________________________________________\nmultiply_30 (Multiply) (None, 7, 7, 1824) 0 activation_30[0][0] \n swish_87[0][0] \n__________________________________________________________________________________________________\nconv2d_118 (Conv2D) (None, 7, 7, 304) 554496 multiply_30[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_88 (BatchNo (None, 7, 7, 304) 1216 conv2d_118[0][0] \n__________________________________________________________________________________________________\ndrop_connect_24 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_88[0][0] \n__________________________________________________________________________________________________\nadd_24 (Add) (None, 7, 7, 304) 0 drop_connect_24[0][0] \n add_23[0][0] \n__________________________________________________________________________________________________\nconv2d_119 (Conv2D) (None, 7, 7, 1824) 554496 add_24[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_89 (BatchNo (None, 7, 7, 1824) 7296 conv2d_119[0][0] \n__________________________________________________________________________________________________\nswish_89 (Swish) (None, 7, 7, 1824) 0 batch_normalization_89[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_31 (DepthwiseC (None, 7, 7, 1824) 45600 swish_89[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_90 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_31[0][0] \n__________________________________________________________________________________________________\nswish_90 (Swish) (None, 7, 7, 1824) 0 batch_normalization_90[0][0] \n__________________________________________________________________________________________________\nlambda_31 (Lambda) (None, 1, 1, 1824) 0 swish_90[0][0] \n__________________________________________________________________________________________________\nconv2d_120 (Conv2D) (None, 1, 1, 76) 138700 lambda_31[0][0] \n__________________________________________________________________________________________________\nswish_91 (Swish) (None, 1, 1, 76) 0 conv2d_120[0][0] \n__________________________________________________________________________________________________\nconv2d_121 (Conv2D) (None, 1, 1, 1824) 140448 swish_91[0][0] \n__________________________________________________________________________________________________\nactivation_31 (Activation) (None, 1, 1, 1824) 0 conv2d_121[0][0] \n__________________________________________________________________________________________________\nmultiply_31 (Multiply) (None, 7, 7, 1824) 0 activation_31[0][0] \n swish_90[0][0] \n__________________________________________________________________________________________________\nconv2d_122 (Conv2D) (None, 7, 7, 304) 554496 multiply_31[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_91 (BatchNo (None, 7, 7, 304) 1216 conv2d_122[0][0] \n__________________________________________________________________________________________________\ndrop_connect_25 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_91[0][0] \n__________________________________________________________________________________________________\nadd_25 (Add) (None, 7, 7, 304) 0 drop_connect_25[0][0] \n add_24[0][0] \n__________________________________________________________________________________________________\nconv2d_123 (Conv2D) (None, 7, 7, 1824) 554496 add_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_92 (BatchNo (None, 7, 7, 1824) 7296 conv2d_123[0][0] \n__________________________________________________________________________________________________\nswish_92 (Swish) (None, 7, 7, 1824) 0 batch_normalization_92[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_32 (DepthwiseC (None, 7, 7, 1824) 45600 swish_92[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_93 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_32[0][0] \n__________________________________________________________________________________________________\nswish_93 (Swish) (None, 7, 7, 1824) 0 batch_normalization_93[0][0] \n__________________________________________________________________________________________________\nlambda_32 (Lambda) (None, 1, 1, 1824) 0 swish_93[0][0] \n__________________________________________________________________________________________________\nconv2d_124 (Conv2D) (None, 1, 1, 76) 138700 lambda_32[0][0] \n__________________________________________________________________________________________________\nswish_94 (Swish) (None, 1, 1, 76) 0 conv2d_124[0][0] \n__________________________________________________________________________________________________\nconv2d_125 (Conv2D) (None, 1, 1, 1824) 140448 swish_94[0][0] \n__________________________________________________________________________________________________\nactivation_32 (Activation) (None, 1, 1, 1824) 0 conv2d_125[0][0] \n__________________________________________________________________________________________________\nmultiply_32 (Multiply) (None, 7, 7, 1824) 0 activation_32[0][0] \n swish_93[0][0] \n__________________________________________________________________________________________________\nconv2d_126 (Conv2D) (None, 7, 7, 304) 554496 multiply_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_94 (BatchNo (None, 7, 7, 304) 1216 conv2d_126[0][0] \n__________________________________________________________________________________________________\ndrop_connect_26 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_94[0][0] \n__________________________________________________________________________________________________\nadd_26 (Add) (None, 7, 7, 304) 0 drop_connect_26[0][0] \n add_25[0][0] \n__________________________________________________________________________________________________\nconv2d_127 (Conv2D) (None, 7, 7, 1824) 554496 add_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_95 (BatchNo (None, 7, 7, 1824) 7296 conv2d_127[0][0] \n__________________________________________________________________________________________________\nswish_95 (Swish) (None, 7, 7, 1824) 0 batch_normalization_95[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_33 (DepthwiseC (None, 7, 7, 1824) 45600 swish_95[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_96 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_33[0][0] \n__________________________________________________________________________________________________\nswish_96 (Swish) (None, 7, 7, 1824) 0 batch_normalization_96[0][0] \n__________________________________________________________________________________________________\nlambda_33 (Lambda) (None, 1, 1, 1824) 0 swish_96[0][0] \n__________________________________________________________________________________________________\nconv2d_128 (Conv2D) (None, 1, 1, 76) 138700 lambda_33[0][0] \n__________________________________________________________________________________________________\nswish_97 (Swish) (None, 1, 1, 76) 0 conv2d_128[0][0] \n__________________________________________________________________________________________________\nconv2d_129 (Conv2D) (None, 1, 1, 1824) 140448 swish_97[0][0] \n__________________________________________________________________________________________________\nactivation_33 (Activation) (None, 1, 1, 1824) 0 conv2d_129[0][0] \n__________________________________________________________________________________________________\nmultiply_33 (Multiply) (None, 7, 7, 1824) 0 activation_33[0][0] \n swish_96[0][0] \n__________________________________________________________________________________________________\nconv2d_130 (Conv2D) (None, 7, 7, 304) 554496 multiply_33[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_97 (BatchNo (None, 7, 7, 304) 1216 conv2d_130[0][0] \n__________________________________________________________________________________________________\ndrop_connect_27 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_97[0][0] \n__________________________________________________________________________________________________\nadd_27 (Add) (None, 7, 7, 304) 0 drop_connect_27[0][0] \n add_26[0][0] \n__________________________________________________________________________________________________\nconv2d_131 (Conv2D) (None, 7, 7, 1824) 554496 add_27[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_98 (BatchNo (None, 7, 7, 1824) 7296 conv2d_131[0][0] \n__________________________________________________________________________________________________\nswish_98 (Swish) (None, 7, 7, 1824) 0 batch_normalization_98[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_34 (DepthwiseC (None, 7, 7, 1824) 45600 swish_98[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_99 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_34[0][0] \n__________________________________________________________________________________________________\nswish_99 (Swish) (None, 7, 7, 1824) 0 batch_normalization_99[0][0] \n__________________________________________________________________________________________________\nlambda_34 (Lambda) (None, 1, 1, 1824) 0 swish_99[0][0] \n__________________________________________________________________________________________________\nconv2d_132 (Conv2D) (None, 1, 1, 76) 138700 lambda_34[0][0] \n__________________________________________________________________________________________________\nswish_100 (Swish) (None, 1, 1, 76) 0 conv2d_132[0][0] \n__________________________________________________________________________________________________\nconv2d_133 (Conv2D) (None, 1, 1, 1824) 140448 swish_100[0][0] \n__________________________________________________________________________________________________\nactivation_34 (Activation) (None, 1, 1, 1824) 0 conv2d_133[0][0] \n__________________________________________________________________________________________________\nmultiply_34 (Multiply) (None, 7, 7, 1824) 0 activation_34[0][0] \n swish_99[0][0] \n__________________________________________________________________________________________________\nconv2d_134 (Conv2D) (None, 7, 7, 304) 554496 multiply_34[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_100 (BatchN (None, 7, 7, 304) 1216 conv2d_134[0][0] \n__________________________________________________________________________________________________\ndrop_connect_28 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_100[0][0] \n__________________________________________________________________________________________________\nadd_28 (Add) (None, 7, 7, 304) 0 drop_connect_28[0][0] \n add_27[0][0] \n__________________________________________________________________________________________________\nconv2d_135 (Conv2D) (None, 7, 7, 1824) 554496 add_28[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_101 (BatchN (None, 7, 7, 1824) 7296 conv2d_135[0][0] \n__________________________________________________________________________________________________\nswish_101 (Swish) (None, 7, 7, 1824) 0 batch_normalization_101[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_35 (DepthwiseC (None, 7, 7, 1824) 45600 swish_101[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_102 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_35[0][0] \n__________________________________________________________________________________________________\nswish_102 (Swish) (None, 7, 7, 1824) 0 batch_normalization_102[0][0] \n__________________________________________________________________________________________________\nlambda_35 (Lambda) (None, 1, 1, 1824) 0 swish_102[0][0] \n__________________________________________________________________________________________________\nconv2d_136 (Conv2D) (None, 1, 1, 76) 138700 lambda_35[0][0] \n__________________________________________________________________________________________________\nswish_103 (Swish) (None, 1, 1, 76) 0 conv2d_136[0][0] \n__________________________________________________________________________________________________\nconv2d_137 (Conv2D) (None, 1, 1, 1824) 140448 swish_103[0][0] \n__________________________________________________________________________________________________\nactivation_35 (Activation) (None, 1, 1, 1824) 0 conv2d_137[0][0] \n__________________________________________________________________________________________________\nmultiply_35 (Multiply) (None, 7, 7, 1824) 0 activation_35[0][0] \n swish_102[0][0] \n__________________________________________________________________________________________________\nconv2d_138 (Conv2D) (None, 7, 7, 304) 554496 multiply_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_103 (BatchN (None, 7, 7, 304) 1216 conv2d_138[0][0] \n__________________________________________________________________________________________________\ndrop_connect_29 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_103[0][0] \n__________________________________________________________________________________________________\nadd_29 (Add) (None, 7, 7, 304) 0 drop_connect_29[0][0] \n add_28[0][0] \n__________________________________________________________________________________________________\nconv2d_139 (Conv2D) (None, 7, 7, 1824) 554496 add_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_104 (BatchN (None, 7, 7, 1824) 7296 conv2d_139[0][0] \n__________________________________________________________________________________________________\nswish_104 (Swish) (None, 7, 7, 1824) 0 batch_normalization_104[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_36 (DepthwiseC (None, 7, 7, 1824) 45600 swish_104[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_105 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_36[0][0] \n__________________________________________________________________________________________________\nswish_105 (Swish) (None, 7, 7, 1824) 0 batch_normalization_105[0][0] \n__________________________________________________________________________________________________\nlambda_36 (Lambda) (None, 1, 1, 1824) 0 swish_105[0][0] \n__________________________________________________________________________________________________\nconv2d_140 (Conv2D) (None, 1, 1, 76) 138700 lambda_36[0][0] \n__________________________________________________________________________________________________\nswish_106 (Swish) (None, 1, 1, 76) 0 conv2d_140[0][0] \n__________________________________________________________________________________________________\nconv2d_141 (Conv2D) (None, 1, 1, 1824) 140448 swish_106[0][0] \n__________________________________________________________________________________________________\nactivation_36 (Activation) (None, 1, 1, 1824) 0 conv2d_141[0][0] \n__________________________________________________________________________________________________\nmultiply_36 (Multiply) (None, 7, 7, 1824) 0 activation_36[0][0] \n swish_105[0][0] \n__________________________________________________________________________________________________\nconv2d_142 (Conv2D) (None, 7, 7, 304) 554496 multiply_36[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_106 (BatchN (None, 7, 7, 304) 1216 conv2d_142[0][0] \n__________________________________________________________________________________________________\ndrop_connect_30 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_106[0][0] \n__________________________________________________________________________________________________\nadd_30 (Add) (None, 7, 7, 304) 0 drop_connect_30[0][0] \n add_29[0][0] \n__________________________________________________________________________________________________\nconv2d_143 (Conv2D) (None, 7, 7, 1824) 554496 add_30[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_107 (BatchN (None, 7, 7, 1824) 7296 conv2d_143[0][0] \n__________________________________________________________________________________________________\nswish_107 (Swish) (None, 7, 7, 1824) 0 batch_normalization_107[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_37 (DepthwiseC (None, 7, 7, 1824) 16416 swish_107[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_108 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_37[0][0] \n__________________________________________________________________________________________________\nswish_108 (Swish) (None, 7, 7, 1824) 0 batch_normalization_108[0][0] \n__________________________________________________________________________________________________\nlambda_37 (Lambda) (None, 1, 1, 1824) 0 swish_108[0][0] \n__________________________________________________________________________________________________\nconv2d_144 (Conv2D) (None, 1, 1, 76) 138700 lambda_37[0][0] \n__________________________________________________________________________________________________\nswish_109 (Swish) (None, 1, 1, 76) 0 conv2d_144[0][0] \n__________________________________________________________________________________________________\nconv2d_145 (Conv2D) (None, 1, 1, 1824) 140448 swish_109[0][0] \n__________________________________________________________________________________________________\nactivation_37 (Activation) (None, 1, 1, 1824) 0 conv2d_145[0][0] \n__________________________________________________________________________________________________\nmultiply_37 (Multiply) (None, 7, 7, 1824) 0 activation_37[0][0] \n swish_108[0][0] \n__________________________________________________________________________________________________\nconv2d_146 (Conv2D) (None, 7, 7, 512) 933888 multiply_37[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_109 (BatchN (None, 7, 7, 512) 2048 conv2d_146[0][0] \n__________________________________________________________________________________________________\nconv2d_147 (Conv2D) (None, 7, 7, 3072) 1572864 batch_normalization_109[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_110 (BatchN (None, 7, 7, 3072) 12288 conv2d_147[0][0] \n__________________________________________________________________________________________________\nswish_110 (Swish) (None, 7, 7, 3072) 0 batch_normalization_110[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_38 (DepthwiseC (None, 7, 7, 3072) 27648 swish_110[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_111 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_38[0][0] \n__________________________________________________________________________________________________\nswish_111 (Swish) (None, 7, 7, 3072) 0 batch_normalization_111[0][0] \n__________________________________________________________________________________________________\nlambda_38 (Lambda) (None, 1, 1, 3072) 0 swish_111[0][0] \n__________________________________________________________________________________________________\nconv2d_148 (Conv2D) (None, 1, 1, 128) 393344 lambda_38[0][0] \n__________________________________________________________________________________________________\nswish_112 (Swish) (None, 1, 1, 128) 0 conv2d_148[0][0] \n__________________________________________________________________________________________________\nconv2d_149 (Conv2D) (None, 1, 1, 3072) 396288 swish_112[0][0] \n__________________________________________________________________________________________________\nactivation_38 (Activation) (None, 1, 1, 3072) 0 conv2d_149[0][0] \n__________________________________________________________________________________________________\nmultiply_38 (Multiply) (None, 7, 7, 3072) 0 activation_38[0][0] \n swish_111[0][0] \n__________________________________________________________________________________________________\nconv2d_150 (Conv2D) (None, 7, 7, 512) 1572864 multiply_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_112 (BatchN (None, 7, 7, 512) 2048 conv2d_150[0][0] \n__________________________________________________________________________________________________\ndrop_connect_31 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_112[0][0] \n__________________________________________________________________________________________________\nadd_31 (Add) (None, 7, 7, 512) 0 drop_connect_31[0][0] \n batch_normalization_109[0][0] \n__________________________________________________________________________________________________\nconv2d_151 (Conv2D) (None, 7, 7, 3072) 1572864 add_31[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_113 (BatchN (None, 7, 7, 3072) 12288 conv2d_151[0][0] \n__________________________________________________________________________________________________\nswish_113 (Swish) (None, 7, 7, 3072) 0 batch_normalization_113[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_39 (DepthwiseC (None, 7, 7, 3072) 27648 swish_113[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_114 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_39[0][0] \n__________________________________________________________________________________________________\nswish_114 (Swish) (None, 7, 7, 3072) 0 batch_normalization_114[0][0] \n__________________________________________________________________________________________________\nlambda_39 (Lambda) (None, 1, 1, 3072) 0 swish_114[0][0] \n__________________________________________________________________________________________________\nconv2d_152 (Conv2D) (None, 1, 1, 128) 393344 lambda_39[0][0] \n__________________________________________________________________________________________________\nswish_115 (Swish) (None, 1, 1, 128) 0 conv2d_152[0][0] \n__________________________________________________________________________________________________\nconv2d_153 (Conv2D) (None, 1, 1, 3072) 396288 swish_115[0][0] \n__________________________________________________________________________________________________\nactivation_39 (Activation) (None, 1, 1, 3072) 0 conv2d_153[0][0] \n__________________________________________________________________________________________________\nmultiply_39 (Multiply) (None, 7, 7, 3072) 0 activation_39[0][0] \n swish_114[0][0] \n__________________________________________________________________________________________________\nconv2d_154 (Conv2D) (None, 7, 7, 512) 1572864 multiply_39[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_115 (BatchN (None, 7, 7, 512) 2048 conv2d_154[0][0] \n__________________________________________________________________________________________________\ndrop_connect_32 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_115[0][0] \n__________________________________________________________________________________________________\nadd_32 (Add) (None, 7, 7, 512) 0 drop_connect_32[0][0] \n add_31[0][0] \n__________________________________________________________________________________________________\nconv2d_155 (Conv2D) (None, 7, 7, 2048) 1048576 add_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_116 (BatchN (None, 7, 7, 2048) 8192 conv2d_155[0][0] \n__________________________________________________________________________________________________\nswish_116 (Swish) (None, 7, 7, 2048) 0 batch_normalization_116[0][0] \n__________________________________________________________________________________________________\nglobal_average_pooling2d_1 (Glo (None, 2048) 0 swish_116[0][0] \n__________________________________________________________________________________________________\nfinal_output (Dense) (None, 1) 2049 global_average_pooling2d_1[0][0] \n==================================================================================================\nTotal params: 28,515,569\nTrainable params: 28,342,833\nNon-trainable params: 172,736\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"history = model.fit_generator(generator=train_generator,\n steps_per_epoch=STEP_SIZE_TRAIN,\n validation_data=valid_generator,\n validation_steps=STEP_SIZE_VALID,\n epochs=EPOCHS,\n callbacks=callback_list,\n verbose=2).history","metadata":{"execution":{"iopub.status.busy":"2024-05-03T02:53:42.381601Z","iopub.execute_input":"2024-05-03T02:53:42.381854Z","iopub.status.idle":"2024-05-03T03:34:50.440026Z","shell.execute_reply.started":"2024-05-03T02:53:42.38181Z","shell.execute_reply":"2024-05-03T03:34:50.438516Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Epoch 1/20\n - 161s - loss: 0.7859 - acc: 0.4801 - val_loss: 0.6131 - val_acc: 0.5749\nEpoch 2/20\n - 119s - loss: 0.6356 - acc: 0.5373 - val_loss: 0.4432 - val_acc: 0.6719\nEpoch 3/20\n - 120s - loss: 0.5899 - acc: 0.5445 - val_loss: 0.4440 - val_acc: 0.6491\nEpoch 4/20\n - 120s - loss: 0.4872 - acc: 0.6208 - val_loss: 0.3908 - val_acc: 0.6548\nEpoch 5/20\n - 120s - loss: 0.4557 - acc: 0.6307 - val_loss: 0.3144 - val_acc: 0.7532\nEpoch 6/20\n - 120s - loss: 0.4224 - acc: 0.6655 - val_loss: 0.3058 - val_acc: 0.7261\nEpoch 7/20\n - 120s - loss: 0.3322 - acc: 0.7133 - val_loss: 0.2955 - val_acc: 0.7703\nEpoch 8/20\n - 120s - loss: 0.2997 - acc: 0.7560 - val_loss: 0.2729 - val_acc: 0.7803\nEpoch 9/20\n - 120s - loss: 0.3070 - acc: 0.7496 - val_loss: 0.2722 - val_acc: 0.7846\nEpoch 10/20\n - 120s - loss: 0.2692 - acc: 0.7780 - val_loss: 0.2603 - val_acc: 0.7632\nEpoch 11/20\n - 120s - loss: 0.2334 - acc: 0.7850 - val_loss: 0.2714 - val_acc: 0.8003\nEpoch 12/20\n - 120s - loss: 0.2261 - acc: 0.7903 - val_loss: 0.2480 - val_acc: 0.7989\nEpoch 13/20\n - 120s - loss: 0.1953 - acc: 0.8182 - val_loss: 0.2836 - val_acc: 0.7903\nEpoch 14/20\n - 120s - loss: 0.1731 - acc: 0.8337 - val_loss: 0.2921 - val_acc: 0.7917\nEpoch 15/20\n - 120s - loss: 0.1567 - acc: 0.8365 - val_loss: 0.2237 - val_acc: 0.7974\nEpoch 16/20\n - 120s - loss: 0.1518 - acc: 0.8542 - val_loss: 0.2586 - val_acc: 0.7960\nEpoch 17/20\n - 120s - loss: 0.1383 - acc: 0.8583 - val_loss: 0.2413 - val_acc: 0.7917\nEpoch 18/20\n - 120s - loss: 0.1205 - acc: 0.8816 - val_loss: 0.2503 - val_acc: 0.8074\nEpoch 19/20\n - 120s - loss: 0.1291 - acc: 0.8717 - val_loss: 0.2407 - val_acc: 0.8117\nEpoch 20/20\n - 120s - loss: 0.1140 - acc: 0.8786 - val_loss: 0.2427 - val_acc: 0.7997\nRestoring model weights from the end of the best epoch\nEpoch 00020: early stopping\n","output_type":"stream"}]},{"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(2, 1, sharex='col', figsize=(20, 6))\n\nax1.plot(cosine_lr_1st.learning_rates)\nax1.set_title('Warm up learning rates')\n\nax2.plot(cosine_lr_2nd.learning_rates)\nax2.set_title('Fine-tune learning rates')\n\nplt.xlabel('Steps')\nplt.ylabel('Learning rate')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:34:50.441405Z","iopub.execute_input":"2024-05-03T03:34:50.441688Z","iopub.status.idle":"2024-05-03T03:34:50.942597Z","shell.execute_reply.started":"2024-05-03T03:34:50.441633Z","shell.execute_reply":"2024-05-03T03:34:50.941167Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(2, 1, sharex='col', figsize=(20, 14))\n\nax1.plot(history['loss'], label='Train loss')\nax1.plot(history['val_loss'], label='Validation loss')\nax1.legend(loc='best')\nax1.set_title('Loss')\n\nax2.plot(history['acc'], label='Train accuracy')\nax2.plot(history['val_acc'], label='Validation accuracy')\nax2.legend(loc='best')\nax2.set_title('Accuracy')\n\nplt.xlabel('Epochs')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:34:50.944902Z","iopub.execute_input":"2024-05-03T03:34:50.945376Z","iopub.status.idle":"2024-05-03T03:34:51.60707Z","shell.execute_reply.started":"2024-05-03T03:34:50.945284Z","shell.execute_reply":"2024-05-03T03:34:51.606369Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Create empty arays to keep the predictions and labels\ndf_preds = pd.DataFrame(columns=['label', 'pred', 'set'])\ntrain_generator.reset()\nvalid_generator.reset()\n\n# Add train predictions and labels\nfor i in range(STEP_SIZE_TRAIN + 1):\n im, lbl = next(train_generator)\n preds = model.predict(im, batch_size=train_generator.batch_size)\n for index in range(len(preds)):\n df_preds.loc[len(df_preds)] = [lbl[index], preds[index][0], 'train']\n\n# Add validation predictions and labels\nfor i in range(STEP_SIZE_VALID + 1):\n im, lbl = next(valid_generator)\n preds = model.predict(im, batch_size=valid_generator.batch_size)\n for index in range(len(preds)):\n df_preds.loc[len(df_preds)] = [lbl[index], preds[index][0], 'validation']\n\ndf_preds['label'] = df_preds['label'].astype('int')","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:34:51.608188Z","iopub.execute_input":"2024-05-03T03:34:51.608418Z","iopub.status.idle":"2024-05-03T03:36:20.36778Z","shell.execute_reply.started":"2024-05-03T03:34:51.608378Z","shell.execute_reply":"2024-05-03T03:36:20.366767Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"def classify(x):\n if x < 0.5:\n return 0\n elif x < 1.5:\n return 1\n elif x < 2.5:\n return 2\n elif x < 3.5:\n return 3\n return 4\n\n# Classify predictions\ndf_preds['predictions'] = df_preds['pred'].apply(lambda x: classify(x))\n\ntrain_preds = df_preds[df_preds['set'] == 'train']\nvalidation_preds = df_preds[df_preds['set'] == 'validation']","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:36:20.369155Z","iopub.execute_input":"2024-05-03T03:36:20.369442Z","iopub.status.idle":"2024-05-03T03:36:20.389437Z","shell.execute_reply.started":"2024-05-03T03:36:20.36939Z","shell.execute_reply":"2024-05-03T03:36:20.388766Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"labels = ['0 - No DR', '1 - Mild', '2 - Moderate', '3 - Severe', '4 - Proliferative DR']\ndef plot_confusion_matrix(train, validation, labels=labels):\n train_labels, train_preds = train\n validation_labels, validation_preds = validation\n fig, (ax1, ax2) = plt.subplots(1, 2, sharex='col', figsize=(24, 7))\n train_cnf_matrix = confusion_matrix(train_labels, train_preds)\n validation_cnf_matrix = confusion_matrix(validation_labels, validation_preds)\n\n train_cnf_matrix_norm = train_cnf_matrix.astype('float') / train_cnf_matrix.sum(axis=1)[:, np.newaxis]\n validation_cnf_matrix_norm = validation_cnf_matrix.astype('float') / validation_cnf_matrix.sum(axis=1)[:, np.newaxis]\n\n train_df_cm = pd.DataFrame(train_cnf_matrix_norm, index=labels, columns=labels)\n validation_df_cm = pd.DataFrame(validation_cnf_matrix_norm, index=labels, columns=labels)\n\n sns.heatmap(train_df_cm, annot=True, fmt='.2f', cmap=\"Blues\",ax=ax1).set_title('Train')\n sns.heatmap(validation_df_cm, annot=True, fmt='.2f', cmap=sns.cubehelix_palette(8),ax=ax2).set_title('Validation')\n plt.show()\n\nplot_confusion_matrix((train_preds['label'], train_preds['predictions']), (validation_preds['label'], validation_preds['predictions']))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:36:20.390786Z","iopub.execute_input":"2024-05-03T03:36:20.391092Z","iopub.status.idle":"2024-05-03T03:36:21.431422Z","shell.execute_reply.started":"2024-05-03T03:36:20.391017Z","shell.execute_reply":"2024-05-03T03:36:21.430197Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"def evaluate_model(train, validation):\n train_labels, train_preds = train\n validation_labels, validation_preds = validation\n print(\"Train Cohen Kappa score: %.3f\" % cohen_kappa_score(train_preds, train_labels, weights='quadratic'))\n print(\"Validation Cohen Kappa score: %.3f\" % cohen_kappa_score(validation_preds, validation_labels, weights='quadratic'))\n print(\"Complete set Cohen Kappa score: %.3f\" % cohen_kappa_score(np.append(train_preds, validation_preds), np.append(train_labels, validation_labels), weights='quadratic'))\n \nevaluate_model((train_preds['label'], train_preds['predictions']), (validation_preds['label'], validation_preds['predictions']))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:36:21.433525Z","iopub.execute_input":"2024-05-03T03:36:21.433943Z","iopub.status.idle":"2024-05-03T03:36:21.469056Z","shell.execute_reply.started":"2024-05-03T03:36:21.433872Z","shell.execute_reply":"2024-05-03T03:36:21.468292Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Train Cohen Kappa score: 0.969\nValidation Cohen Kappa score: 0.917\nComplete set Cohen Kappa score: 0.959\n","output_type":"stream"}]},{"cell_type":"code","source":"def apply_tta(model, generator, steps=10):\n step_size = generator.n//generator.batch_size\n preds_tta = []\n for i in range(steps):\n generator.reset()\n preds = model.predict_generator(generator, steps=step_size)\n preds_tta.append(preds)\n\n return np.mean(preds_tta, axis=0)\n\npreds = apply_tta(model, test_generator)\npredictions = [classify(x) for x in preds]\n\nresults = pd.DataFrame({'id_code':test['id_code'], 'diagnosis':predictions})\nresults['id_code'] = results['id_code'].map(lambda x: str(x)[:-4])","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:36:21.47009Z","iopub.execute_input":"2024-05-03T03:36:21.470356Z","iopub.status.idle":"2024-05-03T03:45:18.316741Z","shell.execute_reply.started":"2024-05-03T03:36:21.470304Z","shell.execute_reply":"2024-05-03T03:45:18.315936Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"# Cleaning created directories\nif os.path.exists(train_dest_path):\n shutil.rmtree(train_dest_path)\nif os.path.exists(validation_dest_path):\n shutil.rmtree(validation_dest_path)\nif os.path.exists(test_dest_path):\n shutil.rmtree(test_dest_path)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:45:18.318111Z","iopub.execute_input":"2024-05-03T03:45:18.318361Z","iopub.status.idle":"2024-05-03T03:45:18.617269Z","shell.execute_reply.started":"2024-05-03T03:45:18.318318Z","shell.execute_reply":"2024-05-03T03:45:18.616529Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"fig = plt.subplots(sharex='col', figsize=(24, 8.7))\nsns.countplot(x=\"diagnosis\", data=results).set_title('Test')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:45:18.618646Z","iopub.execute_input":"2024-05-03T03:45:18.618931Z","iopub.status.idle":"2024-05-03T03:45:19.020197Z","shell.execute_reply.started":"2024-05-03T03:45:18.61888Z","shell.execute_reply":"2024-05-03T03:45:19.019283Z"},"trusted":true},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"results.to_csv('submission_1.csv', index=False)\ndisplay(results.head())","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:45:19.021498Z","iopub.execute_input":"2024-05-03T03:45:19.021764Z","iopub.status.idle":"2024-05-03T03:45:19.237854Z","shell.execute_reply.started":"2024-05-03T03:45:19.02172Z","shell.execute_reply":"2024-05-03T03:45:19.237084Z"},"trusted":true},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":" id_code diagnosis\n0 0005cfc8afb6 2\n1 003f0afdcd15 3\n2 006efc72b638 2\n3 00836aaacf06 2\n4 009245722fa4 2","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosis
00005cfc8afb62
1003f0afdcd153
2006efc72b6382
300836aaacf062
4009245722fa42
\n
"},"metadata":{}}]},{"cell_type":"code","source":"# model.save_weights('../working/effNetB5_bs32_img224_fold1.h5')","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:45:19.239143Z","iopub.execute_input":"2024-05-03T03:45:19.239397Z","iopub.status.idle":"2024-05-03T03:45:19.242819Z","shell.execute_reply.started":"2024-05-03T03:45:19.239355Z","shell.execute_reply":"2024-05-03T03:45:19.241957Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"markdown","source":"FOLD_2","metadata":{}},{"cell_type":"code","source":"import os\nimport sys\nimport cv2\nimport shutil\nimport random\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom tensorflow import set_random_seed\nfrom sklearn.utils import class_weight\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, cohen_kappa_score\nfrom keras import backend as K\nfrom keras.models import Model\nfrom keras.utils import to_categorical\nfrom keras import optimizers, applications\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.layers import Dense, Dropout, GlobalAveragePooling2D, Input\nfrom keras.callbacks import EarlyStopping, ReduceLROnPlateau, Callback, LearningRateScheduler\n\ndef seed_everything(seed=0):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n set_random_seed(0)\n\nseed = 0\nseed_everything(seed)\n%matplotlib inline\nsns.set(style=\"whitegrid\")\nwarnings.filterwarnings(\"ignore\")\nsys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))\nfrom efficientnet import *","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:45:19.244185Z","iopub.execute_input":"2024-05-03T03:45:19.244542Z","iopub.status.idle":"2024-05-03T03:45:19.267484Z","shell.execute_reply.started":"2024-05-03T03:45:19.244473Z","shell.execute_reply":"2024-05-03T03:45:19.266593Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"fold_set = pd.read_csv('/kaggle/input/main-data/5-fold.csv')\nX_train = fold_set[fold_set['fold_1'] == 'train']\nX_val = fold_set[fold_set['fold_1'] == 'validation']\ntest = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')\nprint('Number of train samples: ', X_train.shape[0])\nprint('Number of validation samples: ', X_val.shape[0])\nprint('Number of test samples: ', test.shape[0])\n\n# Preprocecss data\nX_train[\"id_code\"] = X_train[\"id_code\"].apply(lambda x: x + \".png\")\nX_val[\"id_code\"] = X_val[\"id_code\"].apply(lambda x: x + \".png\")\ntest[\"id_code\"] = test[\"id_code\"].apply(lambda x: x + \".png\")\ndisplay(X_train.head())","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:45:19.268745Z","iopub.execute_input":"2024-05-03T03:45:19.26899Z","iopub.status.idle":"2024-05-03T03:45:20.303257Z","shell.execute_reply.started":"2024-05-03T03:45:19.268933Z","shell.execute_reply":"2024-05-03T03:45:20.302582Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"Number of train samples: 2929\nNumber of validation samples: 733\nNumber of test samples: 1928\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" id_code diagnosis height width fold_0 fold_1 fold_2 \\\n0 000c1434d8d7.png 2 2136.0 3216.0 train train train \n1 001639a390f0.png 4 2136.0 3216.0 train train train \n2 0024cdab0c1e.png 1 1736.0 2416.0 validation train train \n3 002c21358ce6.png 0 1050.0 1050.0 train train train \n4 005b95c28852.png 0 1536.0 2048.0 validation train train \n\n fold_3 fold_4 \n0 validation train \n1 train validation \n2 train train \n3 validation train \n4 train train ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosisheightwidthfold_0fold_1fold_2fold_3fold_4
0000c1434d8d7.png22136.03216.0traintraintrainvalidationtrain
1001639a390f0.png42136.03216.0traintraintraintrainvalidation
20024cdab0c1e.png11736.02416.0validationtraintraintraintrain
3002c21358ce6.png01050.01050.0traintraintrainvalidationtrain
4005b95c28852.png01536.02048.0validationtraintraintraintrain
\n
"},"metadata":{}}]},{"cell_type":"code","source":"# Model parameters\nFACTOR = 4\nBATCH_SIZE = 8 * FACTOR\nEPOCHS = 20\nWARMUP_EPOCHS = 5\nLEARNING_RATE = 1e-4 * FACTOR\nWARMUP_LEARNING_RATE = 1e-3 * FACTOR\nHEIGHT = 224\nWIDTH = 224\nCHANNELS = 3\nES_PATIENCE = 5\nRLROP_PATIENCE = 3\nDECAY_DROP = 0.5\nLR_WARMUP_EPOCHS_1st = 2\nLR_WARMUP_EPOCHS_2nd = 5\nSTEP_SIZE = len(X_train) // BATCH_SIZE\nTOTAL_STEPS_1st = WARMUP_EPOCHS * STEP_SIZE\nTOTAL_STEPS_2nd = EPOCHS * STEP_SIZE\nWARMUP_STEPS_1st = LR_WARMUP_EPOCHS_1st * STEP_SIZE\nWARMUP_STEPS_2nd = LR_WARMUP_EPOCHS_2nd * STEP_SIZE","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:45:20.304526Z","iopub.execute_input":"2024-05-03T03:45:20.304766Z","iopub.status.idle":"2024-05-03T03:45:20.311866Z","shell.execute_reply.started":"2024-05-03T03:45:20.304724Z","shell.execute_reply":"2024-05-03T03:45:20.311093Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"train_base_path = '/kaggle/input/aptos2019-blindness-detection/train_images/'\ntest_base_path = '/kaggle/input/aptos2019-blindness-detection/test_images/'\ntrain_dest_path = 'base_dir/train_images/'\nvalidation_dest_path = 'base_dir/validation_images/'\ntest_dest_path = 'base_dir/test_images/'\n\nif os.path.exists(train_dest_path):\n shutil.rmtree(train_dest_path)\nif os.path.exists(validation_dest_path):\n shutil.rmtree(validation_dest_path)\nif os.path.exists(test_dest_path):\n shutil.rmtree(test_dest_path)\n \nos.makedirs(train_dest_path)\nos.makedirs(validation_dest_path)\nos.makedirs(test_dest_path)\n\ndef crop_image(img, tol=7):\n if img.ndim ==2:\n mask = img>tol\n return img[np.ix_(mask.any(1),mask.any(0))]\n elif img.ndim==3:\n gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n mask = gray_img>tol\n check_shape = img[:,:,0][np.ix_(mask.any(1),mask.any(0))].shape[0]\n if (check_shape == 0): # image is too dark so that we crop out everything,\n return img # return original image\n else:\n img1=img[:,:,0][np.ix_(mask.any(1),mask.any(0))]\n img2=img[:,:,1][np.ix_(mask.any(1),mask.any(0))]\n img3=img[:,:,2][np.ix_(mask.any(1),mask.any(0))]\n img = np.stack([img1,img2,img3],axis=-1)\n \n return img\n\ndef circle_crop(img):\n img = crop_image(img)\n\n height, width, depth = img.shape\n largest_side = np.max((height, width))\n img = cv2.resize(img, (largest_side, largest_side))\n\n height, width, depth = img.shape\n\n x = width//2\n y = height//2\n r = np.amin((x, y))\n\n circle_img = np.zeros((height, width), np.uint8)\n cv2.circle(circle_img, (x, y), int(r), 1, thickness=-1)\n img = cv2.bitwise_and(img, img, mask=circle_img)\n img = crop_image(img)\n\n return img\n \ndef preprocess_image(base_path, save_path, image_id, HEIGHT, WIDTH, sigmaX=10):\n image = cv2.imread(base_path + image_id)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image = circle_crop(image)\n image = cv2.resize(image, (HEIGHT, WIDTH))\n image = cv2.addWeighted(image, 4, cv2.GaussianBlur(image, (0,0), sigmaX), -4 , 128)\n cv2.imwrite(save_path + image_id, image)\n \n# Pre-procecss train set\nfor i, image_id in enumerate(X_train['id_code']):\n preprocess_image(train_base_path, train_dest_path, image_id, HEIGHT, WIDTH)\n \n# Pre-procecss validation set\nfor i, image_id in enumerate(X_val['id_code']):\n preprocess_image(train_base_path, validation_dest_path, image_id, HEIGHT, WIDTH)\n \n# Pre-procecss test set\nfor i, image_id in enumerate(test['id_code']):\n preprocess_image(test_base_path, test_dest_path, image_id, HEIGHT, WIDTH)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T03:45:20.313399Z","iopub.execute_input":"2024-05-03T03:45:20.313664Z","iopub.status.idle":"2024-05-03T04:04:34.209368Z","shell.execute_reply.started":"2024-05-03T03:45:20.313615Z","shell.execute_reply":"2024-05-03T04:04:34.208401Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"datagen=ImageDataGenerator(rescale=1./255, \n rotation_range=360,\n horizontal_flip=True,\n vertical_flip=True)\n\ntrain_generator=datagen.flow_from_dataframe(\n dataframe=X_train,\n directory=train_dest_path,\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n class_mode=\"raw\",\n batch_size=BATCH_SIZE,\n target_size=(HEIGHT, WIDTH),\n seed=seed)\n\nvalid_generator=datagen.flow_from_dataframe(\n dataframe=X_val,\n directory=validation_dest_path,\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n class_mode=\"raw\",\n batch_size=BATCH_SIZE,\n target_size=(HEIGHT, WIDTH),\n seed=seed)\n\ntest_generator=datagen.flow_from_dataframe( \n dataframe=test,\n directory=test_dest_path,\n x_col=\"id_code\",\n batch_size=1,\n class_mode=None,\n shuffle=False,\n target_size=(HEIGHT, WIDTH),\n seed=seed)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:04:34.210837Z","iopub.execute_input":"2024-05-03T04:04:34.211111Z","iopub.status.idle":"2024-05-03T04:04:34.292125Z","shell.execute_reply.started":"2024-05-03T04:04:34.211059Z","shell.execute_reply":"2024-05-03T04:04:34.291474Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":"Found 2929 validated image filenames.\nFound 733 validated image filenames.\nFound 1928 validated image filenames.\n","output_type":"stream"}]},{"cell_type":"code","source":"def cosine_decay_with_warmup(global_step,\n learning_rate_base,\n total_steps,\n warmup_learning_rate=0.0,\n warmup_steps=0,\n hold_base_rate_steps=0):\n\n if total_steps < warmup_steps:\n raise ValueError('total_steps must be larger or equal to warmup_steps.')\n learning_rate = 0.5 * learning_rate_base * (1 + np.cos(\n np.pi *\n (global_step - warmup_steps - hold_base_rate_steps\n ) / float(total_steps - warmup_steps - hold_base_rate_steps)))\n if hold_base_rate_steps > 0:\n learning_rate = np.where(global_step > warmup_steps + hold_base_rate_steps,\n learning_rate, learning_rate_base)\n if warmup_steps > 0:\n if learning_rate_base < warmup_learning_rate:\n raise ValueError('learning_rate_base must be larger or equal to warmup_learning_rate.')\n slope = (learning_rate_base - warmup_learning_rate) / warmup_steps\n warmup_rate = slope * global_step + warmup_learning_rate\n learning_rate = np.where(global_step < warmup_steps, warmup_rate,\n learning_rate)\n return np.where(global_step > total_steps, 0.0, learning_rate)\n\n\nclass WarmUpCosineDecayScheduler(Callback):\n\n def __init__(self,\n learning_rate_base,\n total_steps,\n global_step_init=0,\n warmup_learning_rate=0.0,\n warmup_steps=0,\n hold_base_rate_steps=0,\n verbose=0):\n\n super(WarmUpCosineDecayScheduler, self).__init__()\n self.learning_rate_base = learning_rate_base\n self.total_steps = total_steps\n self.global_step = global_step_init\n self.warmup_learning_rate = warmup_learning_rate\n self.warmup_steps = warmup_steps\n self.hold_base_rate_steps = hold_base_rate_steps\n self.verbose = verbose\n self.learning_rates = []\n\n def on_batch_end(self, batch, logs=None):\n self.global_step = self.global_step + 1\n lr = K.get_value(self.model.optimizer.lr)\n self.learning_rates.append(lr)\n\n def on_batch_begin(self, batch, logs=None):\n lr = cosine_decay_with_warmup(global_step=self.global_step,\n learning_rate_base=self.learning_rate_base,\n total_steps=self.total_steps,\n warmup_learning_rate=self.warmup_learning_rate,\n warmup_steps=self.warmup_steps,\n hold_base_rate_steps=self.hold_base_rate_steps)\n K.set_value(self.model.optimizer.lr, lr)\n if self.verbose > 0:\n print('\\nBatch %02d: setting learning rate to %s.' % (self.global_step + 1, lr))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:04:34.293606Z","iopub.execute_input":"2024-05-03T04:04:34.293893Z","iopub.status.idle":"2024-05-03T04:04:34.31194Z","shell.execute_reply.started":"2024-05-03T04:04:34.293838Z","shell.execute_reply":"2024-05-03T04:04:34.311115Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"def create_model(input_shape):\n input_tensor = Input(shape=input_shape)\n base_model = EfficientNetB5(weights=None, \n include_top=False,\n input_tensor=input_tensor)\n base_model.load_weights('../input/efficientnet-keras-weights-b0b5/efficientnet-b5_imagenet_1000_notop.h5')\n\n x = GlobalAveragePooling2D()(base_model.output)\n final_output = Dense(1, activation='linear', name='final_output')(x)\n model = Model(input_tensor, final_output)\n \n return model","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:04:34.313362Z","iopub.execute_input":"2024-05-03T04:04:34.31361Z","iopub.status.idle":"2024-05-03T04:04:34.328718Z","shell.execute_reply.started":"2024-05-03T04:04:34.313568Z","shell.execute_reply":"2024-05-03T04:04:34.328042Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"model = create_model(input_shape=(HEIGHT, WIDTH, CHANNELS))\n\nfor layer in model.layers:\n layer.trainable = False\n\nfor i in range(-2, 0):\n model.layers[i].trainable = True\n\ncosine_lr_1st = WarmUpCosineDecayScheduler(learning_rate_base=WARMUP_LEARNING_RATE,\n total_steps=TOTAL_STEPS_1st,\n warmup_learning_rate=0.0,\n warmup_steps=WARMUP_STEPS_1st,\n hold_base_rate_steps=(2 * STEP_SIZE))\n\nmetric_list = [\"accuracy\"]\ncallback_list = [cosine_lr_1st]\noptimizer = optimizers.Adam(lr=WARMUP_LEARNING_RATE)\nmodel.compile(optimizer=optimizer, loss='mean_squared_error', metrics=metric_list)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:04:34.330086Z","iopub.execute_input":"2024-05-03T04:04:34.330415Z","iopub.status.idle":"2024-05-03T04:05:07.020861Z","shell.execute_reply.started":"2024-05-03T04:04:34.330355Z","shell.execute_reply":"2024-05-03T04:05:07.020025Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":30,"outputs":[{"name":"stdout","text":"__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_2 (InputLayer) (None, 224, 224, 3) 0 \n__________________________________________________________________________________________________\nconv2d_156 (Conv2D) (None, 112, 112, 48) 1296 input_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_117 (BatchN (None, 112, 112, 48) 192 conv2d_156[0][0] \n__________________________________________________________________________________________________\nswish_117 (Swish) (None, 112, 112, 48) 0 batch_normalization_117[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_40 (DepthwiseC (None, 112, 112, 48) 432 swish_117[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_118 (BatchN (None, 112, 112, 48) 192 depthwise_conv2d_40[0][0] \n__________________________________________________________________________________________________\nswish_118 (Swish) (None, 112, 112, 48) 0 batch_normalization_118[0][0] \n__________________________________________________________________________________________________\nlambda_40 (Lambda) (None, 1, 1, 48) 0 swish_118[0][0] \n__________________________________________________________________________________________________\nconv2d_157 (Conv2D) (None, 1, 1, 12) 588 lambda_40[0][0] \n__________________________________________________________________________________________________\nswish_119 (Swish) (None, 1, 1, 12) 0 conv2d_157[0][0] \n__________________________________________________________________________________________________\nconv2d_158 (Conv2D) (None, 1, 1, 48) 624 swish_119[0][0] \n__________________________________________________________________________________________________\nactivation_40 (Activation) (None, 1, 1, 48) 0 conv2d_158[0][0] \n__________________________________________________________________________________________________\nmultiply_40 (Multiply) (None, 112, 112, 48) 0 activation_40[0][0] \n swish_118[0][0] \n__________________________________________________________________________________________________\nconv2d_159 (Conv2D) (None, 112, 112, 24) 1152 multiply_40[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_119 (BatchN (None, 112, 112, 24) 96 conv2d_159[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_41 (DepthwiseC (None, 112, 112, 24) 216 batch_normalization_119[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_120 (BatchN (None, 112, 112, 24) 96 depthwise_conv2d_41[0][0] \n__________________________________________________________________________________________________\nswish_120 (Swish) (None, 112, 112, 24) 0 batch_normalization_120[0][0] \n__________________________________________________________________________________________________\nlambda_41 (Lambda) (None, 1, 1, 24) 0 swish_120[0][0] \n__________________________________________________________________________________________________\nconv2d_160 (Conv2D) (None, 1, 1, 6) 150 lambda_41[0][0] \n__________________________________________________________________________________________________\nswish_121 (Swish) (None, 1, 1, 6) 0 conv2d_160[0][0] \n__________________________________________________________________________________________________\nconv2d_161 (Conv2D) (None, 1, 1, 24) 168 swish_121[0][0] \n__________________________________________________________________________________________________\nactivation_41 (Activation) (None, 1, 1, 24) 0 conv2d_161[0][0] \n__________________________________________________________________________________________________\nmultiply_41 (Multiply) (None, 112, 112, 24) 0 activation_41[0][0] \n swish_120[0][0] \n__________________________________________________________________________________________________\nconv2d_162 (Conv2D) (None, 112, 112, 24) 576 multiply_41[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_121 (BatchN (None, 112, 112, 24) 96 conv2d_162[0][0] \n__________________________________________________________________________________________________\ndrop_connect_33 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_121[0][0] \n__________________________________________________________________________________________________\nadd_33 (Add) (None, 112, 112, 24) 0 drop_connect_33[0][0] \n batch_normalization_119[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_42 (DepthwiseC (None, 112, 112, 24) 216 add_33[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_122 (BatchN (None, 112, 112, 24) 96 depthwise_conv2d_42[0][0] \n__________________________________________________________________________________________________\nswish_122 (Swish) (None, 112, 112, 24) 0 batch_normalization_122[0][0] \n__________________________________________________________________________________________________\nlambda_42 (Lambda) (None, 1, 1, 24) 0 swish_122[0][0] \n__________________________________________________________________________________________________\nconv2d_163 (Conv2D) (None, 1, 1, 6) 150 lambda_42[0][0] \n__________________________________________________________________________________________________\nswish_123 (Swish) (None, 1, 1, 6) 0 conv2d_163[0][0] \n__________________________________________________________________________________________________\nconv2d_164 (Conv2D) (None, 1, 1, 24) 168 swish_123[0][0] \n__________________________________________________________________________________________________\nactivation_42 (Activation) (None, 1, 1, 24) 0 conv2d_164[0][0] \n__________________________________________________________________________________________________\nmultiply_42 (Multiply) (None, 112, 112, 24) 0 activation_42[0][0] \n swish_122[0][0] \n__________________________________________________________________________________________________\nconv2d_165 (Conv2D) (None, 112, 112, 24) 576 multiply_42[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_123 (BatchN (None, 112, 112, 24) 96 conv2d_165[0][0] \n__________________________________________________________________________________________________\ndrop_connect_34 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_123[0][0] \n__________________________________________________________________________________________________\nadd_34 (Add) (None, 112, 112, 24) 0 drop_connect_34[0][0] \n add_33[0][0] \n__________________________________________________________________________________________________\nconv2d_166 (Conv2D) (None, 112, 112, 144 3456 add_34[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_124 (BatchN (None, 112, 112, 144 576 conv2d_166[0][0] \n__________________________________________________________________________________________________\nswish_124 (Swish) (None, 112, 112, 144 0 batch_normalization_124[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_43 (DepthwiseC (None, 56, 56, 144) 1296 swish_124[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_125 (BatchN (None, 56, 56, 144) 576 depthwise_conv2d_43[0][0] \n__________________________________________________________________________________________________\nswish_125 (Swish) (None, 56, 56, 144) 0 batch_normalization_125[0][0] \n__________________________________________________________________________________________________\nlambda_43 (Lambda) (None, 1, 1, 144) 0 swish_125[0][0] \n__________________________________________________________________________________________________\nconv2d_167 (Conv2D) (None, 1, 1, 6) 870 lambda_43[0][0] \n__________________________________________________________________________________________________\nswish_126 (Swish) (None, 1, 1, 6) 0 conv2d_167[0][0] \n__________________________________________________________________________________________________\nconv2d_168 (Conv2D) (None, 1, 1, 144) 1008 swish_126[0][0] \n__________________________________________________________________________________________________\nactivation_43 (Activation) (None, 1, 1, 144) 0 conv2d_168[0][0] \n__________________________________________________________________________________________________\nmultiply_43 (Multiply) (None, 56, 56, 144) 0 activation_43[0][0] \n swish_125[0][0] \n__________________________________________________________________________________________________\nconv2d_169 (Conv2D) (None, 56, 56, 40) 5760 multiply_43[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_126 (BatchN (None, 56, 56, 40) 160 conv2d_169[0][0] \n__________________________________________________________________________________________________\nconv2d_170 (Conv2D) (None, 56, 56, 240) 9600 batch_normalization_126[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_127 (BatchN (None, 56, 56, 240) 960 conv2d_170[0][0] \n__________________________________________________________________________________________________\nswish_127 (Swish) (None, 56, 56, 240) 0 batch_normalization_127[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_44 (DepthwiseC (None, 56, 56, 240) 2160 swish_127[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_128 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_44[0][0] \n__________________________________________________________________________________________________\nswish_128 (Swish) (None, 56, 56, 240) 0 batch_normalization_128[0][0] \n__________________________________________________________________________________________________\nlambda_44 (Lambda) (None, 1, 1, 240) 0 swish_128[0][0] \n__________________________________________________________________________________________________\nconv2d_171 (Conv2D) (None, 1, 1, 10) 2410 lambda_44[0][0] \n__________________________________________________________________________________________________\nswish_129 (Swish) (None, 1, 1, 10) 0 conv2d_171[0][0] \n__________________________________________________________________________________________________\nconv2d_172 (Conv2D) (None, 1, 1, 240) 2640 swish_129[0][0] \n__________________________________________________________________________________________________\nactivation_44 (Activation) (None, 1, 1, 240) 0 conv2d_172[0][0] \n__________________________________________________________________________________________________\nmultiply_44 (Multiply) (None, 56, 56, 240) 0 activation_44[0][0] \n swish_128[0][0] \n__________________________________________________________________________________________________\nconv2d_173 (Conv2D) (None, 56, 56, 40) 9600 multiply_44[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_129 (BatchN (None, 56, 56, 40) 160 conv2d_173[0][0] \n__________________________________________________________________________________________________\ndrop_connect_35 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_129[0][0] \n__________________________________________________________________________________________________\nadd_35 (Add) (None, 56, 56, 40) 0 drop_connect_35[0][0] \n batch_normalization_126[0][0] \n__________________________________________________________________________________________________\nconv2d_174 (Conv2D) (None, 56, 56, 240) 9600 add_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_130 (BatchN (None, 56, 56, 240) 960 conv2d_174[0][0] \n__________________________________________________________________________________________________\nswish_130 (Swish) (None, 56, 56, 240) 0 batch_normalization_130[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_45 (DepthwiseC (None, 56, 56, 240) 2160 swish_130[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_131 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_45[0][0] \n__________________________________________________________________________________________________\nswish_131 (Swish) (None, 56, 56, 240) 0 batch_normalization_131[0][0] \n__________________________________________________________________________________________________\nlambda_45 (Lambda) (None, 1, 1, 240) 0 swish_131[0][0] \n__________________________________________________________________________________________________\nconv2d_175 (Conv2D) (None, 1, 1, 10) 2410 lambda_45[0][0] \n__________________________________________________________________________________________________\nswish_132 (Swish) (None, 1, 1, 10) 0 conv2d_175[0][0] \n__________________________________________________________________________________________________\nconv2d_176 (Conv2D) (None, 1, 1, 240) 2640 swish_132[0][0] \n__________________________________________________________________________________________________\nactivation_45 (Activation) (None, 1, 1, 240) 0 conv2d_176[0][0] \n__________________________________________________________________________________________________\nmultiply_45 (Multiply) (None, 56, 56, 240) 0 activation_45[0][0] \n swish_131[0][0] \n__________________________________________________________________________________________________\nconv2d_177 (Conv2D) (None, 56, 56, 40) 9600 multiply_45[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_132 (BatchN (None, 56, 56, 40) 160 conv2d_177[0][0] \n__________________________________________________________________________________________________\ndrop_connect_36 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_132[0][0] \n__________________________________________________________________________________________________\nadd_36 (Add) (None, 56, 56, 40) 0 drop_connect_36[0][0] \n add_35[0][0] \n__________________________________________________________________________________________________\nconv2d_178 (Conv2D) (None, 56, 56, 240) 9600 add_36[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_133 (BatchN (None, 56, 56, 240) 960 conv2d_178[0][0] \n__________________________________________________________________________________________________\nswish_133 (Swish) (None, 56, 56, 240) 0 batch_normalization_133[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_46 (DepthwiseC (None, 56, 56, 240) 2160 swish_133[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_134 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_46[0][0] \n__________________________________________________________________________________________________\nswish_134 (Swish) (None, 56, 56, 240) 0 batch_normalization_134[0][0] \n__________________________________________________________________________________________________\nlambda_46 (Lambda) (None, 1, 1, 240) 0 swish_134[0][0] \n__________________________________________________________________________________________________\nconv2d_179 (Conv2D) (None, 1, 1, 10) 2410 lambda_46[0][0] \n__________________________________________________________________________________________________\nswish_135 (Swish) (None, 1, 1, 10) 0 conv2d_179[0][0] \n__________________________________________________________________________________________________\nconv2d_180 (Conv2D) (None, 1, 1, 240) 2640 swish_135[0][0] \n__________________________________________________________________________________________________\nactivation_46 (Activation) (None, 1, 1, 240) 0 conv2d_180[0][0] \n__________________________________________________________________________________________________\nmultiply_46 (Multiply) (None, 56, 56, 240) 0 activation_46[0][0] \n swish_134[0][0] \n__________________________________________________________________________________________________\nconv2d_181 (Conv2D) (None, 56, 56, 40) 9600 multiply_46[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_135 (BatchN (None, 56, 56, 40) 160 conv2d_181[0][0] \n__________________________________________________________________________________________________\ndrop_connect_37 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_135[0][0] \n__________________________________________________________________________________________________\nadd_37 (Add) (None, 56, 56, 40) 0 drop_connect_37[0][0] \n add_36[0][0] \n__________________________________________________________________________________________________\nconv2d_182 (Conv2D) (None, 56, 56, 240) 9600 add_37[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_136 (BatchN (None, 56, 56, 240) 960 conv2d_182[0][0] \n__________________________________________________________________________________________________\nswish_136 (Swish) (None, 56, 56, 240) 0 batch_normalization_136[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_47 (DepthwiseC (None, 56, 56, 240) 2160 swish_136[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_137 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_47[0][0] \n__________________________________________________________________________________________________\nswish_137 (Swish) (None, 56, 56, 240) 0 batch_normalization_137[0][0] \n__________________________________________________________________________________________________\nlambda_47 (Lambda) (None, 1, 1, 240) 0 swish_137[0][0] \n__________________________________________________________________________________________________\nconv2d_183 (Conv2D) (None, 1, 1, 10) 2410 lambda_47[0][0] \n__________________________________________________________________________________________________\nswish_138 (Swish) (None, 1, 1, 10) 0 conv2d_183[0][0] \n__________________________________________________________________________________________________\nconv2d_184 (Conv2D) (None, 1, 1, 240) 2640 swish_138[0][0] \n__________________________________________________________________________________________________\nactivation_47 (Activation) (None, 1, 1, 240) 0 conv2d_184[0][0] \n__________________________________________________________________________________________________\nmultiply_47 (Multiply) (None, 56, 56, 240) 0 activation_47[0][0] \n swish_137[0][0] \n__________________________________________________________________________________________________\nconv2d_185 (Conv2D) (None, 56, 56, 40) 9600 multiply_47[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_138 (BatchN (None, 56, 56, 40) 160 conv2d_185[0][0] \n__________________________________________________________________________________________________\ndrop_connect_38 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_138[0][0] \n__________________________________________________________________________________________________\nadd_38 (Add) (None, 56, 56, 40) 0 drop_connect_38[0][0] \n add_37[0][0] \n__________________________________________________________________________________________________\nconv2d_186 (Conv2D) (None, 56, 56, 240) 9600 add_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_139 (BatchN (None, 56, 56, 240) 960 conv2d_186[0][0] \n__________________________________________________________________________________________________\nswish_139 (Swish) (None, 56, 56, 240) 0 batch_normalization_139[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_48 (DepthwiseC (None, 28, 28, 240) 6000 swish_139[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_140 (BatchN (None, 28, 28, 240) 960 depthwise_conv2d_48[0][0] \n__________________________________________________________________________________________________\nswish_140 (Swish) (None, 28, 28, 240) 0 batch_normalization_140[0][0] \n__________________________________________________________________________________________________\nlambda_48 (Lambda) (None, 1, 1, 240) 0 swish_140[0][0] \n__________________________________________________________________________________________________\nconv2d_187 (Conv2D) (None, 1, 1, 10) 2410 lambda_48[0][0] \n__________________________________________________________________________________________________\nswish_141 (Swish) (None, 1, 1, 10) 0 conv2d_187[0][0] \n__________________________________________________________________________________________________\nconv2d_188 (Conv2D) (None, 1, 1, 240) 2640 swish_141[0][0] \n__________________________________________________________________________________________________\nactivation_48 (Activation) (None, 1, 1, 240) 0 conv2d_188[0][0] \n__________________________________________________________________________________________________\nmultiply_48 (Multiply) (None, 28, 28, 240) 0 activation_48[0][0] \n swish_140[0][0] \n__________________________________________________________________________________________________\nconv2d_189 (Conv2D) (None, 28, 28, 64) 15360 multiply_48[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_141 (BatchN (None, 28, 28, 64) 256 conv2d_189[0][0] \n__________________________________________________________________________________________________\nconv2d_190 (Conv2D) (None, 28, 28, 384) 24576 batch_normalization_141[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_142 (BatchN (None, 28, 28, 384) 1536 conv2d_190[0][0] \n__________________________________________________________________________________________________\nswish_142 (Swish) (None, 28, 28, 384) 0 batch_normalization_142[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_49 (DepthwiseC (None, 28, 28, 384) 9600 swish_142[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_143 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_49[0][0] \n__________________________________________________________________________________________________\nswish_143 (Swish) (None, 28, 28, 384) 0 batch_normalization_143[0][0] \n__________________________________________________________________________________________________\nlambda_49 (Lambda) (None, 1, 1, 384) 0 swish_143[0][0] \n__________________________________________________________________________________________________\nconv2d_191 (Conv2D) (None, 1, 1, 16) 6160 lambda_49[0][0] \n__________________________________________________________________________________________________\nswish_144 (Swish) (None, 1, 1, 16) 0 conv2d_191[0][0] \n__________________________________________________________________________________________________\nconv2d_192 (Conv2D) (None, 1, 1, 384) 6528 swish_144[0][0] \n__________________________________________________________________________________________________\nactivation_49 (Activation) (None, 1, 1, 384) 0 conv2d_192[0][0] \n__________________________________________________________________________________________________\nmultiply_49 (Multiply) (None, 28, 28, 384) 0 activation_49[0][0] \n swish_143[0][0] \n__________________________________________________________________________________________________\nconv2d_193 (Conv2D) (None, 28, 28, 64) 24576 multiply_49[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_144 (BatchN (None, 28, 28, 64) 256 conv2d_193[0][0] \n__________________________________________________________________________________________________\ndrop_connect_39 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_144[0][0] \n__________________________________________________________________________________________________\nadd_39 (Add) (None, 28, 28, 64) 0 drop_connect_39[0][0] \n batch_normalization_141[0][0] \n__________________________________________________________________________________________________\nconv2d_194 (Conv2D) (None, 28, 28, 384) 24576 add_39[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_145 (BatchN (None, 28, 28, 384) 1536 conv2d_194[0][0] \n__________________________________________________________________________________________________\nswish_145 (Swish) (None, 28, 28, 384) 0 batch_normalization_145[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_50 (DepthwiseC (None, 28, 28, 384) 9600 swish_145[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_146 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_50[0][0] \n__________________________________________________________________________________________________\nswish_146 (Swish) (None, 28, 28, 384) 0 batch_normalization_146[0][0] \n__________________________________________________________________________________________________\nlambda_50 (Lambda) (None, 1, 1, 384) 0 swish_146[0][0] \n__________________________________________________________________________________________________\nconv2d_195 (Conv2D) (None, 1, 1, 16) 6160 lambda_50[0][0] \n__________________________________________________________________________________________________\nswish_147 (Swish) (None, 1, 1, 16) 0 conv2d_195[0][0] \n__________________________________________________________________________________________________\nconv2d_196 (Conv2D) (None, 1, 1, 384) 6528 swish_147[0][0] \n__________________________________________________________________________________________________\nactivation_50 (Activation) (None, 1, 1, 384) 0 conv2d_196[0][0] \n__________________________________________________________________________________________________\nmultiply_50 (Multiply) (None, 28, 28, 384) 0 activation_50[0][0] \n swish_146[0][0] \n__________________________________________________________________________________________________\nconv2d_197 (Conv2D) (None, 28, 28, 64) 24576 multiply_50[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_147 (BatchN (None, 28, 28, 64) 256 conv2d_197[0][0] \n__________________________________________________________________________________________________\ndrop_connect_40 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_147[0][0] \n__________________________________________________________________________________________________\nadd_40 (Add) (None, 28, 28, 64) 0 drop_connect_40[0][0] \n add_39[0][0] \n__________________________________________________________________________________________________\nconv2d_198 (Conv2D) (None, 28, 28, 384) 24576 add_40[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_148 (BatchN (None, 28, 28, 384) 1536 conv2d_198[0][0] \n__________________________________________________________________________________________________\nswish_148 (Swish) (None, 28, 28, 384) 0 batch_normalization_148[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_51 (DepthwiseC (None, 28, 28, 384) 9600 swish_148[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_149 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_51[0][0] \n__________________________________________________________________________________________________\nswish_149 (Swish) (None, 28, 28, 384) 0 batch_normalization_149[0][0] \n__________________________________________________________________________________________________\nlambda_51 (Lambda) (None, 1, 1, 384) 0 swish_149[0][0] \n__________________________________________________________________________________________________\nconv2d_199 (Conv2D) (None, 1, 1, 16) 6160 lambda_51[0][0] \n__________________________________________________________________________________________________\nswish_150 (Swish) (None, 1, 1, 16) 0 conv2d_199[0][0] \n__________________________________________________________________________________________________\nconv2d_200 (Conv2D) (None, 1, 1, 384) 6528 swish_150[0][0] \n__________________________________________________________________________________________________\nactivation_51 (Activation) (None, 1, 1, 384) 0 conv2d_200[0][0] \n__________________________________________________________________________________________________\nmultiply_51 (Multiply) (None, 28, 28, 384) 0 activation_51[0][0] \n swish_149[0][0] \n__________________________________________________________________________________________________\nconv2d_201 (Conv2D) (None, 28, 28, 64) 24576 multiply_51[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_150 (BatchN (None, 28, 28, 64) 256 conv2d_201[0][0] \n__________________________________________________________________________________________________\ndrop_connect_41 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_150[0][0] \n__________________________________________________________________________________________________\nadd_41 (Add) (None, 28, 28, 64) 0 drop_connect_41[0][0] \n add_40[0][0] \n__________________________________________________________________________________________________\nconv2d_202 (Conv2D) (None, 28, 28, 384) 24576 add_41[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_151 (BatchN (None, 28, 28, 384) 1536 conv2d_202[0][0] \n__________________________________________________________________________________________________\nswish_151 (Swish) (None, 28, 28, 384) 0 batch_normalization_151[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_52 (DepthwiseC (None, 28, 28, 384) 9600 swish_151[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_152 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_52[0][0] \n__________________________________________________________________________________________________\nswish_152 (Swish) (None, 28, 28, 384) 0 batch_normalization_152[0][0] \n__________________________________________________________________________________________________\nlambda_52 (Lambda) (None, 1, 1, 384) 0 swish_152[0][0] \n__________________________________________________________________________________________________\nconv2d_203 (Conv2D) (None, 1, 1, 16) 6160 lambda_52[0][0] \n__________________________________________________________________________________________________\nswish_153 (Swish) (None, 1, 1, 16) 0 conv2d_203[0][0] \n__________________________________________________________________________________________________\nconv2d_204 (Conv2D) (None, 1, 1, 384) 6528 swish_153[0][0] \n__________________________________________________________________________________________________\nactivation_52 (Activation) (None, 1, 1, 384) 0 conv2d_204[0][0] \n__________________________________________________________________________________________________\nmultiply_52 (Multiply) (None, 28, 28, 384) 0 activation_52[0][0] \n swish_152[0][0] \n__________________________________________________________________________________________________\nconv2d_205 (Conv2D) (None, 28, 28, 64) 24576 multiply_52[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_153 (BatchN (None, 28, 28, 64) 256 conv2d_205[0][0] \n__________________________________________________________________________________________________\ndrop_connect_42 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_153[0][0] \n__________________________________________________________________________________________________\nadd_42 (Add) (None, 28, 28, 64) 0 drop_connect_42[0][0] \n add_41[0][0] \n__________________________________________________________________________________________________\nconv2d_206 (Conv2D) (None, 28, 28, 384) 24576 add_42[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_154 (BatchN (None, 28, 28, 384) 1536 conv2d_206[0][0] \n__________________________________________________________________________________________________\nswish_154 (Swish) (None, 28, 28, 384) 0 batch_normalization_154[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_53 (DepthwiseC (None, 14, 14, 384) 3456 swish_154[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_155 (BatchN (None, 14, 14, 384) 1536 depthwise_conv2d_53[0][0] \n__________________________________________________________________________________________________\nswish_155 (Swish) (None, 14, 14, 384) 0 batch_normalization_155[0][0] \n__________________________________________________________________________________________________\nlambda_53 (Lambda) (None, 1, 1, 384) 0 swish_155[0][0] \n__________________________________________________________________________________________________\nconv2d_207 (Conv2D) (None, 1, 1, 16) 6160 lambda_53[0][0] \n__________________________________________________________________________________________________\nswish_156 (Swish) (None, 1, 1, 16) 0 conv2d_207[0][0] \n__________________________________________________________________________________________________\nconv2d_208 (Conv2D) (None, 1, 1, 384) 6528 swish_156[0][0] \n__________________________________________________________________________________________________\nactivation_53 (Activation) (None, 1, 1, 384) 0 conv2d_208[0][0] \n__________________________________________________________________________________________________\nmultiply_53 (Multiply) (None, 14, 14, 384) 0 activation_53[0][0] \n swish_155[0][0] \n__________________________________________________________________________________________________\nconv2d_209 (Conv2D) (None, 14, 14, 128) 49152 multiply_53[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_156 (BatchN (None, 14, 14, 128) 512 conv2d_209[0][0] \n__________________________________________________________________________________________________\nconv2d_210 (Conv2D) (None, 14, 14, 768) 98304 batch_normalization_156[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_157 (BatchN (None, 14, 14, 768) 3072 conv2d_210[0][0] \n__________________________________________________________________________________________________\nswish_157 (Swish) (None, 14, 14, 768) 0 batch_normalization_157[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_54 (DepthwiseC (None, 14, 14, 768) 6912 swish_157[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_158 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_54[0][0] \n__________________________________________________________________________________________________\nswish_158 (Swish) (None, 14, 14, 768) 0 batch_normalization_158[0][0] \n__________________________________________________________________________________________________\nlambda_54 (Lambda) (None, 1, 1, 768) 0 swish_158[0][0] \n__________________________________________________________________________________________________\nconv2d_211 (Conv2D) (None, 1, 1, 32) 24608 lambda_54[0][0] \n__________________________________________________________________________________________________\nswish_159 (Swish) (None, 1, 1, 32) 0 conv2d_211[0][0] \n__________________________________________________________________________________________________\nconv2d_212 (Conv2D) (None, 1, 1, 768) 25344 swish_159[0][0] \n__________________________________________________________________________________________________\nactivation_54 (Activation) (None, 1, 1, 768) 0 conv2d_212[0][0] \n__________________________________________________________________________________________________\nmultiply_54 (Multiply) (None, 14, 14, 768) 0 activation_54[0][0] \n swish_158[0][0] \n__________________________________________________________________________________________________\nconv2d_213 (Conv2D) (None, 14, 14, 128) 98304 multiply_54[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_159 (BatchN (None, 14, 14, 128) 512 conv2d_213[0][0] \n__________________________________________________________________________________________________\ndrop_connect_43 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_159[0][0] \n__________________________________________________________________________________________________\nadd_43 (Add) (None, 14, 14, 128) 0 drop_connect_43[0][0] \n batch_normalization_156[0][0] \n__________________________________________________________________________________________________\nconv2d_214 (Conv2D) (None, 14, 14, 768) 98304 add_43[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_160 (BatchN (None, 14, 14, 768) 3072 conv2d_214[0][0] \n__________________________________________________________________________________________________\nswish_160 (Swish) (None, 14, 14, 768) 0 batch_normalization_160[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_55 (DepthwiseC (None, 14, 14, 768) 6912 swish_160[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_161 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_55[0][0] \n__________________________________________________________________________________________________\nswish_161 (Swish) (None, 14, 14, 768) 0 batch_normalization_161[0][0] \n__________________________________________________________________________________________________\nlambda_55 (Lambda) (None, 1, 1, 768) 0 swish_161[0][0] \n__________________________________________________________________________________________________\nconv2d_215 (Conv2D) (None, 1, 1, 32) 24608 lambda_55[0][0] \n__________________________________________________________________________________________________\nswish_162 (Swish) (None, 1, 1, 32) 0 conv2d_215[0][0] \n__________________________________________________________________________________________________\nconv2d_216 (Conv2D) (None, 1, 1, 768) 25344 swish_162[0][0] \n__________________________________________________________________________________________________\nactivation_55 (Activation) (None, 1, 1, 768) 0 conv2d_216[0][0] \n__________________________________________________________________________________________________\nmultiply_55 (Multiply) (None, 14, 14, 768) 0 activation_55[0][0] \n swish_161[0][0] \n__________________________________________________________________________________________________\nconv2d_217 (Conv2D) (None, 14, 14, 128) 98304 multiply_55[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_162 (BatchN (None, 14, 14, 128) 512 conv2d_217[0][0] \n__________________________________________________________________________________________________\ndrop_connect_44 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_162[0][0] \n__________________________________________________________________________________________________\nadd_44 (Add) (None, 14, 14, 128) 0 drop_connect_44[0][0] \n add_43[0][0] \n__________________________________________________________________________________________________\nconv2d_218 (Conv2D) (None, 14, 14, 768) 98304 add_44[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_163 (BatchN (None, 14, 14, 768) 3072 conv2d_218[0][0] \n__________________________________________________________________________________________________\nswish_163 (Swish) (None, 14, 14, 768) 0 batch_normalization_163[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_56 (DepthwiseC (None, 14, 14, 768) 6912 swish_163[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_164 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_56[0][0] \n__________________________________________________________________________________________________\nswish_164 (Swish) (None, 14, 14, 768) 0 batch_normalization_164[0][0] \n__________________________________________________________________________________________________\nlambda_56 (Lambda) (None, 1, 1, 768) 0 swish_164[0][0] \n__________________________________________________________________________________________________\nconv2d_219 (Conv2D) (None, 1, 1, 32) 24608 lambda_56[0][0] \n__________________________________________________________________________________________________\nswish_165 (Swish) (None, 1, 1, 32) 0 conv2d_219[0][0] \n__________________________________________________________________________________________________\nconv2d_220 (Conv2D) (None, 1, 1, 768) 25344 swish_165[0][0] \n__________________________________________________________________________________________________\nactivation_56 (Activation) (None, 1, 1, 768) 0 conv2d_220[0][0] \n__________________________________________________________________________________________________\nmultiply_56 (Multiply) (None, 14, 14, 768) 0 activation_56[0][0] \n swish_164[0][0] \n__________________________________________________________________________________________________\nconv2d_221 (Conv2D) (None, 14, 14, 128) 98304 multiply_56[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_165 (BatchN (None, 14, 14, 128) 512 conv2d_221[0][0] \n__________________________________________________________________________________________________\ndrop_connect_45 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_165[0][0] \n__________________________________________________________________________________________________\nadd_45 (Add) (None, 14, 14, 128) 0 drop_connect_45[0][0] \n add_44[0][0] \n__________________________________________________________________________________________________\nconv2d_222 (Conv2D) (None, 14, 14, 768) 98304 add_45[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_166 (BatchN (None, 14, 14, 768) 3072 conv2d_222[0][0] \n__________________________________________________________________________________________________\nswish_166 (Swish) (None, 14, 14, 768) 0 batch_normalization_166[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_57 (DepthwiseC (None, 14, 14, 768) 6912 swish_166[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_167 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_57[0][0] \n__________________________________________________________________________________________________\nswish_167 (Swish) (None, 14, 14, 768) 0 batch_normalization_167[0][0] \n__________________________________________________________________________________________________\nlambda_57 (Lambda) (None, 1, 1, 768) 0 swish_167[0][0] \n__________________________________________________________________________________________________\nconv2d_223 (Conv2D) (None, 1, 1, 32) 24608 lambda_57[0][0] \n__________________________________________________________________________________________________\nswish_168 (Swish) (None, 1, 1, 32) 0 conv2d_223[0][0] \n__________________________________________________________________________________________________\nconv2d_224 (Conv2D) (None, 1, 1, 768) 25344 swish_168[0][0] \n__________________________________________________________________________________________________\nactivation_57 (Activation) (None, 1, 1, 768) 0 conv2d_224[0][0] \n__________________________________________________________________________________________________\nmultiply_57 (Multiply) (None, 14, 14, 768) 0 activation_57[0][0] \n swish_167[0][0] \n__________________________________________________________________________________________________\nconv2d_225 (Conv2D) (None, 14, 14, 128) 98304 multiply_57[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_168 (BatchN (None, 14, 14, 128) 512 conv2d_225[0][0] \n__________________________________________________________________________________________________\ndrop_connect_46 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_168[0][0] \n__________________________________________________________________________________________________\nadd_46 (Add) (None, 14, 14, 128) 0 drop_connect_46[0][0] \n add_45[0][0] \n__________________________________________________________________________________________________\nconv2d_226 (Conv2D) (None, 14, 14, 768) 98304 add_46[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_169 (BatchN (None, 14, 14, 768) 3072 conv2d_226[0][0] \n__________________________________________________________________________________________________\nswish_169 (Swish) (None, 14, 14, 768) 0 batch_normalization_169[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_58 (DepthwiseC (None, 14, 14, 768) 6912 swish_169[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_170 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_58[0][0] \n__________________________________________________________________________________________________\nswish_170 (Swish) (None, 14, 14, 768) 0 batch_normalization_170[0][0] \n__________________________________________________________________________________________________\nlambda_58 (Lambda) (None, 1, 1, 768) 0 swish_170[0][0] \n__________________________________________________________________________________________________\nconv2d_227 (Conv2D) (None, 1, 1, 32) 24608 lambda_58[0][0] \n__________________________________________________________________________________________________\nswish_171 (Swish) (None, 1, 1, 32) 0 conv2d_227[0][0] \n__________________________________________________________________________________________________\nconv2d_228 (Conv2D) (None, 1, 1, 768) 25344 swish_171[0][0] \n__________________________________________________________________________________________________\nactivation_58 (Activation) (None, 1, 1, 768) 0 conv2d_228[0][0] \n__________________________________________________________________________________________________\nmultiply_58 (Multiply) (None, 14, 14, 768) 0 activation_58[0][0] \n swish_170[0][0] \n__________________________________________________________________________________________________\nconv2d_229 (Conv2D) (None, 14, 14, 128) 98304 multiply_58[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_171 (BatchN (None, 14, 14, 128) 512 conv2d_229[0][0] \n__________________________________________________________________________________________________\ndrop_connect_47 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_171[0][0] \n__________________________________________________________________________________________________\nadd_47 (Add) (None, 14, 14, 128) 0 drop_connect_47[0][0] \n add_46[0][0] \n__________________________________________________________________________________________________\nconv2d_230 (Conv2D) (None, 14, 14, 768) 98304 add_47[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_172 (BatchN (None, 14, 14, 768) 3072 conv2d_230[0][0] \n__________________________________________________________________________________________________\nswish_172 (Swish) (None, 14, 14, 768) 0 batch_normalization_172[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_59 (DepthwiseC (None, 14, 14, 768) 6912 swish_172[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_173 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_59[0][0] \n__________________________________________________________________________________________________\nswish_173 (Swish) (None, 14, 14, 768) 0 batch_normalization_173[0][0] \n__________________________________________________________________________________________________\nlambda_59 (Lambda) (None, 1, 1, 768) 0 swish_173[0][0] \n__________________________________________________________________________________________________\nconv2d_231 (Conv2D) (None, 1, 1, 32) 24608 lambda_59[0][0] \n__________________________________________________________________________________________________\nswish_174 (Swish) (None, 1, 1, 32) 0 conv2d_231[0][0] \n__________________________________________________________________________________________________\nconv2d_232 (Conv2D) (None, 1, 1, 768) 25344 swish_174[0][0] \n__________________________________________________________________________________________________\nactivation_59 (Activation) (None, 1, 1, 768) 0 conv2d_232[0][0] \n__________________________________________________________________________________________________\nmultiply_59 (Multiply) (None, 14, 14, 768) 0 activation_59[0][0] \n swish_173[0][0] \n__________________________________________________________________________________________________\nconv2d_233 (Conv2D) (None, 14, 14, 128) 98304 multiply_59[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_174 (BatchN (None, 14, 14, 128) 512 conv2d_233[0][0] \n__________________________________________________________________________________________________\ndrop_connect_48 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_174[0][0] \n__________________________________________________________________________________________________\nadd_48 (Add) (None, 14, 14, 128) 0 drop_connect_48[0][0] \n add_47[0][0] \n__________________________________________________________________________________________________\nconv2d_234 (Conv2D) (None, 14, 14, 768) 98304 add_48[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_175 (BatchN (None, 14, 14, 768) 3072 conv2d_234[0][0] \n__________________________________________________________________________________________________\nswish_175 (Swish) (None, 14, 14, 768) 0 batch_normalization_175[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_60 (DepthwiseC (None, 14, 14, 768) 19200 swish_175[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_176 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_60[0][0] \n__________________________________________________________________________________________________\nswish_176 (Swish) (None, 14, 14, 768) 0 batch_normalization_176[0][0] \n__________________________________________________________________________________________________\nlambda_60 (Lambda) (None, 1, 1, 768) 0 swish_176[0][0] \n__________________________________________________________________________________________________\nconv2d_235 (Conv2D) (None, 1, 1, 32) 24608 lambda_60[0][0] \n__________________________________________________________________________________________________\nswish_177 (Swish) (None, 1, 1, 32) 0 conv2d_235[0][0] \n__________________________________________________________________________________________________\nconv2d_236 (Conv2D) (None, 1, 1, 768) 25344 swish_177[0][0] \n__________________________________________________________________________________________________\nactivation_60 (Activation) (None, 1, 1, 768) 0 conv2d_236[0][0] \n__________________________________________________________________________________________________\nmultiply_60 (Multiply) (None, 14, 14, 768) 0 activation_60[0][0] \n swish_176[0][0] \n__________________________________________________________________________________________________\nconv2d_237 (Conv2D) (None, 14, 14, 176) 135168 multiply_60[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_177 (BatchN (None, 14, 14, 176) 704 conv2d_237[0][0] \n__________________________________________________________________________________________________\nconv2d_238 (Conv2D) (None, 14, 14, 1056) 185856 batch_normalization_177[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_178 (BatchN (None, 14, 14, 1056) 4224 conv2d_238[0][0] \n__________________________________________________________________________________________________\nswish_178 (Swish) (None, 14, 14, 1056) 0 batch_normalization_178[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_61 (DepthwiseC (None, 14, 14, 1056) 26400 swish_178[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_179 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_61[0][0] \n__________________________________________________________________________________________________\nswish_179 (Swish) (None, 14, 14, 1056) 0 batch_normalization_179[0][0] \n__________________________________________________________________________________________________\nlambda_61 (Lambda) (None, 1, 1, 1056) 0 swish_179[0][0] \n__________________________________________________________________________________________________\nconv2d_239 (Conv2D) (None, 1, 1, 44) 46508 lambda_61[0][0] \n__________________________________________________________________________________________________\nswish_180 (Swish) (None, 1, 1, 44) 0 conv2d_239[0][0] \n__________________________________________________________________________________________________\nconv2d_240 (Conv2D) (None, 1, 1, 1056) 47520 swish_180[0][0] \n__________________________________________________________________________________________________\nactivation_61 (Activation) (None, 1, 1, 1056) 0 conv2d_240[0][0] \n__________________________________________________________________________________________________\nmultiply_61 (Multiply) (None, 14, 14, 1056) 0 activation_61[0][0] \n swish_179[0][0] \n__________________________________________________________________________________________________\nconv2d_241 (Conv2D) (None, 14, 14, 176) 185856 multiply_61[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_180 (BatchN (None, 14, 14, 176) 704 conv2d_241[0][0] \n__________________________________________________________________________________________________\ndrop_connect_49 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_180[0][0] \n__________________________________________________________________________________________________\nadd_49 (Add) (None, 14, 14, 176) 0 drop_connect_49[0][0] \n batch_normalization_177[0][0] \n__________________________________________________________________________________________________\nconv2d_242 (Conv2D) (None, 14, 14, 1056) 185856 add_49[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_181 (BatchN (None, 14, 14, 1056) 4224 conv2d_242[0][0] \n__________________________________________________________________________________________________\nswish_181 (Swish) (None, 14, 14, 1056) 0 batch_normalization_181[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_62 (DepthwiseC (None, 14, 14, 1056) 26400 swish_181[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_182 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_62[0][0] \n__________________________________________________________________________________________________\nswish_182 (Swish) (None, 14, 14, 1056) 0 batch_normalization_182[0][0] \n__________________________________________________________________________________________________\nlambda_62 (Lambda) (None, 1, 1, 1056) 0 swish_182[0][0] \n__________________________________________________________________________________________________\nconv2d_243 (Conv2D) (None, 1, 1, 44) 46508 lambda_62[0][0] \n__________________________________________________________________________________________________\nswish_183 (Swish) (None, 1, 1, 44) 0 conv2d_243[0][0] \n__________________________________________________________________________________________________\nconv2d_244 (Conv2D) (None, 1, 1, 1056) 47520 swish_183[0][0] \n__________________________________________________________________________________________________\nactivation_62 (Activation) (None, 1, 1, 1056) 0 conv2d_244[0][0] \n__________________________________________________________________________________________________\nmultiply_62 (Multiply) (None, 14, 14, 1056) 0 activation_62[0][0] \n swish_182[0][0] \n__________________________________________________________________________________________________\nconv2d_245 (Conv2D) (None, 14, 14, 176) 185856 multiply_62[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_183 (BatchN (None, 14, 14, 176) 704 conv2d_245[0][0] \n__________________________________________________________________________________________________\ndrop_connect_50 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_183[0][0] \n__________________________________________________________________________________________________\nadd_50 (Add) (None, 14, 14, 176) 0 drop_connect_50[0][0] \n add_49[0][0] \n__________________________________________________________________________________________________\nconv2d_246 (Conv2D) (None, 14, 14, 1056) 185856 add_50[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_184 (BatchN (None, 14, 14, 1056) 4224 conv2d_246[0][0] \n__________________________________________________________________________________________________\nswish_184 (Swish) (None, 14, 14, 1056) 0 batch_normalization_184[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_63 (DepthwiseC (None, 14, 14, 1056) 26400 swish_184[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_185 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_63[0][0] \n__________________________________________________________________________________________________\nswish_185 (Swish) (None, 14, 14, 1056) 0 batch_normalization_185[0][0] \n__________________________________________________________________________________________________\nlambda_63 (Lambda) (None, 1, 1, 1056) 0 swish_185[0][0] \n__________________________________________________________________________________________________\nconv2d_247 (Conv2D) (None, 1, 1, 44) 46508 lambda_63[0][0] \n__________________________________________________________________________________________________\nswish_186 (Swish) (None, 1, 1, 44) 0 conv2d_247[0][0] \n__________________________________________________________________________________________________\nconv2d_248 (Conv2D) (None, 1, 1, 1056) 47520 swish_186[0][0] \n__________________________________________________________________________________________________\nactivation_63 (Activation) (None, 1, 1, 1056) 0 conv2d_248[0][0] \n__________________________________________________________________________________________________\nmultiply_63 (Multiply) (None, 14, 14, 1056) 0 activation_63[0][0] \n swish_185[0][0] \n__________________________________________________________________________________________________\nconv2d_249 (Conv2D) (None, 14, 14, 176) 185856 multiply_63[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_186 (BatchN (None, 14, 14, 176) 704 conv2d_249[0][0] \n__________________________________________________________________________________________________\ndrop_connect_51 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_186[0][0] \n__________________________________________________________________________________________________\nadd_51 (Add) (None, 14, 14, 176) 0 drop_connect_51[0][0] \n add_50[0][0] \n__________________________________________________________________________________________________\nconv2d_250 (Conv2D) (None, 14, 14, 1056) 185856 add_51[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_187 (BatchN (None, 14, 14, 1056) 4224 conv2d_250[0][0] \n__________________________________________________________________________________________________\nswish_187 (Swish) (None, 14, 14, 1056) 0 batch_normalization_187[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_64 (DepthwiseC (None, 14, 14, 1056) 26400 swish_187[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_188 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_64[0][0] \n__________________________________________________________________________________________________\nswish_188 (Swish) (None, 14, 14, 1056) 0 batch_normalization_188[0][0] \n__________________________________________________________________________________________________\nlambda_64 (Lambda) (None, 1, 1, 1056) 0 swish_188[0][0] \n__________________________________________________________________________________________________\nconv2d_251 (Conv2D) (None, 1, 1, 44) 46508 lambda_64[0][0] \n__________________________________________________________________________________________________\nswish_189 (Swish) (None, 1, 1, 44) 0 conv2d_251[0][0] \n__________________________________________________________________________________________________\nconv2d_252 (Conv2D) (None, 1, 1, 1056) 47520 swish_189[0][0] \n__________________________________________________________________________________________________\nactivation_64 (Activation) (None, 1, 1, 1056) 0 conv2d_252[0][0] \n__________________________________________________________________________________________________\nmultiply_64 (Multiply) (None, 14, 14, 1056) 0 activation_64[0][0] \n swish_188[0][0] \n__________________________________________________________________________________________________\nconv2d_253 (Conv2D) (None, 14, 14, 176) 185856 multiply_64[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_189 (BatchN (None, 14, 14, 176) 704 conv2d_253[0][0] \n__________________________________________________________________________________________________\ndrop_connect_52 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_189[0][0] \n__________________________________________________________________________________________________\nadd_52 (Add) (None, 14, 14, 176) 0 drop_connect_52[0][0] \n add_51[0][0] \n__________________________________________________________________________________________________\nconv2d_254 (Conv2D) (None, 14, 14, 1056) 185856 add_52[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_190 (BatchN (None, 14, 14, 1056) 4224 conv2d_254[0][0] \n__________________________________________________________________________________________________\nswish_190 (Swish) (None, 14, 14, 1056) 0 batch_normalization_190[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_65 (DepthwiseC (None, 14, 14, 1056) 26400 swish_190[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_191 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_65[0][0] \n__________________________________________________________________________________________________\nswish_191 (Swish) (None, 14, 14, 1056) 0 batch_normalization_191[0][0] \n__________________________________________________________________________________________________\nlambda_65 (Lambda) (None, 1, 1, 1056) 0 swish_191[0][0] \n__________________________________________________________________________________________________\nconv2d_255 (Conv2D) (None, 1, 1, 44) 46508 lambda_65[0][0] \n__________________________________________________________________________________________________\nswish_192 (Swish) (None, 1, 1, 44) 0 conv2d_255[0][0] \n__________________________________________________________________________________________________\nconv2d_256 (Conv2D) (None, 1, 1, 1056) 47520 swish_192[0][0] \n__________________________________________________________________________________________________\nactivation_65 (Activation) (None, 1, 1, 1056) 0 conv2d_256[0][0] \n__________________________________________________________________________________________________\nmultiply_65 (Multiply) (None, 14, 14, 1056) 0 activation_65[0][0] \n swish_191[0][0] \n__________________________________________________________________________________________________\nconv2d_257 (Conv2D) (None, 14, 14, 176) 185856 multiply_65[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_192 (BatchN (None, 14, 14, 176) 704 conv2d_257[0][0] \n__________________________________________________________________________________________________\ndrop_connect_53 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_192[0][0] \n__________________________________________________________________________________________________\nadd_53 (Add) (None, 14, 14, 176) 0 drop_connect_53[0][0] \n add_52[0][0] \n__________________________________________________________________________________________________\nconv2d_258 (Conv2D) (None, 14, 14, 1056) 185856 add_53[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_193 (BatchN (None, 14, 14, 1056) 4224 conv2d_258[0][0] \n__________________________________________________________________________________________________\nswish_193 (Swish) (None, 14, 14, 1056) 0 batch_normalization_193[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_66 (DepthwiseC (None, 14, 14, 1056) 26400 swish_193[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_194 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_66[0][0] \n__________________________________________________________________________________________________\nswish_194 (Swish) (None, 14, 14, 1056) 0 batch_normalization_194[0][0] \n__________________________________________________________________________________________________\nlambda_66 (Lambda) (None, 1, 1, 1056) 0 swish_194[0][0] \n__________________________________________________________________________________________________\nconv2d_259 (Conv2D) (None, 1, 1, 44) 46508 lambda_66[0][0] \n__________________________________________________________________________________________________\nswish_195 (Swish) (None, 1, 1, 44) 0 conv2d_259[0][0] \n__________________________________________________________________________________________________\nconv2d_260 (Conv2D) (None, 1, 1, 1056) 47520 swish_195[0][0] \n__________________________________________________________________________________________________\nactivation_66 (Activation) (None, 1, 1, 1056) 0 conv2d_260[0][0] \n__________________________________________________________________________________________________\nmultiply_66 (Multiply) (None, 14, 14, 1056) 0 activation_66[0][0] \n swish_194[0][0] \n__________________________________________________________________________________________________\nconv2d_261 (Conv2D) (None, 14, 14, 176) 185856 multiply_66[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_195 (BatchN (None, 14, 14, 176) 704 conv2d_261[0][0] \n__________________________________________________________________________________________________\ndrop_connect_54 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_195[0][0] \n__________________________________________________________________________________________________\nadd_54 (Add) (None, 14, 14, 176) 0 drop_connect_54[0][0] \n add_53[0][0] \n__________________________________________________________________________________________________\nconv2d_262 (Conv2D) (None, 14, 14, 1056) 185856 add_54[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_196 (BatchN (None, 14, 14, 1056) 4224 conv2d_262[0][0] \n__________________________________________________________________________________________________\nswish_196 (Swish) (None, 14, 14, 1056) 0 batch_normalization_196[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_67 (DepthwiseC (None, 7, 7, 1056) 26400 swish_196[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_197 (BatchN (None, 7, 7, 1056) 4224 depthwise_conv2d_67[0][0] \n__________________________________________________________________________________________________\nswish_197 (Swish) (None, 7, 7, 1056) 0 batch_normalization_197[0][0] \n__________________________________________________________________________________________________\nlambda_67 (Lambda) (None, 1, 1, 1056) 0 swish_197[0][0] \n__________________________________________________________________________________________________\nconv2d_263 (Conv2D) (None, 1, 1, 44) 46508 lambda_67[0][0] \n__________________________________________________________________________________________________\nswish_198 (Swish) (None, 1, 1, 44) 0 conv2d_263[0][0] \n__________________________________________________________________________________________________\nconv2d_264 (Conv2D) (None, 1, 1, 1056) 47520 swish_198[0][0] \n__________________________________________________________________________________________________\nactivation_67 (Activation) (None, 1, 1, 1056) 0 conv2d_264[0][0] \n__________________________________________________________________________________________________\nmultiply_67 (Multiply) (None, 7, 7, 1056) 0 activation_67[0][0] \n swish_197[0][0] \n__________________________________________________________________________________________________\nconv2d_265 (Conv2D) (None, 7, 7, 304) 321024 multiply_67[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_198 (BatchN (None, 7, 7, 304) 1216 conv2d_265[0][0] \n__________________________________________________________________________________________________\nconv2d_266 (Conv2D) (None, 7, 7, 1824) 554496 batch_normalization_198[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_199 (BatchN (None, 7, 7, 1824) 7296 conv2d_266[0][0] \n__________________________________________________________________________________________________\nswish_199 (Swish) (None, 7, 7, 1824) 0 batch_normalization_199[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_68 (DepthwiseC (None, 7, 7, 1824) 45600 swish_199[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_200 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_68[0][0] \n__________________________________________________________________________________________________\nswish_200 (Swish) (None, 7, 7, 1824) 0 batch_normalization_200[0][0] \n__________________________________________________________________________________________________\nlambda_68 (Lambda) (None, 1, 1, 1824) 0 swish_200[0][0] \n__________________________________________________________________________________________________\nconv2d_267 (Conv2D) (None, 1, 1, 76) 138700 lambda_68[0][0] \n__________________________________________________________________________________________________\nswish_201 (Swish) (None, 1, 1, 76) 0 conv2d_267[0][0] \n__________________________________________________________________________________________________\nconv2d_268 (Conv2D) (None, 1, 1, 1824) 140448 swish_201[0][0] \n__________________________________________________________________________________________________\nactivation_68 (Activation) (None, 1, 1, 1824) 0 conv2d_268[0][0] \n__________________________________________________________________________________________________\nmultiply_68 (Multiply) (None, 7, 7, 1824) 0 activation_68[0][0] \n swish_200[0][0] \n__________________________________________________________________________________________________\nconv2d_269 (Conv2D) (None, 7, 7, 304) 554496 multiply_68[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_201 (BatchN (None, 7, 7, 304) 1216 conv2d_269[0][0] \n__________________________________________________________________________________________________\ndrop_connect_55 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_201[0][0] \n__________________________________________________________________________________________________\nadd_55 (Add) (None, 7, 7, 304) 0 drop_connect_55[0][0] \n batch_normalization_198[0][0] \n__________________________________________________________________________________________________\nconv2d_270 (Conv2D) (None, 7, 7, 1824) 554496 add_55[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_202 (BatchN (None, 7, 7, 1824) 7296 conv2d_270[0][0] \n__________________________________________________________________________________________________\nswish_202 (Swish) (None, 7, 7, 1824) 0 batch_normalization_202[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_69 (DepthwiseC (None, 7, 7, 1824) 45600 swish_202[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_203 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_69[0][0] \n__________________________________________________________________________________________________\nswish_203 (Swish) (None, 7, 7, 1824) 0 batch_normalization_203[0][0] \n__________________________________________________________________________________________________\nlambda_69 (Lambda) (None, 1, 1, 1824) 0 swish_203[0][0] \n__________________________________________________________________________________________________\nconv2d_271 (Conv2D) (None, 1, 1, 76) 138700 lambda_69[0][0] \n__________________________________________________________________________________________________\nswish_204 (Swish) (None, 1, 1, 76) 0 conv2d_271[0][0] \n__________________________________________________________________________________________________\nconv2d_272 (Conv2D) (None, 1, 1, 1824) 140448 swish_204[0][0] \n__________________________________________________________________________________________________\nactivation_69 (Activation) (None, 1, 1, 1824) 0 conv2d_272[0][0] \n__________________________________________________________________________________________________\nmultiply_69 (Multiply) (None, 7, 7, 1824) 0 activation_69[0][0] \n swish_203[0][0] \n__________________________________________________________________________________________________\nconv2d_273 (Conv2D) (None, 7, 7, 304) 554496 multiply_69[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_204 (BatchN (None, 7, 7, 304) 1216 conv2d_273[0][0] \n__________________________________________________________________________________________________\ndrop_connect_56 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_204[0][0] \n__________________________________________________________________________________________________\nadd_56 (Add) (None, 7, 7, 304) 0 drop_connect_56[0][0] \n add_55[0][0] \n__________________________________________________________________________________________________\nconv2d_274 (Conv2D) (None, 7, 7, 1824) 554496 add_56[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_205 (BatchN (None, 7, 7, 1824) 7296 conv2d_274[0][0] \n__________________________________________________________________________________________________\nswish_205 (Swish) (None, 7, 7, 1824) 0 batch_normalization_205[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_70 (DepthwiseC (None, 7, 7, 1824) 45600 swish_205[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_206 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_70[0][0] \n__________________________________________________________________________________________________\nswish_206 (Swish) (None, 7, 7, 1824) 0 batch_normalization_206[0][0] \n__________________________________________________________________________________________________\nlambda_70 (Lambda) (None, 1, 1, 1824) 0 swish_206[0][0] \n__________________________________________________________________________________________________\nconv2d_275 (Conv2D) (None, 1, 1, 76) 138700 lambda_70[0][0] \n__________________________________________________________________________________________________\nswish_207 (Swish) (None, 1, 1, 76) 0 conv2d_275[0][0] \n__________________________________________________________________________________________________\nconv2d_276 (Conv2D) (None, 1, 1, 1824) 140448 swish_207[0][0] \n__________________________________________________________________________________________________\nactivation_70 (Activation) (None, 1, 1, 1824) 0 conv2d_276[0][0] \n__________________________________________________________________________________________________\nmultiply_70 (Multiply) (None, 7, 7, 1824) 0 activation_70[0][0] \n swish_206[0][0] \n__________________________________________________________________________________________________\nconv2d_277 (Conv2D) (None, 7, 7, 304) 554496 multiply_70[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_207 (BatchN (None, 7, 7, 304) 1216 conv2d_277[0][0] \n__________________________________________________________________________________________________\ndrop_connect_57 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_207[0][0] \n__________________________________________________________________________________________________\nadd_57 (Add) (None, 7, 7, 304) 0 drop_connect_57[0][0] \n add_56[0][0] \n__________________________________________________________________________________________________\nconv2d_278 (Conv2D) (None, 7, 7, 1824) 554496 add_57[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_208 (BatchN (None, 7, 7, 1824) 7296 conv2d_278[0][0] \n__________________________________________________________________________________________________\nswish_208 (Swish) (None, 7, 7, 1824) 0 batch_normalization_208[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_71 (DepthwiseC (None, 7, 7, 1824) 45600 swish_208[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_209 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_71[0][0] \n__________________________________________________________________________________________________\nswish_209 (Swish) (None, 7, 7, 1824) 0 batch_normalization_209[0][0] \n__________________________________________________________________________________________________\nlambda_71 (Lambda) (None, 1, 1, 1824) 0 swish_209[0][0] \n__________________________________________________________________________________________________\nconv2d_279 (Conv2D) (None, 1, 1, 76) 138700 lambda_71[0][0] \n__________________________________________________________________________________________________\nswish_210 (Swish) (None, 1, 1, 76) 0 conv2d_279[0][0] \n__________________________________________________________________________________________________\nconv2d_280 (Conv2D) (None, 1, 1, 1824) 140448 swish_210[0][0] \n__________________________________________________________________________________________________\nactivation_71 (Activation) (None, 1, 1, 1824) 0 conv2d_280[0][0] \n__________________________________________________________________________________________________\nmultiply_71 (Multiply) (None, 7, 7, 1824) 0 activation_71[0][0] \n swish_209[0][0] \n__________________________________________________________________________________________________\nconv2d_281 (Conv2D) (None, 7, 7, 304) 554496 multiply_71[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_210 (BatchN (None, 7, 7, 304) 1216 conv2d_281[0][0] \n__________________________________________________________________________________________________\ndrop_connect_58 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_210[0][0] \n__________________________________________________________________________________________________\nadd_58 (Add) (None, 7, 7, 304) 0 drop_connect_58[0][0] \n add_57[0][0] \n__________________________________________________________________________________________________\nconv2d_282 (Conv2D) (None, 7, 7, 1824) 554496 add_58[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_211 (BatchN (None, 7, 7, 1824) 7296 conv2d_282[0][0] \n__________________________________________________________________________________________________\nswish_211 (Swish) (None, 7, 7, 1824) 0 batch_normalization_211[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_72 (DepthwiseC (None, 7, 7, 1824) 45600 swish_211[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_212 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_72[0][0] \n__________________________________________________________________________________________________\nswish_212 (Swish) (None, 7, 7, 1824) 0 batch_normalization_212[0][0] \n__________________________________________________________________________________________________\nlambda_72 (Lambda) (None, 1, 1, 1824) 0 swish_212[0][0] \n__________________________________________________________________________________________________\nconv2d_283 (Conv2D) (None, 1, 1, 76) 138700 lambda_72[0][0] \n__________________________________________________________________________________________________\nswish_213 (Swish) (None, 1, 1, 76) 0 conv2d_283[0][0] \n__________________________________________________________________________________________________\nconv2d_284 (Conv2D) (None, 1, 1, 1824) 140448 swish_213[0][0] \n__________________________________________________________________________________________________\nactivation_72 (Activation) (None, 1, 1, 1824) 0 conv2d_284[0][0] \n__________________________________________________________________________________________________\nmultiply_72 (Multiply) (None, 7, 7, 1824) 0 activation_72[0][0] \n swish_212[0][0] \n__________________________________________________________________________________________________\nconv2d_285 (Conv2D) (None, 7, 7, 304) 554496 multiply_72[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_213 (BatchN (None, 7, 7, 304) 1216 conv2d_285[0][0] \n__________________________________________________________________________________________________\ndrop_connect_59 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_213[0][0] \n__________________________________________________________________________________________________\nadd_59 (Add) (None, 7, 7, 304) 0 drop_connect_59[0][0] \n add_58[0][0] \n__________________________________________________________________________________________________\nconv2d_286 (Conv2D) (None, 7, 7, 1824) 554496 add_59[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_214 (BatchN (None, 7, 7, 1824) 7296 conv2d_286[0][0] \n__________________________________________________________________________________________________\nswish_214 (Swish) (None, 7, 7, 1824) 0 batch_normalization_214[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_73 (DepthwiseC (None, 7, 7, 1824) 45600 swish_214[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_215 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_73[0][0] \n__________________________________________________________________________________________________\nswish_215 (Swish) (None, 7, 7, 1824) 0 batch_normalization_215[0][0] \n__________________________________________________________________________________________________\nlambda_73 (Lambda) (None, 1, 1, 1824) 0 swish_215[0][0] \n__________________________________________________________________________________________________\nconv2d_287 (Conv2D) (None, 1, 1, 76) 138700 lambda_73[0][0] \n__________________________________________________________________________________________________\nswish_216 (Swish) (None, 1, 1, 76) 0 conv2d_287[0][0] \n__________________________________________________________________________________________________\nconv2d_288 (Conv2D) (None, 1, 1, 1824) 140448 swish_216[0][0] \n__________________________________________________________________________________________________\nactivation_73 (Activation) (None, 1, 1, 1824) 0 conv2d_288[0][0] \n__________________________________________________________________________________________________\nmultiply_73 (Multiply) (None, 7, 7, 1824) 0 activation_73[0][0] \n swish_215[0][0] \n__________________________________________________________________________________________________\nconv2d_289 (Conv2D) (None, 7, 7, 304) 554496 multiply_73[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_216 (BatchN (None, 7, 7, 304) 1216 conv2d_289[0][0] \n__________________________________________________________________________________________________\ndrop_connect_60 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_216[0][0] \n__________________________________________________________________________________________________\nadd_60 (Add) (None, 7, 7, 304) 0 drop_connect_60[0][0] \n add_59[0][0] \n__________________________________________________________________________________________________\nconv2d_290 (Conv2D) (None, 7, 7, 1824) 554496 add_60[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_217 (BatchN (None, 7, 7, 1824) 7296 conv2d_290[0][0] \n__________________________________________________________________________________________________\nswish_217 (Swish) (None, 7, 7, 1824) 0 batch_normalization_217[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_74 (DepthwiseC (None, 7, 7, 1824) 45600 swish_217[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_218 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_74[0][0] \n__________________________________________________________________________________________________\nswish_218 (Swish) (None, 7, 7, 1824) 0 batch_normalization_218[0][0] \n__________________________________________________________________________________________________\nlambda_74 (Lambda) (None, 1, 1, 1824) 0 swish_218[0][0] \n__________________________________________________________________________________________________\nconv2d_291 (Conv2D) (None, 1, 1, 76) 138700 lambda_74[0][0] \n__________________________________________________________________________________________________\nswish_219 (Swish) (None, 1, 1, 76) 0 conv2d_291[0][0] \n__________________________________________________________________________________________________\nconv2d_292 (Conv2D) (None, 1, 1, 1824) 140448 swish_219[0][0] \n__________________________________________________________________________________________________\nactivation_74 (Activation) (None, 1, 1, 1824) 0 conv2d_292[0][0] \n__________________________________________________________________________________________________\nmultiply_74 (Multiply) (None, 7, 7, 1824) 0 activation_74[0][0] \n swish_218[0][0] \n__________________________________________________________________________________________________\nconv2d_293 (Conv2D) (None, 7, 7, 304) 554496 multiply_74[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_219 (BatchN (None, 7, 7, 304) 1216 conv2d_293[0][0] \n__________________________________________________________________________________________________\ndrop_connect_61 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_219[0][0] \n__________________________________________________________________________________________________\nadd_61 (Add) (None, 7, 7, 304) 0 drop_connect_61[0][0] \n add_60[0][0] \n__________________________________________________________________________________________________\nconv2d_294 (Conv2D) (None, 7, 7, 1824) 554496 add_61[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_220 (BatchN (None, 7, 7, 1824) 7296 conv2d_294[0][0] \n__________________________________________________________________________________________________\nswish_220 (Swish) (None, 7, 7, 1824) 0 batch_normalization_220[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_75 (DepthwiseC (None, 7, 7, 1824) 45600 swish_220[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_221 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_75[0][0] \n__________________________________________________________________________________________________\nswish_221 (Swish) (None, 7, 7, 1824) 0 batch_normalization_221[0][0] \n__________________________________________________________________________________________________\nlambda_75 (Lambda) (None, 1, 1, 1824) 0 swish_221[0][0] \n__________________________________________________________________________________________________\nconv2d_295 (Conv2D) (None, 1, 1, 76) 138700 lambda_75[0][0] \n__________________________________________________________________________________________________\nswish_222 (Swish) (None, 1, 1, 76) 0 conv2d_295[0][0] \n__________________________________________________________________________________________________\nconv2d_296 (Conv2D) (None, 1, 1, 1824) 140448 swish_222[0][0] \n__________________________________________________________________________________________________\nactivation_75 (Activation) (None, 1, 1, 1824) 0 conv2d_296[0][0] \n__________________________________________________________________________________________________\nmultiply_75 (Multiply) (None, 7, 7, 1824) 0 activation_75[0][0] \n swish_221[0][0] \n__________________________________________________________________________________________________\nconv2d_297 (Conv2D) (None, 7, 7, 304) 554496 multiply_75[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_222 (BatchN (None, 7, 7, 304) 1216 conv2d_297[0][0] \n__________________________________________________________________________________________________\ndrop_connect_62 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_222[0][0] \n__________________________________________________________________________________________________\nadd_62 (Add) (None, 7, 7, 304) 0 drop_connect_62[0][0] \n add_61[0][0] \n__________________________________________________________________________________________________\nconv2d_298 (Conv2D) (None, 7, 7, 1824) 554496 add_62[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_223 (BatchN (None, 7, 7, 1824) 7296 conv2d_298[0][0] \n__________________________________________________________________________________________________\nswish_223 (Swish) (None, 7, 7, 1824) 0 batch_normalization_223[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_76 (DepthwiseC (None, 7, 7, 1824) 16416 swish_223[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_224 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_76[0][0] \n__________________________________________________________________________________________________\nswish_224 (Swish) (None, 7, 7, 1824) 0 batch_normalization_224[0][0] \n__________________________________________________________________________________________________\nlambda_76 (Lambda) (None, 1, 1, 1824) 0 swish_224[0][0] \n__________________________________________________________________________________________________\nconv2d_299 (Conv2D) (None, 1, 1, 76) 138700 lambda_76[0][0] \n__________________________________________________________________________________________________\nswish_225 (Swish) (None, 1, 1, 76) 0 conv2d_299[0][0] \n__________________________________________________________________________________________________\nconv2d_300 (Conv2D) (None, 1, 1, 1824) 140448 swish_225[0][0] \n__________________________________________________________________________________________________\nactivation_76 (Activation) (None, 1, 1, 1824) 0 conv2d_300[0][0] \n__________________________________________________________________________________________________\nmultiply_76 (Multiply) (None, 7, 7, 1824) 0 activation_76[0][0] \n swish_224[0][0] \n__________________________________________________________________________________________________\nconv2d_301 (Conv2D) (None, 7, 7, 512) 933888 multiply_76[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_225 (BatchN (None, 7, 7, 512) 2048 conv2d_301[0][0] \n__________________________________________________________________________________________________\nconv2d_302 (Conv2D) (None, 7, 7, 3072) 1572864 batch_normalization_225[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_226 (BatchN (None, 7, 7, 3072) 12288 conv2d_302[0][0] \n__________________________________________________________________________________________________\nswish_226 (Swish) (None, 7, 7, 3072) 0 batch_normalization_226[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_77 (DepthwiseC (None, 7, 7, 3072) 27648 swish_226[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_227 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_77[0][0] \n__________________________________________________________________________________________________\nswish_227 (Swish) (None, 7, 7, 3072) 0 batch_normalization_227[0][0] \n__________________________________________________________________________________________________\nlambda_77 (Lambda) (None, 1, 1, 3072) 0 swish_227[0][0] \n__________________________________________________________________________________________________\nconv2d_303 (Conv2D) (None, 1, 1, 128) 393344 lambda_77[0][0] \n__________________________________________________________________________________________________\nswish_228 (Swish) (None, 1, 1, 128) 0 conv2d_303[0][0] \n__________________________________________________________________________________________________\nconv2d_304 (Conv2D) (None, 1, 1, 3072) 396288 swish_228[0][0] \n__________________________________________________________________________________________________\nactivation_77 (Activation) (None, 1, 1, 3072) 0 conv2d_304[0][0] \n__________________________________________________________________________________________________\nmultiply_77 (Multiply) (None, 7, 7, 3072) 0 activation_77[0][0] \n swish_227[0][0] \n__________________________________________________________________________________________________\nconv2d_305 (Conv2D) (None, 7, 7, 512) 1572864 multiply_77[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_228 (BatchN (None, 7, 7, 512) 2048 conv2d_305[0][0] \n__________________________________________________________________________________________________\ndrop_connect_63 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_228[0][0] \n__________________________________________________________________________________________________\nadd_63 (Add) (None, 7, 7, 512) 0 drop_connect_63[0][0] \n batch_normalization_225[0][0] \n__________________________________________________________________________________________________\nconv2d_306 (Conv2D) (None, 7, 7, 3072) 1572864 add_63[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_229 (BatchN (None, 7, 7, 3072) 12288 conv2d_306[0][0] \n__________________________________________________________________________________________________\nswish_229 (Swish) (None, 7, 7, 3072) 0 batch_normalization_229[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_78 (DepthwiseC (None, 7, 7, 3072) 27648 swish_229[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_230 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_78[0][0] \n__________________________________________________________________________________________________\nswish_230 (Swish) (None, 7, 7, 3072) 0 batch_normalization_230[0][0] \n__________________________________________________________________________________________________\nlambda_78 (Lambda) (None, 1, 1, 3072) 0 swish_230[0][0] \n__________________________________________________________________________________________________\nconv2d_307 (Conv2D) (None, 1, 1, 128) 393344 lambda_78[0][0] \n__________________________________________________________________________________________________\nswish_231 (Swish) (None, 1, 1, 128) 0 conv2d_307[0][0] \n__________________________________________________________________________________________________\nconv2d_308 (Conv2D) (None, 1, 1, 3072) 396288 swish_231[0][0] \n__________________________________________________________________________________________________\nactivation_78 (Activation) (None, 1, 1, 3072) 0 conv2d_308[0][0] \n__________________________________________________________________________________________________\nmultiply_78 (Multiply) (None, 7, 7, 3072) 0 activation_78[0][0] \n swish_230[0][0] \n__________________________________________________________________________________________________\nconv2d_309 (Conv2D) (None, 7, 7, 512) 1572864 multiply_78[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_231 (BatchN (None, 7, 7, 512) 2048 conv2d_309[0][0] \n__________________________________________________________________________________________________\ndrop_connect_64 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_231[0][0] \n__________________________________________________________________________________________________\nadd_64 (Add) (None, 7, 7, 512) 0 drop_connect_64[0][0] \n add_63[0][0] \n__________________________________________________________________________________________________\nconv2d_310 (Conv2D) (None, 7, 7, 2048) 1048576 add_64[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_232 (BatchN (None, 7, 7, 2048) 8192 conv2d_310[0][0] \n__________________________________________________________________________________________________\nswish_232 (Swish) (None, 7, 7, 2048) 0 batch_normalization_232[0][0] \n__________________________________________________________________________________________________\nglobal_average_pooling2d_2 (Glo (None, 2048) 0 swish_232[0][0] \n__________________________________________________________________________________________________\nfinal_output (Dense) (None, 1) 2049 global_average_pooling2d_2[0][0] \n==================================================================================================\nTotal params: 28,515,569\nTrainable params: 2,049\nNon-trainable params: 28,513,520\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"STEP_SIZE_TRAIN = train_generator.n//train_generator.batch_size\nSTEP_SIZE_VALID = valid_generator.n//valid_generator.batch_size\n\nhistory_warmup = model.fit_generator(generator=train_generator,\n steps_per_epoch=STEP_SIZE_TRAIN,\n validation_data=valid_generator,\n validation_steps=STEP_SIZE_VALID,\n epochs=WARMUP_EPOCHS,\n callbacks=callback_list,\n verbose=2).history","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:05:07.022237Z","iopub.execute_input":"2024-05-03T04:05:07.022507Z","iopub.status.idle":"2024-05-03T04:08:49.203399Z","shell.execute_reply.started":"2024-05-03T04:05:07.022457Z","shell.execute_reply":"2024-05-03T04:08:49.202398Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"Epoch 1/5\n - 56s - loss: 2.0560 - acc: 0.4375 - val_loss: 1.4053 - val_acc: 0.3295\nEpoch 2/5\n - 41s - loss: 1.1472 - acc: 0.4293 - val_loss: 1.3146 - val_acc: 0.2596\nEpoch 3/5\n - 42s - loss: 0.9512 - acc: 0.4492 - val_loss: 1.3017 - val_acc: 0.2710\nEpoch 4/5\n - 42s - loss: 0.8559 - acc: 0.4582 - val_loss: 1.3732 - val_acc: 0.2668\nEpoch 5/5\n - 41s - loss: 0.8570 - acc: 0.4534 - val_loss: 1.3005 - val_acc: 0.3153\n","output_type":"stream"}]},{"cell_type":"code","source":"for layer in model.layers:\n layer.trainable = True\n\nes = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)\ncosine_lr_2nd = WarmUpCosineDecayScheduler(learning_rate_base=LEARNING_RATE,\n total_steps=TOTAL_STEPS_2nd,\n warmup_learning_rate=0.0,\n warmup_steps=WARMUP_STEPS_2nd,\n hold_base_rate_steps=(3 * STEP_SIZE))\n\ncallback_list = [es, cosine_lr_2nd]\noptimizer = optimizers.Adam(lr=LEARNING_RATE)\nmodel.compile(optimizer=optimizer, loss='mean_squared_error', metrics=metric_list)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:08:49.215945Z","iopub.execute_input":"2024-05-03T04:08:49.216222Z","iopub.status.idle":"2024-05-03T04:08:49.500623Z","shell.execute_reply.started":"2024-05-03T04:08:49.21617Z","shell.execute_reply":"2024-05-03T04:08:49.499389Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_2 (InputLayer) (None, 224, 224, 3) 0 \n__________________________________________________________________________________________________\nconv2d_156 (Conv2D) (None, 112, 112, 48) 1296 input_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_117 (BatchN (None, 112, 112, 48) 192 conv2d_156[0][0] \n__________________________________________________________________________________________________\nswish_117 (Swish) (None, 112, 112, 48) 0 batch_normalization_117[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_40 (DepthwiseC (None, 112, 112, 48) 432 swish_117[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_118 (BatchN (None, 112, 112, 48) 192 depthwise_conv2d_40[0][0] \n__________________________________________________________________________________________________\nswish_118 (Swish) (None, 112, 112, 48) 0 batch_normalization_118[0][0] \n__________________________________________________________________________________________________\nlambda_40 (Lambda) (None, 1, 1, 48) 0 swish_118[0][0] \n__________________________________________________________________________________________________\nconv2d_157 (Conv2D) (None, 1, 1, 12) 588 lambda_40[0][0] \n__________________________________________________________________________________________________\nswish_119 (Swish) (None, 1, 1, 12) 0 conv2d_157[0][0] \n__________________________________________________________________________________________________\nconv2d_158 (Conv2D) (None, 1, 1, 48) 624 swish_119[0][0] \n__________________________________________________________________________________________________\nactivation_40 (Activation) (None, 1, 1, 48) 0 conv2d_158[0][0] \n__________________________________________________________________________________________________\nmultiply_40 (Multiply) (None, 112, 112, 48) 0 activation_40[0][0] \n swish_118[0][0] \n__________________________________________________________________________________________________\nconv2d_159 (Conv2D) (None, 112, 112, 24) 1152 multiply_40[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_119 (BatchN (None, 112, 112, 24) 96 conv2d_159[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_41 (DepthwiseC (None, 112, 112, 24) 216 batch_normalization_119[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_120 (BatchN (None, 112, 112, 24) 96 depthwise_conv2d_41[0][0] \n__________________________________________________________________________________________________\nswish_120 (Swish) (None, 112, 112, 24) 0 batch_normalization_120[0][0] \n__________________________________________________________________________________________________\nlambda_41 (Lambda) (None, 1, 1, 24) 0 swish_120[0][0] \n__________________________________________________________________________________________________\nconv2d_160 (Conv2D) (None, 1, 1, 6) 150 lambda_41[0][0] \n__________________________________________________________________________________________________\nswish_121 (Swish) (None, 1, 1, 6) 0 conv2d_160[0][0] \n__________________________________________________________________________________________________\nconv2d_161 (Conv2D) (None, 1, 1, 24) 168 swish_121[0][0] \n__________________________________________________________________________________________________\nactivation_41 (Activation) (None, 1, 1, 24) 0 conv2d_161[0][0] \n__________________________________________________________________________________________________\nmultiply_41 (Multiply) (None, 112, 112, 24) 0 activation_41[0][0] \n swish_120[0][0] \n__________________________________________________________________________________________________\nconv2d_162 (Conv2D) (None, 112, 112, 24) 576 multiply_41[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_121 (BatchN (None, 112, 112, 24) 96 conv2d_162[0][0] \n__________________________________________________________________________________________________\ndrop_connect_33 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_121[0][0] \n__________________________________________________________________________________________________\nadd_33 (Add) (None, 112, 112, 24) 0 drop_connect_33[0][0] \n batch_normalization_119[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_42 (DepthwiseC (None, 112, 112, 24) 216 add_33[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_122 (BatchN (None, 112, 112, 24) 96 depthwise_conv2d_42[0][0] \n__________________________________________________________________________________________________\nswish_122 (Swish) (None, 112, 112, 24) 0 batch_normalization_122[0][0] \n__________________________________________________________________________________________________\nlambda_42 (Lambda) (None, 1, 1, 24) 0 swish_122[0][0] \n__________________________________________________________________________________________________\nconv2d_163 (Conv2D) (None, 1, 1, 6) 150 lambda_42[0][0] \n__________________________________________________________________________________________________\nswish_123 (Swish) (None, 1, 1, 6) 0 conv2d_163[0][0] \n__________________________________________________________________________________________________\nconv2d_164 (Conv2D) (None, 1, 1, 24) 168 swish_123[0][0] \n__________________________________________________________________________________________________\nactivation_42 (Activation) (None, 1, 1, 24) 0 conv2d_164[0][0] \n__________________________________________________________________________________________________\nmultiply_42 (Multiply) (None, 112, 112, 24) 0 activation_42[0][0] \n swish_122[0][0] \n__________________________________________________________________________________________________\nconv2d_165 (Conv2D) (None, 112, 112, 24) 576 multiply_42[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_123 (BatchN (None, 112, 112, 24) 96 conv2d_165[0][0] \n__________________________________________________________________________________________________\ndrop_connect_34 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_123[0][0] \n__________________________________________________________________________________________________\nadd_34 (Add) (None, 112, 112, 24) 0 drop_connect_34[0][0] \n add_33[0][0] \n__________________________________________________________________________________________________\nconv2d_166 (Conv2D) (None, 112, 112, 144 3456 add_34[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_124 (BatchN (None, 112, 112, 144 576 conv2d_166[0][0] \n__________________________________________________________________________________________________\nswish_124 (Swish) (None, 112, 112, 144 0 batch_normalization_124[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_43 (DepthwiseC (None, 56, 56, 144) 1296 swish_124[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_125 (BatchN (None, 56, 56, 144) 576 depthwise_conv2d_43[0][0] \n__________________________________________________________________________________________________\nswish_125 (Swish) (None, 56, 56, 144) 0 batch_normalization_125[0][0] \n__________________________________________________________________________________________________\nlambda_43 (Lambda) (None, 1, 1, 144) 0 swish_125[0][0] \n__________________________________________________________________________________________________\nconv2d_167 (Conv2D) (None, 1, 1, 6) 870 lambda_43[0][0] \n__________________________________________________________________________________________________\nswish_126 (Swish) (None, 1, 1, 6) 0 conv2d_167[0][0] \n__________________________________________________________________________________________________\nconv2d_168 (Conv2D) (None, 1, 1, 144) 1008 swish_126[0][0] \n__________________________________________________________________________________________________\nactivation_43 (Activation) (None, 1, 1, 144) 0 conv2d_168[0][0] \n__________________________________________________________________________________________________\nmultiply_43 (Multiply) (None, 56, 56, 144) 0 activation_43[0][0] \n swish_125[0][0] \n__________________________________________________________________________________________________\nconv2d_169 (Conv2D) (None, 56, 56, 40) 5760 multiply_43[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_126 (BatchN (None, 56, 56, 40) 160 conv2d_169[0][0] \n__________________________________________________________________________________________________\nconv2d_170 (Conv2D) (None, 56, 56, 240) 9600 batch_normalization_126[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_127 (BatchN (None, 56, 56, 240) 960 conv2d_170[0][0] \n__________________________________________________________________________________________________\nswish_127 (Swish) (None, 56, 56, 240) 0 batch_normalization_127[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_44 (DepthwiseC (None, 56, 56, 240) 2160 swish_127[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_128 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_44[0][0] \n__________________________________________________________________________________________________\nswish_128 (Swish) (None, 56, 56, 240) 0 batch_normalization_128[0][0] \n__________________________________________________________________________________________________\nlambda_44 (Lambda) (None, 1, 1, 240) 0 swish_128[0][0] \n__________________________________________________________________________________________________\nconv2d_171 (Conv2D) (None, 1, 1, 10) 2410 lambda_44[0][0] \n__________________________________________________________________________________________________\nswish_129 (Swish) (None, 1, 1, 10) 0 conv2d_171[0][0] \n__________________________________________________________________________________________________\nconv2d_172 (Conv2D) (None, 1, 1, 240) 2640 swish_129[0][0] \n__________________________________________________________________________________________________\nactivation_44 (Activation) (None, 1, 1, 240) 0 conv2d_172[0][0] \n__________________________________________________________________________________________________\nmultiply_44 (Multiply) (None, 56, 56, 240) 0 activation_44[0][0] \n swish_128[0][0] \n__________________________________________________________________________________________________\nconv2d_173 (Conv2D) (None, 56, 56, 40) 9600 multiply_44[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_129 (BatchN (None, 56, 56, 40) 160 conv2d_173[0][0] \n__________________________________________________________________________________________________\ndrop_connect_35 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_129[0][0] \n__________________________________________________________________________________________________\nadd_35 (Add) (None, 56, 56, 40) 0 drop_connect_35[0][0] \n batch_normalization_126[0][0] \n__________________________________________________________________________________________________\nconv2d_174 (Conv2D) (None, 56, 56, 240) 9600 add_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_130 (BatchN (None, 56, 56, 240) 960 conv2d_174[0][0] \n__________________________________________________________________________________________________\nswish_130 (Swish) (None, 56, 56, 240) 0 batch_normalization_130[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_45 (DepthwiseC (None, 56, 56, 240) 2160 swish_130[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_131 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_45[0][0] \n__________________________________________________________________________________________________\nswish_131 (Swish) (None, 56, 56, 240) 0 batch_normalization_131[0][0] \n__________________________________________________________________________________________________\nlambda_45 (Lambda) (None, 1, 1, 240) 0 swish_131[0][0] \n__________________________________________________________________________________________________\nconv2d_175 (Conv2D) (None, 1, 1, 10) 2410 lambda_45[0][0] \n__________________________________________________________________________________________________\nswish_132 (Swish) (None, 1, 1, 10) 0 conv2d_175[0][0] \n__________________________________________________________________________________________________\nconv2d_176 (Conv2D) (None, 1, 1, 240) 2640 swish_132[0][0] \n__________________________________________________________________________________________________\nactivation_45 (Activation) (None, 1, 1, 240) 0 conv2d_176[0][0] \n__________________________________________________________________________________________________\nmultiply_45 (Multiply) (None, 56, 56, 240) 0 activation_45[0][0] \n swish_131[0][0] \n__________________________________________________________________________________________________\nconv2d_177 (Conv2D) (None, 56, 56, 40) 9600 multiply_45[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_132 (BatchN (None, 56, 56, 40) 160 conv2d_177[0][0] \n__________________________________________________________________________________________________\ndrop_connect_36 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_132[0][0] \n__________________________________________________________________________________________________\nadd_36 (Add) (None, 56, 56, 40) 0 drop_connect_36[0][0] \n add_35[0][0] \n__________________________________________________________________________________________________\nconv2d_178 (Conv2D) (None, 56, 56, 240) 9600 add_36[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_133 (BatchN (None, 56, 56, 240) 960 conv2d_178[0][0] \n__________________________________________________________________________________________________\nswish_133 (Swish) (None, 56, 56, 240) 0 batch_normalization_133[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_46 (DepthwiseC (None, 56, 56, 240) 2160 swish_133[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_134 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_46[0][0] \n__________________________________________________________________________________________________\nswish_134 (Swish) (None, 56, 56, 240) 0 batch_normalization_134[0][0] \n__________________________________________________________________________________________________\nlambda_46 (Lambda) (None, 1, 1, 240) 0 swish_134[0][0] \n__________________________________________________________________________________________________\nconv2d_179 (Conv2D) (None, 1, 1, 10) 2410 lambda_46[0][0] \n__________________________________________________________________________________________________\nswish_135 (Swish) (None, 1, 1, 10) 0 conv2d_179[0][0] \n__________________________________________________________________________________________________\nconv2d_180 (Conv2D) (None, 1, 1, 240) 2640 swish_135[0][0] \n__________________________________________________________________________________________________\nactivation_46 (Activation) (None, 1, 1, 240) 0 conv2d_180[0][0] \n__________________________________________________________________________________________________\nmultiply_46 (Multiply) (None, 56, 56, 240) 0 activation_46[0][0] \n swish_134[0][0] \n__________________________________________________________________________________________________\nconv2d_181 (Conv2D) (None, 56, 56, 40) 9600 multiply_46[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_135 (BatchN (None, 56, 56, 40) 160 conv2d_181[0][0] \n__________________________________________________________________________________________________\ndrop_connect_37 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_135[0][0] \n__________________________________________________________________________________________________\nadd_37 (Add) (None, 56, 56, 40) 0 drop_connect_37[0][0] \n add_36[0][0] \n__________________________________________________________________________________________________\nconv2d_182 (Conv2D) (None, 56, 56, 240) 9600 add_37[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_136 (BatchN (None, 56, 56, 240) 960 conv2d_182[0][0] \n__________________________________________________________________________________________________\nswish_136 (Swish) (None, 56, 56, 240) 0 batch_normalization_136[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_47 (DepthwiseC (None, 56, 56, 240) 2160 swish_136[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_137 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_47[0][0] \n__________________________________________________________________________________________________\nswish_137 (Swish) (None, 56, 56, 240) 0 batch_normalization_137[0][0] \n__________________________________________________________________________________________________\nlambda_47 (Lambda) (None, 1, 1, 240) 0 swish_137[0][0] \n__________________________________________________________________________________________________\nconv2d_183 (Conv2D) (None, 1, 1, 10) 2410 lambda_47[0][0] \n__________________________________________________________________________________________________\nswish_138 (Swish) (None, 1, 1, 10) 0 conv2d_183[0][0] \n__________________________________________________________________________________________________\nconv2d_184 (Conv2D) (None, 1, 1, 240) 2640 swish_138[0][0] \n__________________________________________________________________________________________________\nactivation_47 (Activation) (None, 1, 1, 240) 0 conv2d_184[0][0] \n__________________________________________________________________________________________________\nmultiply_47 (Multiply) (None, 56, 56, 240) 0 activation_47[0][0] \n swish_137[0][0] \n__________________________________________________________________________________________________\nconv2d_185 (Conv2D) (None, 56, 56, 40) 9600 multiply_47[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_138 (BatchN (None, 56, 56, 40) 160 conv2d_185[0][0] \n__________________________________________________________________________________________________\ndrop_connect_38 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_138[0][0] \n__________________________________________________________________________________________________\nadd_38 (Add) (None, 56, 56, 40) 0 drop_connect_38[0][0] \n add_37[0][0] \n__________________________________________________________________________________________________\nconv2d_186 (Conv2D) (None, 56, 56, 240) 9600 add_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_139 (BatchN (None, 56, 56, 240) 960 conv2d_186[0][0] \n__________________________________________________________________________________________________\nswish_139 (Swish) (None, 56, 56, 240) 0 batch_normalization_139[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_48 (DepthwiseC (None, 28, 28, 240) 6000 swish_139[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_140 (BatchN (None, 28, 28, 240) 960 depthwise_conv2d_48[0][0] \n__________________________________________________________________________________________________\nswish_140 (Swish) (None, 28, 28, 240) 0 batch_normalization_140[0][0] \n__________________________________________________________________________________________________\nlambda_48 (Lambda) (None, 1, 1, 240) 0 swish_140[0][0] \n__________________________________________________________________________________________________\nconv2d_187 (Conv2D) (None, 1, 1, 10) 2410 lambda_48[0][0] \n__________________________________________________________________________________________________\nswish_141 (Swish) (None, 1, 1, 10) 0 conv2d_187[0][0] \n__________________________________________________________________________________________________\nconv2d_188 (Conv2D) (None, 1, 1, 240) 2640 swish_141[0][0] \n__________________________________________________________________________________________________\nactivation_48 (Activation) (None, 1, 1, 240) 0 conv2d_188[0][0] \n__________________________________________________________________________________________________\nmultiply_48 (Multiply) (None, 28, 28, 240) 0 activation_48[0][0] \n swish_140[0][0] \n__________________________________________________________________________________________________\nconv2d_189 (Conv2D) (None, 28, 28, 64) 15360 multiply_48[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_141 (BatchN (None, 28, 28, 64) 256 conv2d_189[0][0] \n__________________________________________________________________________________________________\nconv2d_190 (Conv2D) (None, 28, 28, 384) 24576 batch_normalization_141[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_142 (BatchN (None, 28, 28, 384) 1536 conv2d_190[0][0] \n__________________________________________________________________________________________________\nswish_142 (Swish) (None, 28, 28, 384) 0 batch_normalization_142[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_49 (DepthwiseC (None, 28, 28, 384) 9600 swish_142[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_143 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_49[0][0] \n__________________________________________________________________________________________________\nswish_143 (Swish) (None, 28, 28, 384) 0 batch_normalization_143[0][0] \n__________________________________________________________________________________________________\nlambda_49 (Lambda) (None, 1, 1, 384) 0 swish_143[0][0] \n__________________________________________________________________________________________________\nconv2d_191 (Conv2D) (None, 1, 1, 16) 6160 lambda_49[0][0] \n__________________________________________________________________________________________________\nswish_144 (Swish) (None, 1, 1, 16) 0 conv2d_191[0][0] \n__________________________________________________________________________________________________\nconv2d_192 (Conv2D) (None, 1, 1, 384) 6528 swish_144[0][0] \n__________________________________________________________________________________________________\nactivation_49 (Activation) (None, 1, 1, 384) 0 conv2d_192[0][0] \n__________________________________________________________________________________________________\nmultiply_49 (Multiply) (None, 28, 28, 384) 0 activation_49[0][0] \n swish_143[0][0] \n__________________________________________________________________________________________________\nconv2d_193 (Conv2D) (None, 28, 28, 64) 24576 multiply_49[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_144 (BatchN (None, 28, 28, 64) 256 conv2d_193[0][0] \n__________________________________________________________________________________________________\ndrop_connect_39 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_144[0][0] \n__________________________________________________________________________________________________\nadd_39 (Add) (None, 28, 28, 64) 0 drop_connect_39[0][0] \n batch_normalization_141[0][0] \n__________________________________________________________________________________________________\nconv2d_194 (Conv2D) (None, 28, 28, 384) 24576 add_39[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_145 (BatchN (None, 28, 28, 384) 1536 conv2d_194[0][0] \n__________________________________________________________________________________________________\nswish_145 (Swish) (None, 28, 28, 384) 0 batch_normalization_145[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_50 (DepthwiseC (None, 28, 28, 384) 9600 swish_145[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_146 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_50[0][0] \n__________________________________________________________________________________________________\nswish_146 (Swish) (None, 28, 28, 384) 0 batch_normalization_146[0][0] \n__________________________________________________________________________________________________\nlambda_50 (Lambda) (None, 1, 1, 384) 0 swish_146[0][0] \n__________________________________________________________________________________________________\nconv2d_195 (Conv2D) (None, 1, 1, 16) 6160 lambda_50[0][0] \n__________________________________________________________________________________________________\nswish_147 (Swish) (None, 1, 1, 16) 0 conv2d_195[0][0] \n__________________________________________________________________________________________________\nconv2d_196 (Conv2D) (None, 1, 1, 384) 6528 swish_147[0][0] \n__________________________________________________________________________________________________\nactivation_50 (Activation) (None, 1, 1, 384) 0 conv2d_196[0][0] \n__________________________________________________________________________________________________\nmultiply_50 (Multiply) (None, 28, 28, 384) 0 activation_50[0][0] \n swish_146[0][0] \n__________________________________________________________________________________________________\nconv2d_197 (Conv2D) (None, 28, 28, 64) 24576 multiply_50[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_147 (BatchN (None, 28, 28, 64) 256 conv2d_197[0][0] \n__________________________________________________________________________________________________\ndrop_connect_40 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_147[0][0] \n__________________________________________________________________________________________________\nadd_40 (Add) (None, 28, 28, 64) 0 drop_connect_40[0][0] \n add_39[0][0] \n__________________________________________________________________________________________________\nconv2d_198 (Conv2D) (None, 28, 28, 384) 24576 add_40[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_148 (BatchN (None, 28, 28, 384) 1536 conv2d_198[0][0] \n__________________________________________________________________________________________________\nswish_148 (Swish) (None, 28, 28, 384) 0 batch_normalization_148[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_51 (DepthwiseC (None, 28, 28, 384) 9600 swish_148[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_149 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_51[0][0] \n__________________________________________________________________________________________________\nswish_149 (Swish) (None, 28, 28, 384) 0 batch_normalization_149[0][0] \n__________________________________________________________________________________________________\nlambda_51 (Lambda) (None, 1, 1, 384) 0 swish_149[0][0] \n__________________________________________________________________________________________________\nconv2d_199 (Conv2D) (None, 1, 1, 16) 6160 lambda_51[0][0] \n__________________________________________________________________________________________________\nswish_150 (Swish) (None, 1, 1, 16) 0 conv2d_199[0][0] \n__________________________________________________________________________________________________\nconv2d_200 (Conv2D) (None, 1, 1, 384) 6528 swish_150[0][0] \n__________________________________________________________________________________________________\nactivation_51 (Activation) (None, 1, 1, 384) 0 conv2d_200[0][0] \n__________________________________________________________________________________________________\nmultiply_51 (Multiply) (None, 28, 28, 384) 0 activation_51[0][0] \n swish_149[0][0] \n__________________________________________________________________________________________________\nconv2d_201 (Conv2D) (None, 28, 28, 64) 24576 multiply_51[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_150 (BatchN (None, 28, 28, 64) 256 conv2d_201[0][0] \n__________________________________________________________________________________________________\ndrop_connect_41 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_150[0][0] \n__________________________________________________________________________________________________\nadd_41 (Add) (None, 28, 28, 64) 0 drop_connect_41[0][0] \n add_40[0][0] \n__________________________________________________________________________________________________\nconv2d_202 (Conv2D) (None, 28, 28, 384) 24576 add_41[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_151 (BatchN (None, 28, 28, 384) 1536 conv2d_202[0][0] \n__________________________________________________________________________________________________\nswish_151 (Swish) (None, 28, 28, 384) 0 batch_normalization_151[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_52 (DepthwiseC (None, 28, 28, 384) 9600 swish_151[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_152 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_52[0][0] \n__________________________________________________________________________________________________\nswish_152 (Swish) (None, 28, 28, 384) 0 batch_normalization_152[0][0] \n__________________________________________________________________________________________________\nlambda_52 (Lambda) (None, 1, 1, 384) 0 swish_152[0][0] \n__________________________________________________________________________________________________\nconv2d_203 (Conv2D) (None, 1, 1, 16) 6160 lambda_52[0][0] \n__________________________________________________________________________________________________\nswish_153 (Swish) (None, 1, 1, 16) 0 conv2d_203[0][0] \n__________________________________________________________________________________________________\nconv2d_204 (Conv2D) (None, 1, 1, 384) 6528 swish_153[0][0] \n__________________________________________________________________________________________________\nactivation_52 (Activation) (None, 1, 1, 384) 0 conv2d_204[0][0] \n__________________________________________________________________________________________________\nmultiply_52 (Multiply) (None, 28, 28, 384) 0 activation_52[0][0] \n swish_152[0][0] \n__________________________________________________________________________________________________\nconv2d_205 (Conv2D) (None, 28, 28, 64) 24576 multiply_52[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_153 (BatchN (None, 28, 28, 64) 256 conv2d_205[0][0] \n__________________________________________________________________________________________________\ndrop_connect_42 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_153[0][0] \n__________________________________________________________________________________________________\nadd_42 (Add) (None, 28, 28, 64) 0 drop_connect_42[0][0] \n add_41[0][0] \n__________________________________________________________________________________________________\nconv2d_206 (Conv2D) (None, 28, 28, 384) 24576 add_42[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_154 (BatchN (None, 28, 28, 384) 1536 conv2d_206[0][0] \n__________________________________________________________________________________________________\nswish_154 (Swish) (None, 28, 28, 384) 0 batch_normalization_154[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_53 (DepthwiseC (None, 14, 14, 384) 3456 swish_154[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_155 (BatchN (None, 14, 14, 384) 1536 depthwise_conv2d_53[0][0] \n__________________________________________________________________________________________________\nswish_155 (Swish) (None, 14, 14, 384) 0 batch_normalization_155[0][0] \n__________________________________________________________________________________________________\nlambda_53 (Lambda) (None, 1, 1, 384) 0 swish_155[0][0] \n__________________________________________________________________________________________________\nconv2d_207 (Conv2D) (None, 1, 1, 16) 6160 lambda_53[0][0] \n__________________________________________________________________________________________________\nswish_156 (Swish) (None, 1, 1, 16) 0 conv2d_207[0][0] \n__________________________________________________________________________________________________\nconv2d_208 (Conv2D) (None, 1, 1, 384) 6528 swish_156[0][0] \n__________________________________________________________________________________________________\nactivation_53 (Activation) (None, 1, 1, 384) 0 conv2d_208[0][0] \n__________________________________________________________________________________________________\nmultiply_53 (Multiply) (None, 14, 14, 384) 0 activation_53[0][0] \n swish_155[0][0] \n__________________________________________________________________________________________________\nconv2d_209 (Conv2D) (None, 14, 14, 128) 49152 multiply_53[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_156 (BatchN (None, 14, 14, 128) 512 conv2d_209[0][0] \n__________________________________________________________________________________________________\nconv2d_210 (Conv2D) (None, 14, 14, 768) 98304 batch_normalization_156[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_157 (BatchN (None, 14, 14, 768) 3072 conv2d_210[0][0] \n__________________________________________________________________________________________________\nswish_157 (Swish) (None, 14, 14, 768) 0 batch_normalization_157[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_54 (DepthwiseC (None, 14, 14, 768) 6912 swish_157[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_158 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_54[0][0] \n__________________________________________________________________________________________________\nswish_158 (Swish) (None, 14, 14, 768) 0 batch_normalization_158[0][0] \n__________________________________________________________________________________________________\nlambda_54 (Lambda) (None, 1, 1, 768) 0 swish_158[0][0] \n__________________________________________________________________________________________________\nconv2d_211 (Conv2D) (None, 1, 1, 32) 24608 lambda_54[0][0] \n__________________________________________________________________________________________________\nswish_159 (Swish) (None, 1, 1, 32) 0 conv2d_211[0][0] \n__________________________________________________________________________________________________\nconv2d_212 (Conv2D) (None, 1, 1, 768) 25344 swish_159[0][0] \n__________________________________________________________________________________________________\nactivation_54 (Activation) (None, 1, 1, 768) 0 conv2d_212[0][0] \n__________________________________________________________________________________________________\nmultiply_54 (Multiply) (None, 14, 14, 768) 0 activation_54[0][0] \n swish_158[0][0] \n__________________________________________________________________________________________________\nconv2d_213 (Conv2D) (None, 14, 14, 128) 98304 multiply_54[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_159 (BatchN (None, 14, 14, 128) 512 conv2d_213[0][0] \n__________________________________________________________________________________________________\ndrop_connect_43 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_159[0][0] \n__________________________________________________________________________________________________\nadd_43 (Add) (None, 14, 14, 128) 0 drop_connect_43[0][0] \n batch_normalization_156[0][0] \n__________________________________________________________________________________________________\nconv2d_214 (Conv2D) (None, 14, 14, 768) 98304 add_43[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_160 (BatchN (None, 14, 14, 768) 3072 conv2d_214[0][0] \n__________________________________________________________________________________________________\nswish_160 (Swish) (None, 14, 14, 768) 0 batch_normalization_160[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_55 (DepthwiseC (None, 14, 14, 768) 6912 swish_160[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_161 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_55[0][0] \n__________________________________________________________________________________________________\nswish_161 (Swish) (None, 14, 14, 768) 0 batch_normalization_161[0][0] \n__________________________________________________________________________________________________\nlambda_55 (Lambda) (None, 1, 1, 768) 0 swish_161[0][0] \n__________________________________________________________________________________________________\nconv2d_215 (Conv2D) (None, 1, 1, 32) 24608 lambda_55[0][0] \n__________________________________________________________________________________________________\nswish_162 (Swish) (None, 1, 1, 32) 0 conv2d_215[0][0] \n__________________________________________________________________________________________________\nconv2d_216 (Conv2D) (None, 1, 1, 768) 25344 swish_162[0][0] \n__________________________________________________________________________________________________\nactivation_55 (Activation) (None, 1, 1, 768) 0 conv2d_216[0][0] \n__________________________________________________________________________________________________\nmultiply_55 (Multiply) (None, 14, 14, 768) 0 activation_55[0][0] \n swish_161[0][0] \n__________________________________________________________________________________________________\nconv2d_217 (Conv2D) (None, 14, 14, 128) 98304 multiply_55[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_162 (BatchN (None, 14, 14, 128) 512 conv2d_217[0][0] \n__________________________________________________________________________________________________\ndrop_connect_44 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_162[0][0] \n__________________________________________________________________________________________________\nadd_44 (Add) (None, 14, 14, 128) 0 drop_connect_44[0][0] \n add_43[0][0] \n__________________________________________________________________________________________________\nconv2d_218 (Conv2D) (None, 14, 14, 768) 98304 add_44[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_163 (BatchN (None, 14, 14, 768) 3072 conv2d_218[0][0] \n__________________________________________________________________________________________________\nswish_163 (Swish) (None, 14, 14, 768) 0 batch_normalization_163[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_56 (DepthwiseC (None, 14, 14, 768) 6912 swish_163[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_164 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_56[0][0] \n__________________________________________________________________________________________________\nswish_164 (Swish) (None, 14, 14, 768) 0 batch_normalization_164[0][0] \n__________________________________________________________________________________________________\nlambda_56 (Lambda) (None, 1, 1, 768) 0 swish_164[0][0] \n__________________________________________________________________________________________________\nconv2d_219 (Conv2D) (None, 1, 1, 32) 24608 lambda_56[0][0] \n__________________________________________________________________________________________________\nswish_165 (Swish) (None, 1, 1, 32) 0 conv2d_219[0][0] \n__________________________________________________________________________________________________\nconv2d_220 (Conv2D) (None, 1, 1, 768) 25344 swish_165[0][0] \n__________________________________________________________________________________________________\nactivation_56 (Activation) (None, 1, 1, 768) 0 conv2d_220[0][0] \n__________________________________________________________________________________________________\nmultiply_56 (Multiply) (None, 14, 14, 768) 0 activation_56[0][0] \n swish_164[0][0] \n__________________________________________________________________________________________________\nconv2d_221 (Conv2D) (None, 14, 14, 128) 98304 multiply_56[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_165 (BatchN (None, 14, 14, 128) 512 conv2d_221[0][0] \n__________________________________________________________________________________________________\ndrop_connect_45 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_165[0][0] \n__________________________________________________________________________________________________\nadd_45 (Add) (None, 14, 14, 128) 0 drop_connect_45[0][0] \n add_44[0][0] \n__________________________________________________________________________________________________\nconv2d_222 (Conv2D) (None, 14, 14, 768) 98304 add_45[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_166 (BatchN (None, 14, 14, 768) 3072 conv2d_222[0][0] \n__________________________________________________________________________________________________\nswish_166 (Swish) (None, 14, 14, 768) 0 batch_normalization_166[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_57 (DepthwiseC (None, 14, 14, 768) 6912 swish_166[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_167 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_57[0][0] \n__________________________________________________________________________________________________\nswish_167 (Swish) (None, 14, 14, 768) 0 batch_normalization_167[0][0] \n__________________________________________________________________________________________________\nlambda_57 (Lambda) (None, 1, 1, 768) 0 swish_167[0][0] \n__________________________________________________________________________________________________\nconv2d_223 (Conv2D) (None, 1, 1, 32) 24608 lambda_57[0][0] \n__________________________________________________________________________________________________\nswish_168 (Swish) (None, 1, 1, 32) 0 conv2d_223[0][0] \n__________________________________________________________________________________________________\nconv2d_224 (Conv2D) (None, 1, 1, 768) 25344 swish_168[0][0] \n__________________________________________________________________________________________________\nactivation_57 (Activation) (None, 1, 1, 768) 0 conv2d_224[0][0] \n__________________________________________________________________________________________________\nmultiply_57 (Multiply) (None, 14, 14, 768) 0 activation_57[0][0] \n swish_167[0][0] \n__________________________________________________________________________________________________\nconv2d_225 (Conv2D) (None, 14, 14, 128) 98304 multiply_57[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_168 (BatchN (None, 14, 14, 128) 512 conv2d_225[0][0] \n__________________________________________________________________________________________________\ndrop_connect_46 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_168[0][0] \n__________________________________________________________________________________________________\nadd_46 (Add) (None, 14, 14, 128) 0 drop_connect_46[0][0] \n add_45[0][0] \n__________________________________________________________________________________________________\nconv2d_226 (Conv2D) (None, 14, 14, 768) 98304 add_46[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_169 (BatchN (None, 14, 14, 768) 3072 conv2d_226[0][0] \n__________________________________________________________________________________________________\nswish_169 (Swish) (None, 14, 14, 768) 0 batch_normalization_169[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_58 (DepthwiseC (None, 14, 14, 768) 6912 swish_169[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_170 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_58[0][0] \n__________________________________________________________________________________________________\nswish_170 (Swish) (None, 14, 14, 768) 0 batch_normalization_170[0][0] \n__________________________________________________________________________________________________\nlambda_58 (Lambda) (None, 1, 1, 768) 0 swish_170[0][0] \n__________________________________________________________________________________________________\nconv2d_227 (Conv2D) (None, 1, 1, 32) 24608 lambda_58[0][0] \n__________________________________________________________________________________________________\nswish_171 (Swish) (None, 1, 1, 32) 0 conv2d_227[0][0] \n__________________________________________________________________________________________________\nconv2d_228 (Conv2D) (None, 1, 1, 768) 25344 swish_171[0][0] \n__________________________________________________________________________________________________\nactivation_58 (Activation) (None, 1, 1, 768) 0 conv2d_228[0][0] \n__________________________________________________________________________________________________\nmultiply_58 (Multiply) (None, 14, 14, 768) 0 activation_58[0][0] \n swish_170[0][0] \n__________________________________________________________________________________________________\nconv2d_229 (Conv2D) (None, 14, 14, 128) 98304 multiply_58[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_171 (BatchN (None, 14, 14, 128) 512 conv2d_229[0][0] \n__________________________________________________________________________________________________\ndrop_connect_47 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_171[0][0] \n__________________________________________________________________________________________________\nadd_47 (Add) (None, 14, 14, 128) 0 drop_connect_47[0][0] \n add_46[0][0] \n__________________________________________________________________________________________________\nconv2d_230 (Conv2D) (None, 14, 14, 768) 98304 add_47[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_172 (BatchN (None, 14, 14, 768) 3072 conv2d_230[0][0] \n__________________________________________________________________________________________________\nswish_172 (Swish) (None, 14, 14, 768) 0 batch_normalization_172[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_59 (DepthwiseC (None, 14, 14, 768) 6912 swish_172[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_173 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_59[0][0] \n__________________________________________________________________________________________________\nswish_173 (Swish) (None, 14, 14, 768) 0 batch_normalization_173[0][0] \n__________________________________________________________________________________________________\nlambda_59 (Lambda) (None, 1, 1, 768) 0 swish_173[0][0] \n__________________________________________________________________________________________________\nconv2d_231 (Conv2D) (None, 1, 1, 32) 24608 lambda_59[0][0] \n__________________________________________________________________________________________________\nswish_174 (Swish) (None, 1, 1, 32) 0 conv2d_231[0][0] \n__________________________________________________________________________________________________\nconv2d_232 (Conv2D) (None, 1, 1, 768) 25344 swish_174[0][0] \n__________________________________________________________________________________________________\nactivation_59 (Activation) (None, 1, 1, 768) 0 conv2d_232[0][0] \n__________________________________________________________________________________________________\nmultiply_59 (Multiply) (None, 14, 14, 768) 0 activation_59[0][0] \n swish_173[0][0] \n__________________________________________________________________________________________________\nconv2d_233 (Conv2D) (None, 14, 14, 128) 98304 multiply_59[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_174 (BatchN (None, 14, 14, 128) 512 conv2d_233[0][0] \n__________________________________________________________________________________________________\ndrop_connect_48 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_174[0][0] \n__________________________________________________________________________________________________\nadd_48 (Add) (None, 14, 14, 128) 0 drop_connect_48[0][0] \n add_47[0][0] \n__________________________________________________________________________________________________\nconv2d_234 (Conv2D) (None, 14, 14, 768) 98304 add_48[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_175 (BatchN (None, 14, 14, 768) 3072 conv2d_234[0][0] \n__________________________________________________________________________________________________\nswish_175 (Swish) (None, 14, 14, 768) 0 batch_normalization_175[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_60 (DepthwiseC (None, 14, 14, 768) 19200 swish_175[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_176 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_60[0][0] \n__________________________________________________________________________________________________\nswish_176 (Swish) (None, 14, 14, 768) 0 batch_normalization_176[0][0] \n__________________________________________________________________________________________________\nlambda_60 (Lambda) (None, 1, 1, 768) 0 swish_176[0][0] \n__________________________________________________________________________________________________\nconv2d_235 (Conv2D) (None, 1, 1, 32) 24608 lambda_60[0][0] \n__________________________________________________________________________________________________\nswish_177 (Swish) (None, 1, 1, 32) 0 conv2d_235[0][0] \n__________________________________________________________________________________________________\nconv2d_236 (Conv2D) (None, 1, 1, 768) 25344 swish_177[0][0] \n__________________________________________________________________________________________________\nactivation_60 (Activation) (None, 1, 1, 768) 0 conv2d_236[0][0] \n__________________________________________________________________________________________________\nmultiply_60 (Multiply) (None, 14, 14, 768) 0 activation_60[0][0] \n swish_176[0][0] \n__________________________________________________________________________________________________\nconv2d_237 (Conv2D) (None, 14, 14, 176) 135168 multiply_60[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_177 (BatchN (None, 14, 14, 176) 704 conv2d_237[0][0] \n__________________________________________________________________________________________________\nconv2d_238 (Conv2D) (None, 14, 14, 1056) 185856 batch_normalization_177[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_178 (BatchN (None, 14, 14, 1056) 4224 conv2d_238[0][0] \n__________________________________________________________________________________________________\nswish_178 (Swish) (None, 14, 14, 1056) 0 batch_normalization_178[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_61 (DepthwiseC (None, 14, 14, 1056) 26400 swish_178[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_179 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_61[0][0] \n__________________________________________________________________________________________________\nswish_179 (Swish) (None, 14, 14, 1056) 0 batch_normalization_179[0][0] \n__________________________________________________________________________________________________\nlambda_61 (Lambda) (None, 1, 1, 1056) 0 swish_179[0][0] \n__________________________________________________________________________________________________\nconv2d_239 (Conv2D) (None, 1, 1, 44) 46508 lambda_61[0][0] \n__________________________________________________________________________________________________\nswish_180 (Swish) (None, 1, 1, 44) 0 conv2d_239[0][0] \n__________________________________________________________________________________________________\nconv2d_240 (Conv2D) (None, 1, 1, 1056) 47520 swish_180[0][0] \n__________________________________________________________________________________________________\nactivation_61 (Activation) (None, 1, 1, 1056) 0 conv2d_240[0][0] \n__________________________________________________________________________________________________\nmultiply_61 (Multiply) (None, 14, 14, 1056) 0 activation_61[0][0] \n swish_179[0][0] \n__________________________________________________________________________________________________\nconv2d_241 (Conv2D) (None, 14, 14, 176) 185856 multiply_61[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_180 (BatchN (None, 14, 14, 176) 704 conv2d_241[0][0] \n__________________________________________________________________________________________________\ndrop_connect_49 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_180[0][0] \n__________________________________________________________________________________________________\nadd_49 (Add) (None, 14, 14, 176) 0 drop_connect_49[0][0] \n batch_normalization_177[0][0] \n__________________________________________________________________________________________________\nconv2d_242 (Conv2D) (None, 14, 14, 1056) 185856 add_49[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_181 (BatchN (None, 14, 14, 1056) 4224 conv2d_242[0][0] \n__________________________________________________________________________________________________\nswish_181 (Swish) (None, 14, 14, 1056) 0 batch_normalization_181[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_62 (DepthwiseC (None, 14, 14, 1056) 26400 swish_181[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_182 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_62[0][0] \n__________________________________________________________________________________________________\nswish_182 (Swish) (None, 14, 14, 1056) 0 batch_normalization_182[0][0] \n__________________________________________________________________________________________________\nlambda_62 (Lambda) (None, 1, 1, 1056) 0 swish_182[0][0] \n__________________________________________________________________________________________________\nconv2d_243 (Conv2D) (None, 1, 1, 44) 46508 lambda_62[0][0] \n__________________________________________________________________________________________________\nswish_183 (Swish) (None, 1, 1, 44) 0 conv2d_243[0][0] \n__________________________________________________________________________________________________\nconv2d_244 (Conv2D) (None, 1, 1, 1056) 47520 swish_183[0][0] \n__________________________________________________________________________________________________\nactivation_62 (Activation) (None, 1, 1, 1056) 0 conv2d_244[0][0] \n__________________________________________________________________________________________________\nmultiply_62 (Multiply) (None, 14, 14, 1056) 0 activation_62[0][0] \n swish_182[0][0] \n__________________________________________________________________________________________________\nconv2d_245 (Conv2D) (None, 14, 14, 176) 185856 multiply_62[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_183 (BatchN (None, 14, 14, 176) 704 conv2d_245[0][0] \n__________________________________________________________________________________________________\ndrop_connect_50 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_183[0][0] \n__________________________________________________________________________________________________\nadd_50 (Add) (None, 14, 14, 176) 0 drop_connect_50[0][0] \n add_49[0][0] \n__________________________________________________________________________________________________\nconv2d_246 (Conv2D) (None, 14, 14, 1056) 185856 add_50[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_184 (BatchN (None, 14, 14, 1056) 4224 conv2d_246[0][0] \n__________________________________________________________________________________________________\nswish_184 (Swish) (None, 14, 14, 1056) 0 batch_normalization_184[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_63 (DepthwiseC (None, 14, 14, 1056) 26400 swish_184[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_185 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_63[0][0] \n__________________________________________________________________________________________________\nswish_185 (Swish) (None, 14, 14, 1056) 0 batch_normalization_185[0][0] \n__________________________________________________________________________________________________\nlambda_63 (Lambda) (None, 1, 1, 1056) 0 swish_185[0][0] \n__________________________________________________________________________________________________\nconv2d_247 (Conv2D) (None, 1, 1, 44) 46508 lambda_63[0][0] \n__________________________________________________________________________________________________\nswish_186 (Swish) (None, 1, 1, 44) 0 conv2d_247[0][0] \n__________________________________________________________________________________________________\nconv2d_248 (Conv2D) (None, 1, 1, 1056) 47520 swish_186[0][0] \n__________________________________________________________________________________________________\nactivation_63 (Activation) (None, 1, 1, 1056) 0 conv2d_248[0][0] \n__________________________________________________________________________________________________\nmultiply_63 (Multiply) (None, 14, 14, 1056) 0 activation_63[0][0] \n swish_185[0][0] \n__________________________________________________________________________________________________\nconv2d_249 (Conv2D) (None, 14, 14, 176) 185856 multiply_63[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_186 (BatchN (None, 14, 14, 176) 704 conv2d_249[0][0] \n__________________________________________________________________________________________________\ndrop_connect_51 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_186[0][0] \n__________________________________________________________________________________________________\nadd_51 (Add) (None, 14, 14, 176) 0 drop_connect_51[0][0] \n add_50[0][0] \n__________________________________________________________________________________________________\nconv2d_250 (Conv2D) (None, 14, 14, 1056) 185856 add_51[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_187 (BatchN (None, 14, 14, 1056) 4224 conv2d_250[0][0] \n__________________________________________________________________________________________________\nswish_187 (Swish) (None, 14, 14, 1056) 0 batch_normalization_187[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_64 (DepthwiseC (None, 14, 14, 1056) 26400 swish_187[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_188 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_64[0][0] \n__________________________________________________________________________________________________\nswish_188 (Swish) (None, 14, 14, 1056) 0 batch_normalization_188[0][0] \n__________________________________________________________________________________________________\nlambda_64 (Lambda) (None, 1, 1, 1056) 0 swish_188[0][0] \n__________________________________________________________________________________________________\nconv2d_251 (Conv2D) (None, 1, 1, 44) 46508 lambda_64[0][0] \n__________________________________________________________________________________________________\nswish_189 (Swish) (None, 1, 1, 44) 0 conv2d_251[0][0] \n__________________________________________________________________________________________________\nconv2d_252 (Conv2D) (None, 1, 1, 1056) 47520 swish_189[0][0] \n__________________________________________________________________________________________________\nactivation_64 (Activation) (None, 1, 1, 1056) 0 conv2d_252[0][0] \n__________________________________________________________________________________________________\nmultiply_64 (Multiply) (None, 14, 14, 1056) 0 activation_64[0][0] \n swish_188[0][0] \n__________________________________________________________________________________________________\nconv2d_253 (Conv2D) (None, 14, 14, 176) 185856 multiply_64[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_189 (BatchN (None, 14, 14, 176) 704 conv2d_253[0][0] \n__________________________________________________________________________________________________\ndrop_connect_52 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_189[0][0] \n__________________________________________________________________________________________________\nadd_52 (Add) (None, 14, 14, 176) 0 drop_connect_52[0][0] \n add_51[0][0] \n__________________________________________________________________________________________________\nconv2d_254 (Conv2D) (None, 14, 14, 1056) 185856 add_52[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_190 (BatchN (None, 14, 14, 1056) 4224 conv2d_254[0][0] \n__________________________________________________________________________________________________\nswish_190 (Swish) (None, 14, 14, 1056) 0 batch_normalization_190[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_65 (DepthwiseC (None, 14, 14, 1056) 26400 swish_190[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_191 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_65[0][0] \n__________________________________________________________________________________________________\nswish_191 (Swish) (None, 14, 14, 1056) 0 batch_normalization_191[0][0] \n__________________________________________________________________________________________________\nlambda_65 (Lambda) (None, 1, 1, 1056) 0 swish_191[0][0] \n__________________________________________________________________________________________________\nconv2d_255 (Conv2D) (None, 1, 1, 44) 46508 lambda_65[0][0] \n__________________________________________________________________________________________________\nswish_192 (Swish) (None, 1, 1, 44) 0 conv2d_255[0][0] \n__________________________________________________________________________________________________\nconv2d_256 (Conv2D) (None, 1, 1, 1056) 47520 swish_192[0][0] \n__________________________________________________________________________________________________\nactivation_65 (Activation) (None, 1, 1, 1056) 0 conv2d_256[0][0] \n__________________________________________________________________________________________________\nmultiply_65 (Multiply) (None, 14, 14, 1056) 0 activation_65[0][0] \n swish_191[0][0] \n__________________________________________________________________________________________________\nconv2d_257 (Conv2D) (None, 14, 14, 176) 185856 multiply_65[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_192 (BatchN (None, 14, 14, 176) 704 conv2d_257[0][0] \n__________________________________________________________________________________________________\ndrop_connect_53 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_192[0][0] \n__________________________________________________________________________________________________\nadd_53 (Add) (None, 14, 14, 176) 0 drop_connect_53[0][0] \n add_52[0][0] \n__________________________________________________________________________________________________\nconv2d_258 (Conv2D) (None, 14, 14, 1056) 185856 add_53[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_193 (BatchN (None, 14, 14, 1056) 4224 conv2d_258[0][0] \n__________________________________________________________________________________________________\nswish_193 (Swish) (None, 14, 14, 1056) 0 batch_normalization_193[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_66 (DepthwiseC (None, 14, 14, 1056) 26400 swish_193[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_194 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_66[0][0] \n__________________________________________________________________________________________________\nswish_194 (Swish) (None, 14, 14, 1056) 0 batch_normalization_194[0][0] \n__________________________________________________________________________________________________\nlambda_66 (Lambda) (None, 1, 1, 1056) 0 swish_194[0][0] \n__________________________________________________________________________________________________\nconv2d_259 (Conv2D) (None, 1, 1, 44) 46508 lambda_66[0][0] \n__________________________________________________________________________________________________\nswish_195 (Swish) (None, 1, 1, 44) 0 conv2d_259[0][0] \n__________________________________________________________________________________________________\nconv2d_260 (Conv2D) (None, 1, 1, 1056) 47520 swish_195[0][0] \n__________________________________________________________________________________________________\nactivation_66 (Activation) (None, 1, 1, 1056) 0 conv2d_260[0][0] \n__________________________________________________________________________________________________\nmultiply_66 (Multiply) (None, 14, 14, 1056) 0 activation_66[0][0] \n swish_194[0][0] \n__________________________________________________________________________________________________\nconv2d_261 (Conv2D) (None, 14, 14, 176) 185856 multiply_66[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_195 (BatchN (None, 14, 14, 176) 704 conv2d_261[0][0] \n__________________________________________________________________________________________________\ndrop_connect_54 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_195[0][0] \n__________________________________________________________________________________________________\nadd_54 (Add) (None, 14, 14, 176) 0 drop_connect_54[0][0] \n add_53[0][0] \n__________________________________________________________________________________________________\nconv2d_262 (Conv2D) (None, 14, 14, 1056) 185856 add_54[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_196 (BatchN (None, 14, 14, 1056) 4224 conv2d_262[0][0] \n__________________________________________________________________________________________________\nswish_196 (Swish) (None, 14, 14, 1056) 0 batch_normalization_196[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_67 (DepthwiseC (None, 7, 7, 1056) 26400 swish_196[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_197 (BatchN (None, 7, 7, 1056) 4224 depthwise_conv2d_67[0][0] \n__________________________________________________________________________________________________\nswish_197 (Swish) (None, 7, 7, 1056) 0 batch_normalization_197[0][0] \n__________________________________________________________________________________________________\nlambda_67 (Lambda) (None, 1, 1, 1056) 0 swish_197[0][0] \n__________________________________________________________________________________________________\nconv2d_263 (Conv2D) (None, 1, 1, 44) 46508 lambda_67[0][0] \n__________________________________________________________________________________________________\nswish_198 (Swish) (None, 1, 1, 44) 0 conv2d_263[0][0] \n__________________________________________________________________________________________________\nconv2d_264 (Conv2D) (None, 1, 1, 1056) 47520 swish_198[0][0] \n__________________________________________________________________________________________________\nactivation_67 (Activation) (None, 1, 1, 1056) 0 conv2d_264[0][0] \n__________________________________________________________________________________________________\nmultiply_67 (Multiply) (None, 7, 7, 1056) 0 activation_67[0][0] \n swish_197[0][0] \n__________________________________________________________________________________________________\nconv2d_265 (Conv2D) (None, 7, 7, 304) 321024 multiply_67[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_198 (BatchN (None, 7, 7, 304) 1216 conv2d_265[0][0] \n__________________________________________________________________________________________________\nconv2d_266 (Conv2D) (None, 7, 7, 1824) 554496 batch_normalization_198[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_199 (BatchN (None, 7, 7, 1824) 7296 conv2d_266[0][0] \n__________________________________________________________________________________________________\nswish_199 (Swish) (None, 7, 7, 1824) 0 batch_normalization_199[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_68 (DepthwiseC (None, 7, 7, 1824) 45600 swish_199[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_200 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_68[0][0] \n__________________________________________________________________________________________________\nswish_200 (Swish) (None, 7, 7, 1824) 0 batch_normalization_200[0][0] \n__________________________________________________________________________________________________\nlambda_68 (Lambda) (None, 1, 1, 1824) 0 swish_200[0][0] \n__________________________________________________________________________________________________\nconv2d_267 (Conv2D) (None, 1, 1, 76) 138700 lambda_68[0][0] \n__________________________________________________________________________________________________\nswish_201 (Swish) (None, 1, 1, 76) 0 conv2d_267[0][0] \n__________________________________________________________________________________________________\nconv2d_268 (Conv2D) (None, 1, 1, 1824) 140448 swish_201[0][0] \n__________________________________________________________________________________________________\nactivation_68 (Activation) (None, 1, 1, 1824) 0 conv2d_268[0][0] \n__________________________________________________________________________________________________\nmultiply_68 (Multiply) (None, 7, 7, 1824) 0 activation_68[0][0] \n swish_200[0][0] \n__________________________________________________________________________________________________\nconv2d_269 (Conv2D) (None, 7, 7, 304) 554496 multiply_68[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_201 (BatchN (None, 7, 7, 304) 1216 conv2d_269[0][0] \n__________________________________________________________________________________________________\ndrop_connect_55 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_201[0][0] \n__________________________________________________________________________________________________\nadd_55 (Add) (None, 7, 7, 304) 0 drop_connect_55[0][0] \n batch_normalization_198[0][0] \n__________________________________________________________________________________________________\nconv2d_270 (Conv2D) (None, 7, 7, 1824) 554496 add_55[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_202 (BatchN (None, 7, 7, 1824) 7296 conv2d_270[0][0] \n__________________________________________________________________________________________________\nswish_202 (Swish) (None, 7, 7, 1824) 0 batch_normalization_202[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_69 (DepthwiseC (None, 7, 7, 1824) 45600 swish_202[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_203 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_69[0][0] \n__________________________________________________________________________________________________\nswish_203 (Swish) (None, 7, 7, 1824) 0 batch_normalization_203[0][0] \n__________________________________________________________________________________________________\nlambda_69 (Lambda) (None, 1, 1, 1824) 0 swish_203[0][0] \n__________________________________________________________________________________________________\nconv2d_271 (Conv2D) (None, 1, 1, 76) 138700 lambda_69[0][0] \n__________________________________________________________________________________________________\nswish_204 (Swish) (None, 1, 1, 76) 0 conv2d_271[0][0] \n__________________________________________________________________________________________________\nconv2d_272 (Conv2D) (None, 1, 1, 1824) 140448 swish_204[0][0] \n__________________________________________________________________________________________________\nactivation_69 (Activation) (None, 1, 1, 1824) 0 conv2d_272[0][0] \n__________________________________________________________________________________________________\nmultiply_69 (Multiply) (None, 7, 7, 1824) 0 activation_69[0][0] \n swish_203[0][0] \n__________________________________________________________________________________________________\nconv2d_273 (Conv2D) (None, 7, 7, 304) 554496 multiply_69[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_204 (BatchN (None, 7, 7, 304) 1216 conv2d_273[0][0] \n__________________________________________________________________________________________________\ndrop_connect_56 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_204[0][0] \n__________________________________________________________________________________________________\nadd_56 (Add) (None, 7, 7, 304) 0 drop_connect_56[0][0] \n add_55[0][0] \n__________________________________________________________________________________________________\nconv2d_274 (Conv2D) (None, 7, 7, 1824) 554496 add_56[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_205 (BatchN (None, 7, 7, 1824) 7296 conv2d_274[0][0] \n__________________________________________________________________________________________________\nswish_205 (Swish) (None, 7, 7, 1824) 0 batch_normalization_205[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_70 (DepthwiseC (None, 7, 7, 1824) 45600 swish_205[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_206 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_70[0][0] \n__________________________________________________________________________________________________\nswish_206 (Swish) (None, 7, 7, 1824) 0 batch_normalization_206[0][0] \n__________________________________________________________________________________________________\nlambda_70 (Lambda) (None, 1, 1, 1824) 0 swish_206[0][0] \n__________________________________________________________________________________________________\nconv2d_275 (Conv2D) (None, 1, 1, 76) 138700 lambda_70[0][0] \n__________________________________________________________________________________________________\nswish_207 (Swish) (None, 1, 1, 76) 0 conv2d_275[0][0] \n__________________________________________________________________________________________________\nconv2d_276 (Conv2D) (None, 1, 1, 1824) 140448 swish_207[0][0] \n__________________________________________________________________________________________________\nactivation_70 (Activation) (None, 1, 1, 1824) 0 conv2d_276[0][0] \n__________________________________________________________________________________________________\nmultiply_70 (Multiply) (None, 7, 7, 1824) 0 activation_70[0][0] \n swish_206[0][0] \n__________________________________________________________________________________________________\nconv2d_277 (Conv2D) (None, 7, 7, 304) 554496 multiply_70[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_207 (BatchN (None, 7, 7, 304) 1216 conv2d_277[0][0] \n__________________________________________________________________________________________________\ndrop_connect_57 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_207[0][0] \n__________________________________________________________________________________________________\nadd_57 (Add) (None, 7, 7, 304) 0 drop_connect_57[0][0] \n add_56[0][0] \n__________________________________________________________________________________________________\nconv2d_278 (Conv2D) (None, 7, 7, 1824) 554496 add_57[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_208 (BatchN (None, 7, 7, 1824) 7296 conv2d_278[0][0] \n__________________________________________________________________________________________________\nswish_208 (Swish) (None, 7, 7, 1824) 0 batch_normalization_208[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_71 (DepthwiseC (None, 7, 7, 1824) 45600 swish_208[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_209 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_71[0][0] \n__________________________________________________________________________________________________\nswish_209 (Swish) (None, 7, 7, 1824) 0 batch_normalization_209[0][0] \n__________________________________________________________________________________________________\nlambda_71 (Lambda) (None, 1, 1, 1824) 0 swish_209[0][0] \n__________________________________________________________________________________________________\nconv2d_279 (Conv2D) (None, 1, 1, 76) 138700 lambda_71[0][0] \n__________________________________________________________________________________________________\nswish_210 (Swish) (None, 1, 1, 76) 0 conv2d_279[0][0] \n__________________________________________________________________________________________________\nconv2d_280 (Conv2D) (None, 1, 1, 1824) 140448 swish_210[0][0] \n__________________________________________________________________________________________________\nactivation_71 (Activation) (None, 1, 1, 1824) 0 conv2d_280[0][0] \n__________________________________________________________________________________________________\nmultiply_71 (Multiply) (None, 7, 7, 1824) 0 activation_71[0][0] \n swish_209[0][0] \n__________________________________________________________________________________________________\nconv2d_281 (Conv2D) (None, 7, 7, 304) 554496 multiply_71[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_210 (BatchN (None, 7, 7, 304) 1216 conv2d_281[0][0] \n__________________________________________________________________________________________________\ndrop_connect_58 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_210[0][0] \n__________________________________________________________________________________________________\nadd_58 (Add) (None, 7, 7, 304) 0 drop_connect_58[0][0] \n add_57[0][0] \n__________________________________________________________________________________________________\nconv2d_282 (Conv2D) (None, 7, 7, 1824) 554496 add_58[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_211 (BatchN (None, 7, 7, 1824) 7296 conv2d_282[0][0] \n__________________________________________________________________________________________________\nswish_211 (Swish) (None, 7, 7, 1824) 0 batch_normalization_211[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_72 (DepthwiseC (None, 7, 7, 1824) 45600 swish_211[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_212 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_72[0][0] \n__________________________________________________________________________________________________\nswish_212 (Swish) (None, 7, 7, 1824) 0 batch_normalization_212[0][0] \n__________________________________________________________________________________________________\nlambda_72 (Lambda) (None, 1, 1, 1824) 0 swish_212[0][0] \n__________________________________________________________________________________________________\nconv2d_283 (Conv2D) (None, 1, 1, 76) 138700 lambda_72[0][0] \n__________________________________________________________________________________________________\nswish_213 (Swish) (None, 1, 1, 76) 0 conv2d_283[0][0] \n__________________________________________________________________________________________________\nconv2d_284 (Conv2D) (None, 1, 1, 1824) 140448 swish_213[0][0] \n__________________________________________________________________________________________________\nactivation_72 (Activation) (None, 1, 1, 1824) 0 conv2d_284[0][0] \n__________________________________________________________________________________________________\nmultiply_72 (Multiply) (None, 7, 7, 1824) 0 activation_72[0][0] \n swish_212[0][0] \n__________________________________________________________________________________________________\nconv2d_285 (Conv2D) (None, 7, 7, 304) 554496 multiply_72[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_213 (BatchN (None, 7, 7, 304) 1216 conv2d_285[0][0] \n__________________________________________________________________________________________________\ndrop_connect_59 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_213[0][0] \n__________________________________________________________________________________________________\nadd_59 (Add) (None, 7, 7, 304) 0 drop_connect_59[0][0] \n add_58[0][0] \n__________________________________________________________________________________________________\nconv2d_286 (Conv2D) (None, 7, 7, 1824) 554496 add_59[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_214 (BatchN (None, 7, 7, 1824) 7296 conv2d_286[0][0] \n__________________________________________________________________________________________________\nswish_214 (Swish) (None, 7, 7, 1824) 0 batch_normalization_214[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_73 (DepthwiseC (None, 7, 7, 1824) 45600 swish_214[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_215 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_73[0][0] \n__________________________________________________________________________________________________\nswish_215 (Swish) (None, 7, 7, 1824) 0 batch_normalization_215[0][0] \n__________________________________________________________________________________________________\nlambda_73 (Lambda) (None, 1, 1, 1824) 0 swish_215[0][0] \n__________________________________________________________________________________________________\nconv2d_287 (Conv2D) (None, 1, 1, 76) 138700 lambda_73[0][0] \n__________________________________________________________________________________________________\nswish_216 (Swish) (None, 1, 1, 76) 0 conv2d_287[0][0] \n__________________________________________________________________________________________________\nconv2d_288 (Conv2D) (None, 1, 1, 1824) 140448 swish_216[0][0] \n__________________________________________________________________________________________________\nactivation_73 (Activation) (None, 1, 1, 1824) 0 conv2d_288[0][0] \n__________________________________________________________________________________________________\nmultiply_73 (Multiply) (None, 7, 7, 1824) 0 activation_73[0][0] \n swish_215[0][0] \n__________________________________________________________________________________________________\nconv2d_289 (Conv2D) (None, 7, 7, 304) 554496 multiply_73[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_216 (BatchN (None, 7, 7, 304) 1216 conv2d_289[0][0] \n__________________________________________________________________________________________________\ndrop_connect_60 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_216[0][0] \n__________________________________________________________________________________________________\nadd_60 (Add) (None, 7, 7, 304) 0 drop_connect_60[0][0] \n add_59[0][0] \n__________________________________________________________________________________________________\nconv2d_290 (Conv2D) (None, 7, 7, 1824) 554496 add_60[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_217 (BatchN (None, 7, 7, 1824) 7296 conv2d_290[0][0] \n__________________________________________________________________________________________________\nswish_217 (Swish) (None, 7, 7, 1824) 0 batch_normalization_217[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_74 (DepthwiseC (None, 7, 7, 1824) 45600 swish_217[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_218 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_74[0][0] \n__________________________________________________________________________________________________\nswish_218 (Swish) (None, 7, 7, 1824) 0 batch_normalization_218[0][0] \n__________________________________________________________________________________________________\nlambda_74 (Lambda) (None, 1, 1, 1824) 0 swish_218[0][0] \n__________________________________________________________________________________________________\nconv2d_291 (Conv2D) (None, 1, 1, 76) 138700 lambda_74[0][0] \n__________________________________________________________________________________________________\nswish_219 (Swish) (None, 1, 1, 76) 0 conv2d_291[0][0] \n__________________________________________________________________________________________________\nconv2d_292 (Conv2D) (None, 1, 1, 1824) 140448 swish_219[0][0] \n__________________________________________________________________________________________________\nactivation_74 (Activation) (None, 1, 1, 1824) 0 conv2d_292[0][0] \n__________________________________________________________________________________________________\nmultiply_74 (Multiply) (None, 7, 7, 1824) 0 activation_74[0][0] \n swish_218[0][0] \n__________________________________________________________________________________________________\nconv2d_293 (Conv2D) (None, 7, 7, 304) 554496 multiply_74[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_219 (BatchN (None, 7, 7, 304) 1216 conv2d_293[0][0] \n__________________________________________________________________________________________________\ndrop_connect_61 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_219[0][0] \n__________________________________________________________________________________________________\nadd_61 (Add) (None, 7, 7, 304) 0 drop_connect_61[0][0] \n add_60[0][0] \n__________________________________________________________________________________________________\nconv2d_294 (Conv2D) (None, 7, 7, 1824) 554496 add_61[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_220 (BatchN (None, 7, 7, 1824) 7296 conv2d_294[0][0] \n__________________________________________________________________________________________________\nswish_220 (Swish) (None, 7, 7, 1824) 0 batch_normalization_220[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_75 (DepthwiseC (None, 7, 7, 1824) 45600 swish_220[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_221 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_75[0][0] \n__________________________________________________________________________________________________\nswish_221 (Swish) (None, 7, 7, 1824) 0 batch_normalization_221[0][0] \n__________________________________________________________________________________________________\nlambda_75 (Lambda) (None, 1, 1, 1824) 0 swish_221[0][0] \n__________________________________________________________________________________________________\nconv2d_295 (Conv2D) (None, 1, 1, 76) 138700 lambda_75[0][0] \n__________________________________________________________________________________________________\nswish_222 (Swish) (None, 1, 1, 76) 0 conv2d_295[0][0] \n__________________________________________________________________________________________________\nconv2d_296 (Conv2D) (None, 1, 1, 1824) 140448 swish_222[0][0] \n__________________________________________________________________________________________________\nactivation_75 (Activation) (None, 1, 1, 1824) 0 conv2d_296[0][0] \n__________________________________________________________________________________________________\nmultiply_75 (Multiply) (None, 7, 7, 1824) 0 activation_75[0][0] \n swish_221[0][0] \n__________________________________________________________________________________________________\nconv2d_297 (Conv2D) (None, 7, 7, 304) 554496 multiply_75[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_222 (BatchN (None, 7, 7, 304) 1216 conv2d_297[0][0] \n__________________________________________________________________________________________________\ndrop_connect_62 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_222[0][0] \n__________________________________________________________________________________________________\nadd_62 (Add) (None, 7, 7, 304) 0 drop_connect_62[0][0] \n add_61[0][0] \n__________________________________________________________________________________________________\nconv2d_298 (Conv2D) (None, 7, 7, 1824) 554496 add_62[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_223 (BatchN (None, 7, 7, 1824) 7296 conv2d_298[0][0] \n__________________________________________________________________________________________________\nswish_223 (Swish) (None, 7, 7, 1824) 0 batch_normalization_223[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_76 (DepthwiseC (None, 7, 7, 1824) 16416 swish_223[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_224 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_76[0][0] \n__________________________________________________________________________________________________\nswish_224 (Swish) (None, 7, 7, 1824) 0 batch_normalization_224[0][0] \n__________________________________________________________________________________________________\nlambda_76 (Lambda) (None, 1, 1, 1824) 0 swish_224[0][0] \n__________________________________________________________________________________________________\nconv2d_299 (Conv2D) (None, 1, 1, 76) 138700 lambda_76[0][0] \n__________________________________________________________________________________________________\nswish_225 (Swish) (None, 1, 1, 76) 0 conv2d_299[0][0] \n__________________________________________________________________________________________________\nconv2d_300 (Conv2D) (None, 1, 1, 1824) 140448 swish_225[0][0] \n__________________________________________________________________________________________________\nactivation_76 (Activation) (None, 1, 1, 1824) 0 conv2d_300[0][0] \n__________________________________________________________________________________________________\nmultiply_76 (Multiply) (None, 7, 7, 1824) 0 activation_76[0][0] \n swish_224[0][0] \n__________________________________________________________________________________________________\nconv2d_301 (Conv2D) (None, 7, 7, 512) 933888 multiply_76[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_225 (BatchN (None, 7, 7, 512) 2048 conv2d_301[0][0] \n__________________________________________________________________________________________________\nconv2d_302 (Conv2D) (None, 7, 7, 3072) 1572864 batch_normalization_225[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_226 (BatchN (None, 7, 7, 3072) 12288 conv2d_302[0][0] \n__________________________________________________________________________________________________\nswish_226 (Swish) (None, 7, 7, 3072) 0 batch_normalization_226[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_77 (DepthwiseC (None, 7, 7, 3072) 27648 swish_226[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_227 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_77[0][0] \n__________________________________________________________________________________________________\nswish_227 (Swish) (None, 7, 7, 3072) 0 batch_normalization_227[0][0] \n__________________________________________________________________________________________________\nlambda_77 (Lambda) (None, 1, 1, 3072) 0 swish_227[0][0] \n__________________________________________________________________________________________________\nconv2d_303 (Conv2D) (None, 1, 1, 128) 393344 lambda_77[0][0] \n__________________________________________________________________________________________________\nswish_228 (Swish) (None, 1, 1, 128) 0 conv2d_303[0][0] \n__________________________________________________________________________________________________\nconv2d_304 (Conv2D) (None, 1, 1, 3072) 396288 swish_228[0][0] \n__________________________________________________________________________________________________\nactivation_77 (Activation) (None, 1, 1, 3072) 0 conv2d_304[0][0] \n__________________________________________________________________________________________________\nmultiply_77 (Multiply) (None, 7, 7, 3072) 0 activation_77[0][0] \n swish_227[0][0] \n__________________________________________________________________________________________________\nconv2d_305 (Conv2D) (None, 7, 7, 512) 1572864 multiply_77[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_228 (BatchN (None, 7, 7, 512) 2048 conv2d_305[0][0] \n__________________________________________________________________________________________________\ndrop_connect_63 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_228[0][0] \n__________________________________________________________________________________________________\nadd_63 (Add) (None, 7, 7, 512) 0 drop_connect_63[0][0] \n batch_normalization_225[0][0] \n__________________________________________________________________________________________________\nconv2d_306 (Conv2D) (None, 7, 7, 3072) 1572864 add_63[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_229 (BatchN (None, 7, 7, 3072) 12288 conv2d_306[0][0] \n__________________________________________________________________________________________________\nswish_229 (Swish) (None, 7, 7, 3072) 0 batch_normalization_229[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_78 (DepthwiseC (None, 7, 7, 3072) 27648 swish_229[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_230 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_78[0][0] \n__________________________________________________________________________________________________\nswish_230 (Swish) (None, 7, 7, 3072) 0 batch_normalization_230[0][0] \n__________________________________________________________________________________________________\nlambda_78 (Lambda) (None, 1, 1, 3072) 0 swish_230[0][0] \n__________________________________________________________________________________________________\nconv2d_307 (Conv2D) (None, 1, 1, 128) 393344 lambda_78[0][0] \n__________________________________________________________________________________________________\nswish_231 (Swish) (None, 1, 1, 128) 0 conv2d_307[0][0] \n__________________________________________________________________________________________________\nconv2d_308 (Conv2D) (None, 1, 1, 3072) 396288 swish_231[0][0] \n__________________________________________________________________________________________________\nactivation_78 (Activation) (None, 1, 1, 3072) 0 conv2d_308[0][0] \n__________________________________________________________________________________________________\nmultiply_78 (Multiply) (None, 7, 7, 3072) 0 activation_78[0][0] \n swish_230[0][0] \n__________________________________________________________________________________________________\nconv2d_309 (Conv2D) (None, 7, 7, 512) 1572864 multiply_78[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_231 (BatchN (None, 7, 7, 512) 2048 conv2d_309[0][0] \n__________________________________________________________________________________________________\ndrop_connect_64 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_231[0][0] \n__________________________________________________________________________________________________\nadd_64 (Add) (None, 7, 7, 512) 0 drop_connect_64[0][0] \n add_63[0][0] \n__________________________________________________________________________________________________\nconv2d_310 (Conv2D) (None, 7, 7, 2048) 1048576 add_64[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_232 (BatchN (None, 7, 7, 2048) 8192 conv2d_310[0][0] \n__________________________________________________________________________________________________\nswish_232 (Swish) (None, 7, 7, 2048) 0 batch_normalization_232[0][0] \n__________________________________________________________________________________________________\nglobal_average_pooling2d_2 (Glo (None, 2048) 0 swish_232[0][0] \n__________________________________________________________________________________________________\nfinal_output (Dense) (None, 1) 2049 global_average_pooling2d_2[0][0] \n==================================================================================================\nTotal params: 28,515,569\nTrainable params: 28,342,833\nNon-trainable params: 172,736\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"history = model.fit_generator(generator=train_generator,\n steps_per_epoch=STEP_SIZE_TRAIN,\n validation_data=valid_generator,\n validation_steps=STEP_SIZE_VALID,\n epochs=EPOCHS,\n callbacks=callback_list,\n verbose=2).history","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:08:49.503649Z","iopub.execute_input":"2024-05-03T04:08:49.503978Z","iopub.status.idle":"2024-05-03T04:37:40.891241Z","shell.execute_reply.started":"2024-05-03T04:08:49.503916Z","shell.execute_reply":"2024-05-03T04:37:40.890149Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"Epoch 1/20\n - 165s - loss: 0.7981 - acc: 0.4774 - val_loss: 0.6158 - val_acc: 0.5692\nEpoch 2/20\n - 117s - loss: 0.6671 - acc: 0.5293 - val_loss: 0.4883 - val_acc: 0.6305\nEpoch 3/20\n - 118s - loss: 0.5712 - acc: 0.5618 - val_loss: 0.5969 - val_acc: 0.5435\nEpoch 4/20\n - 118s - loss: 0.4995 - acc: 0.6183 - val_loss: 0.4330 - val_acc: 0.6876\nEpoch 5/20\n - 118s - loss: 0.4205 - acc: 0.6553 - val_loss: 0.3942 - val_acc: 0.6205\nEpoch 6/20\n - 118s - loss: 0.4027 - acc: 0.6822 - val_loss: 0.3550 - val_acc: 0.7161\nEpoch 7/20\n - 119s - loss: 0.3429 - acc: 0.7246 - val_loss: 0.3489 - val_acc: 0.7817\nEpoch 8/20\n - 118s - loss: 0.3033 - acc: 0.7536 - val_loss: 0.2719 - val_acc: 0.7946\nEpoch 9/20\n - 118s - loss: 0.2784 - acc: 0.7582 - val_loss: 0.2494 - val_acc: 0.7817\nEpoch 10/20\n - 118s - loss: 0.2551 - acc: 0.7735 - val_loss: 0.3110 - val_acc: 0.7803\nEpoch 11/20\n - 118s - loss: 0.2414 - acc: 0.7914 - val_loss: 0.2957 - val_acc: 0.7732\nEpoch 12/20\n - 118s - loss: 0.2200 - acc: 0.8033 - val_loss: 0.2554 - val_acc: 0.7989\nEpoch 13/20\n - 118s - loss: 0.1899 - acc: 0.8249 - val_loss: 0.3309 - val_acc: 0.7789\nEpoch 14/20\n - 118s - loss: 0.1956 - acc: 0.8146 - val_loss: 0.2751 - val_acc: 0.7746\nRestoring model weights from the end of the best epoch\nEpoch 00014: early stopping\n","output_type":"stream"}]},{"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(2, 1, sharex='col', figsize=(20, 6))\n\nax1.plot(cosine_lr_1st.learning_rates)\nax1.set_title('Warm up learning rates')\n\nax2.plot(cosine_lr_2nd.learning_rates)\nax2.set_title('Fine-tune learning rates')\n\nplt.xlabel('Steps')\nplt.ylabel('Learning rate')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:37:40.892812Z","iopub.execute_input":"2024-05-03T04:37:40.893154Z","iopub.status.idle":"2024-05-03T04:37:41.373487Z","shell.execute_reply.started":"2024-05-03T04:37:40.893087Z","shell.execute_reply":"2024-05-03T04:37:41.372189Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(2, 1, sharex='col', figsize=(20, 14))\n\nax1.plot(history['loss'], label='Train loss')\nax1.plot(history['val_loss'], label='Validation loss')\nax1.legend(loc='best')\nax1.set_title('Loss')\n\nax2.plot(history['acc'], label='Train accuracy')\nax2.plot(history['val_acc'], label='Validation accuracy')\nax2.legend(loc='best')\nax2.set_title('Accuracy')\n\nplt.xlabel('Epochs')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:37:41.37565Z","iopub.execute_input":"2024-05-03T04:37:41.376282Z","iopub.status.idle":"2024-05-03T04:37:42.037956Z","shell.execute_reply.started":"2024-05-03T04:37:41.376015Z","shell.execute_reply":"2024-05-03T04:37:42.037169Z"},"trusted":true},"execution_count":35,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"# Create empty arays to keep the predictions and labels\ndf_preds = pd.DataFrame(columns=['label', 'pred', 'set'])\ntrain_generator.reset()\nvalid_generator.reset()\n\n# Add train predictions and labels\nfor i in range(STEP_SIZE_TRAIN + 1):\n im, lbl = next(train_generator)\n preds = model.predict(im, batch_size=train_generator.batch_size)\n for index in range(len(preds)):\n df_preds.loc[len(df_preds)] = [lbl[index], preds[index][0], 'train']\n\n# Add validation predictions and labels\nfor i in range(STEP_SIZE_VALID + 1):\n im, lbl = next(valid_generator)\n preds = model.predict(im, batch_size=valid_generator.batch_size)\n for index in range(len(preds)):\n df_preds.loc[len(df_preds)] = [lbl[index], preds[index][0], 'validation']\n\ndf_preds['label'] = df_preds['label'].astype('int')","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:37:42.03942Z","iopub.execute_input":"2024-05-03T04:37:42.039683Z","iopub.status.idle":"2024-05-03T04:39:14.615716Z","shell.execute_reply.started":"2024-05-03T04:37:42.039633Z","shell.execute_reply":"2024-05-03T04:39:14.614939Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"def classify(x):\n if x < 0.5:\n return 0\n elif x < 1.5:\n return 1\n elif x < 2.5:\n return 2\n elif x < 3.5:\n return 3\n return 4\n\n# Classify predictions\ndf_preds['predictions'] = df_preds['pred'].apply(lambda x: classify(x))\n\ntrain_preds = df_preds[df_preds['set'] == 'train']\nvalidation_preds = df_preds[df_preds['set'] == 'validation']","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:39:14.61693Z","iopub.execute_input":"2024-05-03T04:39:14.617164Z","iopub.status.idle":"2024-05-03T04:39:14.633014Z","shell.execute_reply.started":"2024-05-03T04:39:14.617125Z","shell.execute_reply":"2024-05-03T04:39:14.632184Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"labels = ['0 - No DR', '1 - Mild', '2 - Moderate', '3 - Severe', '4 - Proliferative DR']\ndef plot_confusion_matrix(train, validation, labels=labels):\n train_labels, train_preds = train\n validation_labels, validation_preds = validation\n fig, (ax1, ax2) = plt.subplots(1, 2, sharex='col', figsize=(24, 7))\n train_cnf_matrix = confusion_matrix(train_labels, train_preds)\n validation_cnf_matrix = confusion_matrix(validation_labels, validation_preds)\n\n train_cnf_matrix_norm = train_cnf_matrix.astype('float') / train_cnf_matrix.sum(axis=1)[:, np.newaxis]\n validation_cnf_matrix_norm = validation_cnf_matrix.astype('float') / validation_cnf_matrix.sum(axis=1)[:, np.newaxis]\n\n train_df_cm = pd.DataFrame(train_cnf_matrix_norm, index=labels, columns=labels)\n validation_df_cm = pd.DataFrame(validation_cnf_matrix_norm, index=labels, columns=labels)\n\n sns.heatmap(train_df_cm, annot=True, fmt='.2f', cmap=\"Blues\",ax=ax1).set_title('Train')\n sns.heatmap(validation_df_cm, annot=True, fmt='.2f', cmap=sns.cubehelix_palette(8),ax=ax2).set_title('Validation')\n plt.show()\n\nplot_confusion_matrix((train_preds['label'], train_preds['predictions']), (validation_preds['label'], validation_preds['predictions']))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:39:14.634428Z","iopub.execute_input":"2024-05-03T04:39:14.634781Z","iopub.status.idle":"2024-05-03T04:39:15.564223Z","shell.execute_reply.started":"2024-05-03T04:39:14.634717Z","shell.execute_reply":"2024-05-03T04:39:15.563326Z"},"trusted":true},"execution_count":38,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"iVBORw0KGgoAAAANSUhEUgAABS8AAAHZCAYAAABnzM5eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3XlclOX6x/HvDIIwAiouiIIioKinLNTU0nLPDRS0o6l5tMVMyyWtbHWrLLP0VLbayTTL1NzN/JW7mUtuabmCuCuUCsq+OL8/LHQaRkcdBhg+7168XvLMNQ/X7ZXDPdfc9/MYzGazWQAAAAAAAABQxBgLOwEAAAAAAAAAyA/NSwAAAAAAAABFEs1LAAAAAAAAAEUSzUsAAAAAAAAARRLNSwAAAAAAAABFEs1LAAAAAAAAAEUSzUsADpGbm6uIiAidOnWqsFMBAAAACs2JEycUHh6unJwcSdJjjz2mhQsX2hV7oz7++GO99NJLN50rABQHpQo7AQCFIyIiIu/P6enp8vDwkJubmyRp3Lhx6tKlyw2dz83NTTt37nRojgAAAEBhePTRR1W/fn0NGzbM4vjKlSs1ZswYrVu3TqVK2fd2+rPPPnNITlu2bNGzzz6r9evX5x174oknHHJuACjKaF4CJdTVjcbWrVvrtdde0z333GMzPicnx+4JGgAAAFCcxcTEaPLkyRo6dKgMBkPe8SVLligqKop5MQA4EdvGAeRrypQpGj58uEaMGKGIiAgtWbJEO3fuVI8ePdSoUSM1b95cr732mrKzsyVdbm6Gh4frxIkTkqRnnnlGr732mh577DFFRESoZ8+eOn78eGEOCQAAALBL27ZtlZycrG3btuUdS05O1po1axQdHa21a9cqOjpaDRo0UIsWLfT+++/bPFffvn01b948SZcvtTRx4kQ1adJEbdq00bp16yxi58+fr44dOyoiIkJt2rTRN998I0lKS0vTgAEDlJiYqIiICEVERCghIUHvv/++nnnmmbznr1q1Sp07d1ajRo3Ut29fxcXF5T3WunVr/e9//1NUVJQaNmyo4cOHKzMz0yF/XwBQkGheArBp5cqVioyM1Pbt29WpUye5ubnppZde0ubNmzV79mxt2LBBc+bMsfn8pUuXatiwYdq6dasCAgL07rvvOjF7AAAA4OZ4enqqY8eOWrRoUd6x77//XiEhIapTp468vLw0ceJEbdu2TZ988olmz56tlStXXve8c+fO1Zo1a7Ro0SLNnz9fK1assHi8QoUK+uSTT7Rjxw698cYbeuONN/T777/LZDJp2rRpqly5snbu3KmdO3fK39/f4rnx8fEaOXKkXnzxRW3atEn33XefnnjiCWVlZVmM4bPPPtOqVat04MABLViw4Bb/pgCg4NG8BGBTgwYN1Lp1axmNRnl6eqp+/fq64447VKpUKQUFBalHjx7aunWrzee3b99et99+u9zd3RUVFaX9+/c7MXsAAADg5kVHR2vFihXKyMiQJC1atEgxMTGSpCZNmig8PFxGo1F16tRR586drzkv/tv333+vfv36KSAgQOXKldPAgQMtHm/ZsqWqV68ug8Ggxo0bq1mzZharP69l+fLlatGihZo1ayZ3d3c9+uijysjIsLhcVN++feXv769y5cqpVatW2rdvn71/HQBQaLhQBwCbAgICLL6Pi4vTxIkT9fvvvys9PV25ubmqX7++zedXqlQp789eXl5KS0srsFwBAAAAR2rUqJH8/Py0atUq1a9fX7/99pumTp0qSfr111/19ttv69ChQ8rOzlZWVpY6dOhw3XMmJiZazLGrVq1q8fi6dev0wQcf6MiRI7p06ZIyMjJUu3Ztu/JNTEy0OJ/RaFRAQIASEhLyjv1zfp6YmGjXuQGgMLHyEoBNV1+cXJLGjBmjWrVq6YcfftCOHTs0dOjQQsoMAAAAKHhdu3bVokWLtHjxYjVr1kwVK1aUJI0cOTLvmpXbt2/Xgw8+KLPZfN3zVapUSadPn877/uo/Z2VlaejQoXrkkUe0ceNGbdu2Tffdd1/eef85N/+nypUr69SpU3nfm81mnT592mp7OQAUNzQvAdgtNTVVPj4+MplMiouLu+b1LgEAAIDiLjo6Wps2bdLcuXMVHR2ddzw1NVVly5ZV6dKltXv3bi1btsyu83Xs2FFffvmlzpw5o+TkZH366ad5j2VlZSkrK0t+fn4qVaqU1q1bp40bN+Y9XqFCBSUlJenixYs2z71u3Tpt2rRJ2dnZ+vzzz+Xh4aGIiIibHD0AFA00LwHYbdSoUVq4cKEaNGig0aNHq2PHjoWdEgAAAFBgAgMDFRERofT0dLVp0ybv+JgxY/Tee+8pIiJCH3zwgd3z4h49eqh58+bq2rWrYmJidP/99+c95u3trZdfflnDhw/XXXfdpWXLlql169Z5j4eGhqpz585q27atGjVqZLEdXJJCQkI0adIkvfrqq2ratKnWrFmjjz/+WB4eHrf4twAAhctgtmdtOwAAAAAAAAA4GSsvAQAAAAAAABRJNC8BAAAAAAAAFEk0LwEAAAAAAAAUSTQvAQAAAAAAABRJpZz5w7winnLmj0M+zm59v7BTKPGMBkNhpwAAeTydOhO4wpFzgvSdUx12LqC4ql+jRWGnUOKtXfpOYacAAChC/Oo3KrSf7ch5we6j6xx2rpvFyksAAAAAAAAARVIhrbcAAAAlmoHPTwEAAABcH+8cAAAAAAAAABRJrLwEAADOx/V/AQAAANiB5iUAAHA+to0DAAAAsAPvHAAAAAAAAAAUSay8BAAAzse2cQAAAAB2oHkJAACcj23jAAAAAOzAOwcAAAAAAAAARRIrLwEAgPOxbRwAAACAHWheAgAA52PbOAAAAAA78M4BAAAAAAAAQJHEyksAAOB8bBsHAAAAYAealwAAwPnYNg4AAADADrxzAAAAAAAAAFAksfISAAA4H9vGAQAAANiB5iUAAHA+to0DAAAAsAPvHAAAAAAAAAAUSay8BAAAzse2cQAAAAB2oHkJAACcj23jAAAAAOzAOwcAAAAAAAAARRIrLwEAgPOx8hIAAACAHWheAgAA5zNyzUsAAAAA18eyBwAAAAAAAABFEisvAQCA87FtHAAAAIAdaF4CAADnM7BtHAAAAMD1sewBAAAAAAAAQJHEyksAAOB8bBsHAAAAYAealwAAwPnYNg4AAADADix7AAAAAAAAAFAksfISAAA4H9vGAQAAANiB5iUAAHA+to0DAAAAsAPLHgAAAAAAAAAUSSW+eVne16Q57wzQnz+/owPLx6tnh0b5xpX19tK08X11dNUbOrrqDb00sJPF403vqKkNXz6jxJ/e1tY5L+ieO0Ockb5LSE5O0oihT+nuuyLUsV1rff/d0nzjzGaz3p38tlo2a6KWzZrov+9Mktlsznv81bGvKDqygxrcXldLFi1wVvouIzkpScOHPqkmje5Uh7attHyZ7TpMeWeS7runie67p4mmvP2WRR3279unB//dTU0a3qEH/91N+/ftc9YQij1qUDRQBycxGB33BaBI8i3roymfvKYt+1ZoxcY56tS1bb5xPr7eeu2dF7R2+yKt3b5Ig4b3z3usStXK2rz3e4uv3UfX6T8DejhpFMVf8sUUjXprilo99IhiBg3V/23YmG+c2WzWB7Nmq/3DA9X+4YGa+uXXeb/Xki5c1OMvj1X7hweqXb8BGvDiGP26/4Azh1GsOaIGknQw/oj6P/eSWvZ5WP2fe0kH4484aQSugToUPmqAm1Xit43/94UeysrOUY02L+iO8EAteG+Qdh88oX2Hz1jEvfVMd5k83VWn82hVKu+j7z8ZomOnz+nLJZtV3tekeVMGatiEOVq0epd6dGikb999QvUixyjpYnohjaz4eOO18XJ3d9eqdT/pwP79Gjp4oGqH11FoWC2LuPnz5mjN6pWaM3+xDAaDnhjwiKoFBunfPR+UJNUOr6P7O3TSu5PfLoxhFHsT/qrDmnUbtX//Pg0ZPFC169RR2D/q8O1fdZi3YLFkMOiJxx5WtaAg9ejZS9lZWRo+ZLD69O2nnr16a97cbzR8yGAtXf5/cvfwKKSRFR/UoGigDk7CtnHA5b306tPKzs5Wy4YxqlMvTFOnv6kDe2MVd+iIRdyzrzwlTy9PdWjWU34Vy2va15N16mSCFs/7XmdOJappvY55sdWCqmjZuq+18vv1Th5N8fXO/76Qeyk3fTftQx06clQj35ikWsE1FBIUaBG3aOVqrd+6XV++PUGSQcNefUNV/Sur2/1t5eVZWi8NelxBAVVkMBi0/pftevbNd7T8fx+plJtb4QysGHFEDbKzczTqrcnq0bmjurdvq0U/rtKotyZr7nuT5e5e4t/W24U6FD5qgJt1U8sVzp496+g8CoXJ00PRbe7UuA+/U2p6ln7edVjfrduj3pGNrWI73XebJn+xUukZ2Tp2+py+WLRJ/bo2lSQ1vSNEiecuasHKnbp0yaxvlv+iP8+nqGubO509pGInPS1Nq378UYOHDJXJVEYRDRqqRcvWWrZ0iVXs0sWL1Lffw/KvUkWV/f3Vt9/DWrp4Yd7jPXv1UZOmd6t06dLOHIJLSEtL08off9CTQ4bJVKaMGjRspBatWmvZksVWsUsXL9J/+j0i/ypV5O/vr779H9aSRZfr8MsvW5WTm6OH/tNPHh4e6vPQf2Q2m7V1y2ZnD6nYoQZFA3UAUBS4wlzby8tTbTvepw/e+Z/S09K1c9serV35syK73W8V26Lt3Zr+8WxlZGTq1IkzWjhnuWJ6dMrnrFJUt/bavnW3Tp04k+/jsJSekaE1m7fq8Qf/LZOXp+6oG657GzXQinU/WcUuX7tBvaI6qXKFCqpcwU+9ojpr+drLTeLSHh6qUa2qjEajzGazjEajLqam6kJKirOHVOw4qgY79u5VTu4lPdi5gzzc3dWjUweZzdK233539pCKJepQ+KgBbsU1m5d//PGHfvvtN+Xk5EiSzp07pzfeeEMdOnRwSnIFrVaNysrNvaTYY4l5x/YcPKm6IQH5xhuuWiViMBhUL6zqX3+2XkBiMEj/Cs3/PLji6NEjcnMzqkZwzbxjtcPDdTj2kFXs4bhY1Q6vYxEXl08cbtzfdQi+qg7h4XUUFxtrFRsXe0i169T5R9yhvx6LVe3a4Rb/VmqFhys2zvo8sEQNigbq4ERsGwdceq5dIyRIuZcu6Wj8ibxjB/fFKqx2zXzjr55LGwxSWHj+cVHd22vJtyscmqsrO3b6jIxGo6pXvfK+JCy4hg6fOGEVG3/8hGoFV8/7vlZwdcUfP2kR89DI59Wid389N/EddWnTUn5lyxZc8i7CUTU4fPykwmoEWcwtQmsEKT6f88AadSh81AC3wuaMf968eWrVqpUGDhyomJgYrV27Vvfff78SEhI0f/58Z+ZYYLxNpZWckmFxLDklXT5lPK1if/x5r555uJ28TaUVElRR/bo2lcnTXZK0+dd4BVQqqx4dGqpUKaP6RDVRSGBFeXmyNfB60tLS5O3tY3HM28dHqamp14319vFRWlqaxbUvcHPS86uDt4/S0vKvg4+395W4q+qQlpYqbx/L8/h4eystn3rCEjUoGqiDE/39yZ8jvoBiyNXn2iaTl1IuWK7KS7mQKlMZL6vYjeu26pHBfWQq46WgGtUU3aOTPD2td9I0uKu+KlQsrx+XryuwvF1NekaGvE0mi2PeJi+lpWfkG1vmqlhvk0lpGRkWc+1Z77yplTM/07hhT6p+nfCCS9yFOKoG+Z/HlO95YI06FD5qgFth84IAX3zxhRYuXKhatWpp+/bt6tevn95++22X+CT4bylpmfL9R6PS19tTF1Ot/6cf+da3mjzq39qzeIzOJadq7ort6tGhoSTpXHKq/v30p3rz6RhNeb6HVm7ap9VbDuhkYpJTxlGcmUwmpab+Y2KbkqIyZcpcNzY1JUUmk8niExfcHK/86pCaIpMp/zqkpFxpwFxdB5OpjFJT/lnPVJnyqScsUYOigToAcBZXn2unpaWrjI/la14ZH5PSUq2vB//mmPf0wvhhWrb2KyUlXdD3S1apY5c2VnFdHmivld+vV3oa15S3l5enp1LTLf++UtPTZfKyXqzh5emp1Kv+blPT0mXy9LSaa5f28ND9ze/Rg8OfVe3gGqoVXKNgkncRjqrBjZwH1qhD4aMGuBU2V16WKlVKtWpdvjlBw4YNFRgY6DKTqb8dOpqoUqWMCq1eKe/Y7bWrad/h01ax5y+k6eGXZqhmuxfV8IHXZTQatO33o3mP/7Q9Vs0fmqRqLUfpkZdnqnawv7b9dtTqPLBUo0awcnJydfTokbxjBw8cUMg/bowhSSGhYTp4YL9F3D9v6oObk38d9is0LMwqNjSslkUdDhzYn1eH0LAwHTx4wOIT+kMHDygs1Po8sEQNigbq4ERsG0cJ5+pz7aOHj6uUm5uqB1fLOxZeN0yxB+OtYi8kX9QLw15T67u6qVu7/jIajPpt136LmNKlPdSuU0stmc+W8RtRPaCKcnNzdfz0lWuEHjpyTCGBgVaxNYMCFXv0yvuXQ0ePqmZQNau4v+Xk5OpkQqLNx3GZo2oQElRNsUePWcwt4o4eU818zgNr1KHwUQPcCpsz/uzsbMXFxSk2NlaxsbEyGo0W37uCtIwsLV79q0YP6iyTp4fuviNEkS3q6+tlW61iawZWlF/ZMjIaDbq/WT090q2Z3px2ZfJ0R3igSpUyyqeMp958OkYnE85r5aZ9zhxOseRlMql123b6aOp7Sk9L064dO7RuzSpFRnWxio3sEq1ZM75QYkKCEhMT9OWM6YrqGpP3eHZ2ljIzM2U2m5WTk6PMzExdunTJmcMptkwmk9q0a6cP339PaWlp2rlju9auXqXILl2tYiO7dNWXM6cr4a86zPxiurpEX67DXXc1lpvRTV/PmqmsrCzN/mqWJKlxk6ZOHU9xRA2KBurgRDQvUcK5+lw7PT1DK1es15MjHpWXl6fubHSbWrZrpmULfrCKDaxeVWXL+cpoNKp5yybq3jtSn74/0yKmTYf7dPFCirb+vNNZQ3AJXp6eatnkLk2b863SMzL06/4D2vDLdnVo0dwqtmOL5pq97Hslnj2nP86d1+yly9Wp5X2SpN8OHtKv+w4oOztHGZlZ+nLRUp1PTta/avGh3PU4qgYN6tWTm9Goucv/T1nZ2Zr3/eV/S41u+5dTx1NcUYfCRw1wKwxmGxcMbN26te0nGQxatWrVDf8wr4inbvg5Ba28r0mfjO2j1k3r6FxSql55b4nmrNimZhGhWjR1sCo1GylJ6t4uQpOefUBlvb106FiiXn53sUVzcsYb/dW+2eV/LD/+vFcjJs7TH+eL3t33zm59v7BTsJKcnKSxr7ykzZt+Vrmy5TT06RHq2DlKO7Zv01NPPK6ff9khSTKbzXp38ttaOP9bSVJM9wc0bMQzeVtZHuvfV9u3/WJx7mmfz1Cjxk2cO6DrMBbRbe7JSUka88qL2vRXHYY9PVKdIi/XYfDAAdq87fKbBbPZrP++M0kL/qpDt+4PaPjIZ/PqsG/fXo0b/bIOx8WqZkioxr76uurWrVdo4ypOqEHRUNLq4GnzAjIFyyvqQ4edK33pYIedC3AWR8+169docaspOZxvWR+NnzRKd9/bSEnnL+jdiZ9q+eKVanBXfX04Y6Ka1usoSbq/cys9N+Yp+fh66+jh4/rvm5/o5/WWc7qPZk7Sb7/u0wfvfF4YQ7HL2qXvFHYK+Uq+mKIJH32qrbt/U1lvbw3q01Pt722mXfv2a8Trb2n1rMt/p2azWR/Mmq0lq9ZKkrq0aaknH+olg8GgHb/v05TpM3UqIVFubm4KrR6kxx98QBH16hbiyIoPR9RAkg7EH9EbH01T/ImTCg6sphcHDVB4zeDCGVQxRB0KX0mrgV/9RoX2sx05L9h9tPCvNW2zeVkQimLzsqQpis3LkqaoNi8BlEyF1rzs8pHDzpW+ZJDDzgUUV0WxeVnSFNXmJQCgcNC8dJzrvmWJi4vToUOHJEm1a9dWSEhIgScFAABcHNu9AUnMtQEAAK7HZvMyMzNTw4cP16ZNm1SjRg2ZzWYdO3ZMzZo105QpU+Th4eHMPAEAAACXwVwbAADAPjaXPUybNk2StH79ei1evFhLlizRunXrZDAY9OmnnzotQQAA4IIMBsd9AcUQc20AAAD72Gxerly5Uq+//rp8fX3zjpUtW1bjx4/XypUrnZIcAABwUdxtHCUcc20AAAD72JzxZ2Vlyc/Pz+q4n5+fMjMzCzQpAAAAwJUx1wYAALCPzWteenp62nySl5dXgSQDAABKCLZ7o4Rjrg0AAApKrQo1CzsFh7LZvDx+/LiGDRtmddxsNuvEiRMFmhQAAHBtBpqXKOGYawMAANjHZvPyxRdftPmkVq1aFUgyAACgZKB5iZKOuTYAAIB9bDYvY2JinJkHAAAAUGIw1wYAALCPzeYlAABAgWHhJQAAAAA70LwEAABOx7ZxAAAAAPYwFnYCAAAAAAAAAJAfu1Zenj9/Xr/++qsMBoPq16+v8uXLF3ReAADAhbHyEriCuTYAAIBt121ebtiwQc8++6zq1asns9msAwcOaNKkSWrWrJkz8gMAAC6I5iVwGXNtAACAa7tu83LKlCn66quvFBoaKkmKi4vTs88+y4QKAAAAuEXMtQEAAK7tus3LnJycvMmUJIWGhionJ6dAkwIAAK6NlZfAZcy1AQAAru26N+zx8/PTggUL8r5fuHCh/Pz8CjQpAADg4gwO/AKKMebaAAAA13bd5uX48eP1zTff6Pbbb1f9+vX1zTffaPz48c7IDQAAAHBpzLUBAACu7brbxqtXr665c+cqNTVVZrNZ3t7ezsgLAAC4MLaNA5cx1wYAALg2m83L2NjYaz4xLCzM4ckAAICSgeYlSjrm2gAAAPax2bx8/PHHrY4ZDAalpqYqOTlZ+/btK9DEAAAAAFfFXBsAALia+Ph4Pf/880pKSlK5cuU0ceJEBQcHW8ScPXtWL7zwgk6fPq3s7Gw1bdpUL7/8skqVsr053OYjq1evtvg+LS1N06dP19dff63+/fvf0mAAAEDJxspLlHTMtQEAgKsZM2aMevfura5du2rx4sUaPXq0Zs6caRHz8ccfKzQ0VJ9++qmys7PVu3dv/fDDD+rUqZPN8173mpc5OTmaPXu2pk2bphYtWmjBggXy9/e/9REBAIASi+YlcBlzbQAAUJRduHBBFy5csDru6+srX1/fvO/Pnj2rvXv3avr06ZKkyMhIvfrqqzp37pz8/Pzy4v7eaXLp0iVlZWUpOzv7unOfazYvFy1apPfff1+33367ZsyYoZo1a97QAAEAAADkj7k2AAAo6mbMmKGpU6daHX/qqac0ZMiQvO9Pnz4tf39/ubm5SZLc3NxUuXJlnT592qJ5OXjwYA0ZMkTNmzdXenq6+vTpo4YNG14zB5vNy6ioKKWlpWnIkCG67bbblJuba3FhcS4iDgAAbhoLL1HCMdcGAADFQb9+/RQTE2N1/OpVlzdixYoVCg8P14wZM5SamqoBAwZoxYoV6tChg83n2GxepqamSpLee+89GQwGmc3mvMcMBoNWrVp1U0kCAACwbRwlHXNtAABQHPxze7gtAQEBSkhIUG5urtzc3JSbm6vExEQFBARYxM2aNUsTJkyQ0WiUj4+PWrdurS1bttxc8/KfFxEHAAAA4BjMtQEAgCupUKGC6tatq2XLlqlr165atmyZ6tata7FlXJICAwO1fv161a9fX1lZWdq0aZPatWt3zXMbCzJxAACA/BgMBod93Yj4+Hj17NlT7du3V8+ePXXkyBGrmLNnz+rxxx9XVFSUOnTooLFjxyonJ8dBIwcAAABc09ixYzVr1iy1b99es2bN0rhx4yRJAwYM0J49eyRJL774orZv366oqChFR0crODhYPXr0uOZ5r3u3cQAAAEcrrG3jY8aMUe/evdW1a1ctXrxYo0eP1syZMy1iPv74Y4WGhurTTz9Vdna2evfurR9++EGdOnUqlJwBAACA4iA0NFTz5s2zOj5t2rS8P1evXj3vjuT2YuUlAAAo1i5cuKATJ05YfV24cMEi7uzZs9q7d68iIyMlSZGRkdq7d6/OnTtnEWcwGJSamqpLly4pKytL2dnZ8vf3d9p4AAAAAFzByksAAOB8Dlx4OWPGDE2dOtXq+FNPPaUhQ4bkfX/69Gn5+/vLzc1NkuTm5qbKlSvr9OnTFtfiGTx4sIYMGaLmzZsrPT1dffr0UcOGDR2XMAAAAAC73VDz8qGHHtKsWbMKKhcAAFBCOHLbeL9+/RQTE2N13J67IuZnxYoVCg8P14wZM5SamqoBAwZoxYoV17wDIuAIzLUBAACs3VDzMjU19ZZ+2PEN/72l5+PWPTX/t8JOocSrX827sFMo8QY2rVnYKUBSIV3yEC7I19fXrkZlQECAEhISlJubKzc3N+Xm5ioxMVEBAQEWcbNmzdKECRNkNBrl4+Oj1q1ba8uWLTQvUeBuZa69duk7DswEN2Prl1sLOwWgSGjct3FhpwDAxdzQNS/d3d0LKg8AAFCCFMbdxitUqKC6detq2bJlkqRly5apbt26FlvGJSkwMFDr16+XJGVlZWnTpk2qVauW4wYP2MBcGwAAwNoNNS/nzp1bUHkAAIASpDCal5I0duxYzZo1S+3bt9esWbM0btw4SdKAAQO0Z88eSdKLL76o7du3KyoqStHR0QoODlaPHj0c/ncA/BNzbQAAAGvcsAcAAJQYoaGhmjdvntXxadOm5f25evXqmj59ujPTAgAAAGADzUsAAOB0jrxhDwAAAADXRfMSAAA4H71LAAAAAHa4bvPy/PnzOnPmjCSpSpUqKl++fIEnBQAAAJQEzLUBAACuzWbz8tixY3rllVe0d+9eVa5cWZKUmJioevXqady4cQoODnZWjgAAwMWwbRwlHXNtAAAA+9hsXj733HPq3bu3pk+fLqPx8k3JL126pKVLl2rUqFGaM2eO05IEAACuheYlSjrm2gAAAPYx2nogKSlJXbp0yZtMSZLRaFTXrl2VnJzslOQAAAAAV8RcGwAAwD42m5flypXTsmXLZDab846ZzWYtWbJEvr6+TkkOAAC4JoPB4LATMBvsAAAgAElEQVQvoDhirg0AAGAfm9vG33zzTY0ZM0bjx4+Xv7+/JCkhIUF16tTRm2++6bQEAQCAC6LniBKOuTYAAIB9bDYvg4ODNWPGDJ07d06nT5+WJAUEBMjPz89pyQEAAACuiLk2AACAfWw2L//m5+fHJAoAADgU272By5hrAwAAXNt1m5cAAACORvMSAAAAgD1s3rAHAAAAAAAAAAoTKy8BAIDTsfISAAAAgD1oXgIAAKejeQkAAADAHmwbBwAAAAAAAFAksfISAAA4HwsvAQAAANiB5iUAAHA6to0DAAAABaNWRf/CTsGh2DYOAAAAAAAAoEhi5SUAAHA6Vl4CAAAAsAfNSwAA4HT0LgEAAADYg23jAAAAAAAAAIokVl4CAACnY9s4AAAAAHvQvAQAAE5H7xIAAACAPdg2DgAAAAAAAKBIYuUlAABwOraNAwAAALAHzUsAAOB09C4BAAAA2IPmJQAAcDqjke4lAAAAgOvjmpcAAAAAAAAAiiRWXgIAAKdj2zgAAAAAe9C8BAAATscNewAAAADYo8Q3Ly8kJ+mN8aO1dfPPKluunJ54arju7xhpFWc2m/XR+5O1dNF8SVJk124aPHRk3puv3Nxc/e+TqVq2eKHS0lIVGFRd738yXT4+vk4dT3FUxsNNDzeupn9V8dHFzBzN331GW44mW8V1va2yOterrJzcS3nHxqw4pD9Ss+Xv46EedwQorKJJBoN05Fy6vt5xSmcuZjlzKMVaRspFrZ0xRcd/3y5P77Jq0v1h1W7SymZ8bk625o4dpOzMDP1n0qy842tnvqtTB3YrOfGUWvV/WnWa3e+M9F1CcnKSxr7ykjZt2qjy5cpryPAR6tQ5yirObDbr3Slva+H8byVJ0d26a/iIZ/Nej8aPfUXbt23VsaNHNfbVCeoa3c2p4yjukpOSNGb0S9r08+U6DB0+Qp0i86/DfydfqUNMt+4aPvJKHfbv26exo19S/OE41QwJ1djxr6tO3bpOHQsAFKbkiyma8NE0bd29R+V8vPVE755qf28zqziz2awPv/pGS1atlSRFtW6hJx/qJYPBoF379mvE629ZxKdnZmrCyGFq1bSxM4ZR7Ll7ldZtPVqpQu0gZadm6ODyzTq965DNeIObUc1G9FQpD3etfX2mJKl8zQA1fNTyPVKp0u7aOXOFEvYcLtD8XYEjamCqWFbhkfeofI0qktGgC8cTtW/xT0r9I8lZwyj2HPGadLXv1q7Xax98oheeeExd2th+34QrHFWDg/FHNOGjaTpy8pSCq1XVi4MGqHbNYOcNBE5X4puX70x8TaXc3bX0x3U6dGC/nh02WGG16ygkNMwibvGCeVq/drVmzF4gg8Gg4YMfU9VqQYp5oKck6X+fTNWeX3fp0y++ln+VAMXHxcrDo3RhDKnYeahhVeVcMmv4on2qXs5Tw+4L1vHzGTp1IdMq9pdjSZq2+YTVcZO7m3aduqDPt55QRnauutzmryH31tBLy21PCmBpw9dTZXQrpf6Tv9Gfx+O0/L3RqhhYU37VgvON37XiW3n5lFN25hmL4xUDayrsrvu0+dvPnZC1a3njtfFyd3fX6nUbdWD/Pg0ZPFC1w+soLKyWRdz8eXO0ZvVKzZ2/WDIYNGjAwwoMDNK/e/aSJNUOr6P2HTrp3cmTCmMYxd6Ev+qwZt1G7f+7DnWs6/DtX3WYt+ByHZ547GFVCwpSj569lJ2VpeFDBqtP337q2au35s39RsOHDNbS5f8ndw+PQhpZ0cLCS8D1vfO/L+Reyk3fTftQh44c1cg3JqlWcA2FBAVaxC1auVrrt27Xl29PkGTQsFffUFX/yup2f1vdWbeOVs+6MqfY8ftePfvmO2p6Z30nj6b4qhdzny7lXNKacdPlU7WiGj7SWRdP/6mUhPP5xtdsEaGslHSV8nPPO3Y+/rRWvjwt73u/kKpq8HAn/bn/WIHn7wocUQN3r9JK/P2IfpuzWjmZ2Qpt10gR/Tvqp0mznTWMYs8Rr0l/u5CSqpkLl1g9F9fmiBpkZ+do1FuT1aNzR3Vv31aLflylUW9N1tz3JsvdvcS3uFxWib5hT3p6mtau+lEDBg2RyVRGd0Q0VPMWrfR/3y2xiv1+2WL1eqifKvtXUaXK/nrwof5avnSRJOnChWTN/fpLjXp5nKoEVJXBYFBIWC2VLk3z8no83AxqGOirhXsSlJlzSYf+TNOuUxd0T3C5GzpP/Ll0bTh8XqlZuco1Sz8c+FMBvp4q4+FWQJm7luzMDB3evlGNo/8jd08vBdS6TcF3NNXBTavzjb/wxxkd3LxaEZ16Wj12W+suCqwbITd393yeCVvS09K08scf9OSQYTKZyiiiQSO1aNla3y1dbBW7ZPEi9e33iPyrVJG/v7/69ntYSxYvzHv8wV591KTp3fLgNeiGpV1dhzJl1KBhI7Vo1VrLlljXYeniRfrP1XXo/7CWLLpch19+2aqc3Bw99J9+8vDwUJ+H/iOz2aytWzY7e0hFlsFgcNgXgKInPSNDazZv1eMP/lsmL0/dUTdc9zZqoBXrfrKKXb52g3pFdVLlChVUuYKfekV11vK16/M97/K1G9SqaWN5eXoW9BBcgpt7KfnfHqJD/7dFuVk5SjpyRol7j6hqg/B8473K+6hqg9o6vHrHNc9btVG4zuyJU252TkGk7VIcVYPk44k6+cs+Zadnynzpko6u/1XelcvL3cR8zx6Ofk366Otv1KNTe5X18XHWEIo9R9Vgx969ysm9pAc7d5CHu7t6dOogs1na9tvvzh4SnMhmW7p79+7XfEPw7bffFkhCznT86FEZ3dxUvUZw3rGwWuHateMXq9j4uFiF1apzJa52uOIPx0qSDscekptbKa1d9YPmfD1TZcp469+9HlL3Hr0LfAzFXRWf0rpklhKu2t59/HyGwiuXyTf+jqq+ei+mrpIzcrTq0FmtjT2Xb1ztSmWUlJ6t1KzcAsnb1SQlnJDBaFS5Klc+8aoQFKJTB/bkG//T7A/VpFt/lXJnBZmjHD16RG5uRtUIrpl3rHZ4HW3fZv16dDjukMLD61jExcWyytgR/q5D8FV1CA+vo22/WNchLvaQatepYxH3dx3iYmNVu3a4xe/RWuHhio2LVbN77yvAEQAoLlx9rn3s9BkZjUZVrxqQdywsuIZ27t1nFRt//IRqBVfP+75WcHXFHz9pFZeRmak1m7dq0vMjCyZpF2SqVE5ms1lpf165JNPF02flF1I13/i60ffq4IrNunSNpqTRvZSq3B6qHdOXOzxfV1QQNZCk8iFVlXEhVdlp1rvVYM2Rr0m/H4rT/rh4PfvYw1r185aCTdyFOKoGh4+fVFiNIIvfoaE1ghR/4oTujrijAEeAwmSzeTlq1ChJ0tq1a3X48GE98MADkqQFCxaoXr16zsmugKWlp8nb29vimLe3t9LS0qxi0/8R6+3to/S0NJnNZiUmnFFKykUdP3ZU3y75QcePH9WwJx5VUPVgNW56T4GPozgr7W5UerZlgzE9O1ee7taLgn85lqx1seeUnJmjED+TnmxeXelZudpyzPL6mOW9SumhhlU1Z+fpAs3dlWRnZMjDy7Jh7OFVRtkZ1v8WDu/YqEu5uQpp0Ewn9//qrBRdXlpamry9LT+59fbxUWpqqo1Yb4u4tL9ej1iFdmvS86uDt4/S0vKvg4+NOqSlpcr7H5/E+3h7Ky2fepZU/L+Kks7V59rpGRnyNpksjnmbvJSWnpFvbJmrYr1NJqVlZFj9XluzeavK+vgooh7XD7ZXqdLuysmwvAZ8TnqmSpW23iFT+baaMrgZlfhbvM3GmiRVuT1EWWkZOnf4lMPzdUUFUYPSZcuoXsy9OrB0o8PzdVWOek26dMmstz+brhGP9JPRWKI3st4wR9Ug//OY8j0PXIfNf22NGzdW48aNtWfPHn300Udq27at2rZtq/fff19btrjGpwsmL5NSUyzfSKampsr0j38IkuTlZVJqaspVcSnyMplkMBhUuvTlbSsPDxik0p6eCqsVrjbtO2rTxvy3u+CKzOxL8nS33Nrt5e6mjOxLVrGnLmQqKSNHZrMUdzZNKw+eVcOgshYxPqXdNLJlTa2JPWvV1IRt7p6eVo3KrPQ0uXta/lvIzszQ5m//p+a9BzszvRLBZLJ8jZGk1JQUlSljvQrZZDIp5aomWGpKikx/vR7h1njlU4eU1BSZTDbqkJJ/HUymMkpN+cd5UlJlyqeeJZXB4LgvoDhy9bm2l6enUtPTLY6lpqfL5GW93dvL01OpaVdiU9PSZfL0tPq9tnzdBnVs0ZzfdzcgJzPbqklWytNDOZnZFsfc3EspvNPd2rdow3XPWbVRuE5tP+DQPF2Zo2vgXsZTdw2I0rGff9fpXbEOz9dVOeo1acEPPyq0RnXdHl7L6nm4NkfV4EbOA9dx3Y8KEhISlJl5ZSl6VlaWEhMTCzQpZwmqUUO5uTk6fuxo3rHYQwdUMyTMKrZmaJhiD175JR178EpcaK3aBZ+sizpzMVNuBqmy95Xtx0HlPHUq+fqfmpjNZl09dTW5GzWiZU3tOnlBy/b+UQDZuq5y/oG6lJurpIQr2yHOnjgsv6o1LOKSE07q4tkELZr4jL4Y0Uv/9+GrSks6py9G9NKFP8/887S4ATVqBCsnJ1dHjx7JO3bwwH6Fhlm/HoWE1tLBA/v/EccEyhFupA6hYZZ1OHBVHULDwnTw4AGZzea8xw8dPKCwUOvzACjZXHWuXT2ginJzc3X89JX5waEjxxQSaH1zi5pBgYo9emU+fujoUdUMqmYRk/DnWe38fZ86tri34JJ2QWl/JMlgNMpU8coH/j5VKyrljOWll0yVysrLz0dNBseo1ej+urNfB5X2NanV6P7yKn9lJ4FnWW/5hVTTSZqXdnNkDUp5ldZdA6KU+PsRHV693anjKO4c9Zq0bc/vWr/1F3V+bLA6PzZYew4c1HszvtLbn31R4GMo7hxVg5Cgaoo9esxinh139Jhq5nMeuI7rNi87duyonj176pNPPtEnn3yiXr16qWPHjs7IrcB5eZnUonU7ffbx+0pPT9PuXTu0Ye1qte/cxSq2Q+cu+uarmfojMUF//JGo2bO+UKeoaElSYFB13RHRUDM//1RZWVk6Eh+nVT+sULN7Wzp5RMVPVq5Z209cUMzt/vJwMyisokl3VvPVz0eSrGLvrOYj01/byWv6ealt7YraefKCJMmz1OXGZewfafp2d4JTx+AK3Et7KqRBM/2yeKayMzN0+tDvOrJrk2rf3doizq9asPq+9aV6jPlAPcZ8oJb9hsvLt5x6jPlA3n6VJEm5OdnKyc6SzNKl3FzlZGfJfMl6JS0seZlMatO2nT6a+p7S09K0c8d2rV2zSp2julrFRnXpqlkzpishIUGJiQmaOWO6unSNyXs8OztLmZmZMpvNysnJUWZmpi5RA7uYTCa1addOH77/ntL+rsPqVYrsYl2HyC5d9eXMq+rwxXR1ib5ch7vuaiw3o5u+njVTWVlZmv3VLElS4yZNnTqeoowb9gCXuepc28vTUy2b3KVpc75VekaGft1/QBt+2a4OLZpbxXZs0Vyzl32vxLPn9Me585q9dLk6tbS8PvD363/S7eG1FFjF31lDcAm52TlK+O2wat3fWG7upVQuuIoq1wvWqR2WzceUM+e09vWZ2jhljjZOmaPf561V5sV0bZwyR+lJV3YSVG1YW0lHzyj97AVnD6XYclQN3Eq7q9FjkTp/5IwOfs8NAG+Uo16TXn5yoGZPmaSZkyZo5qQJqhMaokf/3U1P9Orh7CEVO46qQYN69eRmNGru8v9TVna25n3/gySp0W3/cup44FwG89XtahtWr16trVu3ymw26+6771bLli1v6of9mVL07kZ3ITlJE8a9ol+2bFLZsmX1xJCndX/HSO3auV3PDBmolT9tk3R5ld+H772jpYvmS5Kiortr8NCReW+a/khM0BvjX9HuXTtU3q+C+vR7VNHdi94L2HPLrC+GW9jKeLjp4cbV9K8qPkrJzNG3u89oy9Fk1apk0tP3BWvw/L2SpIF3B+lfVbxVymjQ+fRsrTl0TisPnZUk3RNcTo81DVJmziWLT2Be/v6QzqVl5/tzC0v9at7XDyoEGSkXteaLyTqxd4c8vX3VpPsjqt2klU4d/E3fvfuyBnywyOo5J/f/qlX/m6T/TJqVd2zxW8/q1EHLG/10eWaiqtUpOhdPHti05vWDCkFycpLGvPKiNm/6WeXKltPQp0eqU+co7di+TU8+MUCbftkp6fLr0X8nT9LC+Zdv5hDT/QENH/Fs3uvRo/37avu2rRbnnvb5TN3VuIlzB3QdRbXnlJx0uQ6b/qrDsKdHqlPk5ToMHjhAm7ddVYd3JmnBX3Xo1v0BDR95pQ779u3VuNEv63BcrGqGhGrsq6+rbt2idx07T5tXvy5YDcavdti5doxuff0goAhzxFz73O5tjk/sFiVfTNGEjz7V1t2/qay3twb16an29zbTrn37NeL1t7R61ueSLr+efjBrtpasWitJ6tKmpZ58qJfFhxM9hz2jPl0i1aVNy0IYiX22frn1+kGFwN2rtG7r0UoVagcpOzVDB5dv1uldh1S+ZoAaPhqplS9Ps3qOX0hV1e/VVmtfn2lxvPmzvRS/dpdO/lL03lMUZY6oQdWG4ar/YBvlZGVLV72D/+nt2cpISrF6fmFq3LdxYaeQL0e+Jv1t8JjX1OG+ZurSppUzh1JsOaoGB+KP6I2Ppin+xEkFB1bTi4MGKLxmcOEM6hr86jcqtJ/9/P2jHHauN3+Y6LBz3Sy7mpeOUhSblyVNUWxeljRFtXlZkhTV5mVJU1SblyUNzUvANRTF5mVJU1Sbl4CzFdXmJeBsNC8dx+ZblqFDh15zK9a7775bIAkBAADXx3ZvlHTMtQEAAOxjs3nZqhXLngEAQMGgd4mSjrk2AACAfWw2L2NiYmw9BAAAAOAWMNcGAACwj83m5YwZM9SvXz+99dZb+T7+3HPPFVhSAADAtbFtHCUdc20AAAD72Gxeli5dWpJkMpmclgwAACgZ6F2ipGOuDQAAYB+bzcsHH3xQkvTUU085LRkAAACgJGCuDQAAYB+bzcuvvvrqmk/s06ePw5MBAAAlA9vGUdIx1wYAALCPzeblq6++qttuu021atVyZj4AAKAEoHeJko65NgAAgH1sNi9ff/11LVq0SLGxsYqOjlZkZKTKli3rzNwAAAAAl8RcGwAAwD42m5fdu3dX9+7ddeLECS1cuFC9evVS7dq1NWjQIIWHhzszRwAA4GLYNo6Sjrk2AACAfYzXCwgMDFT//v3Vt29fbdmyRbt373ZGXgAAwIUZDI77Aooz5toAAADXZnPlpdls1oYNG7RgwQIdPHhQHTt21Ny5cxUUFOTM/AAAAACXw1wbAADAPjabl/fdd58qVaqkbt266cknn5TBYFBmZqZiY2MlSWFhYU5LEgAAuBa2jaOkY64NAABgH5vNS3d3dyUlJenzzz/X9OnTZTab8x4zGAxatWqVUxIEAACuh94lSjrm2gAAAPax2bxcvXq1M/MAAAAASgzm2gAAAPax2bwEAAAoKGwbBwAAAGAPmpcAAMDpaF4CAAAAsIexsBMAAAAAAAAAgPyw8hIAADgdCy8BAAAA2OOGVl4+9NBDBZUHAAAoQQwGg8O+AFfBXBsAAMDaDTUvU1NTCyoPAAAAoERjrg0AAGDthraNu7u7F1QeAACgBGHBJGCNuTYAAIC1G2pezp07t6DyAAAAJQjbvQFrzLUBAEBxFh8fr+eff15JSUkqV66cJk6cqODgYKu45cuX66OPPpLZbJbBYND06dNVsWJFm+flhj0AAAAAAAAAbsmYMWPUu3dvde3aVYsXL9bo0aM1c+ZMi5g9e/Zo6tSpmjFjhipVqqSLFy/Kw8PjmueleQkAAJyOhZcAAABAwQit7Oewc124cEEXLlywOu7r6ytfX9+878+ePau9e/dq+vTpkqTIyEi9+uqrOnfunPz8ruTzxRdf6JFHHlGlSpUkST4+PtfNgeYlAABwOiPdSwAAAKDImzFjhqZOnWp1/KmnntKQIUPyvj99+rT8/f3l5uYmSXJzc1PlypV1+vRpi+ZlXFycAgMD1adPH6Wlpaldu3YaNGjQNS8rRfMSAAAAAAAAgJV+/fopJibG6vjVqy5vRG5urg4cOKDp06crKytLjz32mKpWraro6GibzzHezA+Kioq6qQQBAACky9vGHfV1I+Lj49WzZ0+1b99ePXv21JEjR/KNW758uaKiohQZGamoqCj9+eeftz5owE7MtQEAQFHh6+urwMBAq69/Ni8DAgKUkJCg3NxcSZeblImJiQoICLCIq1q1qjp06CAPDw95e3urTZs22r179zVzsLnyMjY21uaTzp8/f93BAQAA2FJYdxsvqIuIAzeKuTYAAHAlFSpUUN26dbVs2TJ17dpVy5YtU926dS22jEuXr4W5bt06de3aVTk5Odq8ebPat29/zXPbbF5GRkaqWrVqMpvNVo8lJSXd5FAAAAAKR0FeRBy4Ucy1AQCAqxk7dqyef/55ffjhh/L19dXEiRMlSQMGDNDQoUN1++23q3Pnzvrtt9/UqVMnGY1GNW/eXA888MA1z2uzeVmtWjV9/fXX8vf3t3qsRYsWtzgcAABQkhkduPDS3jsgFuRFxIEbxVwbAAC4mtDQUM2bN8/q+LRp0/L+bDQa9cILL+iFF16w+7w2m5f333+/Tp48me+Eql27dnb/AAAAgH9yZCPQ3jsg2utmLiIO3Cjm2gAAAPax2bwcNWqUzSe9/PLLN/XDvD25uXlhe6dLvcJOocRr/vqqwk6hxLunmt/1g1Dgbg8qW9gpwEXYewfEqy8i7ubmZtdFxD08PPIuIk7zEo5UEHNtFK7GfRsXdgol3lvPWK/2gfNVCbq5OxDDcaq3rFPYKQAOdVN3GwcAALgVjrzbuL13QLz6IuKSrnkR8Z9++klms1nZ2dnavHmz6tThTQAAAABQGGheAgAApzM48L8bMXbsWM2aNUvt27fXrFmzNG7cOEmXLyK+Z88eSVLnzp1VoUIFderUSdHR0QoLC7vuRcQBAAAAFAz2cQMAgBKjoC4iDgAAAKBg0LwEAABO58i7jQMAAABwXTQvAQCA0znybuMAAAAAXBfXvAQAAAAAAABQJLHyEgAAOB0LLwEAAADYg+YlAABwOiPdSwAAAAB2oHkJAACcjt4lAAAAAHtwzUsAAAAAAAAARRIrLwEAgNNxt3EAAAAA9qB5CQAAnI7eJQAAAAB7sG0cAAAAAAAAQJHEyksAAOB03G0cAAAAgD1oXgIAAKejdQkAAADAHmwbBwAAAAAAAFAksfISAAA4HXcbBwAAAGAPmpcAAMDpjPQuAQAAANiBbeMAAAAAAAAAiiRWXgIAAKdj2zgAAAAAe9C8BAAATkfvEgAAAIA92DYOAAAAAAAAoEhi5SUAAHA6to0DAAAAsAfNSwAA4HTcbRwAAACAPdg2DgAAAAAAAKBIYuUlAABwOraNAwAAALAHzUsAAOB0tC4BAAAA2INt4wAAAAAAAACKJFZeAgAApzOybRwAAACAHWheAgAAp6N3CQAAAMAebBsHAAAAAAAAUCSx8hIAADgddxsHAAAAYA+alwAAwOnoXQIAAACwR4lvXiYnJWnM6Je06eeNKl+uvIYOH6FOkVFWcWazWf+d/LYWzv9WkhTTrbuGj3w2b+XI/n37NHb0S4o/HKeaIaEaO/511alb16ljKa6Sk5M0Ydwr2rLpZ5UrV06Dhj6t9h0jreLMZrM+eG+yliy8XIOo6O56atjIvBo0jagnT0+vvDfEbdt30ktjXnXaOIq7sl6lNL7bv3RPWEUlpWbpvz8c0ne7z1jFfdyvgRrWKJf3vbubUfF/pirm/U2SpCFtQ9W6bmWFVCqjT9bG68PVcU4bQ3GXcjFZn015TXt2bJFP2XLq0X+w7mnVwSruu2+/1IaV3+ls4hl5+5ZV28gH1PmBvnmPTxg1SCeOxCk7O1uVqgSoe9+Banh3C2cOpVhLTk7S2Fde0qZNl38vDBk+Qp065/974d0pV34vRHfrruEjrvxeGD/2FW3ftlXHjh7V2FcnqGt0N6eOAwAKW/LFFE34aJq27t6jcj7eeqJ3T7W/t5lVnNls1odffaMlq9ZKkqJat9CTD/XKez09GH9EEz6apiMnTym4WlW9OGiAatcMdt5AijlH1eHuf/eRZ+nS+vuzp7bN7taLgwY4aRTFm5ePlx4Y8YBqNayt1ORUrfh8hX5dsyvf2KphVRU1KEpVw6opKyNLa2ev0cZFGyVJASEB6vJkVwWEBCgzLVNbl2/Rqq9WOXMoxZpbaQ8FtW0qn+pVlZueoVM/71LSwSNWcZUa1JNf3RB5+JRRTnqm/txzUH/s2GsRU/GOcFW6s65KmTyVfTFV8cvWKjPpopNGUnw56vXozY8/0869+3X8zBm9NGiAOrfivY6rK/HNywmvjZe7u7vWrNuo/fv3acjggapdp47CwmpZxH07b47WrF6peQsWSwaDnnjsYVULCvp/9u47rqmrjQP4LwkBEjaIDNkgo+7Z5da6Z4erjqrVqlWr1bZ2idb1Ompb22pbOyy1b1v3Hi2uaqsi4pahTFEEVIaQBELI+wc2mgZI9A0JhN+3n3xKbk4uz+EY7sNzz7kXQ4eNgLK0FDOnT8XLo8di2IiR2LTxV8ycPhW79h6A2NraTD2rO1YuXQQrsRh7D/6JpMQEzJ4xBY1DwxAUrD0G27dsxJ+HD2LDb9sAgQAzJk9Ao0Y+eP6l4Zo2P/22Fb5+/qbugkX4YEAElGVqdF56BOFeDlgzphUSbt1Dck6xVrvJP8ZpPf9hQlvEpNzVPM+4I8fHB65iWHsfk8RtSX78cgWsxGJ8+ct+pCcn4ePIWfALagwf/2Ctdmq1GpPnzIdvYAhysm5g2fvT4X01wTAAACAASURBVNrAA0936QkAGDX5TTTyC4RIZIVrCZew7L1pWPHtZji7NjBHt+qcpfePC4eO/oXEf44LYbrHhS33jwsbt1QcF6ZMHAcfH1+8NGwEACA0LBy9evfFZ6tWmKMbtR7vNk5k+T7+bj3EViLsWbcGV9PSMXvpCjQO8EeQr3aOsD36EP6MOYOfVi4BIMAbC5fC26Mhnu/ZA0plGd5ZvgpD+/XBC716YPsfB/HO8lXYuHoVxOJ6/6eMQYwxDv+IWrEEvl6eJu5B3Tdo2mCUKVVYNHQhvIK9MW7ROGSlZCEnPVurndRRivFLJmD3V7tw8dhFiKxEcGrwYNLA8HdH4PJfl/HNW1/DxcMFk1dNwc3km4g/GW/qLtVJPl3aQ60qx+VvN0PSwAVBA7tCcTsPirsFWu0EADJ+/xvy23mwcXJA8OBuUN4rRv7VdACAa5MQuDUJQcquwyi5WwBrJ3uoFKVm6FHdY6zfRyEBfuj+7FNYs+FXM/SCzKFe37BHJpMh+o/f8fr0NyC1s0PrNm3RuWs37N65Q6ftrh3bMWbseHh4esLDwwOjXxmHndu3AQBOn45BmaoMo8aMhbW1NV4eNQZqtRoxp06aukt1jlwuw+GDv+O1qTMgldqhZas26Ni5K/bt3qXTdu+uHRg5+hU09PBEw4YeGDl6HPbs2m6GqC2PRCzCc0088Hn0NchKVYhLz8fh+FwMbOld7fu8nW3RJsAFO89labbtOHsTx5Nuo7ikrKbDtigKhRyn/zqEF0a/BluJFGFNW6L1U53w18F9Om37vzQGASHhEIms4OXjj9ZPdcLVK+c1r/sFNoZIVPEHnUAggKqsDHdys3X2Q7rkDx8XpHZo1botOnfphj27dI8LO3dsx+iHjwtjx2Hnjm2a14ePeBlPPvU0rG1sTNmFOkMgMN6DiGofuUKBwydjMGn4S5BKbNEiIgwd27bG/qPHddruPXIMIwb0RUM3NzR0c8WIAf2w98ifAIC4K1dQpirH8H69YS0WY2jf3lCrgdhLl03dpTrJWONAj09sK0bTDk3xx4+/o1RRivTLabhy4gpad2+l07bjCx2RFJuEc4fOQaVUoVReitzrOZrXXTxccO7QWajL1bibdRdpl9PgEeBhyu7UWUIrEZxCfHHr5HmUK8tQnJWLgtRMuIQH6rTNibsCee5dQK1GSX4hClIyYeftrnnds30z3PjzDEruFz1LC4qgKmHxUh9j/j56sXdPtGvWFNZisSm7QGakt3iZlpaGESNGoFu3bgCAy5cv4/PPP6/xwEwhPT0NIpEQAQEPfmGFhYUj+do1nbbJ164iNDz8X+2u3n/tGkJDw7RuPtA4LAzXknX3Q9oy0tMgEong5x+g2dY4NAwpKbo/u5SUawgJDdNql/qvn/GUCWPQt0dHvDN7Bm7evFFjcVsa/wZSqNRqpN+RabYl3rqHEA/7at83qJU3zqTl4UaevKZDtHi3MjMgFIrg5fNg5rBvYGNkpqdU+z61Wo2kS+fQyD9Ia/vHkbMwfmAHzJ85DuHNWyOwMS9jYYh/jgv+Dx0XQqs4LqQkX0VYWPi/2l01SZxEZDksNdfOyLoFoVAIP28vzbaQAH+kZGbqtE29nonGAX6a540D/JB6vSKPS7l+AyH+vlp5drC/L1Ir2Q/pMtY4/GNq5EL0e3Uq5q74BFk5uTUXuAVxb+QOdbkat2/c1mzLSsmqtOjoF+EH+T0ZpnwyFR9s/BBjPxoLJ/cHMy//2nYcrXu0hlAkRAOfBvCP8Me1OP7NaQgbF8f7xcgHS7vluXmwdXOu5l0V7Bo1hOJORaFSbC+FtYMdbN2c8MS4IYgYOxieTzavsbgtibF/H1H9ord4OX/+fEyZMgUODg4AgIiICOzfv7/GAzMFuUwGe3sHrW329g6QyYp12spkMjjYPyjk2Ds4QCaTQa1WQyYrhr2D9n4c7O0hK9bdD2mTy2Sws9cukNnZO1T6s/v3eNnZ22vGAADWfhuFbXv/wG/b9sDdvSHmzJiCsjLO/jOE1FqEIoX2z6pIUQaptaja9w1s5Y0dcTdrMrR6o0Qhg9TOTmub1M4eCrmsindU2LphHcrV5ej0nPY1GWcv+ATfbD2COR99imZtnoJQWK8n2htMVtlxwcEBxZX8TqpoW/lxgfQTCARGexDVZZaaa8sVCthLpVrb7KUSyOSKStvaPdTWXiqFTKGAWq2uYj/SSvdDuow1DgCwZsEH2PrlZ/j1sxVo4OKCOf9ZiTKVqmY7YAGsJdZQFGv/vBXFCthIdFdmODZwQuvn2mDX2p34z8tLcfdWHka8N0LzevypBDTt2AwLdy/CnO/fwun9p5GZxEK+IYRiK6hKlFrbVKVKiPTM3PN8sjkEEOBufMV1/MX2FZ8RBz9vJP68G8lb/4BzaABcm4TUTOAWxJi/j6j+0fvX7L1799CpUyfNHwdCoRBiC5maK5FKUVxcpLWtqLgIUqmdTlupVIqiogd/vBYXFUEqlUIgEEAqtUNx0b/2U1SsU4ggXRVjoF0UKC4qqvRn9+/xkhUXa8YAAFq1aQux2BoODo6Y9da7uHnjBtJSq5+1RhVkpSrY2WhfN8rOxgqy0qoT0tb+zmhgb43fL3M5sjHY2Eoh/9eJE7msGLYSaRXvAP7YuRHHD+7BnAWfVHp9XSsrK7Ro9wwunjmJuJNc9mUIaSXHheKiIthV8jtJKpWiqLjy4wLpJzTig6gus9RcW2Jri2K59sqMYrkcUolt5W1lD9oWy+SQ2tpCIBA80n5Il7HGAQBaPREBsdgKDnZ2mDVuDG7m5CItkzOh9CmVl8JGql2otJXaoEReotO2rESJy39dRmZSJsqUZTj4UzQCmgTARmoLiYME4xePx6GfD+LDfh9gycglCG0biqcGPGWqrtRp5coyiKy1f7eKrMVQKZVVvANo0DwULuFBSNl1GGpVOQBAfb9gnxN3GapSJUrvFePOpatwDKj+cltk3N9HpJ+vl4PRHrWB3pxfJBJBqVRq/pFkZ2dbzAwef/8AlJWpkJ6eptmWlJiA4BDdsybBIY2RlJigeZ6YmIDg+zdvCA4JQVJSotZZgKtJiQgJ5tkXffz8A6AqK0PGQ2NwLSkRQUG6P7ugoBBcTUrUPL+alIDAan7GAgF4ZsZA6bdlsBIK4Of2oFAW5uWAa9lFVb5nUCtv/HElp9oCJxnO08cPKpUKt25kaLZlpCbB51/Lwf9x9MBO7NoYhXeXroGre/XXOipXqZCdxbPyhniU40JQsPZxIemh4wIRkaEsNdf28/KESqXC9axbmm1X0zIQ5KN7Q79AXx9cS09/0C49HYG+jQAAQb6NcC09QyunS07PQGAl+yFdxhqHyrCEYJjcG7kQioRw83bTbPMK8kJ2mu4EgKzUW8BD/9bVqPhaIABcPV2hLi9HXHQcysvLUXi7AOePnEdYu3Cd/ZCukrxCQCiAtdODQoykgQsUd/Irbe/6RDAatm2C5G3RUBY9WAmlyCtEuUoF8M/MR1aTv4/I8unNjEaOHIlp06YhLy8Pn3/+OUaOHInx48ebIrYaJ5VK0f2557Dm89WQyWQ4G3cGRw4dRP+Bg3Ta9h84CD9F/YDs7Gzk5GQjav0PGDh4CACgXbv2EAlF+O+GKJSWluKXnzcAANo/ybNg+kgkUnTp9hzWrf0CcrkM58/F4c+jh9Cn/wCdtn36D8QvG35ETk42cnNy8N+f1qPfgMEAKq49l5QYD5VKBZmsGKtXLYd7Qw8EBlZe+CFtcqUKf1zJxvTuwZCIRWjl54xuEe7Yea7yJeE2VkL0bOpR6ZJxK6EA1lZCCAWCh76u6R7Ufba2ErR9piu2/PQNFAo5ki6fR9yJP/Fs9z46bf86tB+bflyDd5Z8joZe2gfxm9fTcP703ygtUaCsrAx/HdqHhEtnEdGstam6UqdJpFJ07/Ec1n6xGvJ/jguHD6LfAN3jwoCBg7Dhx4eOCz/+gIGDhmheVypLUVJSArVajbKyMpSUlKC8vNyU3anVuGycqIKl5toSW1t0ebId1v22GXKFAucTEnHs9Bn07txBp22fzh3wy+59yLlzF7l38/DLrr3o26UTAKD1E09AJBRi494DKFUqsWnf7wCAtk2bmLQ/dZWxxiHleiaSUtOgUpVDJldgddTPcHd1RUAjzjbTR6momE353NieENuK4f+EP554pgniDp7VaXvm91g0ebYJvIK8IBQJ0f3l7ki9mApFsaLimpkCAVp0bQmBQAB7F3s079wcWSlZlXxX+rfyMhUKkq/D66kWEFqJYOflDqcgH+QlpOq0dQkLgNfTLZG87SBKC7Unc6jLVMhPSkfDNk9AKLaC2F4KtyYhKEzlLGR9jPX7CACUyjKUlJZCDTXKVCqUlJYyz7ZwArUBU9NiY2Nx+PBhqNVqdOvWDW3btn2sb6aohZcfLMjPR+SH7+HEib/h7OSMN2bNRt/+AxB3JhZTX5uIk7EVBxW1Wo1PP16BrVs2AwCef+FFzJz9luaPpvj4K1gw7wOkJF9DYFAw5i9cjIiIJ8zWr6rIa+EsuYKCfCye/wFiTp6Ak7MTps54E7369Me5uFjMmvYaDv99BkDFGHzx2cfYta1iDAYMeRHT3pgNgUCA2JiTWL7kI+RkZ0MikaBZi5aYNnOO1o2AaosOiw+aO4RKOUmssPD5png6xA0FslJ8cuAq9ly4hdb+zvh6bGu0++iQpm3f5p6Y1bMxnlt5TGc/i19ogsGttQtq72++hO1na8+1MX+Y8KS5Q6hU0b0CrPtkIS7FxcDB0QlDx72OZ7r2RuKls1jx4Ux8u+0oAGDWK4OQdzsHVuIHS8Wf7dYb46a/ixsZqfhm1Ue4mZEKoVAID29fDBz2Cto+29Vc3apSM18nc4dQqYKCiuPCyfvHhRmzZqNvv4rjwuuTJ+LE6YeOC6tWYNv948KQF17EzDcfHBcmvDIaZ2JjtPa97vsotGtfu/79Scy0OnXmjgT9jQz06SDOOqG6zRi59t0LsTUQ2f+n4F4Rlqz9BjEXLsHJ3h5TXh6GXh2fxbn4BLy5eDkObfgeQMXv0y83/IKdB48AAAZ274LXR43Q/D5NTE3D0rXrkJp5AwE+jfDelIkICwwwT6fqIGOMQ+zFy1ix7gfk3L0LiY0NmoU1xrTRI+Hr5WnGnulaPmeTuUOolMRBghdnv4TGrRtDVijDvu/24fzhcwhoGoBxi8cjctA8Tdsn+z+FbiO7wdpGjLTLadj++XYU5FbcLCa4ZTB6T+gDdx93KEuUiD8Zj11rd0JZUvXSZ3MY3r+ZuUOolMjGGn49noa9nxdUihLc/Oss8pPSYOftjqCB3XDxq98AABFjB8PaXloxw/K+vMRUZB6uyOuE1mL4dnsSjgGNoCopxZ3L15Adc9EsfaqKX5famRsZ67gwNXIRzl6J19r3l/PfR+smtasG49r88WpnxrD/rTVG21fvFVONtq/Hpbd4uWPHDgwaNEjvNkPUxuJlfVMbi5f1TW0tXtYntbV4Wd/U1uJlfcPiJZF5GSvXro3FSyJTq63Fy/qmthYv65PaWrysb1i8NB69y8bXr19v0DYiIiIiQwkFxnsQ1WXMtYmIiIiqZ1XVCxcvXsSFCxeQl5eHn3/+WbO9qKgIymruyEVERESkD69VSfUdc20iIiIiw1RZvMzOzsalS5cgl8tx6dIlzXY7OzssXbrUJMEREREREVki5tpEREREhqmyeNmjRw/06NEDx48fR4cOund/IiIiInpcXO5N9R1zbSIiIiLDVFm8/EeHDh2QkpKChIQElJaWarYPHjy4RgMjIiIiy8VV40QVmGsTERERVU9v8TIqKgq//fYbcnNz0axZM8TGxqJdu3ZMqIiIiIiI/k/MtYmIiIiqp/du4xs3bsSmTZvg5eWF7777Dps2bYKTk5MpYiMiIiILJRQIjPYgqsuYaxMRERFVT+/MS2tra0ilUpSXl0OtViM0NBQZGRmmiI2IiIgslN6zp0T1BHNtIiIiourpLV5KJBIolUqEh4djxYoV8PLygkKhMEVsREREREQWjbk2ERERUfX0TnyIjIyEUqnE3LlzUVBQgNOnT2P58uWmiI2IiIgslEBgvAdRXcZcm4iIiKh61c68VKlU2L9/P2bMmAGpVIrFixebKi4iIiKyYLxWJRFzbSIiIiJDVDvzUiQS4fTp06aKhYiIiIio3mCuTURERKSf3mXjXbp0wXfffYc7d+5ALpdrHkRERESPi8vGiSow1yYiIiKqnt4b9qxYsULzf4FAALVaDYFAgPj4+BoPjoiIiCyTkEVHIgDMtYmIiIj00Vu8TEhIMEUcRERERET1DnNtIiIiourpXTYOAKmpqYiOjgYAFBcXIz8/v0aDIiIiIssmFAiM9iCq65hrExEREVVNb/Fy69atmDJlCpYuXQoAyM7OxsyZM2s8MCIiIrJcvOYlUQXm2kRERETV01u8jIqKwpYtW+Dg4AAACAoKwu3bt2s8MCIiIiIiS8dcm4iIiKh6eq95KRaLYWdnp7VNJBLVWEBERERk+XjDHqIKzLWJiIiIqqe3eOns7IzU1FQI7q/L2rFjBzw9PWs8MCIiIrJcArB6SQQw1yYiIiLSR2/x8r333sPs2bORmpqKbt26wdbWFl999ZUpYiMiIiILxZmXRBWYaxMRERFVT2/xMjAwEJs2bUJaWhrUajUCAwO5lIWIiIiIyAiYaxMRERFVr8ri5bVr1yrdnpqaCgAICQmpmYiIiIjI4nHmJdV3zLWJiIiIDFNl8XLSpEkQCARQq9XIysqCvb09AODevXvw9vbGoUOHTBYkERERWZZ/ru9HVF8x1yYiIiIyTJXFy38SpkWLFqFNmzbo06cPAGD//v24cuWKaaIjIiIiIrJAzLWJiIiIDCPU1+DChQuaZAoAevfujZMnT9ZoUERERGTZhALjPYjqMubaREREZClSU1MxbNgw9OrVC8OGDUNaWlqVbVNSUtCiRQssW7ZM7371Fi/lcjliY2M1z2NjYyGXyw2LmoiIiKgSAoHxHkR1GXNtIiIishSRkZEYOXIkDhw4gJEjR2LevHmVtlOpVIiMjESPHj0M2q/eu41HRkbizTffhEQiAQCUlJTg448/foTQiYiIiIioMsy1iYiIqDYrLCxEYWGhznZHR0c4Ojpqnt+5cwdXrlzBDz/8AADo378/Fi5ciLt378LV1VXrvd988w26dOkCmUwGmUymNwa9xcu2bdsiOjoaqampUKvVCAoKgrW1td4dExEREVVFaKYpk6mpqZg7dy7y8/Ph7OyMZcuWISAgoNK2KSkpGDJkCEaOHIl33nnHtIFSvcFcm4iIiGqzH3/8EV988YXO9mnTpmH69Oma51lZWfDw8IBIJAIAiEQiNGzYEFlZWVrFy4SEBBw/fhxRUVFYs2aNQTHoLV4CQEZGhmY5i1gsRnBwsEE7JyIiIqqMua5V+c9SlkGDBmHHjh2YN28eoqKidNo96lIWov8Hc20iIiKqrcaOHYshQ4bobH941qWhlEolPvzwQyxdulRT5DSE3uLl9u3bsXLlSnTp0gUA8PXXX2POnDkYOHDgIwdJREREZGy1YSkL0eNirk1ERES12b9z6qp4eXkhOzsbKpUKIpEIKpUKOTk58PLy0rTJzc1FRkYGJk2aBKAij1er1SgqKsLChQur3Lfe4uX333+Pbdu2wd3dXfONJkyYwISKiIiIHpsxV43XhqUsRI+LuTYRERFZAjc3N0RERGD37t0YNGgQdu/ejYiICK0829vbG6dOndI8//zzzyGTyfReosmgZeP/JFP//prqHom14dNyqWbsmdXJ3CHUe0O+/NvcIRCAqAntzR0CAWjmY2+W7yuE8aqXo2vBUhai/wdzbSLjmDqNeXZtcGDjJXOHUO+5BriYOwQC4Nrc3BGY3vz58zF37lysWbMGjo6OWLZsGQBg4sSJmDFjBpo1a/ZY+9VbvPTz88Pq1asxbNgwCAQCbNy4Eb6+vo/1zYiIiIiMrTYsZSF6XMy1iYiIyFIEBwdj06ZNOtvXrVtXafuHV0lVR2/xcsGCBVi0aBEGDhwIgUCAZ555Bh999JFBOyciIiKqjDluNl6TS1mIHhdzbSIiIqLq6S1eurm54ZNPPjFFLERERFRPmOtu4zW1lIXocTHXJiIiIqpelcXLo0ePVvvGzp07Gz0YIiIioppUU0tZiB4Vc20iIiIiw1RZvHzttdcQGhoKZ2dnqNVqrdcEAgETKiIiInpsQnOsGyeqRZhrExERERmmyuLl1KlTsW/fPjg7O+OFF15Ax44dIRQKTRkbERERWSjWLqm+Y65NREREZJgqM6QZM2Zg3759ePnll7Fv3z706dMHK1aswM2bN00ZHxERERGRxWGuTURERGQYvTfsefLJJ9G+fXv8/vvviIyMhLu7O1555RUThEZERESWisvGiSow1yYiIiKqXrXFy+TkZGzduhXR0dFo1aoVPv30Uzz11FOmio2IiIgsFGuXRMy1iYiIiAxRZfFy6NChUKlUGDJkCP773/9CKpUCAORyOQBAIpGYJkIiIiIiIgvDXJuIiIjIMFUWLy9cuAAAuHz5MhYvXqzZrlarIRAIEB8fX/PRERERkUXibUmovmOuTURERDXF09fR3CEYVZXFy4SEBFPGQURERPWIgOvGqZ5jrk1ERERkGE58ICIiIiIiIiIiolpJ793GiYiIiIyN8y6JiIiIiMgQLF4SERGRyQm5bJyIiIiIiAzAZeNERERERERERERUKz1S8fLNN9+sqTiIiIioHhEY8UFkKZhrExEREel6pGXjqampNRUHERER1SNcNU6ki7k2ERERka5HmnmpVqtrKg4iIiIionqNuTYRERGRrkeaebl+/foaCoOIiIjqEwGnXhLpYK5NREREpOuRipfOzs41FQcRERHVI7xjIJEu5tpEREREuvi3AxEREREREREREdVKjzTzkoiIiMgYuGyciIiIiIgMweIlERERmRxLl0REREREZIgql43n5eXh/fffx/jx4/Hzzz9rvTZ9+vQaD4yIiIiIyFIx1yYiIiIyTJXFy8jISDg5OWH48OGIjo7GtGnTUFZWBgC4fv26yQIkIiIiyyMQCIz2IKqLmGsTERERGabK4mV6ejrefvtt9OzZE99//z3c3d3x2muvoaSkxJTxERERkQUSGvFBVBcx1yYiIiIyTJU5f2lpqeZrgUCAyMhIhIaGYtKkSUyqiIiIiIj+D8y1iYiIiAxTZfHS19cXp0+f1tr2zjvvoGXLlkhLS6vpuIiIiMiCcdk41XfMtYmIiIgMU+XdxpcvX17pHwSzZs3CgAEDajQoIiIismwsOVJ9x1ybiIiIyDBVFi+dnZ2rfFNISEiNBENEREREVB8w1yYiIiIyTJXFSyIiIqKawtXeRERERERkCBYviYiIyOSEXDhOREREREQGqPfFy4L8fETOex8n/v4LLs4umDHzTfTtr3udIbVajU9XrcS2LZsBAEOefwEzZ7+luVZRQnw85s97H6kpyQgMCsb8jxYjPCLCpH2pqzgGtUNhYQE+WRqJMzEn4OTkgnGTZ6Bbz7467c6dicHPP3yNa0kJcHBwRNSWfZrX8vPuYO2ny3Hh7BkoFHIEBIXgtemzEd6kuSm7Umc52lohcmAEng52Rb5MidUHk7H/UnalbcM97fFW71CEe9lDXlqO746n4ZdTmQCAFj6OmNM7FIENpLiRr8DSPYk4d73AlF2ps+4VFmDtyo9w/sxJODg64+VXp6Fj9z467S6dPY1NP61D6rUE2Nk7Yu1/d2u9nnD5PNZ/+TEyM1LR0NMbE9+Yi4hmrUzVDSKiWqHgXhGWrF2HmAsX4exgj8kjh6FXx2d12qnVaqz5+VfsPHgEADCgW2e8PmqEJsdLSk3DkrXrkHbjJgIaeeO9KRMRGhhguo7UcRwH8yuUFWPVxt9wJikJjnZ2mNC3L7q1aqPTbuORQ/gjNhbZ+XlwktphwDPPYGiXbprX53z1JdJu3YKyrAyerq4Y27MPnmna1JRdqdNs7GzRaWIfNGoaAEWRHKd/O4rkE/E67Vo//yxaDXwaqjKVZtuWd7/HvdyKfFogEKDNCx0Q2rkZxLbWKMzOx54lv6BUVmKyvtRVxvosrN+/D39dvoiMnBy83L0HxvTsbcpukBnU++LlkkUfQSwW4/DRv5CQEI/pU19DaHg4QkIaa7XbvOk3HD4UjU1bdwACASa/Og6NfH0xdNgIKEtLMXP6VLw8eiyGjRiJTRt/xczpU7Fr7wGIra3N1LO6g2NQO3z58RJYWYnx267DSL6agA/fmo6gkFAEBGlfd8tWIkGv/oPRtaQEv0Z9p/WaXCZHaEQTTJo+B84urjiwexs+fGs6ojbvg0QqNWV36qR3+4ZBqSpH95XHEeZpj9UjWyApuwgpucVa7ZwlYnw5qiVWHriK6Cs5EIuE8HC0AVBRAP1kRHMs2ZOIQ/G56N3UA5+NaI7+q0/gnqLMHN2qU75dvQxWYjG+3fwH0q4lYun7byAgOBS+AcFa7WwkEnTrMwilJb2x9b/fa712r7AAyz6YhYkz38WTHbrhr0MH8J8PZuHLDTth7+Boyu7Ualw2TmT5Pv5uPcRWIuxZtwZX09Ixe+kKNA7wR5Cvj1a77dGH8GfMGfy0cgkAAd5YuBTeHg3xfM8eUCrL8M7yVRjarw9e6NUD2/84iHeWr8LG1asgFtf7P2UMwnEwv8+3bYWVlRU2Ri5A8s0beP/7bxHk1QgBnp5a7dRq4O3hIxHk5YWbd+5g7rqv4e7sgq4tK06ATh04BP4eHhCJRIjPSMc733yFH95+F26OzC8M8cwrz0FVpsKG17+Am39D9J7zEu5m5CLvxm2dtsmnEnBk7e5K9gK0eaEDPBo3ws75G1B0pxAuPg2gUjLPNoSxPgveDRpgYr8B2H3ib3N0g8xAaGjDu3fv1mQcZiGTyRD9x+94bdB2UQAAIABJREFUffobkNrZoXWbtujctRt279yh03bXju0YM3Y8PDw94eHhgdGvjMPO7dsAAKdPx6BMVYZRY8bC2toaL48aA7VajZhTJ03dpTqHY1A7KOQyHD8SjbETX4dEKkXTFq3xdIfOOHhA94Ad/kQz9Og9AJ7ePjqveTXywQvDx8CtgTtEIhH6DnoRZUolMjPSTNCLus1WLET3J9yx5nAK5EoVzl0vwNHE2+jf3FOn7ainffH3tbvYdzEbSpUaslIVUm/LAAAtfJ1wt6gU0VdyUa4G9l7MRp5Mie4R7qbuUp2jkMtx6thBDH9lCiQSKSKatULbpzvj6B97dNo2Dm+Kzs/1g4dXI53XEi+fh5OLG57p/BxEIhE6PdcXjk4uOHXskCm6UWcIjPgfkSWwtFxbrlDg8MkYTBr+EqQSW7SICEPHtq2x/+hxnbZ7jxzDiAF90dDNDQ3dXDFiQD/sPfInACDuyhWUqcoxvF9vWIvFGNq3N9RqIPbSZVN3qU7iOJifvLQExy9ewCu9ekNiY4OmgUF4+okmiI6L1Wk7rGs3NPbxgUgkgm/DhnimSRNcTkvVvB7k7Q2RSAQAEAAoU6mQm59nqq7UaVY2YgS2C8OZzcdQVqJEdtINpMddRUiHJo+0H2upDZr2bos/v9uPojuFAIC8zNtQKVV63knG/Cz0bNsO7cMjILWxMWUXyIz0Fi/Pnz+Prl27YsiQIQCAixcv4sMPP6zxwEwhPT0NIpEQAQGBmm1hYeFIvnZNp23ytasIDQ//V7ur91+7htDQMM2SCgBoHBaGa8m6+yFtHIPaIfN6OoRCEXz8AjTbAkPCkJ6a/H/tNzkpAcoyJbx9fP/PCC2fv5sUqnI1Mu7KNduSsu8hyN1Op20zHycUKpRYP74NDs7pgE+HN4fn/ZmXAgG0PgdARXIb3FB3P6TtZmbF58Db11+zzT+4Ma6npTzajtSoOF2stUmNjLT/7/NERJbJUnPtjKxbEAqF8PP20mwLCfBHSmamTtvU65loHOCned44wA+p128AAFKu30CIv6/WsS3Y3xepleyHdHEczO9Gbi6EAgF83BtqtgV7eSP91q1q36dWq3ExNRX+Htonsj/4/lv0ffdtTP/8M7QICkYo82yDOHm6QF1ejoJbD4q9dzJy4dKoQaXt/VuFYPRXM/DifyYgontLzXZXX3eUq8oR1D4ML3/xOoaumIgnevDSQIYw9meB6he9xculS5di3bp1cHFxAQA0a9YMcXFxNR6YKchlMtjbO2hts7d3gExWrNNWJpPBwd7+QTsHB8hkMqjVashkxbB30N6Pg709ZMW6+yFtHIPaQS6Tw+6hny0A2NnbQy6TPfY+i4uLsHzh+xg1bjLs/jXGpEtqLUJRifZyk6ISFexsRDptPRxtMKCFJ5bvT0KfT/7GzXw5lr5Qcdb4/PUCuDtYo3dTD1gJBRjQwhM+rhLYinX3Q9oUcjmkdtqfA6mdPeTyR/schDVpjrt3cnH80H6UlSlx5MAuZN/MRKlCYcxw67yKQrtxHkR1maXm2nKFAvb/umSMvVQCmVz3d6FcoYDdQ23tpVLIFAqo1eoq9iOtdD+ki+NgfvKSUtjZSrS22UlsISup/vqIUb8fgFpdjl7t2mttXzT+VexctBSLJ0xE27BwCIUGL6as18S21jrXpCyVlUAs0b3EWMrJBGx6+1tsmPI5/vxuP1oPeRbBT1fcS8HO1QE2drZw8nTFr7O+RvTq7Wj9fAc0ahpgim7Uacb+LFD9ovc3nVKpREiI9jXvxGJxjQVkShKpFMXFRVrbioqLIJXqzlCSSqUoKnpQCCsuKoJUKoVAIIBUaofion/tp6gYUjvOdNKHY1A7SKQSnUKvrLjosa9TWVKiQOTbMxDepDmGj5lgjBAtnqxUBTsb7WtG2duIUFyiuwSlRKnCofjbuHLzHkpV5fj6aCpa+jnD3kaEAnkZZv16EaOe8kX0nA54JtgNp1LykFPIC4jrYyuRQCbT/j0iLy6GRPJonwMHJ2e8s3AVdm3+Ga++2BNnT59As9bt4frQWWaquNu4sR5EdZml5toSW1sUy+Va24rlckgltpW3lT1oWyyTQ2prC4FA8Ej7IV0cB/OT2FhDVqJd5C1WlFS73HX7X8cQfSYWi8ZPhLWV7jVFrUQitA+PQGxiAv6+fMnoMVsipaIU1hLtn7m1xBpKealO2/ybdyDLL4JarUbO1Ru4dCAWge3CAEBzbcu4bX9BpSzD3eu5SDkZD98WQTXfiTquJj4LVH/oLV5aW1ujuLhYs0Tg2rVrsLGQ6wr4+wegrEyF9PQ0zbakxAQE/yuBBIDgkMZISkzQPE9MTEDw/RvKBIeEICkpEeqHlgleTUpESLDufkgbx6B28PH1h0pVhhvX0zXbUq4lwT8wuJp3Va60tBQL5s6Em3tDvPF23V/2Zirpd2SwEgrg5/rgbGSoh73OzXoAICm7GGo8+Lf+4J99xe/pM+n5GPVtLLosP4YPtl1BgJsUl24U1mT4FsHbxx/lKhWyMjM029JSrsI34NGT0SYt2mDZmp+wfvthzHj3I9y8no7G4Y92TSUiqh8sNdf28/KESqXC9awHywGvpmUgyEf3mtmBvj64lv4gB7mano5A34prCgf5NsK19AytHC85PQOBleyHdHEczK+RuztU5eXIzM3VbEu5eRP+npUvgd0fcwq/HT6E5a9Ngbuzc7X7VpWXI+vOHaPGa6kKbuVBIBLC0cNFs83Nr2GlN+vRocY/aTbuZOTe36Suuj1VqiY/C2T59BYvJ0+ejAkTJiAnJwdz587F2LFj8cYbb5githonlUrR/bnnsObz1ZDJZDgbdwZHDh1E/4GDdNr2HzgIP0X9gOzsbOTkZCNq/Q8YOLji2kTt2rWHSCjCfzdEobS0FL/8vAEA0P7Jp0zan7qIY1A72EqkeLZzd0R9uwYKuQyXL5zFiWNH0L1Xf5225eXlKC0pgaqsDGq1GqUlJVAqlQCAsjIlFn0wG9Y2tnj7g0VcxvIIFMpyHIrPxZQuQbAVC9HC1wmdw9yx+4LuNWB2nstCt3B3hHrYw0oowMTOAYhLz9csOw/zrNhuZy3CrJ4hyC5U4ESyZd0IoibYSiRo36Ebfl3/FRRyORIunUPs30fQ+bl+Om3Ly8tRWlqCsn8+B6UPPgcAkHI1AWVlSsiKixD11adwc/dAy3bPmLA3tR+XjRNVsNRcW2Jriy5PtsO63zZDrlDgfEIijp0+g96dO+i07dO5A37ZvQ85d+4i924eftm1F327dAIAtH7iCYiEQmzcewClSiU27fsdANC2KU8IGYLjYH4Saxt0aNoMP/6+H/LSElxKTcXfVy6hR+u2Om0Pxp3B9/v24j8TJ8PLzU3rtYycbMQkxKNEWYoylQrRZ2JxMTUFzYMefbJBfVRWokTa6SS0ebEDrGzE8GjcCP5tGuPacd2bTvm3DoG1tOIkknuQF5r0bIP0uIp7KdzLyUdWwnW0GvQMhFYiOHu7IejJcGSc47XN9THWZwGouFlVqVKJcrUaqvJylCqVUJWXm6IbZCYCtVqt95TB9evXcezYMajVanTo0AH+/v763lIpRZn+NqZWkJ+PyA/fw4kTf8PZyRlvzJqNvv0HIO5MLKa+NhEnY88CqLhI7Kcfr8DWLZsBAM+/8CJmzn5Lc5Y8Pv4KFsz7ACnJ1xAYFIz5CxcjIuIJs/WrLqlvY3Arv3ZeG6iwsACrlkQi7vQJODo5Y/zkN9CtZ19cPBeHD+ZMxY7oiju3n487jbenv6r13uat2mLFF9/hwtlYvDVtAmxsbCEQPqgoLFq5Bs1atjZpf6oz5Mu/zR1CpRxtrTB/UASeCnJFvlyJ1dHJ2H8pG638nPDFyy3w7NI/NW1fatsIr3YMgK1YiLMZBVi6NxHZ95eGL32+CZ5tXHGQ//vaHSzbl4Q8mbLS72lOURNq33Vr7hUWYM2KBbgQdwoOjk54+dXp6Ni9D65cOIsl707Hhj0Vd2e9dC4W82e/pvXeJ1q0wUervgEAfLLoPZyNqWjbst0zmDDtbTi5uJq2MwZq5mOvv1EN+D0+V38jA/WMcDfavojMwRi59t0LundrNbeCe0VYsvYbxFy4BCd7e0x5eRh6dXwW5+IT8Obi5Ti04XsAFTnelxt+wc6DRwAAA7t3weujRmhyvMTUNCxduw6pmTcQ4NMI702ZiLDAAPN0qg6qT+NQlJZt7hAqVSgrxscbf0NcUhIc7KR4tW8/dGvVBhdTUvDed99g1+L/AABGL1mE3IJ8iB9aHtu9dRvMfOElpGdnY+VvvyA9JxtCgQCNGrhjRLfu6NCsubm6VaUDG2vnUnYbO1t0mtgHjZoGoKRIgZjfjiD5RDw8w3zQ+62XsP7VTwAAXV8fAJ+mgRCJRSi+ew9Xos/i8u9nNPuRutij08Q+8Az1gbxQhvO7TyLh0HlzdatSvYY2NXcIlTLGZwEAlv/6C/44c1pr33OGDq9118X0G6g7CcJUzq3eYLR9tZwxymj7elzVFi9VKhVef/11fPXVV0b5ZrWxeElkarW1eFmf1NbiZX1TG4uX9ZG5ipd/xBuwTMtAz0VUfqdQotrOmLl2bSxeEplabS1e1je1tXhZn9TW4mV9w+Kl8VS7plMkEkGhUKCc02+JiIiIiIyKuTYRERGRfnpv19SiRQtMmzYN/fv3h91Dd27u3LlzjQZGRERElkvIa1USAWCuTURERKSP3uJlXFwcAOCXX37RbBMIBEyoiIiI6LEJwOolEcBcm4iIiEgfvcXLn376yRRxEBERERHVO8y1iYiIiKpX7TUvgYq7zm3atAkrV64EAGRmZmrOEBMRERE9DoHAeA+iuoy5NhEREVH19BYvly5dipMnTyI6OhoAYGdnhyVLltR4YERERGS5BEb8j6guY65NREREVD29xctTp05h5cqVsLW1BQC4uLigpKSkxgMjIiIiIrJ0zLWJiIiIqqf3mpc2NjYQPLQmq7y8vEYDIiIiIsvHu40TVWCuTURERFQ9vcXL0NBQ7Ny5E2q1GpmZmfjmm2/Qpk0bU8RGREREForLvYkqMNcmIiIiqp7eZeNz585FTEwMcnNzMXToUJSXl+Ptt982RWxERERERBaNuTYRERFR9fTOvLS3t8eiRYtMEQsRERHVE7xLOFEF5tpERERE1dM787JHjx5Yu3Ytbt26ZYp4iIiIqB4QGPFBVJcx1yYiIiKqnt7i5dq1a1FYWIiXXnoJ48aNw65du3gHRCIiIiIiI2CuTURERFQ9vcXLxo0b45133sGRI0cwZswY7Nu3Dx07djRFbERERGShhAKB0R5EdRlzbSIiIqLq6b3m5T+Sk5MRExODixcvokmTJjUZExEREVk4lhyJtDHXJiIiImNxDXAxdwhGpbd4GRUVhe3bt6O4uBiDBw/Gxo0b4eXlZYrYiIiIiIgsGnNtIiIiourpLV4mJibivffeQ9u2bU0RDxEREdUHnHpJBIC5NhEREZE+eq95uXjxYoSHh+Py5cumiIeIiIjqAYER/yOqy5hrExEREVVPb/Hy6NGj6NevH6ZNmwYAuHjxIiZPnlzjgRERERERWTrm2kRERETV01u8XL16NTZv3gwnJycAQLNmzZCRkVHjgREREZHlEgiM9yCqy5hrExEREVXPoLuNu7u7az23traukWCIiIiofmDNkegB5tpEREREVdM789LOzg63b9+G4P7UhlOnTsHBwaHGAyMiIiIisnTMtYmIiIiqp3fm5Zw5czBx4kRkZmZi9OjRSEtLw9q1a00RGxEREVkqTr0kAsBcm4iIiEgfvcXL5s2bIyoqCnFxcQCAVq1awdHRscYDIyIiIsvFu4QTVWCuTURERFQ9vcvGAcDBwQGtWrWCUqnEzZs3azomIiIiIqJ6g7k2ERERUdWqLF7OmTMHCQkJAID8/HwMGDAAn3zyCcaPH49NmzaZLEAiIiKyPLzbONV3zLWJiIiIDFNl8fLKlSsIDw8HAOzYsQPBwcHYs2cPtm7dig0bNpgsQCIiIrI8AiM+iOoi5tpEREREhqnympc2Njaar8+cOYMePXoAADw9PTV3QyQiIiIiokfHXJuIiIgsTWpqKubOnYv8/Hw4Oztj2bJlCAgI0Grz5ZdfYu/evRCJRLCyssKsWbPQsWPHavdb7TUvs7OzoVAoEBMTg/bt22u2l5SUPH5PiIiIiDj1koi5NhEREVmUyMhIjBw5EgcOHMDIkSMxb948nTbNmzfH5s2bsXPnTixZsgSzZs2CQqGodr9VzrycNGkSBg8eDLFYjDZt2iAkJAQAcO7cOXh7e/+f3SEiIqL6zFx3G6+ps8FEj4q5NhEREdUFhYWFKCws1Nnu6OgIR0dHzfM7d+7gypUr+OGHHwAA/fv3x8KFC3H37l24urpq2j2cV4eFhUGtViM/Px+enp5VxlBl8bJPnz5o27Ytbt++rbkeDwB4eXlh4cKFBnaRiIiIqPb452zwoEGDsGPHDsybNw9RUVFabZo3b47x48dDIpEgISEBo0aNwvHjx2Fra2umqMkSMdcmIiKiuuDHH3/EF198obN92rRpmD59uuZ5VlYWPDw8IBKJAAAikQgNGzZEVlaWVvHyYdu3b4efn1+1hUugmuIlALi7u8Pd3V1rm4eHR7U7JCIiItLHmJf0qw1ng4keB3NtIiIiqu3Gjh2LIUOG6Gx/OM9+HDExMfjss8/w/fff621bbfHS2NRqU343qgyv/25+DhKTfuyoEnP6h5o7BALw/t54c4dAAHZOameW72vMw1FtOBtMREREFdq1b2TuEOq9K8fSzR0CAfAbaO4IjOPfEwKq4uXlhezsbKhUKohEIqhUKuTk5MDLy0un7dmzZ/HWW29hzZo1CAoK0rtvVlGIiIioTqsNZ4OJiIiIiOozNzc3REREYPfu3Rg0aBB2796NiIgInUkCFy5cwKxZs7B69Wo0adLEoH2zeElERESmZ8Spl7XhbDARERERUX03f/58zJ07F2vWrIGjoyOWLVsGAJg4cSJmzJiBZs2aYcGCBVAoFFp3Il++fDnCwsKq3O8jFS9HjRqFDRs2PGYXiIiIiCqY427jNXk2mMgYmGsTERFRXRYcHIxNmzbpbF+3bp3m6y1btjzyfoWP0ri4uPiRvwERERFRbTF//nxs2LABvXr1woYNG7BgwQIAFWeDL168CABaZ4MHDRqEQYMGITEx0ZxhUz3BXJuIiIhI1yPNvBSLxTUVBxEREdUj5rqBXE2dDSYyBubaRERERLoeqXi5cePGmoqDiIiI6hEz1S6JajXm2kRERES6HmnZOBEREREREREREZGp8G7jREREZHqceklERERERAZg8ZKIiIhMzhx3GyciIiIiorrnkZaNFxQU1FQcRERERET1GnNtIiIiIl1VFi8TEhLw/PPP48UXX0RycjImTZqETp06oXPnzoiPjzdljERERGRhBALjPYjqIubaRERERIapsni5aNEivP766xg1ahReffVV9O/fH+fPn0dkZCSWLVtmyhiJiIjIwgiM+CCqi5hrExERERmmyuJlcXExunfvjsGDBwMABg4cCADo1q0b8vPzTRMdEREREZEFYq5NREREZJgqb9ijVqs1Xz/77LNar5WXl9dcRERERGT5OGWS6jnm2kRERESGqXLmZaNGjVBUVASgYlnLP27dugWJRFLzkREREZHFEhjxP6K6iLk2ERERkWGqnHn55ZdfVrrd0dERa9asqbGAiIiIiIgsHXNtIiIiIsNUWbysilQqhVQqrYlYiIiIqJ7gXcKJKsdcm4iIiEjbIxcviYiIiP5frF0SEREREZEhqrzmJREREREREREREZE5ceYlERERmR6nXhIRERERkQFYvCQiIiKT413CiYiIiIjIECxeEhERkcnxhj1ERERERGQIXvOSiIiIiIiIiIiIaiXOvCQiIiKT48RLIiIiIiIyBIuXREREZHqsXhIRERERkQG4bJyIiIiIiIiIiIhqJc68JCIiIpPj3caJiIiIiMgQLF4SERGRyfFu40REREREZAguGyciIiIiIiIiIqJaiTMviYiIyOQ48ZKIiIiIqGbYB3iYOwSjYvGSiIiITI/VSyIiIiIiMgCXjRMREREREREREVGtxJmXREREZHK82zgRERERERmi3hcvCwryMf/D93HixF9wcXbB9Jlvom+/ATrt1Go1PvtkJbZt2QwAGPz8C5j55lsQ3L9dakJCPBbMex+pKckIDApG5EeLER4eYdK+1FUF+fmInPc+TvxdMQYzZr6Jvv0rH4NPVz0YgyHPv4CZsx8ag/h4zH9oDOZ/tBjhERwDQxUW5GPJR/MQc+JvODk7Y8r0mejZp79OO7VajTWrV2HX9i0AgAGDnsfUN2ZrxkGlUuHbr77Anh3bIJMVo5GvH7745gc4ODiatD91kbyoEDu/WYmUi2cgdXBEt2Gvotmz3XXandy3BTH7t0FWVABrGwmaPN0Fz418DUKRSNPm1L4tOLV/K4oL8+Hk1hDDZn8ENy9fU3anTrK3EWF6p0C08nFEoaIMUTGZ+DP5rk67EW288VIrLyhVas22GZsvI/teCQCgubcDxj3lCy9HWxQqlNhy7hYOJOSarB91Ae82TmT5Cu4VYcnadYi5cBHODvaYPHIYenV8VqedWq3Gmp9/xc6DRwAAA7p1xuujRmhyi6TUNCxZuw5pN24ioJE33psyEaGBAabrSB1njHHIL7yHt5d/jPQbWSgvL0dAI29MGzMSLcLDTNybuqlQVoxVG3/DmaQkONrZYULfvujWqo1Ou3PXrmJD9O+4euMGHCQSbHjvQ81rOXl5mLBymVZ7RWkpJvUfiJc6d6npLlgEkY01fHs8BQc/b6jkCtz8+xzyk9J02rm3DEeDFmGwktigvLQMeVfTcfN4HKBWw0pig0ad2sG+UUMIxVaQ38nHzWNnIMu+Y/oO1UFiiQ2aDu0Kt1BfKIsVSNp7ElnnrlbZXiAS4tk3h8HKWowji6MAAC6BXmgzQfvvVCsbMc5G7Uf2xZQajZ/Mp94XL5cu+ghisRiHjv6FxIR4TJ/6GkLDwhES0lir3ZZNv+HwoWhs3LIDEAgwZeI4+Pj44qVhI6BUlmLW9KkYOXoshg0fic0bf8Ws6VOxc+8BiMXWZupZ3bHk/hgcPvoXEv4Zg3DdMdh8fww2ba0Yg8mvjkMjX18MHTYCytJSzJw+FS+PHothI0Zi08ZfMXP6VOzaewBia46BIVb+ZxHEVmLsjj6Kq4kJmPPGVISEhiMoOESr3Y4tm3DsyCFE/boVAoEAb0x5Fd4+vhjy4jAAwLdffYGL58/h6/X/haeXF1KSr8Ha2sYcXapz9v6wGiIrMWav3Yxbadfwy4r34eEfjIY+AVrtQls/jZadesHWzh7yokJs+nQBTu3fiqf7vQQAiDu8B2eP7MOItxajQSN/5OVkQWJnb4Ye1T2Tn/VHWbkaY346h0A3Keb1aYzUuzJcz1PotD2enIdVh3UTJJFAgHd7hmD9qUwciM9FiLsdFvcPQ2JOEdLuyk3RDSKiWuHj79ZDbCXCnnVrcDUtHbOXrkDjAH8E+fpotdsefQh/xpzBTyuXABDgjYVL4e3REM/37AGlsgzvLF+Fof364IVePbD9j4N4Z/kqbFy9CmJxvf9TxiDGGAeJrQ3enzIJvl6eEAgE+PP0Gbz1n4+x97u1sHro5ClV7vNtW2FlZYWNkQuQfPMG3v/+WwR5NUKAp6dWO1tra/Rq9yS6tlTil0PRWq81dHHBrsX/0TzPunsHr/xnCTo2a26SPlgCny7toVaV4/K3myFp4IKggV2huJ0Hxd0CrXYFqZm4eyUZqlIlRDbWCOjbCe4tw5F7Nh5CsRiynDu4cewMyuQKuD0RjKCBXXFl/XaUK8vM1LO644khnVBeVo7DC36Ag3cDtBnfD/eybqMoO6/S9oGdW6G0SA4rV7FmW15qFqI/WKd57hrkjdbj+uJ2QkaNx0/mU6+veSmXyRD9x+94ffobkErt0Kp1W3Tu0g17du3Qabtzx3aMHjseHp6e8PDwwOix47BzxzYAwOmYGJSpyjBq9FhYW1tj5KgxUKvViDl10tRdqnNkD4+BnR1at2mLzl27YfdO3THYtWM7xjw8Bq+Mw87t98fg9P0xGFMxBi9zDB6JXC7DkYN/YOLU6ZBK7dCiVRt06NQV+/fs1Gm7d/cODB81Fg09POHe0AMjRr+CvTu3AwAKCwuw8b8/Ye6HC+Dl7Q2BQIDgkMawsWHxUp9ShRzxMcfQ9aVXYG0rgV94M4S2eRoXjv2h09bVwxu294uRarUaAoEQedk3K56Xl+PPLT+h5+ipcPcJgEAggKuHNyT2nPmqj42VEE8HuuDn2EwoysoRn12EmPR8dG3c4JH242Argp21FQ4nVZyBv5ZbjMw8BfxcJDURdp0lMOKDiGofuUKBwydjMGn4S5BKbNEiIgwd27bG/qPHddruPXIMIwb0RUM3NzR0c8WIAf2w98ifAIC4K1dQpirH8H69YS0WY2jf3lCrgdhLl03dpTrJWONgY20N/0beEAqFUKvVEAqFuFdcjMKiIlN3qc6Rl5bg+MULeKVXb0hsbNA0MAhPP9EE0XGxOm3D/fzxXJu28HJ107vf6NhYNAsKgqera02EbXGEViI4hfji1snzKFeWoTgrFwWpmXAJD9RpW1pQBFWpsuKJAIBaDRunity7tLAIuWfjUSaTA2o17ly+BoFICBtn5tr6iMRW8GgWhKsHTkFVWob8tFvIuZIG79aVz+CW/K+9+46Pqsr/P/6eNNIgFCmhhhAS+iqidNAAUpZUqqKyrqKrIqCgq7hfBKWIKChNfiiKWRSQpQQQXBZZO1VEwlICJAQQCIEQIL3N74/IkCGZzASSIUxez33ksc6dO2c+d84weefMuefWqKr67QMVt21vie3W7xCkczHHlcfgsUO7qcHLbducsqn+AAAgAElEQVS2lXUdt0VCwgk5Ozupid/1D6zAoBY6fuxYkX3jjh9VUFCLG/YrmN58/PgxBQYGmU5tkaTmgUHFtgNz1/rAr1AfBFnog+PHjiqwRYsb9vujD44V0wdBQTp2nD6wxcmEBDk5O6txEz/TtuaBQYov5vWLjzum5oHX+yEgMEjxcQX7HT96VM7OLvrvN1s0sE8PDQsfoNUrvyj3+h3BxXOn5eTkZHZqd93GzZR0+kSx+8f89I3efjJE7z4TqcSTx9W+V8GpE1eSk3QlOUlJp+L1/ujhmjt2hL7911IZ8/PtcRh3tAY+7so3SmcuZ5m2xV/MsDjoeF8TH33++D2aP7iN+resbdqekpGr745dVO+gu+RkkILqeKl2VTcdPMcfeIUZDGX3AzgaR8jaJ8+ek5OTkxrX9zVtC/BrorjTp4vsG3/qtJr7NTbdbu7XWPGnfpckxZ36XQFNGpllvGZNGim+mHZQVFn1wzWPjn9VPR/5i16Z+Z5Cez2gmj4+5Ve8g/g9KUlOBoMa1q5j2tbMt74Szp27pXb/88se9bn3vlstr9KoUqOaZDQqK+WqaVtG0iW516pe7P7VA/3U9m9D1fbpofKoXUMXDhR/arPHXTVkcHJW1uWrxd6P6zxrV5fRaFT6heszXa+evSjvesUPwLcM767Yr3eUOKPVydVF9do205k9R8q8XlQsJZ5rsXnzZp09e1YPPPCA/P399f333+v9999XRkaGgoOD7VVjuUlPT5e3d1Wzbd5VqyotLc3Cvt5m+6Wnp8toNCojPa2YdryLbQfmMorrA++qSk8vvg+qWuiD9PQ0eVc1b6eqt7fS6QObZNzw/pYkL29vpaenF7uvV+F+8L7eD0nnzyk19apOJSRo9YYtOnUyQWP+9qQaNfHT/Z26lPtx3MmyMzNUxdPLbJu7p5eyM4s/zbht115q27WXLp49rf0//EfePjUkSVeSL0iSjsf8or/N/FiZ6alaNuMVVatZW+2D/1y+B3GHc3d1Unp2ntm29OxcebgW/Z7vx+PJ+vehJKVk5Ciwjrde7dNMadl5pvUxvz92US/0aKpRXQr+CPzwxxO6kJZd/gcB4I7iyFk7IzNT3p6eZtu8PT2UnlF0GY6MzEx5FdrX29NT6ZmZBTm72HY8i20HRZVVP1wbPF723tvKys7Wd7v2KCeXWU62yMjKlpe7+RehXh7uSs/KsvAI62Li4nQp9ap6tPvTrZZXaTi5uigvK8dsW152jpxdXYvdPyX2hFJiT8jNp6pqtvRXbnrRfzNObq5q/FAXndu1X/nZOcW0gsJcqrgqN9M8D+dmZMmlStE+qNOmqQzOTjp/IF41/etbbLNeW39lp2cqOe5MmdeLisXizMupU6dqzpw5iomJ0QsvvKBZs2bp5Zdf1qBBg7Rx40Z71lhuPD09lZZmPhMmLTVVXl5exe6bWmggLC01VZ6enjIYDPLw9FJqkXbSim0H5jyK6YPUtFR5elrog9Ti+8DT00tpN5y2kpqaJk/6wCYF/WA+0JuWlibPG8LutX3TC/VZ4X6oUsVdkvTEqGdVxd1dAYFB6t23v7b/+H35HoADcHP3UFaG+WBxVkaa3NxLPtW4lm9D1W7YRJs+/UCS5PLHGq9dQobJ3ctb1WvX0729Burovp3lU7gDyczJl6eb+a9FT1dnZeQUnbV6KiVTyek5yjdKhxNTtSEmUV38CwaQG/i465XezTTn2zhFfrxHo1cdUOSffNWhEbNTzHHiOCo3R8/aHu7uSssw/wIuLSNDnh7uxe+bfn3ftPQMebq7F+TsUrSDosqqHwqr4uamh7p10T/XbdDREwnlU7gD8ajipvQs84GvtMwsed7Cskpbftmt7m3byYOlmWyWn5MrZzfzQTJnN1fl5ZQ86Jh9+aoyL6ao4QP3m203ODvLP+QBpZ+7oPN7WMbCFrlZOUUGKl3c3ZR7w6Cys6uLggZ01qF1P1hts36HIJ35hVmXlYHFwcsff/xRa9eu1Zw5cxQVFaWoqCitWLFCI0aMkLODLMrcpImfcnPzlJBwwrQt9shhNQsIKLKvf7Pmij1y+Ib9Ci4o06xZgI7GHpHReP2qs0djjxTbDsyVpg+aBZj3wZHCfRAQoNhi+iCgGX1gi8ZNmigvN1enTl4PoMdij6hpMa9fU/+C97vZfv4F+zVrHiiJ0zhvRq16DZWfl6eLZ6+fxpWYEKfaN1yspzj5+XmmNS/v8m0kZxdXGRjQKbXfL2fKyWCQb7Xrfwj41fLUyUvWL7JjlEyveZOaHvo9JVO/nr4i4x/t7jmZonsbM3hZGKeNo7Jz9Kzd2Lee8vLydOrs9VNjj544Kf+GDYvs27RRQx1LuJ5BjiYkqGmjBpIk/0YNdCzhpFnGO55wUk2LaQdFlVU/FCc3N0+/J54v24IdUIPatZWXn6/TSUmmbXFnzqjJDRfrsVVWTra+3/+b+nTglPHSyLp0RXIyyM3n+tl6HnfVUObFFKuPNTg5qUqhxxmcndR0YE/lpKbr1DYmCNgqPSlFBicned51PRNXrX+XUs8lm+3nWdtHHjWrquNzEXpw0l9098h+qlLNUw9O+os8alzvB3cfb9X0b6DfGbysFCwOXnp4eJhmDtaqVUt+fn5q2rToYrZ3Mg9PT/Xq3Ucfzp+rjPR0/br3F33732/055CwIvuGhIZp2WefKjExUefPJyrqs08VGhYhSbrv/vvl7OSsL5ZFKTs7Wyu+WCZJur9jJ7sez53I09NTvfr00cJ5c5V+rQ+2faOBoUX7YGBomP4ZVagPln6q0PA/+uA+8z5Y/jl9UBoeHp7qGdxHH304TxkZ6dq/b69++G6b+v05tMi+/QeGasWyKCWdT1RS0nktX7ZUA0LDJUkNGzXWn+65V58tWazs7GydiDuurVu+VtfuD9j5iO48bu4eanlfN337r6XKzszQySMHdOSXn9Wue58i++7971dKu1xwRb6k0yf0U/Ry+bW+R5LkWsVdrTs9oJ83rlBWRrquXEzS3m2bFHgP/xasycrN1/YTlzSiQwNVcXFSy7re6uhXXf89eqHIvh2bVJeXW8HgQvPaXgppU1c7TxT0SdyFdPn6uKtd/YJwVa9qFd3XuLriLxZdhgFA5eXoWdvD3V0PdLxPH638lzIyM/Xb4SP6Yfcv6tezW5F9+/fspuUbN+v8xWQlJV/S8g2bNOCBHpKk9q1aydnJSV9u+reyc3K0avMWSVKHNq3tejx3qrLqhwOxR/XboSPKyclVZla2/rlugy5dvqzWzZkoYI2HWxV1a9NWn235WhnZWToQH6+fDx5Q7/Ydiuybn5+v7Jwc5ebnyWiUsnNyipye/9OBGHm7e+huJmmUSn5uni4fPyXfTn+Sk4uzvHxry8e/oS4dji+yb83WAXLxKPgyu0pNH9Xp0FpXT//xBYCTQX4DesiYm6eELT/b8xDueHk5uUo8EKfmD90vZ1cXVferpzqt/HRmr/ngY+q5ZH07LUo/zVmpn+as1P9Wfausqxn6ac5KZaRcPwOw/r2BSkk4p4yLV+x9KLgNDMbCX2MW0rNnTz399NOm2x999JFGjRpluj1ixIhSP1lGBVwG4vLlFL3xfxO1Y/vPqu5TXWNeHK8Bfw7R3l/26Pm/jdL23b9KKrii7/uzZ2nt6n9JkiIGDda4l142nUZx+NBBTXnjH4o7fkxN/Ztp8pvT1KJlq9t2XJZUxBkql1MK+mD7H30w9sXxGjCwoA+ee2aUduwp1AfvzdKaP/ogctBgjRt/vQ8OHTqoKZMK9cFb09SyAvZBWlbFXB/oyuUUTZvyf9q9Y7t8qvvo2Rde1EP9B2rf3l80/oVn9M1PBVdENBqNWvjBe1q/brUkKTR8kJ4bO97UD0nnEzV9yv9p/769qlGzlh4d+aTCBw+9bcdVnK8P39oC6eUlI/WK1v+/WYo7sFce3tXUa/hTatu1lxIO79cXM1/Ta59+JUmKXvSOju3bqeysTHlW9VGrjj314JAnTKeMZ6WnaePHs3V03065e3rrnuAB6hHxWJHTvm63lb+cvd0lFOFdxVljejbV3Q2q6WpWrj7beVrfH09Wq3reeqN/oIZ9WnC1wwnB/rq7oY9cnQ26mJatTf87r43/uz77pKt/DQ1vX1+1vasoPTtP3x27qKhdp1XsL9zbbP3Tt2fmxpmUslsDtH51tzJrC7CXss7ayfuLXrn4drt8NVXTP1ysXfsPyMfbW8+OGKa+3btq36HDemnaO9q27BNJBdliwbLlWv/Nt5Kk0F4P6PlHHzb93joSf0IzPvxI8ad/l1/DBpr47CgFNfW7PQd1ByqLftj7v0Oa82mUziSel7Ozs5o1bqSnhw/WPa1a3sYjKyr1ROLtLqFYV9LT9N6XK7U3NlZVvTz11IA/K/ieexUTF6eJSxZrw7S3JUm/HT+mCYsWmj22nX8zvffs86bbr370/9SiUWP9pV9/ux5DaST/8YVuReNcxU2Ne3eWd2Nf5WVm6cxPvyol9oS86teWf2iwYhatlCQ16t1Z1fzqy8nVVXkZmUo5elJnd+yTMS9fXg3qqPmgh5Sfk2s2Izxu/TalnUmy9NR2d+5UxRzQc/WoojZDH1StwEbKSctU7KYdOrvvqGo09dW9Tw7U1n98VOQxNf3rq93DvfXttCiz7d1efljx3+7T77sP2av8Uus367nb9txlmQtqtiv6ZYu9WRy8fO2110p84IwZM0r9ZBVx8LKyqWBjF5VSRR28rEwq6uBlZVMRBy8ro9s1eHn2ctkNXvr6MHiJO09ZZ+2KOHgJ2FtFHbysbCrq4GVlUlEHLysbBi/LjsWrjd/M4CQAAAAA68jaAAAAtrE4eClJqampWr9+vY4dOyZJCgwM1MCBA+Xt7W2X4gAAgGPiolIAWRsAAMAWFi/Yk5iYqJCQEK1fv17Ozs5ycnLSunXrFBISosREpuMDAIBbYCjDH+AORNYGAACwjcWZlwsWLFBERITGjBljtn3+/PmaP3++3nrrrXIvDgAAAHBEZG0AAADbWBy83LNnj9avX19k+zPPPKPQ0NByLQoAADg2JkyisiNrAwAA2Mbi4KWzs7NcXIre7erqWux2AAAAWxkYvUQlR9YGAACwjcU1L0sKTQQqAAAA4OaRtQEAAGxjMRnFxsaqc+fORbYbjUalpqaWa1EAAMCxcbVxVHZkbQAAANtYHLzcsmWLPesAAACVCWOXqOTI2gAAALaxOHjZoEEDe9YBAAAAVBpkbQAAANuwoA4AALA7Jl4CAAAAsAWDlwAAwO642jgAAAAAW1i82jgAAAAAAAAA3E5WBy+NRqNWrVqlWbNmSZJOnz6tvXv3lnthAADAcRnK8H/AnYysDQAAUDKrg5czZszQjh079M0330iSvLy8NH369HIvDAAAOC6Doex+gDsZWRsAAKBkVgcvd+7cqXfffVfu7u6SpBo1aigrK6vcCwMAAAAcHVkbAACgZFYHL6tUqSJDoWkN+fn55VoQAAAAUFmQtQEAAEpm9WrjgYGBWr9+vYxGo06fPq3Fixfr3nvvtUdtAADAQXG6N1CArA0AAFAyqzMvX331Ve3atUtJSUkaOnSo8vPz9corr9ijNgAAAMChkbUBAABKZnXmpbe3t6ZOnWqPWgAAQCXBVcKBAmRtAACAklmdedm7d299+OGHOnfunD3qAQAAlQBXGwcKkLUBAABKZnXw8sMPP9SVK1c0ZMgQPfHEE9qwYQNXQAQAAADKAFkbAACgZFYHL5s3b66///3v+vbbb/X4449r8+bN6t69uz1qAwAADspQhj/AnYysDQAAUDKra15ec/z4ce3atUsxMTFq3bp1edYEAAAcHaOOgBmyNgAAQPGsDl5GRUVp3bp1SktLU3h4uL788kv5+vraozYAAADAoZG1AQAASmZ18PLIkSOaOHGiOnToYI96AABAJcDVxoECZG0AAICSWR28nDZtmj3qAAAAlQhXCQcKkLUBAABKZnHw8uWXX9asWbM0aNAgGYr5C+Nf//pXuRYGAAAAOCqyNgAAgG0sDl6OHDlSkvT3v//dbsUAAIDKgYmXqOzI2gAAALaxOHjZpk0bSdLZs2cVFhZmdl90dHT5VgUAABwbo5eo5MjaAAAAtnGytsPSpUtt2gYAAFDRxcfHa9iwYerbt6+GDRumEydOFNknLy9PU6ZMUe/evdWnTx+tWrXK/oWi0iBrAwAAR1FeWdvizMuYmBjt379fly5d0ueff27anpqaqpycnJs7CgAAAN2+q42/8cYbeuSRRxQWFqbo6GhNmjRJUVFRZvts2LBBJ0+e1JYtW5SSkqLw8HB17txZDRs2vC01wzGRtQEAgKMpr6xtceZlYmKiDhw4oIyMDB04cMD0k5SUpBkzZpTdkQEAgErHYCi7nytXruj06dNFfq5cuWL2nBcvXtTBgwc1cOBASdLAgQN18OBBJScnm+23adMmDRkyRE5OTqpZs6Z69+6tr7/+2m6vDSoHsjYAALgTVISsbXHmZe/evdW7d2/9+OOP6tat280eoxkP1zJpBrijubtY/GcHOxlxL7OnKgL6oXJzL8OPwo8++0zz588vsn306NF64YUXTLfPnj2runXrytnZWZLk7OysOnXq6OzZs6pZs6bZfvXr1zfd9vX11blz58quYEBln7VrtutQBlUBd7aa7W53BZCkxre7AABlmgvmzZt327O21T8dunXrpri4OB0+fFjZ2dmm7eHh4dYeCgAAUO5GjhypiIiIIturVat2G6oBSoesDQAAKrKKkLWtDl5GRUVp5cqVSkpKUtu2bbVnzx7dd999BCoAAFAhVKtWzabw5Ovrq8TEROXl5cnZ2Vl5eXk6f/68fH19i+x35swZtWtXMIXnxm+HgbJE1gYAABVZRcjaVq82/uWXX2rVqlXy9fXVkiVLtGrVKvn4+FgtGgAAoCKpVauWWrZsqY0bN0qSNm7cqJYtW5qdxiJJ/fr106pVq5Sfn6/k5GRt3bpVffv2vR0loxIgawMAAEdQnlnb6uClm5ubPD09lZ+fL6PRqMDAQJ08efIWDgcAAOD2mDx5spYtW6a+fftq2bJlmjJliiRp1KhRiomJkSSFhYWpYcOGeuihhzR06FA9//zzatSo0e0sGw6MrA0AABxFeWVtg9FoNJa0w4gRI7R06VJNnDhRtWvXlq+vr7788ktt2LChjA4NAAAAqJzI2gAAACWzOngZGxurhg0bKiMjQ7Nnz9bVq1f17LPPqmXLlvaqEQAAAHBIZG0AAICSlTh4mZeXpwULFmjMmDH2rAkAAABweGRtAAAA60pc89LZ2Vm7d++2Vy0AAABApUHWBgAAsM558uTJk0va4dKlS/r111/VpEkTGQwG5ebmKjc3V66urnYqEQAAAHBMZG0AAICSWb3a+KxZszRr1ix17dpV7du31z333KP27dvbo7ZSiY+P17Bhw9S3b18NGzZMJ06cuKl2goODNXDgQOXn55tti42NLVU7a9asUYcOHRQeHq7+/fsrNDRU8+fPV2Zmplm7/fr1U2hoqPr3769Vq1bdVM0V3cyZMxUcHKygoKBSv46FBQcHq1u3bsrLyzNtW716tYKCgrRs2TJJ0vLly7V06VJJBX1g6TSsnTt3KjIy8qZruR0uXbqkUaNGqW/fvgoJCdHo0aOVnJx8U20FBQVp8ODBZtvmzp2roKAg/fe//y11ezfzb8SarVu3av/+/WXaZnl77rnnFBoaqvDwcD3yyCM6dOjQTbWzefNmhYeHKywsTP369dP48ePLuNKKYf78+bf0uRAUFKSQkBDTa759+/ZSt3H69Gl17NjRdDssLMz0Ob13714NHDhQ4eHh2rFjx03VWJwb39sxMTFl2senT59Wq1atFBYWppCQEPXr10//+Mc/dO7cOdM+r776qnr06KGwsDD17dtXb731ltnvPQD2Q9Yma98qsvatI2ffGcjatiNnFyBnOw4XazscPnzYHnXcsjfeeEOPPPKIwsLCFB0drUmTJikqKuqm2kpPT1d0dLQiIiJuqaYuXbpo7ty5kqSLFy/q9ddf17hx47Ro0SLTPnPnzlVgYKBiY2MVGRmpHj16qG7durf0vBVNr1699Pjjj2vEiBG33Fbt2rX1448/qmfPnpKkdevWqXXr1qb7H3744Vt+jorKYDDoqaeeMv0CmDlzpt59911Nnz79ptrLz8/XsWPHFBAQIKPRqE2bNikwMLAsSy5Rbm6uXFwsfwRt3bpVbdq0Ubt27exW062aOXOmqlatKqmg/okTJ2rt2rWlauP8+fOaMmWK1q5dK19fXxmNRrt9Dlvrk7L0v//9T/v27VP9+vVvqZ0VK1bIy8tLW7du1bhx47R9+3Y5OV3/Xi4/P18Gg0EGg8Gm9qKjo83+Ozw8XE899VSpairte7tt27Z67733SvUc1lStWtV0LNnZ2frwww81fPhwbdiwwfQeffrpp/Xoo48qNTVVERERuvfeezVgwIAyrQOAdWTtm0fWLkDWvnXk7DsDWds25GxytiOyOvNSKvimdevWrZKktLQ0paSklGtRpXXx4kUdPHhQAwcOlCQNHDhQBw8evOlvy0aPHq158+YpOzu7yH0JCQkaOXKkQkJCFBERoe+//96mNmvVqqWZM2dq+/btOnr0aJH7AwMDVa1aNSUmJt5UzRVZhw4d5OvrWyZtRUREaM2aNZKkU6dOKSMjwywIzJs3TzNnziz2sXPmzFGfPn306KOP6ttvvy2TeuypevXqZt9c3X333Tpz5sxNtxceHm56LXfu3KnAwEBVr17ddP+FCxf0/PPPKyQkRCEhIVq3bp3pvj179igkJESDBw/W1KlTVfi6X3FxcXrqqac0aNAghYaGavXq1ab7goKC9PHHH+uxxx7T/PnzdeTIET3yyCOKiIjQgAEDTN/k//DDD9q2bZsWL16ssLAw03OvXbtWQ4YMUWRkpB5//HHFxcXd9PGXh2u/rCQpNTXV5l/khV24cEEuLi6mvjAYDGZXnP3tt9/02GOPKTIyUpGRkab38sSJE/XZZ5+Z9ouNjVWvXr1kNBqVmpqq119/XYMHD1ZISIimTp1qmlXx2GOPafbs2Ro5cqSee+45SdJ3332n4cOHKzIyUsOGDdO+fftKfRwlyc7O1ptvvqk33njjpl6j4nTt2lUpKSlKSUnRvHnzNGHCBD333HMKCwvTlStXtH//fg0bNkwhISEaNmyYxdkGQUFBSktL08cff6zNmzcrKirK9C1xeb23C89Oudl+LImbm5vGjh2runXrav369UXu9/b2VuvWrW/p8wTArSFrX0fWLj2y9q0jZ1f8nC2RtW1BziZnOyqrw/5r1qzR4sWLlZOTo969eysxMVFvvvmm6Q1SEZw9e1Z169aVs7OzpILFz+vUqaOzZ8+qZs2apW6vTZs2atOmjZYvX66RI0ea3TdhwgQNHTpUQ4YM0bFjxzRixAht3rzZpufx8fFRkyZNdPToUTVv3tzsvl9++UU1atRQixYtSl1vZdKxY0d98cUXunz5stauXavw8HAdOHDA6uO2bdumbdu2ad26dXJ3d9fzzz9vh2rLT35+vpYvX67g4OCbbqN///4aMWKExo8fr7Vr1yoiIkKffPKJ6f6pU6eqefPmWrBggc6fP6/IyEi1atVKfn5+evHFF/Xuu++qY8eO2rRpk/75z39KKvgmbMKECZo1a5aaNWum1NRUDRo0SHfffbeaNWtmqv3a/qmpqVq6dKnc3NyUlpamIUOGqHv37urevbuCg4PVpk0bPfroo5IKgtzmzZv1+eefy83NTd99950mTpyoFStW3PRrUB5ef/11/fTTTzIajfr4449L/fgWLVqoXbt2euCBB9SxY0e1b99eYWFhqlGjhq5cuaI33nhDixcvVp06dXT+/HkNHjxYGzduVGRkpKZNm2b6zFqzZo0iIiJkMBg0Y8YM3XfffZo2bZry8/M1YcIErV69WkOHDpVU8It7yZIlcnFx0cmTJ7Vw4UItWbJE3t7eOnr0qEaNGlWmf4R88MEHCg0NVaNGjcqszc2bN6tevXqmz+I9e/ZozZo1qlmzprKzszVmzBhNnz5dXbp00fbt2zVmzBht2bLFYntPPfWUjh07ZnoPlud7e+fOnabnvZV+tKZt27bF/kF/8eJFHTlyRC+88IJN7QAoW2RtsnZFQtYmZ1fknC2Rta0hZ5OzHZXVwcuoqCitXr3adBqCv7+/Lly4UO6F3W7jxo3T448/brZeSWpqqg4dOqRBgwZJkgICAtSyZUvt27fP5l9uhb85k6QxY8bIaDTq1KlTmj9/vtzc3MruIByQwWBQ//799dVXX2nTpk1avny5TYFq586dGjBggLy8vCRJgwcP1sKFC8u73HLz1ltvydPT0/ShfDM8PT1199136z//+Y/27t2radOmmYWq7du369VXX5Uk1alTRz179tTOnTuVn58vDw8P07fTAwYM0KRJkyRJJ06c0PHjx/XSSy+Z2snJyVFcXJzpF0/hU8QyMzM1efJkHTlyRAaDQefPn9fhw4dN+xa2bds2HT58WEOGDJFU8G/pypUrN3385WXatGmSCk6zeuedd/TRRx+V6vFOTk5auHChYmNjtXv3bm3dulVLlizRhg0b9Ntvv+n06dMaNWqUaX+DwaCEhAR16NBBaWlpOnz4sAICArRx40atXLlSUsFrt3//fn366aeSCl73wqfMhYSEmE6/+OGHH3Ty5EmzU89yc3N14cIF3XXXXTf3ohTy66+/KiYmRhMmTLjltiRp+PDhcnJy0l133aUFCxaYtvfo0cMUsOLj4+Xq6qouXbpIkjp37ixXV1fFx8ebPhOsKc/3dmG30o+ltXjxYq1cuVLx8fF67LHHrNYGoHyQtcnaFQlZm5wtVdycLZG1S0LOJmc7MquDl66urkXedNe+da0ofH19lZiYqLy8PDk7OysvL0/nz58v9vSJIR01QqAAAAs1SURBVEOGKDs7W15eXvriiy8stunv76+ePXua3rwlsXU69uXLl3Xy5EmzUy+urcOzefNmvfzyy/r3v/9dJgMEdyJb+yYyMlJDhgzR/fffrxo1atjU9o1B9k42c+ZMJSQkaNGiRWZrjlxz6dIl/eUvf5EkNW3aVO+//77FtiIiIjRu3DhFRkYWu3bIje9ta+91o9GoGjVqmK1nciNPT0/Tf8+ePVu1a9fW22+/LRcXF/31r39VVlaWxbYHDRqksWPHllhDRREeHq5Jkybp0qVLRd6ntrzXAwMDFRgYqBEjRmjAgAHatWuX3NzcFBQUpM8//7zYx1w7PeL+++9Xs2bN1KBBA0kFr93ChQstfgNbuE8kqXv37nrnnXdKe8g22b17t+Li4tSrVy9J0rlz5/Tkk09qxowZ6tatm2k/W9/H19biuVHhbUajsdj3bmlOpSnP9/aNbrYfrYmJiVFoaKjp9rW1eOLi4vTwww+rU6dOpjXOANgPWZusbS9kbevI2XdGzpbI2sUhZ1tHzr5zWV3zsnr16oqPjze9+aKjo1WvXr1yL6w0atWqpZYtW2rjxo2SpI0bN6ply5bFnl6yatUqRUdHl/gL+5oXXnhBX3zxhdLS0iQVrFfQsmVL06LAx48f1+HDh/WnP/3JalvJycmaOHGiOnfurICAgCL39+/fX127dtXixYuttuWobO2bRo0a6cUXXzStGWKLzp07a/PmzUpPT1deXp7ZGhp3kjlz5ujAgQNasGCBxZkD1z74o6OjSwxUktSpUyc988wzxS7w3rlzZ9M3UUlJSfruu+/UsWNH+fv7KzMzU7t375Ykff3117p69aqkgl9+7u7uZuv2HD9+XKmpqcU+/9WrV1WvXj25uLgoNjZWe/bsMd3n7e1talcquNJidHS06UpueXl5Ns0EsJe0tDSdPXvWdHvbtm3y8fExW9/ompLe64mJifr1119Nt8+dO6fk5GQ1bNhQ99xzjxISEsyuyLd//37THwwRERHauHGjVq1aZXaFz+DgYC1evNi0bktycrJOnTpV7HF07dpVP/zwg9lpD2V5Ncqnn35aP/74o+n0snr16mnJkiVmgUoq3fvYGn9/f2VnZ5tetx07dig3N1d+fn42t1Ge7+0blUU/Fpadna358+fr3LlzZqHqGn9/f40ZM+aWX2cAN4esTda2F7J2ycjZFTdnS2RtW5CzydmOzOrMy4kTJ2r8+PGKj49XcHCw3N3dza7gV1FMnjxZr776qhYuXKhq1apZXEi6NOrVq6ewsDCzKf7vvvuuJk2apKVLl8rFxUXvvPOOxTV4fv75Z4WHhyszM1Nubm7q06eP2RT0G40fP16RkZEaNWqUateufcv1VxRTp07Vli1bdOHCBT3xxBOqXr26vvrqq1tqc9iwYaXa/8EHH9S+ffsUHh6uOnXqqGPHjnfcgu1Hjx7VokWL5Ofnp+HDh0uSGjZsaDaFv7QMBoP++te/FnvfP/7xD02aNEkhISGSCtagurZ+1OzZszVlyhRVqVJFnTp1Ml3JzsXFRYsWLdL06dO1ZMkS5efnq1atWhY/rJ999lm98sorWr9+vRo3bqz77rvPdF9oaKhee+01ff3113riiScUHh6ucePG6dlnn1VeXp5ycnLUr18/tWnT5qaPvyxlZGRo7NixysjIkJOTk3x8fLRo0aJSL5Sdm5urefPm6ffff5e7u7vy8/M1btw4tWrVSpK0cOFCzZo1S9OnT1dOTo4aNWpkep769esrICBAu3bt0uzZs01tTpw4UbNmzVJYWJgMBoNcXV01ceLEYr9Z9PPz06xZs/T6668rMzNTOTk5at++/R13NcrC3NzcNHfuXE2bNk3p6eny9PTUBx98UKpTB8vzvX3jzKWy6MerV68qLCzM9G+lQ4cOWrFihdlC94UNGzZMUVFR2rp1q3r37m3z6wLg1pG1ydq3iqx968jZFTtnS2TtioqcTc62F4PRwhz/Xbt26f7771d2dracnZ114sQJGY1GNW3atMKdygIAAADcScjaAAAAtrF42vjbb78tqWCU2NnZWc2aNVNAQABhCgAAALhFZG0AAADbWDxtPCcnR5988omSk5OLXbC2uLU7AAAAAFhH1gYAALCNxcHLN998U9HR0crMzKxwi/UCAAAAdzKyNgAAgG0srnl5zZIlS/Tkk0/aqx4AAACg0iBrAwAAlMzi4GV2drbc3NyUkZFR7AM9PDzKtTAAAADAUZG1AQAAbGNx8DIiIkJr165VixYtZDAYZDQazf7/0KFD9q4VAAAAcAhkbQAAANtYPW0cAAAAAAAAAG4Hp9tdAAAAAAAAAAAUx+LVxjt16iSDwVBk+7VTWbZv316uhQEAAACOiqwNAABgG4unjf/+++8lPrBBgwblUhAAAADg6MjaAAAAtrFpzcvc3FzFx8fLYDDIz89PLi4WJ2wCAAAAKAWyNgAAgGVWk1FMTIzGjBkjNzc3GY1G5ebmat68eWrdurU96gMAAAAcFlkbAACgZFZnXg4fPlxjx45V586dJUk7duzQ+++/rxUrVtilQAAAAMBRkbUBAABKZvVq4xkZGaYwJRUsLp6RkVGuRQEAAACVAVkbAACgZFYHLz08PLRjxw7T7V27dsnDw6NciwIAAAAqA7I2AABAyayeNr5//36NHTtWbm5ukqScnBzNnTtXbdq0sUuBAAAAgKMiawMAAJTM6uDl1atX5e7urvj4eBmNRvn7+8vV1dVe9QEAAAAOi6wNAABQshIHL41Go8LCwrR+/Xp71gQAAAA4PLI2AACAdSWueWkwGNSoUSNdvnzZXvUAAAAAlQJZGwAAwDoXazt4enoqIiJCPXr0kKenp2n7K6+8Uq6FAQAAAI6OrA0AAFAyq4OXTZo0UZMmTexRCwAAAFCpkLUBAABKZvWCPZcuXVKNGjXsVQ8AAABQaZC1AQAASmZxzcvt27erU6dO6tKlix588EEdOnTInnUBAAAADousDQAAYBuLMy8jIyP1/PPPq2vXrtq0aZM2bdqkjz/+2N71AQAAAA6HrA0AAGAbizMv8/Ly1KtXL7m7uysyMlIXLlywZ10AAACAwyJrAwAA2KbEC/ZkZmbq2sRMo9FodtvDw6P8qwMAAAAcFFkbAADAOounjbdo0UIGg0GF775222AwsC4PAAAAcJPI2gAAALaxerVxAAAAAAAAALgdLK55CQAAAAAAAAC3E4OXAAAAAAAAACokBi8BAAAAAAAAVEgMXgIAAAAAAACokEo1ePnSSy+VVx0AAABApUbWBgAAKKpUg5fx8fHlVQcAAABQqZG1AQAAiirV4KXRaCyvOgAAAIBKjawNAABQlMFYipSUkpKi6tWrl2c9AAAAQKVE1gYAACiqVIOXAAAAAAAAAGAvXG0cAAAAAAAAQIXE4CUAAAAAAACACqlUg5c///xzedUBAAAAVGpkbQAAgKIsrnl57NixItuefPJJffLJJzIajQoICCj34gAAAABHRNYGAACwjcXByxYtWqh+/fpm2xITE1W3bl0ZDAZ98803dikQAAAAcDRkbQAAANu4WLpj9OjR+u233zR58mQ1aNBAkhQcHKxt27bZrTgAAADAEZG1AQAAbGNxzcvRo0frxRdf1Pjx47V8+XJJksFgsFthAAAAgKMiawMAANjG4mnj12RnZ2vu3LmKiYlRfHy8vv/+e3vVBgAAADg0sjYAAEDJrA5eXrNv3z7t2rVLTz/9dHnXBAAAAFQqZG0AAIDi2Tx4CQAAAAAAAAD2ZHHNSwAAAAAAAAC4nRi8BAAAAAAAAFAhMXgJAAAAAAAAoEJi8BIAAAAAAABAhfT/AalWSnTzwq/rAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"code","source":"def evaluate_model(train, validation):\n train_labels, train_preds = train\n validation_labels, validation_preds = validation\n print(\"Train Cohen Kappa score: %.3f\" % cohen_kappa_score(train_preds, train_labels, weights='quadratic'))\n print(\"Validation Cohen Kappa score: %.3f\" % cohen_kappa_score(validation_preds, validation_labels, weights='quadratic'))\n print(\"Complete set Cohen Kappa score: %.3f\" % cohen_kappa_score(np.append(train_preds, validation_preds), np.append(train_labels, validation_labels), weights='quadratic'))\n \nevaluate_model((train_preds['label'], train_preds['predictions']), (validation_preds['label'], validation_preds['predictions']))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:39:15.565889Z","iopub.execute_input":"2024-05-03T04:39:15.566548Z","iopub.status.idle":"2024-05-03T04:39:15.592899Z","shell.execute_reply.started":"2024-05-03T04:39:15.566486Z","shell.execute_reply":"2024-05-03T04:39:15.59209Z"},"trusted":true},"execution_count":39,"outputs":[{"name":"stdout","text":"Train Cohen Kappa score: 0.940\nValidation Cohen Kappa score: 0.908\nComplete set Cohen Kappa score: 0.933\n","output_type":"stream"}]},{"cell_type":"code","source":"def apply_tta(model, generator, steps=10):\n step_size = generator.n//generator.batch_size\n preds_tta = []\n for i in range(steps):\n generator.reset()\n preds = model.predict_generator(generator, steps=step_size)\n preds_tta.append(preds)\n\n return np.mean(preds_tta, axis=0)\n\npreds = apply_tta(model, test_generator)\npredictions = [classify(x) for x in preds]\n\nresults = pd.DataFrame({'id_code':test['id_code'], 'diagnosis':predictions})\nresults['id_code'] = results['id_code'].map(lambda x: str(x)[:-4])","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:39:15.594405Z","iopub.execute_input":"2024-05-03T04:39:15.595024Z","iopub.status.idle":"2024-05-03T04:49:04.05629Z","shell.execute_reply.started":"2024-05-03T04:39:15.594967Z","shell.execute_reply":"2024-05-03T04:49:04.055507Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"code","source":"# Cleaning created directories\nif os.path.exists(train_dest_path):\n shutil.rmtree(train_dest_path)\nif os.path.exists(validation_dest_path):\n shutil.rmtree(validation_dest_path)\nif os.path.exists(test_dest_path):\n shutil.rmtree(test_dest_path)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:49:04.05766Z","iopub.execute_input":"2024-05-03T04:49:04.057942Z","iopub.status.idle":"2024-05-03T04:49:04.33058Z","shell.execute_reply.started":"2024-05-03T04:49:04.057888Z","shell.execute_reply":"2024-05-03T04:49:04.329918Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"fig = plt.subplots(sharex='col', figsize=(24, 8.7))\nsns.countplot(x=\"diagnosis\", data=results, palette=\"GnBu_d\").set_title('Test')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:49:04.33232Z","iopub.execute_input":"2024-05-03T04:49:04.33266Z","iopub.status.idle":"2024-05-03T04:49:04.719824Z","shell.execute_reply.started":"2024-05-03T04:49:04.332599Z","shell.execute_reply":"2024-05-03T04:49:04.719086Z"},"trusted":true},"execution_count":42,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"results.to_csv('submission_2.csv', index=False)\ndisplay(results.head())","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:49:04.721469Z","iopub.execute_input":"2024-05-03T04:49:04.721827Z","iopub.status.idle":"2024-05-03T04:49:04.741969Z","shell.execute_reply.started":"2024-05-03T04:49:04.721756Z","shell.execute_reply":"2024-05-03T04:49:04.74109Z"},"trusted":true},"execution_count":43,"outputs":[{"output_type":"display_data","data":{"text/plain":" id_code diagnosis\n0 0005cfc8afb6 2\n1 003f0afdcd15 4\n2 006efc72b638 3\n3 00836aaacf06 2\n4 009245722fa4 3","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosis
00005cfc8afb62
1003f0afdcd154
2006efc72b6383
300836aaacf062
4009245722fa43
\n
"},"metadata":{}}]},{"cell_type":"code","source":"# model.save_weights('../working/effNetB5_bs32_img224_fold2.h5')","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:49:04.743237Z","iopub.execute_input":"2024-05-03T04:49:04.743499Z","iopub.status.idle":"2024-05-03T04:49:04.746899Z","shell.execute_reply.started":"2024-05-03T04:49:04.743449Z","shell.execute_reply":"2024-05-03T04:49:04.74608Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"markdown","source":"FOLD 3\n","metadata":{}},{"cell_type":"code","source":"import os\nimport sys\nimport cv2\nimport shutil\nimport random\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom tensorflow import set_random_seed\nfrom sklearn.utils import class_weight\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, cohen_kappa_score\nfrom keras import backend as K\nfrom keras.models import Model\nfrom keras.utils import to_categorical\nfrom keras import optimizers, applications\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.layers import Dense, Dropout, GlobalAveragePooling2D, Input\nfrom keras.callbacks import EarlyStopping, ReduceLROnPlateau, Callback, LearningRateScheduler\n\ndef seed_everything(seed=0):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n set_random_seed(0)\n\nseed = 0\nseed_everything(seed)\n%matplotlib inline\nsns.set(style=\"whitegrid\")\nwarnings.filterwarnings(\"ignore\")\nsys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))\nfrom efficientnet import *","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:49:04.748182Z","iopub.execute_input":"2024-05-03T04:49:04.748427Z","iopub.status.idle":"2024-05-03T04:49:04.771416Z","shell.execute_reply.started":"2024-05-03T04:49:04.748384Z","shell.execute_reply":"2024-05-03T04:49:04.770721Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"fold_set = pd.read_csv('/kaggle/input/main-data/5-fold.csv')\nX_train = fold_set[fold_set['fold_2'] == 'train']\nX_val = fold_set[fold_set['fold_2'] == 'validation']\ntest = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')\nprint('Number of train samples: ', X_train.shape[0])\nprint('Number of validation samples: ', X_val.shape[0])\nprint('Number of test samples: ', test.shape[0])\n\n# Preprocecss data\nX_train[\"id_code\"] = X_train[\"id_code\"].apply(lambda x: x + \".png\")\nX_val[\"id_code\"] = X_val[\"id_code\"].apply(lambda x: x + \".png\")\ntest[\"id_code\"] = test[\"id_code\"].apply(lambda x: x + \".png\")\ndisplay(X_train.head())","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:49:04.772488Z","iopub.execute_input":"2024-05-03T04:49:04.772797Z","iopub.status.idle":"2024-05-03T04:49:06.667337Z","shell.execute_reply.started":"2024-05-03T04:49:04.772736Z","shell.execute_reply":"2024-05-03T04:49:06.666561Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"Number of train samples: 2930\nNumber of validation samples: 732\nNumber of test samples: 1928\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" id_code diagnosis height width fold_0 fold_1 fold_2 \\\n0 000c1434d8d7.png 2 2136.0 3216.0 train train train \n1 001639a390f0.png 4 2136.0 3216.0 train train train \n2 0024cdab0c1e.png 1 1736.0 2416.0 validation train train \n3 002c21358ce6.png 0 1050.0 1050.0 train train train \n4 005b95c28852.png 0 1536.0 2048.0 validation train train \n\n fold_3 fold_4 \n0 validation train \n1 train validation \n2 train train \n3 validation train \n4 train train ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosisheightwidthfold_0fold_1fold_2fold_3fold_4
0000c1434d8d7.png22136.03216.0traintraintrainvalidationtrain
1001639a390f0.png42136.03216.0traintraintraintrainvalidation
20024cdab0c1e.png11736.02416.0validationtraintraintraintrain
3002c21358ce6.png01050.01050.0traintraintrainvalidationtrain
4005b95c28852.png01536.02048.0validationtraintraintraintrain
\n
"},"metadata":{}}]},{"cell_type":"code","source":"# Model parameters\nFACTOR = 4\nBATCH_SIZE = 8 * FACTOR\nEPOCHS = 20\nWARMUP_EPOCHS = 5\nLEARNING_RATE = 1e-4 * FACTOR\nWARMUP_LEARNING_RATE = 1e-3 * FACTOR\nHEIGHT = 224\nWIDTH = 224\nCHANNELS = 3\nES_PATIENCE = 5\nRLROP_PATIENCE = 3\nDECAY_DROP = 0.5\nLR_WARMUP_EPOCHS_1st = 2\nLR_WARMUP_EPOCHS_2nd = 5\nSTEP_SIZE = len(X_train) // BATCH_SIZE\nTOTAL_STEPS_1st = WARMUP_EPOCHS * STEP_SIZE\nTOTAL_STEPS_2nd = EPOCHS * STEP_SIZE\nWARMUP_STEPS_1st = LR_WARMUP_EPOCHS_1st * STEP_SIZE\nWARMUP_STEPS_2nd = LR_WARMUP_EPOCHS_2nd * STEP_SIZE","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:49:06.668768Z","iopub.execute_input":"2024-05-03T04:49:06.669063Z","iopub.status.idle":"2024-05-03T04:49:06.676021Z","shell.execute_reply.started":"2024-05-03T04:49:06.668992Z","shell.execute_reply":"2024-05-03T04:49:06.675117Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"train_base_path = '../input/aptos2019-blindness-detection/train_images/'\ntest_base_path = '../input/aptos2019-blindness-detection/test_images/'\ntrain_dest_path = 'base_dir/train_images/'\nvalidation_dest_path = 'base_dir/validation_images/'\ntest_dest_path = 'base_dir/test_images/'\n\n# Making sure directories don't exist\nif os.path.exists(train_dest_path):\n shutil.rmtree(train_dest_path)\nif os.path.exists(validation_dest_path):\n shutil.rmtree(validation_dest_path)\nif os.path.exists(test_dest_path):\n shutil.rmtree(test_dest_path)\n \n# Creating train, validation and test directories\nos.makedirs(train_dest_path)\nos.makedirs(validation_dest_path)\nos.makedirs(test_dest_path)\n\ndef crop_image(img, tol=7):\n if img.ndim ==2:\n mask = img>tol\n return img[np.ix_(mask.any(1),mask.any(0))]\n elif img.ndim==3:\n gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n mask = gray_img>tol\n check_shape = img[:,:,0][np.ix_(mask.any(1),mask.any(0))].shape[0]\n if (check_shape == 0): # image is too dark so that we crop out everything,\n return img # return original image\n else:\n img1=img[:,:,0][np.ix_(mask.any(1),mask.any(0))]\n img2=img[:,:,1][np.ix_(mask.any(1),mask.any(0))]\n img3=img[:,:,2][np.ix_(mask.any(1),mask.any(0))]\n img = np.stack([img1,img2,img3],axis=-1)\n \n return img\n\ndef circle_crop(img):\n img = crop_image(img)\n\n height, width, depth = img.shape\n largest_side = np.max((height, width))\n img = cv2.resize(img, (largest_side, largest_side))\n\n height, width, depth = img.shape\n\n x = width//2\n y = height//2\n r = np.amin((x, y))\n\n circle_img = np.zeros((height, width), np.uint8)\n cv2.circle(circle_img, (x, y), int(r), 1, thickness=-1)\n img = cv2.bitwise_and(img, img, mask=circle_img)\n img = crop_image(img)\n\n return img\n \ndef preprocess_image(base_path, save_path, image_id, HEIGHT, WIDTH, sigmaX=10):\n image = cv2.imread(base_path + image_id)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image = circle_crop(image)\n image = cv2.resize(image, (HEIGHT, WIDTH))\n image = cv2.addWeighted(image, 4, cv2.GaussianBlur(image, (0,0), sigmaX), -4 , 128)\n cv2.imwrite(save_path + image_id, image)\n \n# Pre-procecss train set\nfor i, image_id in enumerate(X_train['id_code']):\n preprocess_image(train_base_path, train_dest_path, image_id, HEIGHT, WIDTH)\n \n# Pre-procecss validation set\nfor i, image_id in enumerate(X_val['id_code']):\n preprocess_image(train_base_path, validation_dest_path, image_id, HEIGHT, WIDTH)\n \n# Pre-procecss test set\nfor i, image_id in enumerate(test['id_code']):\n preprocess_image(test_base_path, test_dest_path, image_id, HEIGHT, WIDTH)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T04:49:06.677742Z","iopub.execute_input":"2024-05-03T04:49:06.67812Z","iopub.status.idle":"2024-05-03T05:08:23.080019Z","shell.execute_reply.started":"2024-05-03T04:49:06.678054Z","shell.execute_reply":"2024-05-03T05:08:23.079321Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"code","source":"datagen=ImageDataGenerator(rescale=1./255, \n rotation_range=360,\n horizontal_flip=True,\n vertical_flip=True)\n\ntrain_generator=datagen.flow_from_dataframe(\n dataframe=X_train,\n directory=train_dest_path,\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n class_mode=\"raw\",\n batch_size=BATCH_SIZE,\n target_size=(HEIGHT, WIDTH),\n seed=seed)\n\nvalid_generator=datagen.flow_from_dataframe(\n dataframe=X_val,\n directory=validation_dest_path,\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n class_mode=\"raw\",\n batch_size=BATCH_SIZE,\n target_size=(HEIGHT, WIDTH),\n seed=seed)\n\ntest_generator=datagen.flow_from_dataframe( \n dataframe=test,\n directory=test_dest_path,\n x_col=\"id_code\",\n batch_size=1,\n class_mode=None,\n shuffle=False,\n target_size=(HEIGHT, WIDTH),\n seed=seed)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:08:23.081362Z","iopub.execute_input":"2024-05-03T05:08:23.081669Z","iopub.status.idle":"2024-05-03T05:08:23.162474Z","shell.execute_reply.started":"2024-05-03T05:08:23.081615Z","shell.execute_reply":"2024-05-03T05:08:23.161546Z"},"trusted":true},"execution_count":49,"outputs":[{"name":"stdout","text":"Found 2930 validated image filenames.\nFound 732 validated image filenames.\nFound 1928 validated image filenames.\n","output_type":"stream"}]},{"cell_type":"code","source":"def cosine_decay_with_warmup(global_step,\n learning_rate_base,\n total_steps,\n warmup_learning_rate=0.0,\n warmup_steps=0,\n hold_base_rate_steps=0):\n\n if total_steps < warmup_steps:\n raise ValueError('total_steps must be larger or equal to warmup_steps.')\n learning_rate = 0.5 * learning_rate_base * (1 + np.cos(\n np.pi *\n (global_step - warmup_steps - hold_base_rate_steps\n ) / float(total_steps - warmup_steps - hold_base_rate_steps)))\n if hold_base_rate_steps > 0:\n learning_rate = np.where(global_step > warmup_steps + hold_base_rate_steps,\n learning_rate, learning_rate_base)\n if warmup_steps > 0:\n if learning_rate_base < warmup_learning_rate:\n raise ValueError('learning_rate_base must be larger or equal to warmup_learning_rate.')\n slope = (learning_rate_base - warmup_learning_rate) / warmup_steps\n warmup_rate = slope * global_step + warmup_learning_rate\n learning_rate = np.where(global_step < warmup_steps, warmup_rate,\n learning_rate)\n return np.where(global_step > total_steps, 0.0, learning_rate)\n\n\nclass WarmUpCosineDecayScheduler(Callback):\n\n def __init__(self,\n learning_rate_base,\n total_steps,\n global_step_init=0,\n warmup_learning_rate=0.0,\n warmup_steps=0,\n hold_base_rate_steps=0,\n verbose=0):\n\n super(WarmUpCosineDecayScheduler, self).__init__()\n self.learning_rate_base = learning_rate_base\n self.total_steps = total_steps\n self.global_step = global_step_init\n self.warmup_learning_rate = warmup_learning_rate\n self.warmup_steps = warmup_steps\n self.hold_base_rate_steps = hold_base_rate_steps\n self.verbose = verbose\n self.learning_rates = []\n\n def on_batch_end(self, batch, logs=None):\n self.global_step = self.global_step + 1\n lr = K.get_value(self.model.optimizer.lr)\n self.learning_rates.append(lr)\n\n def on_batch_begin(self, batch, logs=None):\n lr = cosine_decay_with_warmup(global_step=self.global_step,\n learning_rate_base=self.learning_rate_base,\n total_steps=self.total_steps,\n warmup_learning_rate=self.warmup_learning_rate,\n warmup_steps=self.warmup_steps,\n hold_base_rate_steps=self.hold_base_rate_steps)\n K.set_value(self.model.optimizer.lr, lr)\n if self.verbose > 0:\n print('\\nBatch %02d: setting learning rate to %s.' % (self.global_step + 1, lr))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:08:23.163855Z","iopub.execute_input":"2024-05-03T05:08:23.164171Z","iopub.status.idle":"2024-05-03T05:08:23.18221Z","shell.execute_reply.started":"2024-05-03T05:08:23.164115Z","shell.execute_reply":"2024-05-03T05:08:23.181342Z"},"trusted":true},"execution_count":50,"outputs":[]},{"cell_type":"code","source":"def create_model(input_shape):\n input_tensor = Input(shape=input_shape)\n base_model = EfficientNetB5(weights=None, \n include_top=False,\n input_tensor=input_tensor)\n base_model.load_weights('../input/efficientnet-keras-weights-b0b5/efficientnet-b5_imagenet_1000_notop.h5')\n\n x = GlobalAveragePooling2D()(base_model.output)\n final_output = Dense(1, activation='linear', name='final_output')(x)\n model = Model(input_tensor, final_output)\n \n return model","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:08:23.183673Z","iopub.execute_input":"2024-05-03T05:08:23.183957Z","iopub.status.idle":"2024-05-03T05:08:23.19656Z","shell.execute_reply.started":"2024-05-03T05:08:23.183907Z","shell.execute_reply":"2024-05-03T05:08:23.195878Z"},"trusted":true},"execution_count":51,"outputs":[]},{"cell_type":"code","source":"model = create_model(input_shape=(HEIGHT, WIDTH, CHANNELS))\n\nfor layer in model.layers:\n layer.trainable = False\n\nfor i in range(-2, 0):\n model.layers[i].trainable = True\n\ncosine_lr_1st = WarmUpCosineDecayScheduler(learning_rate_base=WARMUP_LEARNING_RATE,\n total_steps=TOTAL_STEPS_1st,\n warmup_learning_rate=0.0,\n warmup_steps=WARMUP_STEPS_1st,\n hold_base_rate_steps=(2 * STEP_SIZE))\n\nmetric_list = [\"accuracy\"]\ncallback_list = [cosine_lr_1st]\noptimizer = optimizers.Adam(lr=WARMUP_LEARNING_RATE)\nmodel.compile(optimizer=optimizer, loss='mean_squared_error', metrics=metric_list)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:08:23.197729Z","iopub.execute_input":"2024-05-03T05:08:23.197954Z","iopub.status.idle":"2024-05-03T05:09:03.645694Z","shell.execute_reply.started":"2024-05-03T05:08:23.197916Z","shell.execute_reply":"2024-05-03T05:09:03.64504Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":52,"outputs":[{"name":"stdout","text":"__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_3 (InputLayer) (None, 224, 224, 3) 0 \n__________________________________________________________________________________________________\nconv2d_311 (Conv2D) (None, 112, 112, 48) 1296 input_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_233 (BatchN (None, 112, 112, 48) 192 conv2d_311[0][0] \n__________________________________________________________________________________________________\nswish_233 (Swish) (None, 112, 112, 48) 0 batch_normalization_233[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_79 (DepthwiseC (None, 112, 112, 48) 432 swish_233[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_234 (BatchN (None, 112, 112, 48) 192 depthwise_conv2d_79[0][0] \n__________________________________________________________________________________________________\nswish_234 (Swish) (None, 112, 112, 48) 0 batch_normalization_234[0][0] \n__________________________________________________________________________________________________\nlambda_79 (Lambda) (None, 1, 1, 48) 0 swish_234[0][0] \n__________________________________________________________________________________________________\nconv2d_312 (Conv2D) (None, 1, 1, 12) 588 lambda_79[0][0] \n__________________________________________________________________________________________________\nswish_235 (Swish) (None, 1, 1, 12) 0 conv2d_312[0][0] \n__________________________________________________________________________________________________\nconv2d_313 (Conv2D) (None, 1, 1, 48) 624 swish_235[0][0] \n__________________________________________________________________________________________________\nactivation_79 (Activation) (None, 1, 1, 48) 0 conv2d_313[0][0] \n__________________________________________________________________________________________________\nmultiply_79 (Multiply) (None, 112, 112, 48) 0 activation_79[0][0] \n swish_234[0][0] \n__________________________________________________________________________________________________\nconv2d_314 (Conv2D) (None, 112, 112, 24) 1152 multiply_79[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_235 (BatchN (None, 112, 112, 24) 96 conv2d_314[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_80 (DepthwiseC (None, 112, 112, 24) 216 batch_normalization_235[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_236 (BatchN (None, 112, 112, 24) 96 depthwise_conv2d_80[0][0] \n__________________________________________________________________________________________________\nswish_236 (Swish) (None, 112, 112, 24) 0 batch_normalization_236[0][0] \n__________________________________________________________________________________________________\nlambda_80 (Lambda) (None, 1, 1, 24) 0 swish_236[0][0] \n__________________________________________________________________________________________________\nconv2d_315 (Conv2D) (None, 1, 1, 6) 150 lambda_80[0][0] \n__________________________________________________________________________________________________\nswish_237 (Swish) (None, 1, 1, 6) 0 conv2d_315[0][0] \n__________________________________________________________________________________________________\nconv2d_316 (Conv2D) (None, 1, 1, 24) 168 swish_237[0][0] \n__________________________________________________________________________________________________\nactivation_80 (Activation) (None, 1, 1, 24) 0 conv2d_316[0][0] \n__________________________________________________________________________________________________\nmultiply_80 (Multiply) (None, 112, 112, 24) 0 activation_80[0][0] \n swish_236[0][0] \n__________________________________________________________________________________________________\nconv2d_317 (Conv2D) (None, 112, 112, 24) 576 multiply_80[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_237 (BatchN (None, 112, 112, 24) 96 conv2d_317[0][0] \n__________________________________________________________________________________________________\ndrop_connect_65 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_237[0][0] \n__________________________________________________________________________________________________\nadd_65 (Add) (None, 112, 112, 24) 0 drop_connect_65[0][0] \n batch_normalization_235[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_81 (DepthwiseC (None, 112, 112, 24) 216 add_65[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_238 (BatchN (None, 112, 112, 24) 96 depthwise_conv2d_81[0][0] \n__________________________________________________________________________________________________\nswish_238 (Swish) (None, 112, 112, 24) 0 batch_normalization_238[0][0] \n__________________________________________________________________________________________________\nlambda_81 (Lambda) (None, 1, 1, 24) 0 swish_238[0][0] \n__________________________________________________________________________________________________\nconv2d_318 (Conv2D) (None, 1, 1, 6) 150 lambda_81[0][0] \n__________________________________________________________________________________________________\nswish_239 (Swish) (None, 1, 1, 6) 0 conv2d_318[0][0] \n__________________________________________________________________________________________________\nconv2d_319 (Conv2D) (None, 1, 1, 24) 168 swish_239[0][0] \n__________________________________________________________________________________________________\nactivation_81 (Activation) (None, 1, 1, 24) 0 conv2d_319[0][0] \n__________________________________________________________________________________________________\nmultiply_81 (Multiply) (None, 112, 112, 24) 0 activation_81[0][0] \n swish_238[0][0] \n__________________________________________________________________________________________________\nconv2d_320 (Conv2D) (None, 112, 112, 24) 576 multiply_81[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_239 (BatchN (None, 112, 112, 24) 96 conv2d_320[0][0] \n__________________________________________________________________________________________________\ndrop_connect_66 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_239[0][0] \n__________________________________________________________________________________________________\nadd_66 (Add) (None, 112, 112, 24) 0 drop_connect_66[0][0] \n add_65[0][0] \n__________________________________________________________________________________________________\nconv2d_321 (Conv2D) (None, 112, 112, 144 3456 add_66[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_240 (BatchN (None, 112, 112, 144 576 conv2d_321[0][0] \n__________________________________________________________________________________________________\nswish_240 (Swish) (None, 112, 112, 144 0 batch_normalization_240[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_82 (DepthwiseC (None, 56, 56, 144) 1296 swish_240[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_241 (BatchN (None, 56, 56, 144) 576 depthwise_conv2d_82[0][0] \n__________________________________________________________________________________________________\nswish_241 (Swish) (None, 56, 56, 144) 0 batch_normalization_241[0][0] \n__________________________________________________________________________________________________\nlambda_82 (Lambda) (None, 1, 1, 144) 0 swish_241[0][0] \n__________________________________________________________________________________________________\nconv2d_322 (Conv2D) (None, 1, 1, 6) 870 lambda_82[0][0] \n__________________________________________________________________________________________________\nswish_242 (Swish) (None, 1, 1, 6) 0 conv2d_322[0][0] \n__________________________________________________________________________________________________\nconv2d_323 (Conv2D) (None, 1, 1, 144) 1008 swish_242[0][0] \n__________________________________________________________________________________________________\nactivation_82 (Activation) (None, 1, 1, 144) 0 conv2d_323[0][0] \n__________________________________________________________________________________________________\nmultiply_82 (Multiply) (None, 56, 56, 144) 0 activation_82[0][0] \n swish_241[0][0] \n__________________________________________________________________________________________________\nconv2d_324 (Conv2D) (None, 56, 56, 40) 5760 multiply_82[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_242 (BatchN (None, 56, 56, 40) 160 conv2d_324[0][0] \n__________________________________________________________________________________________________\nconv2d_325 (Conv2D) (None, 56, 56, 240) 9600 batch_normalization_242[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_243 (BatchN (None, 56, 56, 240) 960 conv2d_325[0][0] \n__________________________________________________________________________________________________\nswish_243 (Swish) (None, 56, 56, 240) 0 batch_normalization_243[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_83 (DepthwiseC (None, 56, 56, 240) 2160 swish_243[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_244 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_83[0][0] \n__________________________________________________________________________________________________\nswish_244 (Swish) (None, 56, 56, 240) 0 batch_normalization_244[0][0] \n__________________________________________________________________________________________________\nlambda_83 (Lambda) (None, 1, 1, 240) 0 swish_244[0][0] \n__________________________________________________________________________________________________\nconv2d_326 (Conv2D) (None, 1, 1, 10) 2410 lambda_83[0][0] \n__________________________________________________________________________________________________\nswish_245 (Swish) (None, 1, 1, 10) 0 conv2d_326[0][0] \n__________________________________________________________________________________________________\nconv2d_327 (Conv2D) (None, 1, 1, 240) 2640 swish_245[0][0] \n__________________________________________________________________________________________________\nactivation_83 (Activation) (None, 1, 1, 240) 0 conv2d_327[0][0] \n__________________________________________________________________________________________________\nmultiply_83 (Multiply) (None, 56, 56, 240) 0 activation_83[0][0] \n swish_244[0][0] \n__________________________________________________________________________________________________\nconv2d_328 (Conv2D) (None, 56, 56, 40) 9600 multiply_83[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_245 (BatchN (None, 56, 56, 40) 160 conv2d_328[0][0] \n__________________________________________________________________________________________________\ndrop_connect_67 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_245[0][0] \n__________________________________________________________________________________________________\nadd_67 (Add) (None, 56, 56, 40) 0 drop_connect_67[0][0] \n batch_normalization_242[0][0] \n__________________________________________________________________________________________________\nconv2d_329 (Conv2D) (None, 56, 56, 240) 9600 add_67[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_246 (BatchN (None, 56, 56, 240) 960 conv2d_329[0][0] \n__________________________________________________________________________________________________\nswish_246 (Swish) (None, 56, 56, 240) 0 batch_normalization_246[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_84 (DepthwiseC (None, 56, 56, 240) 2160 swish_246[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_247 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_84[0][0] \n__________________________________________________________________________________________________\nswish_247 (Swish) (None, 56, 56, 240) 0 batch_normalization_247[0][0] \n__________________________________________________________________________________________________\nlambda_84 (Lambda) (None, 1, 1, 240) 0 swish_247[0][0] \n__________________________________________________________________________________________________\nconv2d_330 (Conv2D) (None, 1, 1, 10) 2410 lambda_84[0][0] \n__________________________________________________________________________________________________\nswish_248 (Swish) (None, 1, 1, 10) 0 conv2d_330[0][0] \n__________________________________________________________________________________________________\nconv2d_331 (Conv2D) (None, 1, 1, 240) 2640 swish_248[0][0] \n__________________________________________________________________________________________________\nactivation_84 (Activation) (None, 1, 1, 240) 0 conv2d_331[0][0] \n__________________________________________________________________________________________________\nmultiply_84 (Multiply) (None, 56, 56, 240) 0 activation_84[0][0] \n swish_247[0][0] \n__________________________________________________________________________________________________\nconv2d_332 (Conv2D) (None, 56, 56, 40) 9600 multiply_84[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_248 (BatchN (None, 56, 56, 40) 160 conv2d_332[0][0] \n__________________________________________________________________________________________________\ndrop_connect_68 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_248[0][0] \n__________________________________________________________________________________________________\nadd_68 (Add) (None, 56, 56, 40) 0 drop_connect_68[0][0] \n add_67[0][0] \n__________________________________________________________________________________________________\nconv2d_333 (Conv2D) (None, 56, 56, 240) 9600 add_68[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_249 (BatchN (None, 56, 56, 240) 960 conv2d_333[0][0] \n__________________________________________________________________________________________________\nswish_249 (Swish) (None, 56, 56, 240) 0 batch_normalization_249[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_85 (DepthwiseC (None, 56, 56, 240) 2160 swish_249[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_250 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_85[0][0] \n__________________________________________________________________________________________________\nswish_250 (Swish) (None, 56, 56, 240) 0 batch_normalization_250[0][0] \n__________________________________________________________________________________________________\nlambda_85 (Lambda) (None, 1, 1, 240) 0 swish_250[0][0] \n__________________________________________________________________________________________________\nconv2d_334 (Conv2D) (None, 1, 1, 10) 2410 lambda_85[0][0] \n__________________________________________________________________________________________________\nswish_251 (Swish) (None, 1, 1, 10) 0 conv2d_334[0][0] \n__________________________________________________________________________________________________\nconv2d_335 (Conv2D) (None, 1, 1, 240) 2640 swish_251[0][0] \n__________________________________________________________________________________________________\nactivation_85 (Activation) (None, 1, 1, 240) 0 conv2d_335[0][0] \n__________________________________________________________________________________________________\nmultiply_85 (Multiply) (None, 56, 56, 240) 0 activation_85[0][0] \n swish_250[0][0] \n__________________________________________________________________________________________________\nconv2d_336 (Conv2D) (None, 56, 56, 40) 9600 multiply_85[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_251 (BatchN (None, 56, 56, 40) 160 conv2d_336[0][0] \n__________________________________________________________________________________________________\ndrop_connect_69 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_251[0][0] \n__________________________________________________________________________________________________\nadd_69 (Add) (None, 56, 56, 40) 0 drop_connect_69[0][0] \n add_68[0][0] \n__________________________________________________________________________________________________\nconv2d_337 (Conv2D) (None, 56, 56, 240) 9600 add_69[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_252 (BatchN (None, 56, 56, 240) 960 conv2d_337[0][0] \n__________________________________________________________________________________________________\nswish_252 (Swish) (None, 56, 56, 240) 0 batch_normalization_252[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_86 (DepthwiseC (None, 56, 56, 240) 2160 swish_252[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_253 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_86[0][0] \n__________________________________________________________________________________________________\nswish_253 (Swish) (None, 56, 56, 240) 0 batch_normalization_253[0][0] \n__________________________________________________________________________________________________\nlambda_86 (Lambda) (None, 1, 1, 240) 0 swish_253[0][0] \n__________________________________________________________________________________________________\nconv2d_338 (Conv2D) (None, 1, 1, 10) 2410 lambda_86[0][0] \n__________________________________________________________________________________________________\nswish_254 (Swish) (None, 1, 1, 10) 0 conv2d_338[0][0] \n__________________________________________________________________________________________________\nconv2d_339 (Conv2D) (None, 1, 1, 240) 2640 swish_254[0][0] \n__________________________________________________________________________________________________\nactivation_86 (Activation) (None, 1, 1, 240) 0 conv2d_339[0][0] \n__________________________________________________________________________________________________\nmultiply_86 (Multiply) (None, 56, 56, 240) 0 activation_86[0][0] \n swish_253[0][0] \n__________________________________________________________________________________________________\nconv2d_340 (Conv2D) (None, 56, 56, 40) 9600 multiply_86[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_254 (BatchN (None, 56, 56, 40) 160 conv2d_340[0][0] \n__________________________________________________________________________________________________\ndrop_connect_70 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_254[0][0] \n__________________________________________________________________________________________________\nadd_70 (Add) (None, 56, 56, 40) 0 drop_connect_70[0][0] \n add_69[0][0] \n__________________________________________________________________________________________________\nconv2d_341 (Conv2D) (None, 56, 56, 240) 9600 add_70[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_255 (BatchN (None, 56, 56, 240) 960 conv2d_341[0][0] \n__________________________________________________________________________________________________\nswish_255 (Swish) (None, 56, 56, 240) 0 batch_normalization_255[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_87 (DepthwiseC (None, 28, 28, 240) 6000 swish_255[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_256 (BatchN (None, 28, 28, 240) 960 depthwise_conv2d_87[0][0] \n__________________________________________________________________________________________________\nswish_256 (Swish) (None, 28, 28, 240) 0 batch_normalization_256[0][0] \n__________________________________________________________________________________________________\nlambda_87 (Lambda) (None, 1, 1, 240) 0 swish_256[0][0] \n__________________________________________________________________________________________________\nconv2d_342 (Conv2D) (None, 1, 1, 10) 2410 lambda_87[0][0] \n__________________________________________________________________________________________________\nswish_257 (Swish) (None, 1, 1, 10) 0 conv2d_342[0][0] \n__________________________________________________________________________________________________\nconv2d_343 (Conv2D) (None, 1, 1, 240) 2640 swish_257[0][0] \n__________________________________________________________________________________________________\nactivation_87 (Activation) (None, 1, 1, 240) 0 conv2d_343[0][0] \n__________________________________________________________________________________________________\nmultiply_87 (Multiply) (None, 28, 28, 240) 0 activation_87[0][0] \n swish_256[0][0] \n__________________________________________________________________________________________________\nconv2d_344 (Conv2D) (None, 28, 28, 64) 15360 multiply_87[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_257 (BatchN (None, 28, 28, 64) 256 conv2d_344[0][0] \n__________________________________________________________________________________________________\nconv2d_345 (Conv2D) (None, 28, 28, 384) 24576 batch_normalization_257[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_258 (BatchN (None, 28, 28, 384) 1536 conv2d_345[0][0] \n__________________________________________________________________________________________________\nswish_258 (Swish) (None, 28, 28, 384) 0 batch_normalization_258[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_88 (DepthwiseC (None, 28, 28, 384) 9600 swish_258[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_259 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_88[0][0] \n__________________________________________________________________________________________________\nswish_259 (Swish) (None, 28, 28, 384) 0 batch_normalization_259[0][0] \n__________________________________________________________________________________________________\nlambda_88 (Lambda) (None, 1, 1, 384) 0 swish_259[0][0] \n__________________________________________________________________________________________________\nconv2d_346 (Conv2D) (None, 1, 1, 16) 6160 lambda_88[0][0] \n__________________________________________________________________________________________________\nswish_260 (Swish) (None, 1, 1, 16) 0 conv2d_346[0][0] \n__________________________________________________________________________________________________\nconv2d_347 (Conv2D) (None, 1, 1, 384) 6528 swish_260[0][0] \n__________________________________________________________________________________________________\nactivation_88 (Activation) (None, 1, 1, 384) 0 conv2d_347[0][0] \n__________________________________________________________________________________________________\nmultiply_88 (Multiply) (None, 28, 28, 384) 0 activation_88[0][0] \n swish_259[0][0] \n__________________________________________________________________________________________________\nconv2d_348 (Conv2D) (None, 28, 28, 64) 24576 multiply_88[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_260 (BatchN (None, 28, 28, 64) 256 conv2d_348[0][0] \n__________________________________________________________________________________________________\ndrop_connect_71 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_260[0][0] \n__________________________________________________________________________________________________\nadd_71 (Add) (None, 28, 28, 64) 0 drop_connect_71[0][0] \n batch_normalization_257[0][0] \n__________________________________________________________________________________________________\nconv2d_349 (Conv2D) (None, 28, 28, 384) 24576 add_71[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_261 (BatchN (None, 28, 28, 384) 1536 conv2d_349[0][0] \n__________________________________________________________________________________________________\nswish_261 (Swish) (None, 28, 28, 384) 0 batch_normalization_261[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_89 (DepthwiseC (None, 28, 28, 384) 9600 swish_261[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_262 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_89[0][0] \n__________________________________________________________________________________________________\nswish_262 (Swish) (None, 28, 28, 384) 0 batch_normalization_262[0][0] \n__________________________________________________________________________________________________\nlambda_89 (Lambda) (None, 1, 1, 384) 0 swish_262[0][0] \n__________________________________________________________________________________________________\nconv2d_350 (Conv2D) (None, 1, 1, 16) 6160 lambda_89[0][0] \n__________________________________________________________________________________________________\nswish_263 (Swish) (None, 1, 1, 16) 0 conv2d_350[0][0] \n__________________________________________________________________________________________________\nconv2d_351 (Conv2D) (None, 1, 1, 384) 6528 swish_263[0][0] \n__________________________________________________________________________________________________\nactivation_89 (Activation) (None, 1, 1, 384) 0 conv2d_351[0][0] \n__________________________________________________________________________________________________\nmultiply_89 (Multiply) (None, 28, 28, 384) 0 activation_89[0][0] \n swish_262[0][0] \n__________________________________________________________________________________________________\nconv2d_352 (Conv2D) (None, 28, 28, 64) 24576 multiply_89[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_263 (BatchN (None, 28, 28, 64) 256 conv2d_352[0][0] \n__________________________________________________________________________________________________\ndrop_connect_72 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_263[0][0] \n__________________________________________________________________________________________________\nadd_72 (Add) (None, 28, 28, 64) 0 drop_connect_72[0][0] \n add_71[0][0] \n__________________________________________________________________________________________________\nconv2d_353 (Conv2D) (None, 28, 28, 384) 24576 add_72[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_264 (BatchN (None, 28, 28, 384) 1536 conv2d_353[0][0] \n__________________________________________________________________________________________________\nswish_264 (Swish) (None, 28, 28, 384) 0 batch_normalization_264[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_90 (DepthwiseC (None, 28, 28, 384) 9600 swish_264[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_265 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_90[0][0] \n__________________________________________________________________________________________________\nswish_265 (Swish) (None, 28, 28, 384) 0 batch_normalization_265[0][0] \n__________________________________________________________________________________________________\nlambda_90 (Lambda) (None, 1, 1, 384) 0 swish_265[0][0] \n__________________________________________________________________________________________________\nconv2d_354 (Conv2D) (None, 1, 1, 16) 6160 lambda_90[0][0] \n__________________________________________________________________________________________________\nswish_266 (Swish) (None, 1, 1, 16) 0 conv2d_354[0][0] \n__________________________________________________________________________________________________\nconv2d_355 (Conv2D) (None, 1, 1, 384) 6528 swish_266[0][0] \n__________________________________________________________________________________________________\nactivation_90 (Activation) (None, 1, 1, 384) 0 conv2d_355[0][0] \n__________________________________________________________________________________________________\nmultiply_90 (Multiply) (None, 28, 28, 384) 0 activation_90[0][0] \n swish_265[0][0] \n__________________________________________________________________________________________________\nconv2d_356 (Conv2D) (None, 28, 28, 64) 24576 multiply_90[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_266 (BatchN (None, 28, 28, 64) 256 conv2d_356[0][0] \n__________________________________________________________________________________________________\ndrop_connect_73 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_266[0][0] \n__________________________________________________________________________________________________\nadd_73 (Add) (None, 28, 28, 64) 0 drop_connect_73[0][0] \n add_72[0][0] \n__________________________________________________________________________________________________\nconv2d_357 (Conv2D) (None, 28, 28, 384) 24576 add_73[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_267 (BatchN (None, 28, 28, 384) 1536 conv2d_357[0][0] \n__________________________________________________________________________________________________\nswish_267 (Swish) (None, 28, 28, 384) 0 batch_normalization_267[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_91 (DepthwiseC (None, 28, 28, 384) 9600 swish_267[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_268 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_91[0][0] \n__________________________________________________________________________________________________\nswish_268 (Swish) (None, 28, 28, 384) 0 batch_normalization_268[0][0] \n__________________________________________________________________________________________________\nlambda_91 (Lambda) (None, 1, 1, 384) 0 swish_268[0][0] \n__________________________________________________________________________________________________\nconv2d_358 (Conv2D) (None, 1, 1, 16) 6160 lambda_91[0][0] \n__________________________________________________________________________________________________\nswish_269 (Swish) (None, 1, 1, 16) 0 conv2d_358[0][0] \n__________________________________________________________________________________________________\nconv2d_359 (Conv2D) (None, 1, 1, 384) 6528 swish_269[0][0] \n__________________________________________________________________________________________________\nactivation_91 (Activation) (None, 1, 1, 384) 0 conv2d_359[0][0] \n__________________________________________________________________________________________________\nmultiply_91 (Multiply) (None, 28, 28, 384) 0 activation_91[0][0] \n swish_268[0][0] \n__________________________________________________________________________________________________\nconv2d_360 (Conv2D) (None, 28, 28, 64) 24576 multiply_91[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_269 (BatchN (None, 28, 28, 64) 256 conv2d_360[0][0] \n__________________________________________________________________________________________________\ndrop_connect_74 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_269[0][0] \n__________________________________________________________________________________________________\nadd_74 (Add) (None, 28, 28, 64) 0 drop_connect_74[0][0] \n add_73[0][0] \n__________________________________________________________________________________________________\nconv2d_361 (Conv2D) (None, 28, 28, 384) 24576 add_74[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_270 (BatchN (None, 28, 28, 384) 1536 conv2d_361[0][0] \n__________________________________________________________________________________________________\nswish_270 (Swish) (None, 28, 28, 384) 0 batch_normalization_270[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_92 (DepthwiseC (None, 14, 14, 384) 3456 swish_270[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_271 (BatchN (None, 14, 14, 384) 1536 depthwise_conv2d_92[0][0] \n__________________________________________________________________________________________________\nswish_271 (Swish) (None, 14, 14, 384) 0 batch_normalization_271[0][0] \n__________________________________________________________________________________________________\nlambda_92 (Lambda) (None, 1, 1, 384) 0 swish_271[0][0] \n__________________________________________________________________________________________________\nconv2d_362 (Conv2D) (None, 1, 1, 16) 6160 lambda_92[0][0] \n__________________________________________________________________________________________________\nswish_272 (Swish) (None, 1, 1, 16) 0 conv2d_362[0][0] \n__________________________________________________________________________________________________\nconv2d_363 (Conv2D) (None, 1, 1, 384) 6528 swish_272[0][0] \n__________________________________________________________________________________________________\nactivation_92 (Activation) (None, 1, 1, 384) 0 conv2d_363[0][0] \n__________________________________________________________________________________________________\nmultiply_92 (Multiply) (None, 14, 14, 384) 0 activation_92[0][0] \n swish_271[0][0] \n__________________________________________________________________________________________________\nconv2d_364 (Conv2D) (None, 14, 14, 128) 49152 multiply_92[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_272 (BatchN (None, 14, 14, 128) 512 conv2d_364[0][0] \n__________________________________________________________________________________________________\nconv2d_365 (Conv2D) (None, 14, 14, 768) 98304 batch_normalization_272[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_273 (BatchN (None, 14, 14, 768) 3072 conv2d_365[0][0] \n__________________________________________________________________________________________________\nswish_273 (Swish) (None, 14, 14, 768) 0 batch_normalization_273[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_93 (DepthwiseC (None, 14, 14, 768) 6912 swish_273[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_274 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_93[0][0] \n__________________________________________________________________________________________________\nswish_274 (Swish) (None, 14, 14, 768) 0 batch_normalization_274[0][0] \n__________________________________________________________________________________________________\nlambda_93 (Lambda) (None, 1, 1, 768) 0 swish_274[0][0] \n__________________________________________________________________________________________________\nconv2d_366 (Conv2D) (None, 1, 1, 32) 24608 lambda_93[0][0] \n__________________________________________________________________________________________________\nswish_275 (Swish) (None, 1, 1, 32) 0 conv2d_366[0][0] \n__________________________________________________________________________________________________\nconv2d_367 (Conv2D) (None, 1, 1, 768) 25344 swish_275[0][0] \n__________________________________________________________________________________________________\nactivation_93 (Activation) (None, 1, 1, 768) 0 conv2d_367[0][0] \n__________________________________________________________________________________________________\nmultiply_93 (Multiply) (None, 14, 14, 768) 0 activation_93[0][0] \n swish_274[0][0] \n__________________________________________________________________________________________________\nconv2d_368 (Conv2D) (None, 14, 14, 128) 98304 multiply_93[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_275 (BatchN (None, 14, 14, 128) 512 conv2d_368[0][0] \n__________________________________________________________________________________________________\ndrop_connect_75 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_275[0][0] \n__________________________________________________________________________________________________\nadd_75 (Add) (None, 14, 14, 128) 0 drop_connect_75[0][0] \n batch_normalization_272[0][0] \n__________________________________________________________________________________________________\nconv2d_369 (Conv2D) (None, 14, 14, 768) 98304 add_75[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_276 (BatchN (None, 14, 14, 768) 3072 conv2d_369[0][0] \n__________________________________________________________________________________________________\nswish_276 (Swish) (None, 14, 14, 768) 0 batch_normalization_276[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_94 (DepthwiseC (None, 14, 14, 768) 6912 swish_276[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_277 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_94[0][0] \n__________________________________________________________________________________________________\nswish_277 (Swish) (None, 14, 14, 768) 0 batch_normalization_277[0][0] \n__________________________________________________________________________________________________\nlambda_94 (Lambda) (None, 1, 1, 768) 0 swish_277[0][0] \n__________________________________________________________________________________________________\nconv2d_370 (Conv2D) (None, 1, 1, 32) 24608 lambda_94[0][0] \n__________________________________________________________________________________________________\nswish_278 (Swish) (None, 1, 1, 32) 0 conv2d_370[0][0] \n__________________________________________________________________________________________________\nconv2d_371 (Conv2D) (None, 1, 1, 768) 25344 swish_278[0][0] \n__________________________________________________________________________________________________\nactivation_94 (Activation) (None, 1, 1, 768) 0 conv2d_371[0][0] \n__________________________________________________________________________________________________\nmultiply_94 (Multiply) (None, 14, 14, 768) 0 activation_94[0][0] \n swish_277[0][0] \n__________________________________________________________________________________________________\nconv2d_372 (Conv2D) (None, 14, 14, 128) 98304 multiply_94[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_278 (BatchN (None, 14, 14, 128) 512 conv2d_372[0][0] \n__________________________________________________________________________________________________\ndrop_connect_76 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_278[0][0] \n__________________________________________________________________________________________________\nadd_76 (Add) (None, 14, 14, 128) 0 drop_connect_76[0][0] \n add_75[0][0] \n__________________________________________________________________________________________________\nconv2d_373 (Conv2D) (None, 14, 14, 768) 98304 add_76[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_279 (BatchN (None, 14, 14, 768) 3072 conv2d_373[0][0] \n__________________________________________________________________________________________________\nswish_279 (Swish) (None, 14, 14, 768) 0 batch_normalization_279[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_95 (DepthwiseC (None, 14, 14, 768) 6912 swish_279[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_280 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_95[0][0] \n__________________________________________________________________________________________________\nswish_280 (Swish) (None, 14, 14, 768) 0 batch_normalization_280[0][0] \n__________________________________________________________________________________________________\nlambda_95 (Lambda) (None, 1, 1, 768) 0 swish_280[0][0] \n__________________________________________________________________________________________________\nconv2d_374 (Conv2D) (None, 1, 1, 32) 24608 lambda_95[0][0] \n__________________________________________________________________________________________________\nswish_281 (Swish) (None, 1, 1, 32) 0 conv2d_374[0][0] \n__________________________________________________________________________________________________\nconv2d_375 (Conv2D) (None, 1, 1, 768) 25344 swish_281[0][0] \n__________________________________________________________________________________________________\nactivation_95 (Activation) (None, 1, 1, 768) 0 conv2d_375[0][0] \n__________________________________________________________________________________________________\nmultiply_95 (Multiply) (None, 14, 14, 768) 0 activation_95[0][0] \n swish_280[0][0] \n__________________________________________________________________________________________________\nconv2d_376 (Conv2D) (None, 14, 14, 128) 98304 multiply_95[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_281 (BatchN (None, 14, 14, 128) 512 conv2d_376[0][0] \n__________________________________________________________________________________________________\ndrop_connect_77 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_281[0][0] \n__________________________________________________________________________________________________\nadd_77 (Add) (None, 14, 14, 128) 0 drop_connect_77[0][0] \n add_76[0][0] \n__________________________________________________________________________________________________\nconv2d_377 (Conv2D) (None, 14, 14, 768) 98304 add_77[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_282 (BatchN (None, 14, 14, 768) 3072 conv2d_377[0][0] \n__________________________________________________________________________________________________\nswish_282 (Swish) (None, 14, 14, 768) 0 batch_normalization_282[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_96 (DepthwiseC (None, 14, 14, 768) 6912 swish_282[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_283 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_96[0][0] \n__________________________________________________________________________________________________\nswish_283 (Swish) (None, 14, 14, 768) 0 batch_normalization_283[0][0] \n__________________________________________________________________________________________________\nlambda_96 (Lambda) (None, 1, 1, 768) 0 swish_283[0][0] \n__________________________________________________________________________________________________\nconv2d_378 (Conv2D) (None, 1, 1, 32) 24608 lambda_96[0][0] \n__________________________________________________________________________________________________\nswish_284 (Swish) (None, 1, 1, 32) 0 conv2d_378[0][0] \n__________________________________________________________________________________________________\nconv2d_379 (Conv2D) (None, 1, 1, 768) 25344 swish_284[0][0] \n__________________________________________________________________________________________________\nactivation_96 (Activation) (None, 1, 1, 768) 0 conv2d_379[0][0] \n__________________________________________________________________________________________________\nmultiply_96 (Multiply) (None, 14, 14, 768) 0 activation_96[0][0] \n swish_283[0][0] \n__________________________________________________________________________________________________\nconv2d_380 (Conv2D) (None, 14, 14, 128) 98304 multiply_96[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_284 (BatchN (None, 14, 14, 128) 512 conv2d_380[0][0] \n__________________________________________________________________________________________________\ndrop_connect_78 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_284[0][0] \n__________________________________________________________________________________________________\nadd_78 (Add) (None, 14, 14, 128) 0 drop_connect_78[0][0] \n add_77[0][0] \n__________________________________________________________________________________________________\nconv2d_381 (Conv2D) (None, 14, 14, 768) 98304 add_78[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_285 (BatchN (None, 14, 14, 768) 3072 conv2d_381[0][0] \n__________________________________________________________________________________________________\nswish_285 (Swish) (None, 14, 14, 768) 0 batch_normalization_285[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_97 (DepthwiseC (None, 14, 14, 768) 6912 swish_285[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_286 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_97[0][0] \n__________________________________________________________________________________________________\nswish_286 (Swish) (None, 14, 14, 768) 0 batch_normalization_286[0][0] \n__________________________________________________________________________________________________\nlambda_97 (Lambda) (None, 1, 1, 768) 0 swish_286[0][0] \n__________________________________________________________________________________________________\nconv2d_382 (Conv2D) (None, 1, 1, 32) 24608 lambda_97[0][0] \n__________________________________________________________________________________________________\nswish_287 (Swish) (None, 1, 1, 32) 0 conv2d_382[0][0] \n__________________________________________________________________________________________________\nconv2d_383 (Conv2D) (None, 1, 1, 768) 25344 swish_287[0][0] \n__________________________________________________________________________________________________\nactivation_97 (Activation) (None, 1, 1, 768) 0 conv2d_383[0][0] \n__________________________________________________________________________________________________\nmultiply_97 (Multiply) (None, 14, 14, 768) 0 activation_97[0][0] \n swish_286[0][0] \n__________________________________________________________________________________________________\nconv2d_384 (Conv2D) (None, 14, 14, 128) 98304 multiply_97[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_287 (BatchN (None, 14, 14, 128) 512 conv2d_384[0][0] \n__________________________________________________________________________________________________\ndrop_connect_79 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_287[0][0] \n__________________________________________________________________________________________________\nadd_79 (Add) (None, 14, 14, 128) 0 drop_connect_79[0][0] \n add_78[0][0] \n__________________________________________________________________________________________________\nconv2d_385 (Conv2D) (None, 14, 14, 768) 98304 add_79[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_288 (BatchN (None, 14, 14, 768) 3072 conv2d_385[0][0] \n__________________________________________________________________________________________________\nswish_288 (Swish) (None, 14, 14, 768) 0 batch_normalization_288[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_98 (DepthwiseC (None, 14, 14, 768) 6912 swish_288[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_289 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_98[0][0] \n__________________________________________________________________________________________________\nswish_289 (Swish) (None, 14, 14, 768) 0 batch_normalization_289[0][0] \n__________________________________________________________________________________________________\nlambda_98 (Lambda) (None, 1, 1, 768) 0 swish_289[0][0] \n__________________________________________________________________________________________________\nconv2d_386 (Conv2D) (None, 1, 1, 32) 24608 lambda_98[0][0] \n__________________________________________________________________________________________________\nswish_290 (Swish) (None, 1, 1, 32) 0 conv2d_386[0][0] \n__________________________________________________________________________________________________\nconv2d_387 (Conv2D) (None, 1, 1, 768) 25344 swish_290[0][0] \n__________________________________________________________________________________________________\nactivation_98 (Activation) (None, 1, 1, 768) 0 conv2d_387[0][0] \n__________________________________________________________________________________________________\nmultiply_98 (Multiply) (None, 14, 14, 768) 0 activation_98[0][0] \n swish_289[0][0] \n__________________________________________________________________________________________________\nconv2d_388 (Conv2D) (None, 14, 14, 128) 98304 multiply_98[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_290 (BatchN (None, 14, 14, 128) 512 conv2d_388[0][0] \n__________________________________________________________________________________________________\ndrop_connect_80 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_290[0][0] \n__________________________________________________________________________________________________\nadd_80 (Add) (None, 14, 14, 128) 0 drop_connect_80[0][0] \n add_79[0][0] \n__________________________________________________________________________________________________\nconv2d_389 (Conv2D) (None, 14, 14, 768) 98304 add_80[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_291 (BatchN (None, 14, 14, 768) 3072 conv2d_389[0][0] \n__________________________________________________________________________________________________\nswish_291 (Swish) (None, 14, 14, 768) 0 batch_normalization_291[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_99 (DepthwiseC (None, 14, 14, 768) 19200 swish_291[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_292 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_99[0][0] \n__________________________________________________________________________________________________\nswish_292 (Swish) (None, 14, 14, 768) 0 batch_normalization_292[0][0] \n__________________________________________________________________________________________________\nlambda_99 (Lambda) (None, 1, 1, 768) 0 swish_292[0][0] \n__________________________________________________________________________________________________\nconv2d_390 (Conv2D) (None, 1, 1, 32) 24608 lambda_99[0][0] \n__________________________________________________________________________________________________\nswish_293 (Swish) (None, 1, 1, 32) 0 conv2d_390[0][0] \n__________________________________________________________________________________________________\nconv2d_391 (Conv2D) (None, 1, 1, 768) 25344 swish_293[0][0] \n__________________________________________________________________________________________________\nactivation_99 (Activation) (None, 1, 1, 768) 0 conv2d_391[0][0] \n__________________________________________________________________________________________________\nmultiply_99 (Multiply) (None, 14, 14, 768) 0 activation_99[0][0] \n swish_292[0][0] \n__________________________________________________________________________________________________\nconv2d_392 (Conv2D) (None, 14, 14, 176) 135168 multiply_99[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_293 (BatchN (None, 14, 14, 176) 704 conv2d_392[0][0] \n__________________________________________________________________________________________________\nconv2d_393 (Conv2D) (None, 14, 14, 1056) 185856 batch_normalization_293[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_294 (BatchN (None, 14, 14, 1056) 4224 conv2d_393[0][0] \n__________________________________________________________________________________________________\nswish_294 (Swish) (None, 14, 14, 1056) 0 batch_normalization_294[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_100 (Depthwise (None, 14, 14, 1056) 26400 swish_294[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_295 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_100[0][0] \n__________________________________________________________________________________________________\nswish_295 (Swish) (None, 14, 14, 1056) 0 batch_normalization_295[0][0] \n__________________________________________________________________________________________________\nlambda_100 (Lambda) (None, 1, 1, 1056) 0 swish_295[0][0] \n__________________________________________________________________________________________________\nconv2d_394 (Conv2D) (None, 1, 1, 44) 46508 lambda_100[0][0] \n__________________________________________________________________________________________________\nswish_296 (Swish) (None, 1, 1, 44) 0 conv2d_394[0][0] \n__________________________________________________________________________________________________\nconv2d_395 (Conv2D) (None, 1, 1, 1056) 47520 swish_296[0][0] \n__________________________________________________________________________________________________\nactivation_100 (Activation) (None, 1, 1, 1056) 0 conv2d_395[0][0] \n__________________________________________________________________________________________________\nmultiply_100 (Multiply) (None, 14, 14, 1056) 0 activation_100[0][0] \n swish_295[0][0] \n__________________________________________________________________________________________________\nconv2d_396 (Conv2D) (None, 14, 14, 176) 185856 multiply_100[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_296 (BatchN (None, 14, 14, 176) 704 conv2d_396[0][0] \n__________________________________________________________________________________________________\ndrop_connect_81 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_296[0][0] \n__________________________________________________________________________________________________\nadd_81 (Add) (None, 14, 14, 176) 0 drop_connect_81[0][0] \n batch_normalization_293[0][0] \n__________________________________________________________________________________________________\nconv2d_397 (Conv2D) (None, 14, 14, 1056) 185856 add_81[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_297 (BatchN (None, 14, 14, 1056) 4224 conv2d_397[0][0] \n__________________________________________________________________________________________________\nswish_297 (Swish) (None, 14, 14, 1056) 0 batch_normalization_297[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_101 (Depthwise (None, 14, 14, 1056) 26400 swish_297[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_298 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_101[0][0] \n__________________________________________________________________________________________________\nswish_298 (Swish) (None, 14, 14, 1056) 0 batch_normalization_298[0][0] \n__________________________________________________________________________________________________\nlambda_101 (Lambda) (None, 1, 1, 1056) 0 swish_298[0][0] \n__________________________________________________________________________________________________\nconv2d_398 (Conv2D) (None, 1, 1, 44) 46508 lambda_101[0][0] \n__________________________________________________________________________________________________\nswish_299 (Swish) (None, 1, 1, 44) 0 conv2d_398[0][0] \n__________________________________________________________________________________________________\nconv2d_399 (Conv2D) (None, 1, 1, 1056) 47520 swish_299[0][0] \n__________________________________________________________________________________________________\nactivation_101 (Activation) (None, 1, 1, 1056) 0 conv2d_399[0][0] \n__________________________________________________________________________________________________\nmultiply_101 (Multiply) (None, 14, 14, 1056) 0 activation_101[0][0] \n swish_298[0][0] \n__________________________________________________________________________________________________\nconv2d_400 (Conv2D) (None, 14, 14, 176) 185856 multiply_101[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_299 (BatchN (None, 14, 14, 176) 704 conv2d_400[0][0] \n__________________________________________________________________________________________________\ndrop_connect_82 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_299[0][0] \n__________________________________________________________________________________________________\nadd_82 (Add) (None, 14, 14, 176) 0 drop_connect_82[0][0] \n add_81[0][0] \n__________________________________________________________________________________________________\nconv2d_401 (Conv2D) (None, 14, 14, 1056) 185856 add_82[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_300 (BatchN (None, 14, 14, 1056) 4224 conv2d_401[0][0] \n__________________________________________________________________________________________________\nswish_300 (Swish) (None, 14, 14, 1056) 0 batch_normalization_300[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_102 (Depthwise (None, 14, 14, 1056) 26400 swish_300[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_301 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_102[0][0] \n__________________________________________________________________________________________________\nswish_301 (Swish) (None, 14, 14, 1056) 0 batch_normalization_301[0][0] \n__________________________________________________________________________________________________\nlambda_102 (Lambda) (None, 1, 1, 1056) 0 swish_301[0][0] \n__________________________________________________________________________________________________\nconv2d_402 (Conv2D) (None, 1, 1, 44) 46508 lambda_102[0][0] \n__________________________________________________________________________________________________\nswish_302 (Swish) (None, 1, 1, 44) 0 conv2d_402[0][0] \n__________________________________________________________________________________________________\nconv2d_403 (Conv2D) (None, 1, 1, 1056) 47520 swish_302[0][0] \n__________________________________________________________________________________________________\nactivation_102 (Activation) (None, 1, 1, 1056) 0 conv2d_403[0][0] \n__________________________________________________________________________________________________\nmultiply_102 (Multiply) (None, 14, 14, 1056) 0 activation_102[0][0] \n swish_301[0][0] \n__________________________________________________________________________________________________\nconv2d_404 (Conv2D) (None, 14, 14, 176) 185856 multiply_102[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_302 (BatchN (None, 14, 14, 176) 704 conv2d_404[0][0] \n__________________________________________________________________________________________________\ndrop_connect_83 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_302[0][0] \n__________________________________________________________________________________________________\nadd_83 (Add) (None, 14, 14, 176) 0 drop_connect_83[0][0] \n add_82[0][0] \n__________________________________________________________________________________________________\nconv2d_405 (Conv2D) (None, 14, 14, 1056) 185856 add_83[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_303 (BatchN (None, 14, 14, 1056) 4224 conv2d_405[0][0] \n__________________________________________________________________________________________________\nswish_303 (Swish) (None, 14, 14, 1056) 0 batch_normalization_303[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_103 (Depthwise (None, 14, 14, 1056) 26400 swish_303[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_304 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_103[0][0] \n__________________________________________________________________________________________________\nswish_304 (Swish) (None, 14, 14, 1056) 0 batch_normalization_304[0][0] \n__________________________________________________________________________________________________\nlambda_103 (Lambda) (None, 1, 1, 1056) 0 swish_304[0][0] \n__________________________________________________________________________________________________\nconv2d_406 (Conv2D) (None, 1, 1, 44) 46508 lambda_103[0][0] \n__________________________________________________________________________________________________\nswish_305 (Swish) (None, 1, 1, 44) 0 conv2d_406[0][0] \n__________________________________________________________________________________________________\nconv2d_407 (Conv2D) (None, 1, 1, 1056) 47520 swish_305[0][0] \n__________________________________________________________________________________________________\nactivation_103 (Activation) (None, 1, 1, 1056) 0 conv2d_407[0][0] \n__________________________________________________________________________________________________\nmultiply_103 (Multiply) (None, 14, 14, 1056) 0 activation_103[0][0] \n swish_304[0][0] \n__________________________________________________________________________________________________\nconv2d_408 (Conv2D) (None, 14, 14, 176) 185856 multiply_103[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_305 (BatchN (None, 14, 14, 176) 704 conv2d_408[0][0] \n__________________________________________________________________________________________________\ndrop_connect_84 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_305[0][0] \n__________________________________________________________________________________________________\nadd_84 (Add) (None, 14, 14, 176) 0 drop_connect_84[0][0] \n add_83[0][0] \n__________________________________________________________________________________________________\nconv2d_409 (Conv2D) (None, 14, 14, 1056) 185856 add_84[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_306 (BatchN (None, 14, 14, 1056) 4224 conv2d_409[0][0] \n__________________________________________________________________________________________________\nswish_306 (Swish) (None, 14, 14, 1056) 0 batch_normalization_306[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_104 (Depthwise (None, 14, 14, 1056) 26400 swish_306[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_307 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_104[0][0] \n__________________________________________________________________________________________________\nswish_307 (Swish) (None, 14, 14, 1056) 0 batch_normalization_307[0][0] \n__________________________________________________________________________________________________\nlambda_104 (Lambda) (None, 1, 1, 1056) 0 swish_307[0][0] \n__________________________________________________________________________________________________\nconv2d_410 (Conv2D) (None, 1, 1, 44) 46508 lambda_104[0][0] \n__________________________________________________________________________________________________\nswish_308 (Swish) (None, 1, 1, 44) 0 conv2d_410[0][0] \n__________________________________________________________________________________________________\nconv2d_411 (Conv2D) (None, 1, 1, 1056) 47520 swish_308[0][0] \n__________________________________________________________________________________________________\nactivation_104 (Activation) (None, 1, 1, 1056) 0 conv2d_411[0][0] \n__________________________________________________________________________________________________\nmultiply_104 (Multiply) (None, 14, 14, 1056) 0 activation_104[0][0] \n swish_307[0][0] \n__________________________________________________________________________________________________\nconv2d_412 (Conv2D) (None, 14, 14, 176) 185856 multiply_104[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_308 (BatchN (None, 14, 14, 176) 704 conv2d_412[0][0] \n__________________________________________________________________________________________________\ndrop_connect_85 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_308[0][0] \n__________________________________________________________________________________________________\nadd_85 (Add) (None, 14, 14, 176) 0 drop_connect_85[0][0] \n add_84[0][0] \n__________________________________________________________________________________________________\nconv2d_413 (Conv2D) (None, 14, 14, 1056) 185856 add_85[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_309 (BatchN (None, 14, 14, 1056) 4224 conv2d_413[0][0] \n__________________________________________________________________________________________________\nswish_309 (Swish) (None, 14, 14, 1056) 0 batch_normalization_309[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_105 (Depthwise (None, 14, 14, 1056) 26400 swish_309[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_310 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_105[0][0] \n__________________________________________________________________________________________________\nswish_310 (Swish) (None, 14, 14, 1056) 0 batch_normalization_310[0][0] \n__________________________________________________________________________________________________\nlambda_105 (Lambda) (None, 1, 1, 1056) 0 swish_310[0][0] \n__________________________________________________________________________________________________\nconv2d_414 (Conv2D) (None, 1, 1, 44) 46508 lambda_105[0][0] \n__________________________________________________________________________________________________\nswish_311 (Swish) (None, 1, 1, 44) 0 conv2d_414[0][0] \n__________________________________________________________________________________________________\nconv2d_415 (Conv2D) (None, 1, 1, 1056) 47520 swish_311[0][0] \n__________________________________________________________________________________________________\nactivation_105 (Activation) (None, 1, 1, 1056) 0 conv2d_415[0][0] \n__________________________________________________________________________________________________\nmultiply_105 (Multiply) (None, 14, 14, 1056) 0 activation_105[0][0] \n swish_310[0][0] \n__________________________________________________________________________________________________\nconv2d_416 (Conv2D) (None, 14, 14, 176) 185856 multiply_105[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_311 (BatchN (None, 14, 14, 176) 704 conv2d_416[0][0] \n__________________________________________________________________________________________________\ndrop_connect_86 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_311[0][0] \n__________________________________________________________________________________________________\nadd_86 (Add) (None, 14, 14, 176) 0 drop_connect_86[0][0] \n add_85[0][0] \n__________________________________________________________________________________________________\nconv2d_417 (Conv2D) (None, 14, 14, 1056) 185856 add_86[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_312 (BatchN (None, 14, 14, 1056) 4224 conv2d_417[0][0] \n__________________________________________________________________________________________________\nswish_312 (Swish) (None, 14, 14, 1056) 0 batch_normalization_312[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_106 (Depthwise (None, 7, 7, 1056) 26400 swish_312[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_313 (BatchN (None, 7, 7, 1056) 4224 depthwise_conv2d_106[0][0] \n__________________________________________________________________________________________________\nswish_313 (Swish) (None, 7, 7, 1056) 0 batch_normalization_313[0][0] \n__________________________________________________________________________________________________\nlambda_106 (Lambda) (None, 1, 1, 1056) 0 swish_313[0][0] \n__________________________________________________________________________________________________\nconv2d_418 (Conv2D) (None, 1, 1, 44) 46508 lambda_106[0][0] \n__________________________________________________________________________________________________\nswish_314 (Swish) (None, 1, 1, 44) 0 conv2d_418[0][0] \n__________________________________________________________________________________________________\nconv2d_419 (Conv2D) (None, 1, 1, 1056) 47520 swish_314[0][0] \n__________________________________________________________________________________________________\nactivation_106 (Activation) (None, 1, 1, 1056) 0 conv2d_419[0][0] \n__________________________________________________________________________________________________\nmultiply_106 (Multiply) (None, 7, 7, 1056) 0 activation_106[0][0] \n swish_313[0][0] \n__________________________________________________________________________________________________\nconv2d_420 (Conv2D) (None, 7, 7, 304) 321024 multiply_106[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_314 (BatchN (None, 7, 7, 304) 1216 conv2d_420[0][0] \n__________________________________________________________________________________________________\nconv2d_421 (Conv2D) (None, 7, 7, 1824) 554496 batch_normalization_314[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_315 (BatchN (None, 7, 7, 1824) 7296 conv2d_421[0][0] \n__________________________________________________________________________________________________\nswish_315 (Swish) (None, 7, 7, 1824) 0 batch_normalization_315[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_107 (Depthwise (None, 7, 7, 1824) 45600 swish_315[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_316 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_107[0][0] \n__________________________________________________________________________________________________\nswish_316 (Swish) (None, 7, 7, 1824) 0 batch_normalization_316[0][0] \n__________________________________________________________________________________________________\nlambda_107 (Lambda) (None, 1, 1, 1824) 0 swish_316[0][0] \n__________________________________________________________________________________________________\nconv2d_422 (Conv2D) (None, 1, 1, 76) 138700 lambda_107[0][0] \n__________________________________________________________________________________________________\nswish_317 (Swish) (None, 1, 1, 76) 0 conv2d_422[0][0] \n__________________________________________________________________________________________________\nconv2d_423 (Conv2D) (None, 1, 1, 1824) 140448 swish_317[0][0] \n__________________________________________________________________________________________________\nactivation_107 (Activation) (None, 1, 1, 1824) 0 conv2d_423[0][0] \n__________________________________________________________________________________________________\nmultiply_107 (Multiply) (None, 7, 7, 1824) 0 activation_107[0][0] \n swish_316[0][0] \n__________________________________________________________________________________________________\nconv2d_424 (Conv2D) (None, 7, 7, 304) 554496 multiply_107[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_317 (BatchN (None, 7, 7, 304) 1216 conv2d_424[0][0] \n__________________________________________________________________________________________________\ndrop_connect_87 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_317[0][0] \n__________________________________________________________________________________________________\nadd_87 (Add) (None, 7, 7, 304) 0 drop_connect_87[0][0] \n batch_normalization_314[0][0] \n__________________________________________________________________________________________________\nconv2d_425 (Conv2D) (None, 7, 7, 1824) 554496 add_87[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_318 (BatchN (None, 7, 7, 1824) 7296 conv2d_425[0][0] \n__________________________________________________________________________________________________\nswish_318 (Swish) (None, 7, 7, 1824) 0 batch_normalization_318[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_108 (Depthwise (None, 7, 7, 1824) 45600 swish_318[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_319 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_108[0][0] \n__________________________________________________________________________________________________\nswish_319 (Swish) (None, 7, 7, 1824) 0 batch_normalization_319[0][0] \n__________________________________________________________________________________________________\nlambda_108 (Lambda) (None, 1, 1, 1824) 0 swish_319[0][0] \n__________________________________________________________________________________________________\nconv2d_426 (Conv2D) (None, 1, 1, 76) 138700 lambda_108[0][0] \n__________________________________________________________________________________________________\nswish_320 (Swish) (None, 1, 1, 76) 0 conv2d_426[0][0] \n__________________________________________________________________________________________________\nconv2d_427 (Conv2D) (None, 1, 1, 1824) 140448 swish_320[0][0] \n__________________________________________________________________________________________________\nactivation_108 (Activation) (None, 1, 1, 1824) 0 conv2d_427[0][0] \n__________________________________________________________________________________________________\nmultiply_108 (Multiply) (None, 7, 7, 1824) 0 activation_108[0][0] \n swish_319[0][0] \n__________________________________________________________________________________________________\nconv2d_428 (Conv2D) (None, 7, 7, 304) 554496 multiply_108[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_320 (BatchN (None, 7, 7, 304) 1216 conv2d_428[0][0] \n__________________________________________________________________________________________________\ndrop_connect_88 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_320[0][0] \n__________________________________________________________________________________________________\nadd_88 (Add) (None, 7, 7, 304) 0 drop_connect_88[0][0] \n add_87[0][0] \n__________________________________________________________________________________________________\nconv2d_429 (Conv2D) (None, 7, 7, 1824) 554496 add_88[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_321 (BatchN (None, 7, 7, 1824) 7296 conv2d_429[0][0] \n__________________________________________________________________________________________________\nswish_321 (Swish) (None, 7, 7, 1824) 0 batch_normalization_321[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_109 (Depthwise (None, 7, 7, 1824) 45600 swish_321[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_322 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_109[0][0] \n__________________________________________________________________________________________________\nswish_322 (Swish) (None, 7, 7, 1824) 0 batch_normalization_322[0][0] \n__________________________________________________________________________________________________\nlambda_109 (Lambda) (None, 1, 1, 1824) 0 swish_322[0][0] \n__________________________________________________________________________________________________\nconv2d_430 (Conv2D) (None, 1, 1, 76) 138700 lambda_109[0][0] \n__________________________________________________________________________________________________\nswish_323 (Swish) (None, 1, 1, 76) 0 conv2d_430[0][0] \n__________________________________________________________________________________________________\nconv2d_431 (Conv2D) (None, 1, 1, 1824) 140448 swish_323[0][0] \n__________________________________________________________________________________________________\nactivation_109 (Activation) (None, 1, 1, 1824) 0 conv2d_431[0][0] \n__________________________________________________________________________________________________\nmultiply_109 (Multiply) (None, 7, 7, 1824) 0 activation_109[0][0] \n swish_322[0][0] \n__________________________________________________________________________________________________\nconv2d_432 (Conv2D) (None, 7, 7, 304) 554496 multiply_109[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_323 (BatchN (None, 7, 7, 304) 1216 conv2d_432[0][0] \n__________________________________________________________________________________________________\ndrop_connect_89 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_323[0][0] \n__________________________________________________________________________________________________\nadd_89 (Add) (None, 7, 7, 304) 0 drop_connect_89[0][0] \n add_88[0][0] \n__________________________________________________________________________________________________\nconv2d_433 (Conv2D) (None, 7, 7, 1824) 554496 add_89[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_324 (BatchN (None, 7, 7, 1824) 7296 conv2d_433[0][0] \n__________________________________________________________________________________________________\nswish_324 (Swish) (None, 7, 7, 1824) 0 batch_normalization_324[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_110 (Depthwise (None, 7, 7, 1824) 45600 swish_324[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_325 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_110[0][0] \n__________________________________________________________________________________________________\nswish_325 (Swish) (None, 7, 7, 1824) 0 batch_normalization_325[0][0] \n__________________________________________________________________________________________________\nlambda_110 (Lambda) (None, 1, 1, 1824) 0 swish_325[0][0] \n__________________________________________________________________________________________________\nconv2d_434 (Conv2D) (None, 1, 1, 76) 138700 lambda_110[0][0] \n__________________________________________________________________________________________________\nswish_326 (Swish) (None, 1, 1, 76) 0 conv2d_434[0][0] \n__________________________________________________________________________________________________\nconv2d_435 (Conv2D) (None, 1, 1, 1824) 140448 swish_326[0][0] \n__________________________________________________________________________________________________\nactivation_110 (Activation) (None, 1, 1, 1824) 0 conv2d_435[0][0] \n__________________________________________________________________________________________________\nmultiply_110 (Multiply) (None, 7, 7, 1824) 0 activation_110[0][0] \n swish_325[0][0] \n__________________________________________________________________________________________________\nconv2d_436 (Conv2D) (None, 7, 7, 304) 554496 multiply_110[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_326 (BatchN (None, 7, 7, 304) 1216 conv2d_436[0][0] \n__________________________________________________________________________________________________\ndrop_connect_90 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_326[0][0] \n__________________________________________________________________________________________________\nadd_90 (Add) (None, 7, 7, 304) 0 drop_connect_90[0][0] \n add_89[0][0] \n__________________________________________________________________________________________________\nconv2d_437 (Conv2D) (None, 7, 7, 1824) 554496 add_90[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_327 (BatchN (None, 7, 7, 1824) 7296 conv2d_437[0][0] \n__________________________________________________________________________________________________\nswish_327 (Swish) (None, 7, 7, 1824) 0 batch_normalization_327[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_111 (Depthwise (None, 7, 7, 1824) 45600 swish_327[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_328 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_111[0][0] \n__________________________________________________________________________________________________\nswish_328 (Swish) (None, 7, 7, 1824) 0 batch_normalization_328[0][0] \n__________________________________________________________________________________________________\nlambda_111 (Lambda) (None, 1, 1, 1824) 0 swish_328[0][0] \n__________________________________________________________________________________________________\nconv2d_438 (Conv2D) (None, 1, 1, 76) 138700 lambda_111[0][0] \n__________________________________________________________________________________________________\nswish_329 (Swish) (None, 1, 1, 76) 0 conv2d_438[0][0] \n__________________________________________________________________________________________________\nconv2d_439 (Conv2D) (None, 1, 1, 1824) 140448 swish_329[0][0] \n__________________________________________________________________________________________________\nactivation_111 (Activation) (None, 1, 1, 1824) 0 conv2d_439[0][0] \n__________________________________________________________________________________________________\nmultiply_111 (Multiply) (None, 7, 7, 1824) 0 activation_111[0][0] \n swish_328[0][0] \n__________________________________________________________________________________________________\nconv2d_440 (Conv2D) (None, 7, 7, 304) 554496 multiply_111[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_329 (BatchN (None, 7, 7, 304) 1216 conv2d_440[0][0] \n__________________________________________________________________________________________________\ndrop_connect_91 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_329[0][0] \n__________________________________________________________________________________________________\nadd_91 (Add) (None, 7, 7, 304) 0 drop_connect_91[0][0] \n add_90[0][0] \n__________________________________________________________________________________________________\nconv2d_441 (Conv2D) (None, 7, 7, 1824) 554496 add_91[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_330 (BatchN (None, 7, 7, 1824) 7296 conv2d_441[0][0] \n__________________________________________________________________________________________________\nswish_330 (Swish) (None, 7, 7, 1824) 0 batch_normalization_330[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_112 (Depthwise (None, 7, 7, 1824) 45600 swish_330[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_331 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_112[0][0] \n__________________________________________________________________________________________________\nswish_331 (Swish) (None, 7, 7, 1824) 0 batch_normalization_331[0][0] \n__________________________________________________________________________________________________\nlambda_112 (Lambda) (None, 1, 1, 1824) 0 swish_331[0][0] \n__________________________________________________________________________________________________\nconv2d_442 (Conv2D) (None, 1, 1, 76) 138700 lambda_112[0][0] \n__________________________________________________________________________________________________\nswish_332 (Swish) (None, 1, 1, 76) 0 conv2d_442[0][0] \n__________________________________________________________________________________________________\nconv2d_443 (Conv2D) (None, 1, 1, 1824) 140448 swish_332[0][0] \n__________________________________________________________________________________________________\nactivation_112 (Activation) (None, 1, 1, 1824) 0 conv2d_443[0][0] \n__________________________________________________________________________________________________\nmultiply_112 (Multiply) (None, 7, 7, 1824) 0 activation_112[0][0] \n swish_331[0][0] \n__________________________________________________________________________________________________\nconv2d_444 (Conv2D) (None, 7, 7, 304) 554496 multiply_112[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_332 (BatchN (None, 7, 7, 304) 1216 conv2d_444[0][0] \n__________________________________________________________________________________________________\ndrop_connect_92 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_332[0][0] \n__________________________________________________________________________________________________\nadd_92 (Add) (None, 7, 7, 304) 0 drop_connect_92[0][0] \n add_91[0][0] \n__________________________________________________________________________________________________\nconv2d_445 (Conv2D) (None, 7, 7, 1824) 554496 add_92[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_333 (BatchN (None, 7, 7, 1824) 7296 conv2d_445[0][0] \n__________________________________________________________________________________________________\nswish_333 (Swish) (None, 7, 7, 1824) 0 batch_normalization_333[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_113 (Depthwise (None, 7, 7, 1824) 45600 swish_333[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_334 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_113[0][0] \n__________________________________________________________________________________________________\nswish_334 (Swish) (None, 7, 7, 1824) 0 batch_normalization_334[0][0] \n__________________________________________________________________________________________________\nlambda_113 (Lambda) (None, 1, 1, 1824) 0 swish_334[0][0] \n__________________________________________________________________________________________________\nconv2d_446 (Conv2D) (None, 1, 1, 76) 138700 lambda_113[0][0] \n__________________________________________________________________________________________________\nswish_335 (Swish) (None, 1, 1, 76) 0 conv2d_446[0][0] \n__________________________________________________________________________________________________\nconv2d_447 (Conv2D) (None, 1, 1, 1824) 140448 swish_335[0][0] \n__________________________________________________________________________________________________\nactivation_113 (Activation) (None, 1, 1, 1824) 0 conv2d_447[0][0] \n__________________________________________________________________________________________________\nmultiply_113 (Multiply) (None, 7, 7, 1824) 0 activation_113[0][0] \n swish_334[0][0] \n__________________________________________________________________________________________________\nconv2d_448 (Conv2D) (None, 7, 7, 304) 554496 multiply_113[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_335 (BatchN (None, 7, 7, 304) 1216 conv2d_448[0][0] \n__________________________________________________________________________________________________\ndrop_connect_93 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_335[0][0] \n__________________________________________________________________________________________________\nadd_93 (Add) (None, 7, 7, 304) 0 drop_connect_93[0][0] \n add_92[0][0] \n__________________________________________________________________________________________________\nconv2d_449 (Conv2D) (None, 7, 7, 1824) 554496 add_93[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_336 (BatchN (None, 7, 7, 1824) 7296 conv2d_449[0][0] \n__________________________________________________________________________________________________\nswish_336 (Swish) (None, 7, 7, 1824) 0 batch_normalization_336[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_114 (Depthwise (None, 7, 7, 1824) 45600 swish_336[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_337 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_114[0][0] \n__________________________________________________________________________________________________\nswish_337 (Swish) (None, 7, 7, 1824) 0 batch_normalization_337[0][0] \n__________________________________________________________________________________________________\nlambda_114 (Lambda) (None, 1, 1, 1824) 0 swish_337[0][0] \n__________________________________________________________________________________________________\nconv2d_450 (Conv2D) (None, 1, 1, 76) 138700 lambda_114[0][0] \n__________________________________________________________________________________________________\nswish_338 (Swish) (None, 1, 1, 76) 0 conv2d_450[0][0] \n__________________________________________________________________________________________________\nconv2d_451 (Conv2D) (None, 1, 1, 1824) 140448 swish_338[0][0] \n__________________________________________________________________________________________________\nactivation_114 (Activation) (None, 1, 1, 1824) 0 conv2d_451[0][0] \n__________________________________________________________________________________________________\nmultiply_114 (Multiply) (None, 7, 7, 1824) 0 activation_114[0][0] \n swish_337[0][0] \n__________________________________________________________________________________________________\nconv2d_452 (Conv2D) (None, 7, 7, 304) 554496 multiply_114[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_338 (BatchN (None, 7, 7, 304) 1216 conv2d_452[0][0] \n__________________________________________________________________________________________________\ndrop_connect_94 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_338[0][0] \n__________________________________________________________________________________________________\nadd_94 (Add) (None, 7, 7, 304) 0 drop_connect_94[0][0] \n add_93[0][0] \n__________________________________________________________________________________________________\nconv2d_453 (Conv2D) (None, 7, 7, 1824) 554496 add_94[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_339 (BatchN (None, 7, 7, 1824) 7296 conv2d_453[0][0] \n__________________________________________________________________________________________________\nswish_339 (Swish) (None, 7, 7, 1824) 0 batch_normalization_339[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_115 (Depthwise (None, 7, 7, 1824) 16416 swish_339[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_340 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_115[0][0] \n__________________________________________________________________________________________________\nswish_340 (Swish) (None, 7, 7, 1824) 0 batch_normalization_340[0][0] \n__________________________________________________________________________________________________\nlambda_115 (Lambda) (None, 1, 1, 1824) 0 swish_340[0][0] \n__________________________________________________________________________________________________\nconv2d_454 (Conv2D) (None, 1, 1, 76) 138700 lambda_115[0][0] \n__________________________________________________________________________________________________\nswish_341 (Swish) (None, 1, 1, 76) 0 conv2d_454[0][0] \n__________________________________________________________________________________________________\nconv2d_455 (Conv2D) (None, 1, 1, 1824) 140448 swish_341[0][0] \n__________________________________________________________________________________________________\nactivation_115 (Activation) (None, 1, 1, 1824) 0 conv2d_455[0][0] \n__________________________________________________________________________________________________\nmultiply_115 (Multiply) (None, 7, 7, 1824) 0 activation_115[0][0] \n swish_340[0][0] \n__________________________________________________________________________________________________\nconv2d_456 (Conv2D) (None, 7, 7, 512) 933888 multiply_115[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_341 (BatchN (None, 7, 7, 512) 2048 conv2d_456[0][0] \n__________________________________________________________________________________________________\nconv2d_457 (Conv2D) (None, 7, 7, 3072) 1572864 batch_normalization_341[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_342 (BatchN (None, 7, 7, 3072) 12288 conv2d_457[0][0] \n__________________________________________________________________________________________________\nswish_342 (Swish) (None, 7, 7, 3072) 0 batch_normalization_342[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_116 (Depthwise (None, 7, 7, 3072) 27648 swish_342[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_343 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_116[0][0] \n__________________________________________________________________________________________________\nswish_343 (Swish) (None, 7, 7, 3072) 0 batch_normalization_343[0][0] \n__________________________________________________________________________________________________\nlambda_116 (Lambda) (None, 1, 1, 3072) 0 swish_343[0][0] \n__________________________________________________________________________________________________\nconv2d_458 (Conv2D) (None, 1, 1, 128) 393344 lambda_116[0][0] \n__________________________________________________________________________________________________\nswish_344 (Swish) (None, 1, 1, 128) 0 conv2d_458[0][0] \n__________________________________________________________________________________________________\nconv2d_459 (Conv2D) (None, 1, 1, 3072) 396288 swish_344[0][0] \n__________________________________________________________________________________________________\nactivation_116 (Activation) (None, 1, 1, 3072) 0 conv2d_459[0][0] \n__________________________________________________________________________________________________\nmultiply_116 (Multiply) (None, 7, 7, 3072) 0 activation_116[0][0] \n swish_343[0][0] \n__________________________________________________________________________________________________\nconv2d_460 (Conv2D) (None, 7, 7, 512) 1572864 multiply_116[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_344 (BatchN (None, 7, 7, 512) 2048 conv2d_460[0][0] \n__________________________________________________________________________________________________\ndrop_connect_95 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_344[0][0] \n__________________________________________________________________________________________________\nadd_95 (Add) (None, 7, 7, 512) 0 drop_connect_95[0][0] \n batch_normalization_341[0][0] \n__________________________________________________________________________________________________\nconv2d_461 (Conv2D) (None, 7, 7, 3072) 1572864 add_95[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_345 (BatchN (None, 7, 7, 3072) 12288 conv2d_461[0][0] \n__________________________________________________________________________________________________\nswish_345 (Swish) (None, 7, 7, 3072) 0 batch_normalization_345[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_117 (Depthwise (None, 7, 7, 3072) 27648 swish_345[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_346 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_117[0][0] \n__________________________________________________________________________________________________\nswish_346 (Swish) (None, 7, 7, 3072) 0 batch_normalization_346[0][0] \n__________________________________________________________________________________________________\nlambda_117 (Lambda) (None, 1, 1, 3072) 0 swish_346[0][0] \n__________________________________________________________________________________________________\nconv2d_462 (Conv2D) (None, 1, 1, 128) 393344 lambda_117[0][0] \n__________________________________________________________________________________________________\nswish_347 (Swish) (None, 1, 1, 128) 0 conv2d_462[0][0] \n__________________________________________________________________________________________________\nconv2d_463 (Conv2D) (None, 1, 1, 3072) 396288 swish_347[0][0] \n__________________________________________________________________________________________________\nactivation_117 (Activation) (None, 1, 1, 3072) 0 conv2d_463[0][0] \n__________________________________________________________________________________________________\nmultiply_117 (Multiply) (None, 7, 7, 3072) 0 activation_117[0][0] \n swish_346[0][0] \n__________________________________________________________________________________________________\nconv2d_464 (Conv2D) (None, 7, 7, 512) 1572864 multiply_117[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_347 (BatchN (None, 7, 7, 512) 2048 conv2d_464[0][0] \n__________________________________________________________________________________________________\ndrop_connect_96 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_347[0][0] \n__________________________________________________________________________________________________\nadd_96 (Add) (None, 7, 7, 512) 0 drop_connect_96[0][0] \n add_95[0][0] \n__________________________________________________________________________________________________\nconv2d_465 (Conv2D) (None, 7, 7, 2048) 1048576 add_96[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_348 (BatchN (None, 7, 7, 2048) 8192 conv2d_465[0][0] \n__________________________________________________________________________________________________\nswish_348 (Swish) (None, 7, 7, 2048) 0 batch_normalization_348[0][0] \n__________________________________________________________________________________________________\nglobal_average_pooling2d_3 (Glo (None, 2048) 0 swish_348[0][0] \n__________________________________________________________________________________________________\nfinal_output (Dense) (None, 1) 2049 global_average_pooling2d_3[0][0] \n==================================================================================================\nTotal params: 28,515,569\nTrainable params: 2,049\nNon-trainable params: 28,513,520\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"STEP_SIZE_TRAIN = train_generator.n//train_generator.batch_size\nSTEP_SIZE_VALID = valid_generator.n//valid_generator.batch_size\n\nhistory_warmup = model.fit_generator(generator=train_generator,\n steps_per_epoch=STEP_SIZE_TRAIN,\n validation_data=valid_generator,\n validation_steps=STEP_SIZE_VALID,\n epochs=WARMUP_EPOCHS,\n callbacks=callback_list,\n verbose=2).history","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:09:03.646882Z","iopub.execute_input":"2024-05-03T05:09:03.647112Z","iopub.status.idle":"2024-05-03T05:12:52.253846Z","shell.execute_reply.started":"2024-05-03T05:09:03.647074Z","shell.execute_reply":"2024-05-03T05:12:52.253027Z"},"trusted":true},"execution_count":53,"outputs":[{"name":"stdout","text":"Epoch 1/5\n - 64s - loss: 2.1023 - acc: 0.4289 - val_loss: 1.4369 - val_acc: 0.2358\nEpoch 2/5\n - 42s - loss: 1.1487 - acc: 0.4251 - val_loss: 1.5489 - val_acc: 0.3429\nEpoch 3/5\n - 41s - loss: 0.9949 - acc: 0.4347 - val_loss: 1.5335 - val_acc: 0.3029\nEpoch 4/5\n - 41s - loss: 0.8674 - acc: 0.4632 - val_loss: 2.1755 - val_acc: 0.4100\nEpoch 5/5\n - 41s - loss: 0.9005 - acc: 0.4509 - val_loss: 2.1584 - val_acc: 0.4200\n","output_type":"stream"}]},{"cell_type":"code","source":"for layer in model.layers:\n layer.trainable = True\n\nes = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)\ncosine_lr_2nd = WarmUpCosineDecayScheduler(learning_rate_base=LEARNING_RATE,\n total_steps=TOTAL_STEPS_2nd,\n warmup_learning_rate=0.0,\n warmup_steps=WARMUP_STEPS_2nd,\n hold_base_rate_steps=(3 * STEP_SIZE))\n\ncallback_list = [es, cosine_lr_2nd]\noptimizer = optimizers.Adam(lr=LEARNING_RATE)\nmodel.compile(optimizer=optimizer, loss='mean_squared_error', metrics=metric_list)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:12:52.255387Z","iopub.execute_input":"2024-05-03T05:12:52.255642Z","iopub.status.idle":"2024-05-03T05:12:52.493737Z","shell.execute_reply.started":"2024-05-03T05:12:52.255592Z","shell.execute_reply":"2024-05-03T05:12:52.492959Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":54,"outputs":[{"name":"stdout","text":"__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_3 (InputLayer) (None, 224, 224, 3) 0 \n__________________________________________________________________________________________________\nconv2d_311 (Conv2D) (None, 112, 112, 48) 1296 input_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_233 (BatchN (None, 112, 112, 48) 192 conv2d_311[0][0] \n__________________________________________________________________________________________________\nswish_233 (Swish) (None, 112, 112, 48) 0 batch_normalization_233[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_79 (DepthwiseC (None, 112, 112, 48) 432 swish_233[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_234 (BatchN (None, 112, 112, 48) 192 depthwise_conv2d_79[0][0] \n__________________________________________________________________________________________________\nswish_234 (Swish) (None, 112, 112, 48) 0 batch_normalization_234[0][0] \n__________________________________________________________________________________________________\nlambda_79 (Lambda) (None, 1, 1, 48) 0 swish_234[0][0] \n__________________________________________________________________________________________________\nconv2d_312 (Conv2D) (None, 1, 1, 12) 588 lambda_79[0][0] \n__________________________________________________________________________________________________\nswish_235 (Swish) (None, 1, 1, 12) 0 conv2d_312[0][0] \n__________________________________________________________________________________________________\nconv2d_313 (Conv2D) (None, 1, 1, 48) 624 swish_235[0][0] \n__________________________________________________________________________________________________\nactivation_79 (Activation) (None, 1, 1, 48) 0 conv2d_313[0][0] \n__________________________________________________________________________________________________\nmultiply_79 (Multiply) (None, 112, 112, 48) 0 activation_79[0][0] \n swish_234[0][0] \n__________________________________________________________________________________________________\nconv2d_314 (Conv2D) (None, 112, 112, 24) 1152 multiply_79[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_235 (BatchN (None, 112, 112, 24) 96 conv2d_314[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_80 (DepthwiseC (None, 112, 112, 24) 216 batch_normalization_235[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_236 (BatchN (None, 112, 112, 24) 96 depthwise_conv2d_80[0][0] \n__________________________________________________________________________________________________\nswish_236 (Swish) (None, 112, 112, 24) 0 batch_normalization_236[0][0] \n__________________________________________________________________________________________________\nlambda_80 (Lambda) (None, 1, 1, 24) 0 swish_236[0][0] \n__________________________________________________________________________________________________\nconv2d_315 (Conv2D) (None, 1, 1, 6) 150 lambda_80[0][0] \n__________________________________________________________________________________________________\nswish_237 (Swish) (None, 1, 1, 6) 0 conv2d_315[0][0] \n__________________________________________________________________________________________________\nconv2d_316 (Conv2D) (None, 1, 1, 24) 168 swish_237[0][0] \n__________________________________________________________________________________________________\nactivation_80 (Activation) (None, 1, 1, 24) 0 conv2d_316[0][0] \n__________________________________________________________________________________________________\nmultiply_80 (Multiply) (None, 112, 112, 24) 0 activation_80[0][0] \n swish_236[0][0] \n__________________________________________________________________________________________________\nconv2d_317 (Conv2D) (None, 112, 112, 24) 576 multiply_80[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_237 (BatchN (None, 112, 112, 24) 96 conv2d_317[0][0] \n__________________________________________________________________________________________________\ndrop_connect_65 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_237[0][0] \n__________________________________________________________________________________________________\nadd_65 (Add) (None, 112, 112, 24) 0 drop_connect_65[0][0] \n batch_normalization_235[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_81 (DepthwiseC (None, 112, 112, 24) 216 add_65[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_238 (BatchN (None, 112, 112, 24) 96 depthwise_conv2d_81[0][0] \n__________________________________________________________________________________________________\nswish_238 (Swish) (None, 112, 112, 24) 0 batch_normalization_238[0][0] \n__________________________________________________________________________________________________\nlambda_81 (Lambda) (None, 1, 1, 24) 0 swish_238[0][0] \n__________________________________________________________________________________________________\nconv2d_318 (Conv2D) (None, 1, 1, 6) 150 lambda_81[0][0] \n__________________________________________________________________________________________________\nswish_239 (Swish) (None, 1, 1, 6) 0 conv2d_318[0][0] \n__________________________________________________________________________________________________\nconv2d_319 (Conv2D) (None, 1, 1, 24) 168 swish_239[0][0] \n__________________________________________________________________________________________________\nactivation_81 (Activation) (None, 1, 1, 24) 0 conv2d_319[0][0] \n__________________________________________________________________________________________________\nmultiply_81 (Multiply) (None, 112, 112, 24) 0 activation_81[0][0] \n swish_238[0][0] \n__________________________________________________________________________________________________\nconv2d_320 (Conv2D) (None, 112, 112, 24) 576 multiply_81[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_239 (BatchN (None, 112, 112, 24) 96 conv2d_320[0][0] \n__________________________________________________________________________________________________\ndrop_connect_66 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_239[0][0] \n__________________________________________________________________________________________________\nadd_66 (Add) (None, 112, 112, 24) 0 drop_connect_66[0][0] \n add_65[0][0] \n__________________________________________________________________________________________________\nconv2d_321 (Conv2D) (None, 112, 112, 144 3456 add_66[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_240 (BatchN (None, 112, 112, 144 576 conv2d_321[0][0] \n__________________________________________________________________________________________________\nswish_240 (Swish) (None, 112, 112, 144 0 batch_normalization_240[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_82 (DepthwiseC (None, 56, 56, 144) 1296 swish_240[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_241 (BatchN (None, 56, 56, 144) 576 depthwise_conv2d_82[0][0] \n__________________________________________________________________________________________________\nswish_241 (Swish) (None, 56, 56, 144) 0 batch_normalization_241[0][0] \n__________________________________________________________________________________________________\nlambda_82 (Lambda) (None, 1, 1, 144) 0 swish_241[0][0] \n__________________________________________________________________________________________________\nconv2d_322 (Conv2D) (None, 1, 1, 6) 870 lambda_82[0][0] \n__________________________________________________________________________________________________\nswish_242 (Swish) (None, 1, 1, 6) 0 conv2d_322[0][0] \n__________________________________________________________________________________________________\nconv2d_323 (Conv2D) (None, 1, 1, 144) 1008 swish_242[0][0] \n__________________________________________________________________________________________________\nactivation_82 (Activation) (None, 1, 1, 144) 0 conv2d_323[0][0] \n__________________________________________________________________________________________________\nmultiply_82 (Multiply) (None, 56, 56, 144) 0 activation_82[0][0] \n swish_241[0][0] \n__________________________________________________________________________________________________\nconv2d_324 (Conv2D) (None, 56, 56, 40) 5760 multiply_82[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_242 (BatchN (None, 56, 56, 40) 160 conv2d_324[0][0] \n__________________________________________________________________________________________________\nconv2d_325 (Conv2D) (None, 56, 56, 240) 9600 batch_normalization_242[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_243 (BatchN (None, 56, 56, 240) 960 conv2d_325[0][0] \n__________________________________________________________________________________________________\nswish_243 (Swish) (None, 56, 56, 240) 0 batch_normalization_243[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_83 (DepthwiseC (None, 56, 56, 240) 2160 swish_243[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_244 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_83[0][0] \n__________________________________________________________________________________________________\nswish_244 (Swish) (None, 56, 56, 240) 0 batch_normalization_244[0][0] \n__________________________________________________________________________________________________\nlambda_83 (Lambda) (None, 1, 1, 240) 0 swish_244[0][0] \n__________________________________________________________________________________________________\nconv2d_326 (Conv2D) (None, 1, 1, 10) 2410 lambda_83[0][0] \n__________________________________________________________________________________________________\nswish_245 (Swish) (None, 1, 1, 10) 0 conv2d_326[0][0] \n__________________________________________________________________________________________________\nconv2d_327 (Conv2D) (None, 1, 1, 240) 2640 swish_245[0][0] \n__________________________________________________________________________________________________\nactivation_83 (Activation) (None, 1, 1, 240) 0 conv2d_327[0][0] \n__________________________________________________________________________________________________\nmultiply_83 (Multiply) (None, 56, 56, 240) 0 activation_83[0][0] \n swish_244[0][0] \n__________________________________________________________________________________________________\nconv2d_328 (Conv2D) (None, 56, 56, 40) 9600 multiply_83[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_245 (BatchN (None, 56, 56, 40) 160 conv2d_328[0][0] \n__________________________________________________________________________________________________\ndrop_connect_67 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_245[0][0] \n__________________________________________________________________________________________________\nadd_67 (Add) (None, 56, 56, 40) 0 drop_connect_67[0][0] \n batch_normalization_242[0][0] \n__________________________________________________________________________________________________\nconv2d_329 (Conv2D) (None, 56, 56, 240) 9600 add_67[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_246 (BatchN (None, 56, 56, 240) 960 conv2d_329[0][0] \n__________________________________________________________________________________________________\nswish_246 (Swish) (None, 56, 56, 240) 0 batch_normalization_246[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_84 (DepthwiseC (None, 56, 56, 240) 2160 swish_246[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_247 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_84[0][0] \n__________________________________________________________________________________________________\nswish_247 (Swish) (None, 56, 56, 240) 0 batch_normalization_247[0][0] \n__________________________________________________________________________________________________\nlambda_84 (Lambda) (None, 1, 1, 240) 0 swish_247[0][0] \n__________________________________________________________________________________________________\nconv2d_330 (Conv2D) (None, 1, 1, 10) 2410 lambda_84[0][0] \n__________________________________________________________________________________________________\nswish_248 (Swish) (None, 1, 1, 10) 0 conv2d_330[0][0] \n__________________________________________________________________________________________________\nconv2d_331 (Conv2D) (None, 1, 1, 240) 2640 swish_248[0][0] \n__________________________________________________________________________________________________\nactivation_84 (Activation) (None, 1, 1, 240) 0 conv2d_331[0][0] \n__________________________________________________________________________________________________\nmultiply_84 (Multiply) (None, 56, 56, 240) 0 activation_84[0][0] \n swish_247[0][0] \n__________________________________________________________________________________________________\nconv2d_332 (Conv2D) (None, 56, 56, 40) 9600 multiply_84[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_248 (BatchN (None, 56, 56, 40) 160 conv2d_332[0][0] \n__________________________________________________________________________________________________\ndrop_connect_68 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_248[0][0] \n__________________________________________________________________________________________________\nadd_68 (Add) (None, 56, 56, 40) 0 drop_connect_68[0][0] \n add_67[0][0] \n__________________________________________________________________________________________________\nconv2d_333 (Conv2D) (None, 56, 56, 240) 9600 add_68[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_249 (BatchN (None, 56, 56, 240) 960 conv2d_333[0][0] \n__________________________________________________________________________________________________\nswish_249 (Swish) (None, 56, 56, 240) 0 batch_normalization_249[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_85 (DepthwiseC (None, 56, 56, 240) 2160 swish_249[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_250 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_85[0][0] \n__________________________________________________________________________________________________\nswish_250 (Swish) (None, 56, 56, 240) 0 batch_normalization_250[0][0] \n__________________________________________________________________________________________________\nlambda_85 (Lambda) (None, 1, 1, 240) 0 swish_250[0][0] \n__________________________________________________________________________________________________\nconv2d_334 (Conv2D) (None, 1, 1, 10) 2410 lambda_85[0][0] \n__________________________________________________________________________________________________\nswish_251 (Swish) (None, 1, 1, 10) 0 conv2d_334[0][0] \n__________________________________________________________________________________________________\nconv2d_335 (Conv2D) (None, 1, 1, 240) 2640 swish_251[0][0] \n__________________________________________________________________________________________________\nactivation_85 (Activation) (None, 1, 1, 240) 0 conv2d_335[0][0] \n__________________________________________________________________________________________________\nmultiply_85 (Multiply) (None, 56, 56, 240) 0 activation_85[0][0] \n swish_250[0][0] \n__________________________________________________________________________________________________\nconv2d_336 (Conv2D) (None, 56, 56, 40) 9600 multiply_85[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_251 (BatchN (None, 56, 56, 40) 160 conv2d_336[0][0] \n__________________________________________________________________________________________________\ndrop_connect_69 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_251[0][0] \n__________________________________________________________________________________________________\nadd_69 (Add) (None, 56, 56, 40) 0 drop_connect_69[0][0] \n add_68[0][0] \n__________________________________________________________________________________________________\nconv2d_337 (Conv2D) (None, 56, 56, 240) 9600 add_69[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_252 (BatchN (None, 56, 56, 240) 960 conv2d_337[0][0] \n__________________________________________________________________________________________________\nswish_252 (Swish) (None, 56, 56, 240) 0 batch_normalization_252[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_86 (DepthwiseC (None, 56, 56, 240) 2160 swish_252[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_253 (BatchN (None, 56, 56, 240) 960 depthwise_conv2d_86[0][0] \n__________________________________________________________________________________________________\nswish_253 (Swish) (None, 56, 56, 240) 0 batch_normalization_253[0][0] \n__________________________________________________________________________________________________\nlambda_86 (Lambda) (None, 1, 1, 240) 0 swish_253[0][0] \n__________________________________________________________________________________________________\nconv2d_338 (Conv2D) (None, 1, 1, 10) 2410 lambda_86[0][0] \n__________________________________________________________________________________________________\nswish_254 (Swish) (None, 1, 1, 10) 0 conv2d_338[0][0] \n__________________________________________________________________________________________________\nconv2d_339 (Conv2D) (None, 1, 1, 240) 2640 swish_254[0][0] \n__________________________________________________________________________________________________\nactivation_86 (Activation) (None, 1, 1, 240) 0 conv2d_339[0][0] \n__________________________________________________________________________________________________\nmultiply_86 (Multiply) (None, 56, 56, 240) 0 activation_86[0][0] \n swish_253[0][0] \n__________________________________________________________________________________________________\nconv2d_340 (Conv2D) (None, 56, 56, 40) 9600 multiply_86[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_254 (BatchN (None, 56, 56, 40) 160 conv2d_340[0][0] \n__________________________________________________________________________________________________\ndrop_connect_70 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_254[0][0] \n__________________________________________________________________________________________________\nadd_70 (Add) (None, 56, 56, 40) 0 drop_connect_70[0][0] \n add_69[0][0] \n__________________________________________________________________________________________________\nconv2d_341 (Conv2D) (None, 56, 56, 240) 9600 add_70[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_255 (BatchN (None, 56, 56, 240) 960 conv2d_341[0][0] \n__________________________________________________________________________________________________\nswish_255 (Swish) (None, 56, 56, 240) 0 batch_normalization_255[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_87 (DepthwiseC (None, 28, 28, 240) 6000 swish_255[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_256 (BatchN (None, 28, 28, 240) 960 depthwise_conv2d_87[0][0] \n__________________________________________________________________________________________________\nswish_256 (Swish) (None, 28, 28, 240) 0 batch_normalization_256[0][0] \n__________________________________________________________________________________________________\nlambda_87 (Lambda) (None, 1, 1, 240) 0 swish_256[0][0] \n__________________________________________________________________________________________________\nconv2d_342 (Conv2D) (None, 1, 1, 10) 2410 lambda_87[0][0] \n__________________________________________________________________________________________________\nswish_257 (Swish) (None, 1, 1, 10) 0 conv2d_342[0][0] \n__________________________________________________________________________________________________\nconv2d_343 (Conv2D) (None, 1, 1, 240) 2640 swish_257[0][0] \n__________________________________________________________________________________________________\nactivation_87 (Activation) (None, 1, 1, 240) 0 conv2d_343[0][0] \n__________________________________________________________________________________________________\nmultiply_87 (Multiply) (None, 28, 28, 240) 0 activation_87[0][0] \n swish_256[0][0] \n__________________________________________________________________________________________________\nconv2d_344 (Conv2D) (None, 28, 28, 64) 15360 multiply_87[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_257 (BatchN (None, 28, 28, 64) 256 conv2d_344[0][0] \n__________________________________________________________________________________________________\nconv2d_345 (Conv2D) (None, 28, 28, 384) 24576 batch_normalization_257[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_258 (BatchN (None, 28, 28, 384) 1536 conv2d_345[0][0] \n__________________________________________________________________________________________________\nswish_258 (Swish) (None, 28, 28, 384) 0 batch_normalization_258[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_88 (DepthwiseC (None, 28, 28, 384) 9600 swish_258[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_259 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_88[0][0] \n__________________________________________________________________________________________________\nswish_259 (Swish) (None, 28, 28, 384) 0 batch_normalization_259[0][0] \n__________________________________________________________________________________________________\nlambda_88 (Lambda) (None, 1, 1, 384) 0 swish_259[0][0] \n__________________________________________________________________________________________________\nconv2d_346 (Conv2D) (None, 1, 1, 16) 6160 lambda_88[0][0] \n__________________________________________________________________________________________________\nswish_260 (Swish) (None, 1, 1, 16) 0 conv2d_346[0][0] \n__________________________________________________________________________________________________\nconv2d_347 (Conv2D) (None, 1, 1, 384) 6528 swish_260[0][0] \n__________________________________________________________________________________________________\nactivation_88 (Activation) (None, 1, 1, 384) 0 conv2d_347[0][0] \n__________________________________________________________________________________________________\nmultiply_88 (Multiply) (None, 28, 28, 384) 0 activation_88[0][0] \n swish_259[0][0] \n__________________________________________________________________________________________________\nconv2d_348 (Conv2D) (None, 28, 28, 64) 24576 multiply_88[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_260 (BatchN (None, 28, 28, 64) 256 conv2d_348[0][0] \n__________________________________________________________________________________________________\ndrop_connect_71 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_260[0][0] \n__________________________________________________________________________________________________\nadd_71 (Add) (None, 28, 28, 64) 0 drop_connect_71[0][0] \n batch_normalization_257[0][0] \n__________________________________________________________________________________________________\nconv2d_349 (Conv2D) (None, 28, 28, 384) 24576 add_71[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_261 (BatchN (None, 28, 28, 384) 1536 conv2d_349[0][0] \n__________________________________________________________________________________________________\nswish_261 (Swish) (None, 28, 28, 384) 0 batch_normalization_261[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_89 (DepthwiseC (None, 28, 28, 384) 9600 swish_261[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_262 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_89[0][0] \n__________________________________________________________________________________________________\nswish_262 (Swish) (None, 28, 28, 384) 0 batch_normalization_262[0][0] \n__________________________________________________________________________________________________\nlambda_89 (Lambda) (None, 1, 1, 384) 0 swish_262[0][0] \n__________________________________________________________________________________________________\nconv2d_350 (Conv2D) (None, 1, 1, 16) 6160 lambda_89[0][0] \n__________________________________________________________________________________________________\nswish_263 (Swish) (None, 1, 1, 16) 0 conv2d_350[0][0] \n__________________________________________________________________________________________________\nconv2d_351 (Conv2D) (None, 1, 1, 384) 6528 swish_263[0][0] \n__________________________________________________________________________________________________\nactivation_89 (Activation) (None, 1, 1, 384) 0 conv2d_351[0][0] \n__________________________________________________________________________________________________\nmultiply_89 (Multiply) (None, 28, 28, 384) 0 activation_89[0][0] \n swish_262[0][0] \n__________________________________________________________________________________________________\nconv2d_352 (Conv2D) (None, 28, 28, 64) 24576 multiply_89[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_263 (BatchN (None, 28, 28, 64) 256 conv2d_352[0][0] \n__________________________________________________________________________________________________\ndrop_connect_72 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_263[0][0] \n__________________________________________________________________________________________________\nadd_72 (Add) (None, 28, 28, 64) 0 drop_connect_72[0][0] \n add_71[0][0] \n__________________________________________________________________________________________________\nconv2d_353 (Conv2D) (None, 28, 28, 384) 24576 add_72[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_264 (BatchN (None, 28, 28, 384) 1536 conv2d_353[0][0] \n__________________________________________________________________________________________________\nswish_264 (Swish) (None, 28, 28, 384) 0 batch_normalization_264[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_90 (DepthwiseC (None, 28, 28, 384) 9600 swish_264[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_265 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_90[0][0] \n__________________________________________________________________________________________________\nswish_265 (Swish) (None, 28, 28, 384) 0 batch_normalization_265[0][0] \n__________________________________________________________________________________________________\nlambda_90 (Lambda) (None, 1, 1, 384) 0 swish_265[0][0] \n__________________________________________________________________________________________________\nconv2d_354 (Conv2D) (None, 1, 1, 16) 6160 lambda_90[0][0] \n__________________________________________________________________________________________________\nswish_266 (Swish) (None, 1, 1, 16) 0 conv2d_354[0][0] \n__________________________________________________________________________________________________\nconv2d_355 (Conv2D) (None, 1, 1, 384) 6528 swish_266[0][0] \n__________________________________________________________________________________________________\nactivation_90 (Activation) (None, 1, 1, 384) 0 conv2d_355[0][0] \n__________________________________________________________________________________________________\nmultiply_90 (Multiply) (None, 28, 28, 384) 0 activation_90[0][0] \n swish_265[0][0] \n__________________________________________________________________________________________________\nconv2d_356 (Conv2D) (None, 28, 28, 64) 24576 multiply_90[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_266 (BatchN (None, 28, 28, 64) 256 conv2d_356[0][0] \n__________________________________________________________________________________________________\ndrop_connect_73 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_266[0][0] \n__________________________________________________________________________________________________\nadd_73 (Add) (None, 28, 28, 64) 0 drop_connect_73[0][0] \n add_72[0][0] \n__________________________________________________________________________________________________\nconv2d_357 (Conv2D) (None, 28, 28, 384) 24576 add_73[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_267 (BatchN (None, 28, 28, 384) 1536 conv2d_357[0][0] \n__________________________________________________________________________________________________\nswish_267 (Swish) (None, 28, 28, 384) 0 batch_normalization_267[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_91 (DepthwiseC (None, 28, 28, 384) 9600 swish_267[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_268 (BatchN (None, 28, 28, 384) 1536 depthwise_conv2d_91[0][0] \n__________________________________________________________________________________________________\nswish_268 (Swish) (None, 28, 28, 384) 0 batch_normalization_268[0][0] \n__________________________________________________________________________________________________\nlambda_91 (Lambda) (None, 1, 1, 384) 0 swish_268[0][0] \n__________________________________________________________________________________________________\nconv2d_358 (Conv2D) (None, 1, 1, 16) 6160 lambda_91[0][0] \n__________________________________________________________________________________________________\nswish_269 (Swish) (None, 1, 1, 16) 0 conv2d_358[0][0] \n__________________________________________________________________________________________________\nconv2d_359 (Conv2D) (None, 1, 1, 384) 6528 swish_269[0][0] \n__________________________________________________________________________________________________\nactivation_91 (Activation) (None, 1, 1, 384) 0 conv2d_359[0][0] \n__________________________________________________________________________________________________\nmultiply_91 (Multiply) (None, 28, 28, 384) 0 activation_91[0][0] \n swish_268[0][0] \n__________________________________________________________________________________________________\nconv2d_360 (Conv2D) (None, 28, 28, 64) 24576 multiply_91[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_269 (BatchN (None, 28, 28, 64) 256 conv2d_360[0][0] \n__________________________________________________________________________________________________\ndrop_connect_74 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_269[0][0] \n__________________________________________________________________________________________________\nadd_74 (Add) (None, 28, 28, 64) 0 drop_connect_74[0][0] \n add_73[0][0] \n__________________________________________________________________________________________________\nconv2d_361 (Conv2D) (None, 28, 28, 384) 24576 add_74[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_270 (BatchN (None, 28, 28, 384) 1536 conv2d_361[0][0] \n__________________________________________________________________________________________________\nswish_270 (Swish) (None, 28, 28, 384) 0 batch_normalization_270[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_92 (DepthwiseC (None, 14, 14, 384) 3456 swish_270[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_271 (BatchN (None, 14, 14, 384) 1536 depthwise_conv2d_92[0][0] \n__________________________________________________________________________________________________\nswish_271 (Swish) (None, 14, 14, 384) 0 batch_normalization_271[0][0] \n__________________________________________________________________________________________________\nlambda_92 (Lambda) (None, 1, 1, 384) 0 swish_271[0][0] \n__________________________________________________________________________________________________\nconv2d_362 (Conv2D) (None, 1, 1, 16) 6160 lambda_92[0][0] \n__________________________________________________________________________________________________\nswish_272 (Swish) (None, 1, 1, 16) 0 conv2d_362[0][0] \n__________________________________________________________________________________________________\nconv2d_363 (Conv2D) (None, 1, 1, 384) 6528 swish_272[0][0] \n__________________________________________________________________________________________________\nactivation_92 (Activation) (None, 1, 1, 384) 0 conv2d_363[0][0] \n__________________________________________________________________________________________________\nmultiply_92 (Multiply) (None, 14, 14, 384) 0 activation_92[0][0] \n swish_271[0][0] \n__________________________________________________________________________________________________\nconv2d_364 (Conv2D) (None, 14, 14, 128) 49152 multiply_92[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_272 (BatchN (None, 14, 14, 128) 512 conv2d_364[0][0] \n__________________________________________________________________________________________________\nconv2d_365 (Conv2D) (None, 14, 14, 768) 98304 batch_normalization_272[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_273 (BatchN (None, 14, 14, 768) 3072 conv2d_365[0][0] \n__________________________________________________________________________________________________\nswish_273 (Swish) (None, 14, 14, 768) 0 batch_normalization_273[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_93 (DepthwiseC (None, 14, 14, 768) 6912 swish_273[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_274 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_93[0][0] \n__________________________________________________________________________________________________\nswish_274 (Swish) (None, 14, 14, 768) 0 batch_normalization_274[0][0] \n__________________________________________________________________________________________________\nlambda_93 (Lambda) (None, 1, 1, 768) 0 swish_274[0][0] \n__________________________________________________________________________________________________\nconv2d_366 (Conv2D) (None, 1, 1, 32) 24608 lambda_93[0][0] \n__________________________________________________________________________________________________\nswish_275 (Swish) (None, 1, 1, 32) 0 conv2d_366[0][0] \n__________________________________________________________________________________________________\nconv2d_367 (Conv2D) (None, 1, 1, 768) 25344 swish_275[0][0] \n__________________________________________________________________________________________________\nactivation_93 (Activation) (None, 1, 1, 768) 0 conv2d_367[0][0] \n__________________________________________________________________________________________________\nmultiply_93 (Multiply) (None, 14, 14, 768) 0 activation_93[0][0] \n swish_274[0][0] \n__________________________________________________________________________________________________\nconv2d_368 (Conv2D) (None, 14, 14, 128) 98304 multiply_93[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_275 (BatchN (None, 14, 14, 128) 512 conv2d_368[0][0] \n__________________________________________________________________________________________________\ndrop_connect_75 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_275[0][0] \n__________________________________________________________________________________________________\nadd_75 (Add) (None, 14, 14, 128) 0 drop_connect_75[0][0] \n batch_normalization_272[0][0] \n__________________________________________________________________________________________________\nconv2d_369 (Conv2D) (None, 14, 14, 768) 98304 add_75[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_276 (BatchN (None, 14, 14, 768) 3072 conv2d_369[0][0] \n__________________________________________________________________________________________________\nswish_276 (Swish) (None, 14, 14, 768) 0 batch_normalization_276[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_94 (DepthwiseC (None, 14, 14, 768) 6912 swish_276[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_277 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_94[0][0] \n__________________________________________________________________________________________________\nswish_277 (Swish) (None, 14, 14, 768) 0 batch_normalization_277[0][0] \n__________________________________________________________________________________________________\nlambda_94 (Lambda) (None, 1, 1, 768) 0 swish_277[0][0] \n__________________________________________________________________________________________________\nconv2d_370 (Conv2D) (None, 1, 1, 32) 24608 lambda_94[0][0] \n__________________________________________________________________________________________________\nswish_278 (Swish) (None, 1, 1, 32) 0 conv2d_370[0][0] \n__________________________________________________________________________________________________\nconv2d_371 (Conv2D) (None, 1, 1, 768) 25344 swish_278[0][0] \n__________________________________________________________________________________________________\nactivation_94 (Activation) (None, 1, 1, 768) 0 conv2d_371[0][0] \n__________________________________________________________________________________________________\nmultiply_94 (Multiply) (None, 14, 14, 768) 0 activation_94[0][0] \n swish_277[0][0] \n__________________________________________________________________________________________________\nconv2d_372 (Conv2D) (None, 14, 14, 128) 98304 multiply_94[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_278 (BatchN (None, 14, 14, 128) 512 conv2d_372[0][0] \n__________________________________________________________________________________________________\ndrop_connect_76 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_278[0][0] \n__________________________________________________________________________________________________\nadd_76 (Add) (None, 14, 14, 128) 0 drop_connect_76[0][0] \n add_75[0][0] \n__________________________________________________________________________________________________\nconv2d_373 (Conv2D) (None, 14, 14, 768) 98304 add_76[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_279 (BatchN (None, 14, 14, 768) 3072 conv2d_373[0][0] \n__________________________________________________________________________________________________\nswish_279 (Swish) (None, 14, 14, 768) 0 batch_normalization_279[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_95 (DepthwiseC (None, 14, 14, 768) 6912 swish_279[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_280 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_95[0][0] \n__________________________________________________________________________________________________\nswish_280 (Swish) (None, 14, 14, 768) 0 batch_normalization_280[0][0] \n__________________________________________________________________________________________________\nlambda_95 (Lambda) (None, 1, 1, 768) 0 swish_280[0][0] \n__________________________________________________________________________________________________\nconv2d_374 (Conv2D) (None, 1, 1, 32) 24608 lambda_95[0][0] \n__________________________________________________________________________________________________\nswish_281 (Swish) (None, 1, 1, 32) 0 conv2d_374[0][0] \n__________________________________________________________________________________________________\nconv2d_375 (Conv2D) (None, 1, 1, 768) 25344 swish_281[0][0] \n__________________________________________________________________________________________________\nactivation_95 (Activation) (None, 1, 1, 768) 0 conv2d_375[0][0] \n__________________________________________________________________________________________________\nmultiply_95 (Multiply) (None, 14, 14, 768) 0 activation_95[0][0] \n swish_280[0][0] \n__________________________________________________________________________________________________\nconv2d_376 (Conv2D) (None, 14, 14, 128) 98304 multiply_95[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_281 (BatchN (None, 14, 14, 128) 512 conv2d_376[0][0] \n__________________________________________________________________________________________________\ndrop_connect_77 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_281[0][0] \n__________________________________________________________________________________________________\nadd_77 (Add) (None, 14, 14, 128) 0 drop_connect_77[0][0] \n add_76[0][0] \n__________________________________________________________________________________________________\nconv2d_377 (Conv2D) (None, 14, 14, 768) 98304 add_77[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_282 (BatchN (None, 14, 14, 768) 3072 conv2d_377[0][0] \n__________________________________________________________________________________________________\nswish_282 (Swish) (None, 14, 14, 768) 0 batch_normalization_282[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_96 (DepthwiseC (None, 14, 14, 768) 6912 swish_282[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_283 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_96[0][0] \n__________________________________________________________________________________________________\nswish_283 (Swish) (None, 14, 14, 768) 0 batch_normalization_283[0][0] \n__________________________________________________________________________________________________\nlambda_96 (Lambda) (None, 1, 1, 768) 0 swish_283[0][0] \n__________________________________________________________________________________________________\nconv2d_378 (Conv2D) (None, 1, 1, 32) 24608 lambda_96[0][0] \n__________________________________________________________________________________________________\nswish_284 (Swish) (None, 1, 1, 32) 0 conv2d_378[0][0] \n__________________________________________________________________________________________________\nconv2d_379 (Conv2D) (None, 1, 1, 768) 25344 swish_284[0][0] \n__________________________________________________________________________________________________\nactivation_96 (Activation) (None, 1, 1, 768) 0 conv2d_379[0][0] \n__________________________________________________________________________________________________\nmultiply_96 (Multiply) (None, 14, 14, 768) 0 activation_96[0][0] \n swish_283[0][0] \n__________________________________________________________________________________________________\nconv2d_380 (Conv2D) (None, 14, 14, 128) 98304 multiply_96[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_284 (BatchN (None, 14, 14, 128) 512 conv2d_380[0][0] \n__________________________________________________________________________________________________\ndrop_connect_78 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_284[0][0] \n__________________________________________________________________________________________________\nadd_78 (Add) (None, 14, 14, 128) 0 drop_connect_78[0][0] \n add_77[0][0] \n__________________________________________________________________________________________________\nconv2d_381 (Conv2D) (None, 14, 14, 768) 98304 add_78[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_285 (BatchN (None, 14, 14, 768) 3072 conv2d_381[0][0] \n__________________________________________________________________________________________________\nswish_285 (Swish) (None, 14, 14, 768) 0 batch_normalization_285[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_97 (DepthwiseC (None, 14, 14, 768) 6912 swish_285[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_286 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_97[0][0] \n__________________________________________________________________________________________________\nswish_286 (Swish) (None, 14, 14, 768) 0 batch_normalization_286[0][0] \n__________________________________________________________________________________________________\nlambda_97 (Lambda) (None, 1, 1, 768) 0 swish_286[0][0] \n__________________________________________________________________________________________________\nconv2d_382 (Conv2D) (None, 1, 1, 32) 24608 lambda_97[0][0] \n__________________________________________________________________________________________________\nswish_287 (Swish) (None, 1, 1, 32) 0 conv2d_382[0][0] \n__________________________________________________________________________________________________\nconv2d_383 (Conv2D) (None, 1, 1, 768) 25344 swish_287[0][0] \n__________________________________________________________________________________________________\nactivation_97 (Activation) (None, 1, 1, 768) 0 conv2d_383[0][0] \n__________________________________________________________________________________________________\nmultiply_97 (Multiply) (None, 14, 14, 768) 0 activation_97[0][0] \n swish_286[0][0] \n__________________________________________________________________________________________________\nconv2d_384 (Conv2D) (None, 14, 14, 128) 98304 multiply_97[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_287 (BatchN (None, 14, 14, 128) 512 conv2d_384[0][0] \n__________________________________________________________________________________________________\ndrop_connect_79 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_287[0][0] \n__________________________________________________________________________________________________\nadd_79 (Add) (None, 14, 14, 128) 0 drop_connect_79[0][0] \n add_78[0][0] \n__________________________________________________________________________________________________\nconv2d_385 (Conv2D) (None, 14, 14, 768) 98304 add_79[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_288 (BatchN (None, 14, 14, 768) 3072 conv2d_385[0][0] \n__________________________________________________________________________________________________\nswish_288 (Swish) (None, 14, 14, 768) 0 batch_normalization_288[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_98 (DepthwiseC (None, 14, 14, 768) 6912 swish_288[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_289 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_98[0][0] \n__________________________________________________________________________________________________\nswish_289 (Swish) (None, 14, 14, 768) 0 batch_normalization_289[0][0] \n__________________________________________________________________________________________________\nlambda_98 (Lambda) (None, 1, 1, 768) 0 swish_289[0][0] \n__________________________________________________________________________________________________\nconv2d_386 (Conv2D) (None, 1, 1, 32) 24608 lambda_98[0][0] \n__________________________________________________________________________________________________\nswish_290 (Swish) (None, 1, 1, 32) 0 conv2d_386[0][0] \n__________________________________________________________________________________________________\nconv2d_387 (Conv2D) (None, 1, 1, 768) 25344 swish_290[0][0] \n__________________________________________________________________________________________________\nactivation_98 (Activation) (None, 1, 1, 768) 0 conv2d_387[0][0] \n__________________________________________________________________________________________________\nmultiply_98 (Multiply) (None, 14, 14, 768) 0 activation_98[0][0] \n swish_289[0][0] \n__________________________________________________________________________________________________\nconv2d_388 (Conv2D) (None, 14, 14, 128) 98304 multiply_98[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_290 (BatchN (None, 14, 14, 128) 512 conv2d_388[0][0] \n__________________________________________________________________________________________________\ndrop_connect_80 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_290[0][0] \n__________________________________________________________________________________________________\nadd_80 (Add) (None, 14, 14, 128) 0 drop_connect_80[0][0] \n add_79[0][0] \n__________________________________________________________________________________________________\nconv2d_389 (Conv2D) (None, 14, 14, 768) 98304 add_80[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_291 (BatchN (None, 14, 14, 768) 3072 conv2d_389[0][0] \n__________________________________________________________________________________________________\nswish_291 (Swish) (None, 14, 14, 768) 0 batch_normalization_291[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_99 (DepthwiseC (None, 14, 14, 768) 19200 swish_291[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_292 (BatchN (None, 14, 14, 768) 3072 depthwise_conv2d_99[0][0] \n__________________________________________________________________________________________________\nswish_292 (Swish) (None, 14, 14, 768) 0 batch_normalization_292[0][0] \n__________________________________________________________________________________________________\nlambda_99 (Lambda) (None, 1, 1, 768) 0 swish_292[0][0] \n__________________________________________________________________________________________________\nconv2d_390 (Conv2D) (None, 1, 1, 32) 24608 lambda_99[0][0] \n__________________________________________________________________________________________________\nswish_293 (Swish) (None, 1, 1, 32) 0 conv2d_390[0][0] \n__________________________________________________________________________________________________\nconv2d_391 (Conv2D) (None, 1, 1, 768) 25344 swish_293[0][0] \n__________________________________________________________________________________________________\nactivation_99 (Activation) (None, 1, 1, 768) 0 conv2d_391[0][0] \n__________________________________________________________________________________________________\nmultiply_99 (Multiply) (None, 14, 14, 768) 0 activation_99[0][0] \n swish_292[0][0] \n__________________________________________________________________________________________________\nconv2d_392 (Conv2D) (None, 14, 14, 176) 135168 multiply_99[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_293 (BatchN (None, 14, 14, 176) 704 conv2d_392[0][0] \n__________________________________________________________________________________________________\nconv2d_393 (Conv2D) (None, 14, 14, 1056) 185856 batch_normalization_293[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_294 (BatchN (None, 14, 14, 1056) 4224 conv2d_393[0][0] \n__________________________________________________________________________________________________\nswish_294 (Swish) (None, 14, 14, 1056) 0 batch_normalization_294[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_100 (Depthwise (None, 14, 14, 1056) 26400 swish_294[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_295 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_100[0][0] \n__________________________________________________________________________________________________\nswish_295 (Swish) (None, 14, 14, 1056) 0 batch_normalization_295[0][0] \n__________________________________________________________________________________________________\nlambda_100 (Lambda) (None, 1, 1, 1056) 0 swish_295[0][0] \n__________________________________________________________________________________________________\nconv2d_394 (Conv2D) (None, 1, 1, 44) 46508 lambda_100[0][0] \n__________________________________________________________________________________________________\nswish_296 (Swish) (None, 1, 1, 44) 0 conv2d_394[0][0] \n__________________________________________________________________________________________________\nconv2d_395 (Conv2D) (None, 1, 1, 1056) 47520 swish_296[0][0] \n__________________________________________________________________________________________________\nactivation_100 (Activation) (None, 1, 1, 1056) 0 conv2d_395[0][0] \n__________________________________________________________________________________________________\nmultiply_100 (Multiply) (None, 14, 14, 1056) 0 activation_100[0][0] \n swish_295[0][0] \n__________________________________________________________________________________________________\nconv2d_396 (Conv2D) (None, 14, 14, 176) 185856 multiply_100[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_296 (BatchN (None, 14, 14, 176) 704 conv2d_396[0][0] \n__________________________________________________________________________________________________\ndrop_connect_81 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_296[0][0] \n__________________________________________________________________________________________________\nadd_81 (Add) (None, 14, 14, 176) 0 drop_connect_81[0][0] \n batch_normalization_293[0][0] \n__________________________________________________________________________________________________\nconv2d_397 (Conv2D) (None, 14, 14, 1056) 185856 add_81[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_297 (BatchN (None, 14, 14, 1056) 4224 conv2d_397[0][0] \n__________________________________________________________________________________________________\nswish_297 (Swish) (None, 14, 14, 1056) 0 batch_normalization_297[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_101 (Depthwise (None, 14, 14, 1056) 26400 swish_297[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_298 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_101[0][0] \n__________________________________________________________________________________________________\nswish_298 (Swish) (None, 14, 14, 1056) 0 batch_normalization_298[0][0] \n__________________________________________________________________________________________________\nlambda_101 (Lambda) (None, 1, 1, 1056) 0 swish_298[0][0] \n__________________________________________________________________________________________________\nconv2d_398 (Conv2D) (None, 1, 1, 44) 46508 lambda_101[0][0] \n__________________________________________________________________________________________________\nswish_299 (Swish) (None, 1, 1, 44) 0 conv2d_398[0][0] \n__________________________________________________________________________________________________\nconv2d_399 (Conv2D) (None, 1, 1, 1056) 47520 swish_299[0][0] \n__________________________________________________________________________________________________\nactivation_101 (Activation) (None, 1, 1, 1056) 0 conv2d_399[0][0] \n__________________________________________________________________________________________________\nmultiply_101 (Multiply) (None, 14, 14, 1056) 0 activation_101[0][0] \n swish_298[0][0] \n__________________________________________________________________________________________________\nconv2d_400 (Conv2D) (None, 14, 14, 176) 185856 multiply_101[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_299 (BatchN (None, 14, 14, 176) 704 conv2d_400[0][0] \n__________________________________________________________________________________________________\ndrop_connect_82 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_299[0][0] \n__________________________________________________________________________________________________\nadd_82 (Add) (None, 14, 14, 176) 0 drop_connect_82[0][0] \n add_81[0][0] \n__________________________________________________________________________________________________\nconv2d_401 (Conv2D) (None, 14, 14, 1056) 185856 add_82[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_300 (BatchN (None, 14, 14, 1056) 4224 conv2d_401[0][0] \n__________________________________________________________________________________________________\nswish_300 (Swish) (None, 14, 14, 1056) 0 batch_normalization_300[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_102 (Depthwise (None, 14, 14, 1056) 26400 swish_300[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_301 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_102[0][0] \n__________________________________________________________________________________________________\nswish_301 (Swish) (None, 14, 14, 1056) 0 batch_normalization_301[0][0] \n__________________________________________________________________________________________________\nlambda_102 (Lambda) (None, 1, 1, 1056) 0 swish_301[0][0] \n__________________________________________________________________________________________________\nconv2d_402 (Conv2D) (None, 1, 1, 44) 46508 lambda_102[0][0] \n__________________________________________________________________________________________________\nswish_302 (Swish) (None, 1, 1, 44) 0 conv2d_402[0][0] \n__________________________________________________________________________________________________\nconv2d_403 (Conv2D) (None, 1, 1, 1056) 47520 swish_302[0][0] \n__________________________________________________________________________________________________\nactivation_102 (Activation) (None, 1, 1, 1056) 0 conv2d_403[0][0] \n__________________________________________________________________________________________________\nmultiply_102 (Multiply) (None, 14, 14, 1056) 0 activation_102[0][0] \n swish_301[0][0] \n__________________________________________________________________________________________________\nconv2d_404 (Conv2D) (None, 14, 14, 176) 185856 multiply_102[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_302 (BatchN (None, 14, 14, 176) 704 conv2d_404[0][0] \n__________________________________________________________________________________________________\ndrop_connect_83 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_302[0][0] \n__________________________________________________________________________________________________\nadd_83 (Add) (None, 14, 14, 176) 0 drop_connect_83[0][0] \n add_82[0][0] \n__________________________________________________________________________________________________\nconv2d_405 (Conv2D) (None, 14, 14, 1056) 185856 add_83[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_303 (BatchN (None, 14, 14, 1056) 4224 conv2d_405[0][0] \n__________________________________________________________________________________________________\nswish_303 (Swish) (None, 14, 14, 1056) 0 batch_normalization_303[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_103 (Depthwise (None, 14, 14, 1056) 26400 swish_303[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_304 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_103[0][0] \n__________________________________________________________________________________________________\nswish_304 (Swish) (None, 14, 14, 1056) 0 batch_normalization_304[0][0] \n__________________________________________________________________________________________________\nlambda_103 (Lambda) (None, 1, 1, 1056) 0 swish_304[0][0] \n__________________________________________________________________________________________________\nconv2d_406 (Conv2D) (None, 1, 1, 44) 46508 lambda_103[0][0] \n__________________________________________________________________________________________________\nswish_305 (Swish) (None, 1, 1, 44) 0 conv2d_406[0][0] \n__________________________________________________________________________________________________\nconv2d_407 (Conv2D) (None, 1, 1, 1056) 47520 swish_305[0][0] \n__________________________________________________________________________________________________\nactivation_103 (Activation) (None, 1, 1, 1056) 0 conv2d_407[0][0] \n__________________________________________________________________________________________________\nmultiply_103 (Multiply) (None, 14, 14, 1056) 0 activation_103[0][0] \n swish_304[0][0] \n__________________________________________________________________________________________________\nconv2d_408 (Conv2D) (None, 14, 14, 176) 185856 multiply_103[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_305 (BatchN (None, 14, 14, 176) 704 conv2d_408[0][0] \n__________________________________________________________________________________________________\ndrop_connect_84 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_305[0][0] \n__________________________________________________________________________________________________\nadd_84 (Add) (None, 14, 14, 176) 0 drop_connect_84[0][0] \n add_83[0][0] \n__________________________________________________________________________________________________\nconv2d_409 (Conv2D) (None, 14, 14, 1056) 185856 add_84[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_306 (BatchN (None, 14, 14, 1056) 4224 conv2d_409[0][0] \n__________________________________________________________________________________________________\nswish_306 (Swish) (None, 14, 14, 1056) 0 batch_normalization_306[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_104 (Depthwise (None, 14, 14, 1056) 26400 swish_306[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_307 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_104[0][0] \n__________________________________________________________________________________________________\nswish_307 (Swish) (None, 14, 14, 1056) 0 batch_normalization_307[0][0] \n__________________________________________________________________________________________________\nlambda_104 (Lambda) (None, 1, 1, 1056) 0 swish_307[0][0] \n__________________________________________________________________________________________________\nconv2d_410 (Conv2D) (None, 1, 1, 44) 46508 lambda_104[0][0] \n__________________________________________________________________________________________________\nswish_308 (Swish) (None, 1, 1, 44) 0 conv2d_410[0][0] \n__________________________________________________________________________________________________\nconv2d_411 (Conv2D) (None, 1, 1, 1056) 47520 swish_308[0][0] \n__________________________________________________________________________________________________\nactivation_104 (Activation) (None, 1, 1, 1056) 0 conv2d_411[0][0] \n__________________________________________________________________________________________________\nmultiply_104 (Multiply) (None, 14, 14, 1056) 0 activation_104[0][0] \n swish_307[0][0] \n__________________________________________________________________________________________________\nconv2d_412 (Conv2D) (None, 14, 14, 176) 185856 multiply_104[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_308 (BatchN (None, 14, 14, 176) 704 conv2d_412[0][0] \n__________________________________________________________________________________________________\ndrop_connect_85 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_308[0][0] \n__________________________________________________________________________________________________\nadd_85 (Add) (None, 14, 14, 176) 0 drop_connect_85[0][0] \n add_84[0][0] \n__________________________________________________________________________________________________\nconv2d_413 (Conv2D) (None, 14, 14, 1056) 185856 add_85[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_309 (BatchN (None, 14, 14, 1056) 4224 conv2d_413[0][0] \n__________________________________________________________________________________________________\nswish_309 (Swish) (None, 14, 14, 1056) 0 batch_normalization_309[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_105 (Depthwise (None, 14, 14, 1056) 26400 swish_309[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_310 (BatchN (None, 14, 14, 1056) 4224 depthwise_conv2d_105[0][0] \n__________________________________________________________________________________________________\nswish_310 (Swish) (None, 14, 14, 1056) 0 batch_normalization_310[0][0] \n__________________________________________________________________________________________________\nlambda_105 (Lambda) (None, 1, 1, 1056) 0 swish_310[0][0] \n__________________________________________________________________________________________________\nconv2d_414 (Conv2D) (None, 1, 1, 44) 46508 lambda_105[0][0] \n__________________________________________________________________________________________________\nswish_311 (Swish) (None, 1, 1, 44) 0 conv2d_414[0][0] \n__________________________________________________________________________________________________\nconv2d_415 (Conv2D) (None, 1, 1, 1056) 47520 swish_311[0][0] \n__________________________________________________________________________________________________\nactivation_105 (Activation) (None, 1, 1, 1056) 0 conv2d_415[0][0] \n__________________________________________________________________________________________________\nmultiply_105 (Multiply) (None, 14, 14, 1056) 0 activation_105[0][0] \n swish_310[0][0] \n__________________________________________________________________________________________________\nconv2d_416 (Conv2D) (None, 14, 14, 176) 185856 multiply_105[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_311 (BatchN (None, 14, 14, 176) 704 conv2d_416[0][0] \n__________________________________________________________________________________________________\ndrop_connect_86 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_311[0][0] \n__________________________________________________________________________________________________\nadd_86 (Add) (None, 14, 14, 176) 0 drop_connect_86[0][0] \n add_85[0][0] \n__________________________________________________________________________________________________\nconv2d_417 (Conv2D) (None, 14, 14, 1056) 185856 add_86[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_312 (BatchN (None, 14, 14, 1056) 4224 conv2d_417[0][0] \n__________________________________________________________________________________________________\nswish_312 (Swish) (None, 14, 14, 1056) 0 batch_normalization_312[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_106 (Depthwise (None, 7, 7, 1056) 26400 swish_312[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_313 (BatchN (None, 7, 7, 1056) 4224 depthwise_conv2d_106[0][0] \n__________________________________________________________________________________________________\nswish_313 (Swish) (None, 7, 7, 1056) 0 batch_normalization_313[0][0] \n__________________________________________________________________________________________________\nlambda_106 (Lambda) (None, 1, 1, 1056) 0 swish_313[0][0] \n__________________________________________________________________________________________________\nconv2d_418 (Conv2D) (None, 1, 1, 44) 46508 lambda_106[0][0] \n__________________________________________________________________________________________________\nswish_314 (Swish) (None, 1, 1, 44) 0 conv2d_418[0][0] \n__________________________________________________________________________________________________\nconv2d_419 (Conv2D) (None, 1, 1, 1056) 47520 swish_314[0][0] \n__________________________________________________________________________________________________\nactivation_106 (Activation) (None, 1, 1, 1056) 0 conv2d_419[0][0] \n__________________________________________________________________________________________________\nmultiply_106 (Multiply) (None, 7, 7, 1056) 0 activation_106[0][0] \n swish_313[0][0] \n__________________________________________________________________________________________________\nconv2d_420 (Conv2D) (None, 7, 7, 304) 321024 multiply_106[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_314 (BatchN (None, 7, 7, 304) 1216 conv2d_420[0][0] \n__________________________________________________________________________________________________\nconv2d_421 (Conv2D) (None, 7, 7, 1824) 554496 batch_normalization_314[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_315 (BatchN (None, 7, 7, 1824) 7296 conv2d_421[0][0] \n__________________________________________________________________________________________________\nswish_315 (Swish) (None, 7, 7, 1824) 0 batch_normalization_315[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_107 (Depthwise (None, 7, 7, 1824) 45600 swish_315[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_316 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_107[0][0] \n__________________________________________________________________________________________________\nswish_316 (Swish) (None, 7, 7, 1824) 0 batch_normalization_316[0][0] \n__________________________________________________________________________________________________\nlambda_107 (Lambda) (None, 1, 1, 1824) 0 swish_316[0][0] \n__________________________________________________________________________________________________\nconv2d_422 (Conv2D) (None, 1, 1, 76) 138700 lambda_107[0][0] \n__________________________________________________________________________________________________\nswish_317 (Swish) (None, 1, 1, 76) 0 conv2d_422[0][0] \n__________________________________________________________________________________________________\nconv2d_423 (Conv2D) (None, 1, 1, 1824) 140448 swish_317[0][0] \n__________________________________________________________________________________________________\nactivation_107 (Activation) (None, 1, 1, 1824) 0 conv2d_423[0][0] \n__________________________________________________________________________________________________\nmultiply_107 (Multiply) (None, 7, 7, 1824) 0 activation_107[0][0] \n swish_316[0][0] \n__________________________________________________________________________________________________\nconv2d_424 (Conv2D) (None, 7, 7, 304) 554496 multiply_107[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_317 (BatchN (None, 7, 7, 304) 1216 conv2d_424[0][0] \n__________________________________________________________________________________________________\ndrop_connect_87 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_317[0][0] \n__________________________________________________________________________________________________\nadd_87 (Add) (None, 7, 7, 304) 0 drop_connect_87[0][0] \n batch_normalization_314[0][0] \n__________________________________________________________________________________________________\nconv2d_425 (Conv2D) (None, 7, 7, 1824) 554496 add_87[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_318 (BatchN (None, 7, 7, 1824) 7296 conv2d_425[0][0] \n__________________________________________________________________________________________________\nswish_318 (Swish) (None, 7, 7, 1824) 0 batch_normalization_318[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_108 (Depthwise (None, 7, 7, 1824) 45600 swish_318[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_319 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_108[0][0] \n__________________________________________________________________________________________________\nswish_319 (Swish) (None, 7, 7, 1824) 0 batch_normalization_319[0][0] \n__________________________________________________________________________________________________\nlambda_108 (Lambda) (None, 1, 1, 1824) 0 swish_319[0][0] \n__________________________________________________________________________________________________\nconv2d_426 (Conv2D) (None, 1, 1, 76) 138700 lambda_108[0][0] \n__________________________________________________________________________________________________\nswish_320 (Swish) (None, 1, 1, 76) 0 conv2d_426[0][0] \n__________________________________________________________________________________________________\nconv2d_427 (Conv2D) (None, 1, 1, 1824) 140448 swish_320[0][0] \n__________________________________________________________________________________________________\nactivation_108 (Activation) (None, 1, 1, 1824) 0 conv2d_427[0][0] \n__________________________________________________________________________________________________\nmultiply_108 (Multiply) (None, 7, 7, 1824) 0 activation_108[0][0] \n swish_319[0][0] \n__________________________________________________________________________________________________\nconv2d_428 (Conv2D) (None, 7, 7, 304) 554496 multiply_108[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_320 (BatchN (None, 7, 7, 304) 1216 conv2d_428[0][0] \n__________________________________________________________________________________________________\ndrop_connect_88 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_320[0][0] \n__________________________________________________________________________________________________\nadd_88 (Add) (None, 7, 7, 304) 0 drop_connect_88[0][0] \n add_87[0][0] \n__________________________________________________________________________________________________\nconv2d_429 (Conv2D) (None, 7, 7, 1824) 554496 add_88[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_321 (BatchN (None, 7, 7, 1824) 7296 conv2d_429[0][0] \n__________________________________________________________________________________________________\nswish_321 (Swish) (None, 7, 7, 1824) 0 batch_normalization_321[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_109 (Depthwise (None, 7, 7, 1824) 45600 swish_321[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_322 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_109[0][0] \n__________________________________________________________________________________________________\nswish_322 (Swish) (None, 7, 7, 1824) 0 batch_normalization_322[0][0] \n__________________________________________________________________________________________________\nlambda_109 (Lambda) (None, 1, 1, 1824) 0 swish_322[0][0] \n__________________________________________________________________________________________________\nconv2d_430 (Conv2D) (None, 1, 1, 76) 138700 lambda_109[0][0] \n__________________________________________________________________________________________________\nswish_323 (Swish) (None, 1, 1, 76) 0 conv2d_430[0][0] \n__________________________________________________________________________________________________\nconv2d_431 (Conv2D) (None, 1, 1, 1824) 140448 swish_323[0][0] \n__________________________________________________________________________________________________\nactivation_109 (Activation) (None, 1, 1, 1824) 0 conv2d_431[0][0] \n__________________________________________________________________________________________________\nmultiply_109 (Multiply) (None, 7, 7, 1824) 0 activation_109[0][0] \n swish_322[0][0] \n__________________________________________________________________________________________________\nconv2d_432 (Conv2D) (None, 7, 7, 304) 554496 multiply_109[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_323 (BatchN (None, 7, 7, 304) 1216 conv2d_432[0][0] \n__________________________________________________________________________________________________\ndrop_connect_89 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_323[0][0] \n__________________________________________________________________________________________________\nadd_89 (Add) (None, 7, 7, 304) 0 drop_connect_89[0][0] \n add_88[0][0] \n__________________________________________________________________________________________________\nconv2d_433 (Conv2D) (None, 7, 7, 1824) 554496 add_89[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_324 (BatchN (None, 7, 7, 1824) 7296 conv2d_433[0][0] \n__________________________________________________________________________________________________\nswish_324 (Swish) (None, 7, 7, 1824) 0 batch_normalization_324[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_110 (Depthwise (None, 7, 7, 1824) 45600 swish_324[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_325 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_110[0][0] \n__________________________________________________________________________________________________\nswish_325 (Swish) (None, 7, 7, 1824) 0 batch_normalization_325[0][0] \n__________________________________________________________________________________________________\nlambda_110 (Lambda) (None, 1, 1, 1824) 0 swish_325[0][0] \n__________________________________________________________________________________________________\nconv2d_434 (Conv2D) (None, 1, 1, 76) 138700 lambda_110[0][0] \n__________________________________________________________________________________________________\nswish_326 (Swish) (None, 1, 1, 76) 0 conv2d_434[0][0] \n__________________________________________________________________________________________________\nconv2d_435 (Conv2D) (None, 1, 1, 1824) 140448 swish_326[0][0] \n__________________________________________________________________________________________________\nactivation_110 (Activation) (None, 1, 1, 1824) 0 conv2d_435[0][0] \n__________________________________________________________________________________________________\nmultiply_110 (Multiply) (None, 7, 7, 1824) 0 activation_110[0][0] \n swish_325[0][0] \n__________________________________________________________________________________________________\nconv2d_436 (Conv2D) (None, 7, 7, 304) 554496 multiply_110[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_326 (BatchN (None, 7, 7, 304) 1216 conv2d_436[0][0] \n__________________________________________________________________________________________________\ndrop_connect_90 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_326[0][0] \n__________________________________________________________________________________________________\nadd_90 (Add) (None, 7, 7, 304) 0 drop_connect_90[0][0] \n add_89[0][0] \n__________________________________________________________________________________________________\nconv2d_437 (Conv2D) (None, 7, 7, 1824) 554496 add_90[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_327 (BatchN (None, 7, 7, 1824) 7296 conv2d_437[0][0] \n__________________________________________________________________________________________________\nswish_327 (Swish) (None, 7, 7, 1824) 0 batch_normalization_327[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_111 (Depthwise (None, 7, 7, 1824) 45600 swish_327[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_328 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_111[0][0] \n__________________________________________________________________________________________________\nswish_328 (Swish) (None, 7, 7, 1824) 0 batch_normalization_328[0][0] \n__________________________________________________________________________________________________\nlambda_111 (Lambda) (None, 1, 1, 1824) 0 swish_328[0][0] \n__________________________________________________________________________________________________\nconv2d_438 (Conv2D) (None, 1, 1, 76) 138700 lambda_111[0][0] \n__________________________________________________________________________________________________\nswish_329 (Swish) (None, 1, 1, 76) 0 conv2d_438[0][0] \n__________________________________________________________________________________________________\nconv2d_439 (Conv2D) (None, 1, 1, 1824) 140448 swish_329[0][0] \n__________________________________________________________________________________________________\nactivation_111 (Activation) (None, 1, 1, 1824) 0 conv2d_439[0][0] \n__________________________________________________________________________________________________\nmultiply_111 (Multiply) (None, 7, 7, 1824) 0 activation_111[0][0] \n swish_328[0][0] \n__________________________________________________________________________________________________\nconv2d_440 (Conv2D) (None, 7, 7, 304) 554496 multiply_111[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_329 (BatchN (None, 7, 7, 304) 1216 conv2d_440[0][0] \n__________________________________________________________________________________________________\ndrop_connect_91 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_329[0][0] \n__________________________________________________________________________________________________\nadd_91 (Add) (None, 7, 7, 304) 0 drop_connect_91[0][0] \n add_90[0][0] \n__________________________________________________________________________________________________\nconv2d_441 (Conv2D) (None, 7, 7, 1824) 554496 add_91[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_330 (BatchN (None, 7, 7, 1824) 7296 conv2d_441[0][0] \n__________________________________________________________________________________________________\nswish_330 (Swish) (None, 7, 7, 1824) 0 batch_normalization_330[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_112 (Depthwise (None, 7, 7, 1824) 45600 swish_330[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_331 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_112[0][0] \n__________________________________________________________________________________________________\nswish_331 (Swish) (None, 7, 7, 1824) 0 batch_normalization_331[0][0] \n__________________________________________________________________________________________________\nlambda_112 (Lambda) (None, 1, 1, 1824) 0 swish_331[0][0] \n__________________________________________________________________________________________________\nconv2d_442 (Conv2D) (None, 1, 1, 76) 138700 lambda_112[0][0] \n__________________________________________________________________________________________________\nswish_332 (Swish) (None, 1, 1, 76) 0 conv2d_442[0][0] \n__________________________________________________________________________________________________\nconv2d_443 (Conv2D) (None, 1, 1, 1824) 140448 swish_332[0][0] \n__________________________________________________________________________________________________\nactivation_112 (Activation) (None, 1, 1, 1824) 0 conv2d_443[0][0] \n__________________________________________________________________________________________________\nmultiply_112 (Multiply) (None, 7, 7, 1824) 0 activation_112[0][0] \n swish_331[0][0] \n__________________________________________________________________________________________________\nconv2d_444 (Conv2D) (None, 7, 7, 304) 554496 multiply_112[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_332 (BatchN (None, 7, 7, 304) 1216 conv2d_444[0][0] \n__________________________________________________________________________________________________\ndrop_connect_92 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_332[0][0] \n__________________________________________________________________________________________________\nadd_92 (Add) (None, 7, 7, 304) 0 drop_connect_92[0][0] \n add_91[0][0] \n__________________________________________________________________________________________________\nconv2d_445 (Conv2D) (None, 7, 7, 1824) 554496 add_92[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_333 (BatchN (None, 7, 7, 1824) 7296 conv2d_445[0][0] \n__________________________________________________________________________________________________\nswish_333 (Swish) (None, 7, 7, 1824) 0 batch_normalization_333[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_113 (Depthwise (None, 7, 7, 1824) 45600 swish_333[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_334 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_113[0][0] \n__________________________________________________________________________________________________\nswish_334 (Swish) (None, 7, 7, 1824) 0 batch_normalization_334[0][0] \n__________________________________________________________________________________________________\nlambda_113 (Lambda) (None, 1, 1, 1824) 0 swish_334[0][0] \n__________________________________________________________________________________________________\nconv2d_446 (Conv2D) (None, 1, 1, 76) 138700 lambda_113[0][0] \n__________________________________________________________________________________________________\nswish_335 (Swish) (None, 1, 1, 76) 0 conv2d_446[0][0] \n__________________________________________________________________________________________________\nconv2d_447 (Conv2D) (None, 1, 1, 1824) 140448 swish_335[0][0] \n__________________________________________________________________________________________________\nactivation_113 (Activation) (None, 1, 1, 1824) 0 conv2d_447[0][0] \n__________________________________________________________________________________________________\nmultiply_113 (Multiply) (None, 7, 7, 1824) 0 activation_113[0][0] \n swish_334[0][0] \n__________________________________________________________________________________________________\nconv2d_448 (Conv2D) (None, 7, 7, 304) 554496 multiply_113[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_335 (BatchN (None, 7, 7, 304) 1216 conv2d_448[0][0] \n__________________________________________________________________________________________________\ndrop_connect_93 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_335[0][0] \n__________________________________________________________________________________________________\nadd_93 (Add) (None, 7, 7, 304) 0 drop_connect_93[0][0] \n add_92[0][0] \n__________________________________________________________________________________________________\nconv2d_449 (Conv2D) (None, 7, 7, 1824) 554496 add_93[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_336 (BatchN (None, 7, 7, 1824) 7296 conv2d_449[0][0] \n__________________________________________________________________________________________________\nswish_336 (Swish) (None, 7, 7, 1824) 0 batch_normalization_336[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_114 (Depthwise (None, 7, 7, 1824) 45600 swish_336[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_337 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_114[0][0] \n__________________________________________________________________________________________________\nswish_337 (Swish) (None, 7, 7, 1824) 0 batch_normalization_337[0][0] \n__________________________________________________________________________________________________\nlambda_114 (Lambda) (None, 1, 1, 1824) 0 swish_337[0][0] \n__________________________________________________________________________________________________\nconv2d_450 (Conv2D) (None, 1, 1, 76) 138700 lambda_114[0][0] \n__________________________________________________________________________________________________\nswish_338 (Swish) (None, 1, 1, 76) 0 conv2d_450[0][0] \n__________________________________________________________________________________________________\nconv2d_451 (Conv2D) (None, 1, 1, 1824) 140448 swish_338[0][0] \n__________________________________________________________________________________________________\nactivation_114 (Activation) (None, 1, 1, 1824) 0 conv2d_451[0][0] \n__________________________________________________________________________________________________\nmultiply_114 (Multiply) (None, 7, 7, 1824) 0 activation_114[0][0] \n swish_337[0][0] \n__________________________________________________________________________________________________\nconv2d_452 (Conv2D) (None, 7, 7, 304) 554496 multiply_114[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_338 (BatchN (None, 7, 7, 304) 1216 conv2d_452[0][0] \n__________________________________________________________________________________________________\ndrop_connect_94 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_338[0][0] \n__________________________________________________________________________________________________\nadd_94 (Add) (None, 7, 7, 304) 0 drop_connect_94[0][0] \n add_93[0][0] \n__________________________________________________________________________________________________\nconv2d_453 (Conv2D) (None, 7, 7, 1824) 554496 add_94[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_339 (BatchN (None, 7, 7, 1824) 7296 conv2d_453[0][0] \n__________________________________________________________________________________________________\nswish_339 (Swish) (None, 7, 7, 1824) 0 batch_normalization_339[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_115 (Depthwise (None, 7, 7, 1824) 16416 swish_339[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_340 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_115[0][0] \n__________________________________________________________________________________________________\nswish_340 (Swish) (None, 7, 7, 1824) 0 batch_normalization_340[0][0] \n__________________________________________________________________________________________________\nlambda_115 (Lambda) (None, 1, 1, 1824) 0 swish_340[0][0] \n__________________________________________________________________________________________________\nconv2d_454 (Conv2D) (None, 1, 1, 76) 138700 lambda_115[0][0] \n__________________________________________________________________________________________________\nswish_341 (Swish) (None, 1, 1, 76) 0 conv2d_454[0][0] \n__________________________________________________________________________________________________\nconv2d_455 (Conv2D) (None, 1, 1, 1824) 140448 swish_341[0][0] \n__________________________________________________________________________________________________\nactivation_115 (Activation) (None, 1, 1, 1824) 0 conv2d_455[0][0] \n__________________________________________________________________________________________________\nmultiply_115 (Multiply) (None, 7, 7, 1824) 0 activation_115[0][0] \n swish_340[0][0] \n__________________________________________________________________________________________________\nconv2d_456 (Conv2D) (None, 7, 7, 512) 933888 multiply_115[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_341 (BatchN (None, 7, 7, 512) 2048 conv2d_456[0][0] \n__________________________________________________________________________________________________\nconv2d_457 (Conv2D) (None, 7, 7, 3072) 1572864 batch_normalization_341[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_342 (BatchN (None, 7, 7, 3072) 12288 conv2d_457[0][0] \n__________________________________________________________________________________________________\nswish_342 (Swish) (None, 7, 7, 3072) 0 batch_normalization_342[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_116 (Depthwise (None, 7, 7, 3072) 27648 swish_342[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_343 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_116[0][0] \n__________________________________________________________________________________________________\nswish_343 (Swish) (None, 7, 7, 3072) 0 batch_normalization_343[0][0] \n__________________________________________________________________________________________________\nlambda_116 (Lambda) (None, 1, 1, 3072) 0 swish_343[0][0] \n__________________________________________________________________________________________________\nconv2d_458 (Conv2D) (None, 1, 1, 128) 393344 lambda_116[0][0] \n__________________________________________________________________________________________________\nswish_344 (Swish) (None, 1, 1, 128) 0 conv2d_458[0][0] \n__________________________________________________________________________________________________\nconv2d_459 (Conv2D) (None, 1, 1, 3072) 396288 swish_344[0][0] \n__________________________________________________________________________________________________\nactivation_116 (Activation) (None, 1, 1, 3072) 0 conv2d_459[0][0] \n__________________________________________________________________________________________________\nmultiply_116 (Multiply) (None, 7, 7, 3072) 0 activation_116[0][0] \n swish_343[0][0] \n__________________________________________________________________________________________________\nconv2d_460 (Conv2D) (None, 7, 7, 512) 1572864 multiply_116[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_344 (BatchN (None, 7, 7, 512) 2048 conv2d_460[0][0] \n__________________________________________________________________________________________________\ndrop_connect_95 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_344[0][0] \n__________________________________________________________________________________________________\nadd_95 (Add) (None, 7, 7, 512) 0 drop_connect_95[0][0] \n batch_normalization_341[0][0] \n__________________________________________________________________________________________________\nconv2d_461 (Conv2D) (None, 7, 7, 3072) 1572864 add_95[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_345 (BatchN (None, 7, 7, 3072) 12288 conv2d_461[0][0] \n__________________________________________________________________________________________________\nswish_345 (Swish) (None, 7, 7, 3072) 0 batch_normalization_345[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_117 (Depthwise (None, 7, 7, 3072) 27648 swish_345[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_346 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_117[0][0] \n__________________________________________________________________________________________________\nswish_346 (Swish) (None, 7, 7, 3072) 0 batch_normalization_346[0][0] \n__________________________________________________________________________________________________\nlambda_117 (Lambda) (None, 1, 1, 3072) 0 swish_346[0][0] \n__________________________________________________________________________________________________\nconv2d_462 (Conv2D) (None, 1, 1, 128) 393344 lambda_117[0][0] \n__________________________________________________________________________________________________\nswish_347 (Swish) (None, 1, 1, 128) 0 conv2d_462[0][0] \n__________________________________________________________________________________________________\nconv2d_463 (Conv2D) (None, 1, 1, 3072) 396288 swish_347[0][0] \n__________________________________________________________________________________________________\nactivation_117 (Activation) (None, 1, 1, 3072) 0 conv2d_463[0][0] \n__________________________________________________________________________________________________\nmultiply_117 (Multiply) (None, 7, 7, 3072) 0 activation_117[0][0] \n swish_346[0][0] \n__________________________________________________________________________________________________\nconv2d_464 (Conv2D) (None, 7, 7, 512) 1572864 multiply_117[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_347 (BatchN (None, 7, 7, 512) 2048 conv2d_464[0][0] \n__________________________________________________________________________________________________\ndrop_connect_96 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_347[0][0] \n__________________________________________________________________________________________________\nadd_96 (Add) (None, 7, 7, 512) 0 drop_connect_96[0][0] \n add_95[0][0] \n__________________________________________________________________________________________________\nconv2d_465 (Conv2D) (None, 7, 7, 2048) 1048576 add_96[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_348 (BatchN (None, 7, 7, 2048) 8192 conv2d_465[0][0] \n__________________________________________________________________________________________________\nswish_348 (Swish) (None, 7, 7, 2048) 0 batch_normalization_348[0][0] \n__________________________________________________________________________________________________\nglobal_average_pooling2d_3 (Glo (None, 2048) 0 swish_348[0][0] \n__________________________________________________________________________________________________\nfinal_output (Dense) (None, 1) 2049 global_average_pooling2d_3[0][0] \n==================================================================================================\nTotal params: 28,515,569\nTrainable params: 28,342,833\nNon-trainable params: 172,736\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"history = model.fit_generator(generator=train_generator,\n steps_per_epoch=STEP_SIZE_TRAIN,\n validation_data=valid_generator,\n validation_steps=STEP_SIZE_VALID,\n epochs=EPOCHS,\n callbacks=callback_list,\n verbose=2).history","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:12:52.49535Z","iopub.execute_input":"2024-05-03T05:12:52.495834Z","iopub.status.idle":"2024-05-03T05:54:22.292823Z","shell.execute_reply.started":"2024-05-03T05:12:52.49562Z","shell.execute_reply":"2024-05-03T05:54:22.291615Z"},"trusted":true},"execution_count":55,"outputs":[{"name":"stdout","text":"Epoch 1/20\n - 176s - loss: 0.7661 - acc: 0.4842 - val_loss: 0.6680 - val_acc: 0.5557\nEpoch 2/20\n - 120s - loss: 0.6428 - acc: 0.5273 - val_loss: 0.5869 - val_acc: 0.5800\nEpoch 3/20\n - 120s - loss: 0.5614 - acc: 0.5614 - val_loss: 0.4859 - val_acc: 0.6514\nEpoch 4/20\n - 120s - loss: 0.4834 - acc: 0.6102 - val_loss: 0.4763 - val_acc: 0.6871\nEpoch 5/20\n - 120s - loss: 0.4380 - acc: 0.6318 - val_loss: 0.4406 - val_acc: 0.6457\nEpoch 6/20\n - 120s - loss: 0.3644 - acc: 0.6827 - val_loss: 0.3194 - val_acc: 0.7543\nEpoch 7/20\n - 121s - loss: 0.3306 - acc: 0.7109 - val_loss: 0.4030 - val_acc: 0.7357\nEpoch 8/20\n - 120s - loss: 0.3065 - acc: 0.7382 - val_loss: 0.2900 - val_acc: 0.7643\nEpoch 9/20\n - 120s - loss: 0.2701 - acc: 0.7639 - val_loss: 0.3171 - val_acc: 0.7671\nEpoch 10/20\n - 120s - loss: 0.2582 - acc: 0.7771 - val_loss: 0.3069 - val_acc: 0.7957\nEpoch 11/20\n - 120s - loss: 0.2166 - acc: 0.7969 - val_loss: 0.3507 - val_acc: 0.7471\nEpoch 12/20\n - 121s - loss: 0.2165 - acc: 0.8042 - val_loss: 0.2898 - val_acc: 0.7800\nEpoch 13/20\n - 120s - loss: 0.1956 - acc: 0.8137 - val_loss: 0.3153 - val_acc: 0.7629\nEpoch 14/20\n - 120s - loss: 0.1766 - acc: 0.8280 - val_loss: 0.2849 - val_acc: 0.7914\nEpoch 15/20\n - 120s - loss: 0.1694 - acc: 0.8356 - val_loss: 0.3427 - val_acc: 0.7900\nEpoch 16/20\n - 121s - loss: 0.1487 - acc: 0.8459 - val_loss: 0.2663 - val_acc: 0.8014\nEpoch 17/20\n - 120s - loss: 0.1398 - acc: 0.8508 - val_loss: 0.3030 - val_acc: 0.8000\nEpoch 18/20\n - 121s - loss: 0.1195 - acc: 0.8692 - val_loss: 0.2842 - val_acc: 0.7986\nEpoch 19/20\n - 120s - loss: 0.1215 - acc: 0.8765 - val_loss: 0.2939 - val_acc: 0.8086\nEpoch 20/20\n - 120s - loss: 0.1255 - acc: 0.8760 - val_loss: 0.2769 - val_acc: 0.7969\n","output_type":"stream"}]},{"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(2, 1, sharex='col', figsize=(20, 6))\n\nax1.plot(cosine_lr_1st.learning_rates)\nax1.set_title('Warm up learning rates')\n\nax2.plot(cosine_lr_2nd.learning_rates)\nax2.set_title('Fine-tune learning rates')\n\nplt.xlabel('Steps')\nplt.ylabel('Learning rate')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:54:22.294741Z","iopub.execute_input":"2024-05-03T05:54:22.295024Z","iopub.status.idle":"2024-05-03T05:54:22.805026Z","shell.execute_reply.started":"2024-05-03T05:54:22.294969Z","shell.execute_reply":"2024-05-03T05:54:22.80355Z"},"trusted":true},"execution_count":56,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"iVBORw0KGgoAAAANSUhEUgAABLEAAAGJCAYAAAB1rqEzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xtc1vX9//HnxcU5BAUELxRP5IEET4DO0ixDsUSx+prm0m0trWaz8/S2/aay+tZsh9pcbulqzdWmX8oySc0oTa2mgkfEU4qiclIQAZHTdV2/P5zXIk8XCHwu4HG/3byN6/N+fz6f1xtfNX31fr8w2e12uwAAAAAAAAAX5mZ0AAAAAAAAAMD1UMQCAAAAAACAy6OIBQAAAAAAAJdHEQsAAAAAAAAujyIWAAAAAAAAXB5FLAAAAAAAALg8ilgAAAAuatq0aUpJSTHk3ePGjdPWrVsNeTcAAMCVUMQCAAAtxhtvvKEZM2bUuTZmzJgrXvv444+bM7RW5+OPP9bQoUONDkOSNGrUKH311VdGhwEAAAxGEQsAALQYsbGx2rFjh6xWqyTp9OnTqq2tVVZWVp1rx48fV2xsbL2fX1tb26jxuipXWqcrxQIAAFwbRSwAANBiREdHq7a2Vvv375ckbd++XUOHDlWPHj3qXOvatatCQ0MlSS+++KJGjhypwYMH67777lN6errjeYsWLdLs2bP13HPPafDgwfrggw/qXBs0aJDGjx+v7OxsvfHGGxo2bJhGjhypLVu2XDXGPn366Pjx447Pc+fO1auvvipJ2rp1q26//Xb95S9/0dChQzVq1Ch99NFHTq//vffe09133624uDj9+Mc/1qlTpxxjDVnnk08+qZ/97GcaNGiQxo0bp7179zru+fbup+vN3bdvnyZOnKhBgwZp9uzZeuqppxxr/q6VK1dqypQpeumllzRkyBAtWrRIOTk5mj59uoYOHaqhQ4fq2WefVWlpqSTp+eefV25urh577DENGjRIS5culSTt2rVLU6ZMUWxsrCZMmFDn6OPKlSt11113adCgQfX+HgMAANdFEQsAALQYnp6e6t+/v6NAk56erpiYGMXExNS59u1dWNHR0frwww+1bds2JSYm6sknn1RVVZVj/LPPPtPYsWOVnp6u8ePHS5I2bNigpKQkbd++XZGRkfrxj38sm82mTZs2adasWZo3b16D13DmzBmdPXtWmzdv1q9//WvNmzdPR48eve59aWlpeuONN/SnP/1JX3/9tWJiYvTss8/e0Do///xzjRs3Tunp6Ro1apReeOGFq77/anOrq6v1xBNP6N5773W8Oy0t7Zpr2bNnj8LDw/XVV1/p8ccfl91u16OPPqrNmzdr7dq1ys/P16JFiyRJv/nNbxQWFqa//OUv2rlzp2bMmKGCggI9+uijevzxx7Vt2zbNmTNHs2fPVnFxsSoqKvTiiy9q6dKl2rlzp5YvX67IyMjrfn8BAIDro4gFAABalCFDhmj79u2S/luwiomJqXNtyJAhjvlJSUnq0KGD3N3d9fDDD6u6ulrZ2dmO8YEDByo+Pl5ubm7y9vaWdPHY4ogRI+Tu7q6xY8fq7Nmzmjlzpjw8PHTPPffo1KlTjp1CDfHkk0/K09NTQ4YM0ciRI7V27drr3rN8+XLNnDlTERERcnd312OPPab9+/c7dmM1ZJ0xMTEaOXKkzGazkpKSdODAgau+/2pzd+/erdraWk2fPl0eHh4aM2aMoqOjr7mWkJAQTZs2Te7u7vL29la3bt102223ydPTU4GBgfrRj37k+P28klWrVun222/XyJEj5ebmpttuu01RUVH64osvJElubm46fPiwKisrFRISol69el33+wsAAFyfu9EBAAAA1EdsbKzeffddnTt3TsXFxerevbuCg4M1d+5cnTt3TocPH66zE+utt95SSkqKCgsLZTKZVF5errNnzzrGO3XqdNk7goKCHF97e3urQ4cOMpvNjs+SVFFRIX9//3rH7+/vL19fX8fnsLAwFRYWXve+3NxcvfTSS1q4cKHjmt1uV0FBgTp37tygdQYHB9dZZ1VVlWpra+XufvkfEa82t7CwUKGhoTKZTI5xi8VyzbV8N5aioiK9+OKLSk9P1/nz52W326/5vc3NzdW6deu0YcMGx7Xa2loNHTpUvr6+evXVV/XWW2/pF7/4hQYPHqw5c+YoIiLimjEBAADXRxELAAC0KIMGDVJ5eblWrFihwYMHS5L8/PwUEhKiFStWKCQkROHh4ZIu7spaunSp3n77bfXq1Utubm6Ki4uT3W53PO/bxZfG4OPjowsXLjg+nz592tGfS5JKS0tVUVHhKGTl5eU5tVPIYrHoscce04QJEy4bM2Kdl3Ts2FEFBQWy2+2Od+Tl5Tl+D67ku7H87ne/k8lk0kcffaQOHTooLS1Nv/rVr656v8ViUVJSkl588cUrjo8YMUIjRoxQZWWlXnvtNf3yl7/UP//5zwasDgAAuBKOEwIAgBbF29tbUVFRevvtt+vsuIqJibns2vnz52U2mxUYGKja2lr96U9/Unl5eZPG17dvX6WmpspqtWrTpk1XPBa3aNEiVVdXKz09XRs3btTYsWOv+9wpU6ZoyZIlOnz4sCSprKzMcQzRiHVeMnDgQJnNZr3zzjuqra1VWlpanabvzjh//rx8fX3l7++vgoIC/fWvf60zHhwcrBMnTjg+T5gwQRs2bNDmzZtltVpVVVWlrVu3Kj8/X2fOnNFnn32miooKeXp6ytfX17GLDgAAtGwUsQAAQIsTFxenoqIixcTEOK7FxMSoqKhIcXFxjmvDhw/X7bffroSEBI0aNUpeXl7XPep2o37xi19ow4YNio2N1erVqxUfH19nPDg4WP7+/hoxYoSee+45LViwwKmjbqNHj9YjjzyiZ555RoMHD1ZiYqI2bdokyZh1XuLp6alFixbpvffeU1xcnD766CPdcccd8vT0dPoZTzzxhLKyshQbG6uZM2dqzJgxdcZnzpypP//5z4qNjdWbb74pi8WixYsX1/mJkW+++aZsNptsNpv+9re/acSIEY7+afPnz2/sZQMAAAOY7N/eZw4AAIAms3XrVj3//POO4lNrNWnSJE2ZMkX333+/0aEAAIBWhJ1YAAAAuCHbtm3T6dOnVVtbqw8++EAHDx7UiBEjjA4LAAC0MjR2BwAAwA3Jzs7WU089pYqKCoWHh+uPf/yjQkJCjA4LAAC0MhwnBAAAAAAAgMvjOCEAAAAAAABcHkWsa7Db7aqqqhKb1QAAAAAAAIxFEesaqqurlZmZqerqaqNDaRT79u0zOgS0EOQK6oN8gbPIFdQH+QJnkStwFrmC+iBfXBNFrDaksrLS6BDQQpArqA/yBc4iV1Af5AucRa7AWeQK6oN8cU0UsQAAAAAAAODyXKKIlZ2drcmTJyshIUGTJ0/WsWPHLptjtVqVnJys+Ph4jR49WikpKU6NXXL06FENGDBACxcubMqlAAAAAAAAoAm4RBFr/vz5mjp1qj755BNNnTpV8+bNu2zO6tWrlZOTo/Xr12vFihVatGiRTp48ed0x6WKRa/78+YqPj2+2NQEAAAAAAKDxGF7EKioqUlZWlhITEyVJiYmJysrKUnFxcZ15a9as0aRJk+Tm5qbAwEDFx8dr3bp11x2TpCVLluiOO+5Q9+7dm21dAAAAAAAAaDzuRgeQl5en0NBQmc1mSZLZbFZISIjy8vIUGBhYZ15YWJjjs8ViUX5+/nXHDhw4oC1btmjZsmVavHhxcywJcMrXe/P094/3qcZqd2q+ycnnmpycaLrGEyurKuX9SVq9X97YMTr7ROef59xcL093Dbg5WONH9FRQgI/zDwcAAAAANBnDi1hNqaamRr/85S/18ssvO4pkDZGZmdmIURkrIyPD6BAg6UxpjZasK1TATWaFBXped77duTpXvdh1jYe285IujTv58sYOsWnW7JyKyhp9sPEbfbT5iCYM6aDo7r6NH0wrw79b4CxyBfVBvsBZ5AqcRa6gPsiXphUTE1PvewwvYlksFhUUFMhqtcpsNstqtaqwsFAWi+Wyebm5uerfv7+kuruvrjZ2+vRp5eTkaObMmZKk0tJS2e12lZeX64UXXnA6xqioKHl5eTXGcg2VkZHRoCRB46qqseq5P2ySl6eHXpl9h4Lbu95OH3JFyjtzXn9YsVPvf1Wk4NDOShze0+iQXBb5AmeRK6gP8gXOIlfgLHIF9UG+uCbDe2IFBQUpMjJSqampkqTU1FRFRkbWOUooSWPHjlVKSopsNpuKi4uVlpamhISEa46FhYVp69at+vzzz/X555/rBz/4gR544IF6FbCAxrbkg706lleqZ6YOdskCFi6yBN+kFx4dpqH9OmnJh3v178w8o0MCAAAAgDbN8CKWJC1YsEDvvPOOEhIS9M477yg5OVmSNGPGDO3du1eSlJSUpC5dumjMmDF64IEHNGvWLIWHh193DHAlGzJOaP3W45p0Vy/FRoYaHQ6uw8PdrOenxSqiS3u9tnynTp+9YHRIAAAAANBmGX6cUJIiIiKUkpJy2fWlS5c6vjabzY7i1ndda+zbfvrTnzY8SOAGnSgo0+L3dqtfzyB9P6Gv0eHASV4eZj3/UIye/N1G/SlllxbM+J5M9ekkDwAAAABoFC6xEwto7Sqra7Vw2XZ5eV4siJjN/KPXkoQF+2naPZHacbCQY4UAAAAAYBD+Jg00gzdW7lVOQZmemRqjoAD6YLVE427toe4Wf7350T7VWm1GhwMAAAAAbQ5FLKCJfbY9R2nbc/TAXb01uE+I0eGggcxmN02/J1IFxRXakH7C6HAAAAAAoM2hiAU0oeP5pVr8/h5FRwTrQfpgtXixkaG6Oby9VqQdYjcWAAAAADQzilhAE6msutgHy9fLXc89FCOzG83AWzqTyaSpY/qooLhCGzPYjQUAAAAAzYkiFtAE7Ha7/rxyj04Wluu578co0N/b6JDQSGIjQ9Xd4q+PNh+V3W43OhwAAAAAaDMoYgFNIG1bjj5PP6Epo/toQO+ORoeDRmQymZQ4vIeyc0uVlV1sdDgAAAAA0GZQxAIa2bG8Uv1l5R4N6BWsyaP7GB0OmsDIQV10k4+HPv4y2+hQAAAAAKDNoIgFNKKKyhr9+u/bdZOPh579Pn2wWitvL3eNHtJVX+3JVXFppdHhAAAAAECbQBELaCR2u12L39ujvDPleu6hGHVoRx+s1mzssO6y2uzamHHS6FAAAAAAoE2giAU0kvVbj+uLnSf1YEJf9b+ZPlitXeeOfurTrYM+T8+hwTsAAAAANAOKWEAjyM49pzc+2KuBvTtq0l29jQ4HzWRUbLiO55fp6KlzRocCAAAAAK0eRSzgBl3qg9XO11PPTqUPVlsyYmBnuZvd9HnGCaNDAQAAAIBWjyIWcAPsdrv+lLJb+UXn9fxDMWrfzsvokNCM2vl6Ku6WUG3aeUpWG0cKAQAAAKApUcQCbsDar49p865TeujuSEVFBBsdDgwwYkBnlZRV6cCxYqNDAQAAAIBWjSIW0EBHTpZo6YeZGtw3RPff2cvocGCQmMgQebi76cs9uUaHAgAAAACtGkUsoAHOX6jRwmXpCvDz1DMPDpYbfbDaLF9vDw3uE6Kv9+TKxpFCAAAAAGgyFLGAerLb7Vr0f7tUcLZCP5sWqwA/+mC1dbf2D9OZc5U6dOKs0aEAAAAAQKtFEQuop4+/zNaXe3I1/e5I3dIjyOhw4AKG9Oskd7NJX+3JMzoUAAAAAGi1KGIB9XD4xFm9+VGmYiNDde8dNxsdDlyEn4+HBvTqqK/25Mpu50ghAAAAADQFiliAk8r/0werfTtvPU0fLHzHsOgwFRRX6FheqdGhAAAAAECrRBELcILdbtcfV+zUmZILmjMtVv43eRodElxMbGSIJCl9f4HBkQAAAABA60QRC3DC6s1H9fXePP1g3C3q2z3Q6HDggoICfNSzcwBFLAAAAABoIhSxgOs4lHNWf0vdp6H9OmniyAijw4ELi4sM1YFjxSqvqDY6FAAAAABodShiAddQVlGthcu2K9DfW09NGSSTiT5YuLrYyFDZ7NKOg4VGhwIAAAAArY5LFLGys7M1efJkJSQkaPLkyTp27Nhlc6xWq5KTkxUfH6/Ro0crJSXFqbH3339f48ePV1JSksaPH69ly5Y1x5LQCtjtdv1h+U4Vl1ZqzvQ4+fnSBwvX1qtrB/nf5KntHCkEAAAAgEbnbnQAkjR//nxNnTpVSUlJWrVqlebNm3dZsWn16tXKycnR+vXrVVJSookTJ2rYsGHq0qXLNccSEhJ03333yWQyqby8XOPHj9eQIUPUt29fg1aLlmLVpiPaui9fjyRFqXfXDkaHgxbA7GbS4L4h2nGgUFabXWZ+giUAAAAANBrDd2IVFRUpKytLiYmJkqTExERlZWWpuLi4zrw1a9Zo0qRJcnNzU2BgoOLj47Vu3brrjvn5+TmOgFVWVqqmpoYjYbiuA8eL9XZqloZFWzRhRE+jw0ELEts3VKXnq3X4xFmjQwEAAACAVsXwIlZeXp5CQ0NlNpslSWazWSEhIcrLy7tsXlhYmOOzxWJRfn7+dcck6bPPPtO4ceN055136pFHHlGfPn2acklo4UrPV2vhsnQFt/fR7Mn0wUL9DOoTIpNJ2nXotNGhAAAAAECr4hLHCZvaXXfdpbvuuku5ubmaNWuWbr/9dvXs6fzumszMzCaMrnllZGQYHYJLs9nt+tcXRTpbWqmHR4foYNYeo0MyDLnScJ06eGjLjqPqFVhudCjNhnyBs8gV1Af5AmeRK3AWuYL6IF+aVkxMTL3vMbyIZbFYVFBQIKvVKrPZLKvVqsLCQlkslsvm5ebmqn///pLq7r661ti3hYWFKTo6Whs3bqxXESsqKkpeXl4NXaLLyMjIaFCStCXvf35Yh3NP6dF7o5U4vO0eIyRXbsytefu0atMR3RI1QD5ehv9rtsmRL3AWuYL6IF/gLHIFziJXUB/ki2sy/DhhUFCQIiMjlZqaKklKTU1VZGSkAgMD68wbO3asUlJSZLPZVFxcrLS0NCUkJFx37MiRI45nFBcXa+vWrerdu3czrQ4tSVZ2kZat3a/b+odp3G09jA4HLdjA3h1Va7Vr39Eio0MBAAAAgFbDJbYILFiwQHPnztXixYvl7++vhQsXSpJmzJih2bNnKzo6WklJSdq9e7fGjBkjSZo1a5bCw8Ml6ZpjK1as0Jdffil3d3fZ7XY99NBDGj58uAGrhCs7V16lV/6RrtAOvvrpAwPpg4UbEtkjSB7ubtp16LRiI0ONDgcAAAAAWgWXKGJFREQoJSXlsutLly51fG02m5WcnHzF+6819vOf/7xxgkSrZbPZ9ft/7dC58mr9ZvYI3eTjYXRIaOG8PMy6pUegdh+muTsAAAAANBbDjxMCRnt/w2HtOFCoGROjdHOX9kaHg1ZiYO8QHcsr1dnSSqNDAQAAAIBWgSIW2rTMI2f0ztr9GjGws+4e1t3ocNCKDOzVUZLYjQUAAAAAjYQiFtqskrIq/eadDHUKuklPTBpAHyw0qh6dA9TO10O7KGIBAAAAQKOgiIU2yWaz6/f/zFBZRbXmTI+Trzd9sNC4zG4m9b+5o3YfOi273W50OAAAAADQ4lHEQpuU8tkh7Tx0WjMnRqtn5wCjw0ErNaB3R505V6ncM+eNDgUAAAAAWjyKWGhz9nxzWv/85IBGDuqihO91MzoctGLREUGSLvZeAwAAAADcGIpYaFPOllXqt+9kyBLsp1n0wUIT69zRT+3beWnvN0VGhwIAAAAALR5FLLQZVptdv3s3Q+crazX3B3Hy8XI3OiS0ciaTSdERwco8eoa+WAAAAABwgyhioc34v08PavfhM3rs3mh1t/gbHQ7aiKiIIBWdq1R+UYXRoQAAAABAi0YRC23C7kOn9a9PD+rOmC6KH9LV6HDQhkT1vNgXay99sQAAAADghlDEQqtXXFqp376boS4hfvrJ/fTBQvMKD22nAD9PmrsDAAAAwA2iiIVWzWq16bfvZOhCda3mTI+TN32w0MxMJpOiegYr82gRfbEAAAAA4AZQxEKr9q/1B7X3yBk9fl9/detEHywYIyoiSKfPXlBBMX2xAAAAAKChKGKh1dpxsFD/99khxcd11V1x9MGCcaIjgiVJmUeKDI4EAAAAAFouilholYrOXdDv/5mh8NB2evS+aKPDQRsXHtpO7Xw9ae4OAAAAADeAIhZaHavVpt+8k6GqaqvmTo+Ttyd9sGAsNzeToiKClHmUnVgAAAAA0FAUsdDqvPvJAe07WqSf/M8AhYe2MzocQNLFvliFxRUqpC8WAAAAADQIRSy0Kun7C5Ty2WGNGdpNd8aEGx0O4ODoi3WUI4UAAAAA0BAUsdBqnCm5oN//c4e6W/w18176YMG1dOvkLz8fD5q7AwAAAEADUcRCq1BrtemVf6Sr1mrVnOmx8vIwGx0SUIebm0n9egbR3B0AAAAAGogiFlqFd9bu1/5jxZr1PwPVJYQ+WHBNURHByi+q0JmSC0aHAgAAAAAtDkUstHjbsvL1/oZvNHZYd40c3MXocICriooIkiR+SiEAAAAANABFLLRohWcr9Nq/dqhnWIBmJEUZHQ5wTT3CAuTr7a59FLEAAAAAoN4oYqHFqqm91AfLrjk/iJUnfbDg4sxuJt3SI0j7+AmFAAAAAFBvLlHEys7O1uTJk5WQkKDJkyfr2LFjl82xWq1KTk5WfHy8Ro8erZSUFKfGXn/9dY0bN04TJkzQfffdp82bNzfHktAMlq3J0sHjZ/XTBwYqLNjP6HAAp/TrGaQTBeUqKasyOhQAAAAAaFHcjQ5AkubPn6+pU6cqKSlJq1at0rx587Rs2bI6c1avXq2cnBytX79eJSUlmjhxooYNG6YuXbpcc6x///56+OGH5ePjowMHDuihhx7Sli1b5O3tbdBq0Ri2Zubpwy+O6J5bu2vEwM5GhwM47VJfrH3ZRbqtf5jB0QAAAABAy2H4TqyioiJlZWUpMTFRkpSYmKisrCwVFxfXmbdmzRpNmjRJbm5uCgwMVHx8vNatW3fdsREjRsjHx0eS1KdPH9ntdpWUlDTjCtHYCoor9OrynYroEqBH6IOFFiaic3t5eZqVeYQjhQAAAABQH4YXsfLy8hQaGiqz+WI/I7PZrJCQEOXl5V02Lyzsv7sWLBaL8vPzrzv2bR9++KG6du2qTp06NcVS0Awu9sHaLrvdrjnT4uThTh8stCwe7m7q260Dzd0BAAAAoJ5c4jhhc9i2bZv+8Ic/6K233qr3vZmZmU0QkTEyMjKMDuGGrM0o0aGccj0wPFC5xw8o97jREbVeLT1XXFmgd7V2Hy7Vlq+3y8fT8P+W0CjIFziLXEF9kC9wFrkCZ5ErqA/ypWnFxMTU+x7Di1gWi0UFBQWyWq0ym82yWq0qLCyUxWK5bF5ubq769+8vqe7uq2uNSdLOnTv1/PPPa/HixerZs2e9Y4yKipKXl1dDl+gyMjIyGpQkruLrvbnaevCkxo/oqWkTo40Op1Vr6bni6jzbn9GGvV/K0z9cMf1a/s5Q8gXOIldQH+QLnEWuwFnkCuqDfHFNhm8BCAoKUmRkpFJTUyVJqampioyMVGBgYJ15Y8eOVUpKimw2m4qLi5WWlqaEhITrju3Zs0dPP/20/vjHP6pfv37Nuzg0mvyi8/rD8p3qFd5eP0q8xehwgBvSp2sHuZvdtJe+WAAAAADgNMN3YknSggULNHfuXC1evFj+/v5auHChJGnGjBmaPXu2oqOjlZSUpN27d2vMmDGSpFmzZik8PFySrjmWnJysyspKzZs3z/G+V155RX369GnOJeIG1NRatXDZdslk0s+mxdIHCy2ep4dZfeiLBQAAAAD14hJFrIiICKWkpFx2fenSpY6vzWazkpOTr3j/tcbef//9xgkShnnro3365uQ5/fyHQ9Qp6CajwwEaRVTPIKV8flgVlTXy9fYwOhwAAAAAcHmGHycErmXL7lNK/TJbSbdHaFi05fo3AC1Ev55BstnsOnDsrNGhAAAAAECLQBELLiv3TLn+uGKX+nTtoB+Mow8WWpfI7oEyu5mUeZS+WAAAAADgDIpYcEnVNVYtXJYus9ulPlikKloXby933dylvTKP0BcLAAAAAJxBZQAu6a8fZeroqXN6+sHBCgn0NTocoElERQTp8ImzqqqxGh0KAAAAALg8ilhwOZt3ntLar47p3jtu1pB+nYwOB2gy/XoGqdZq18HjxUaHAgAAAAAujyIWXMqp0+ValLJTkd0DNf2eSKPDAZpUZI8gmUzSPo4UAgAAAMB1UcSCy6iqserXf98ud7NZzz8UK3cz6YnWzc/HQz3CApR5lCIWAAAAAFwPVQK4jKUf7tWxvFI9M3WwOnbwMTocoFlE9QzSgWPFqqm1GR0KAAAAALg0ilhwCRt3nNQn/z6u+++8WbGRoUaHAzSbqIggVdfadPjEWaNDAQAAAACXRhELhjtRUKbXU3bplh6BmnY3fbDQttzSI0iStI8jhQAAAABwTRSxYKjK6lotXLZdnh5m/WxarMz0wUIbE+Dnpa6d2tEXCwAAAACug4oBDLXkg73KKSjTs1NjFBRAHyy0Tf16Bml/dpGsVvpiAQAAAMDVUMSCYT5Pz9Gn23I06a7eGtw3xOhwAMNE9wzWhSqrjuaeMzoUAAAAAHBZFLFgiJz8Ui1+f4+iIoI0dUwfo8MBDNUv4mJfrMwjHCkEAAAAgKuhiIVmV1lVq18vS5e3p1nPfT+GPlho8wL9vRUWfBPN3QEAAADgGqgeoNn9eeUenSws03Pfpw8WcEm/nkHad7RINpvd6FAAAAAAwCVRxEKzStt2XJ+nn9Dk+D4a2Js+WMAlURFBKr9Qo+P5pUaHAgAAAAAuiSIWms3xvFL9eeVe9b85WFPogwXUEdUzWBJ9sQAAAADgaihioVlcqKrVr5dtl6+3+8U+WG4mo0MCXEpIoK86dvBR5tEzRocCAAAAAC6JIhaanN1u1+L3dyv3dLme+36MOvh7Gx0S4JKiI4KVeYS+WAAAAABwJRSx0OTWb83RxoyTmjKmrwb06mh0OIDLGtCro0rPV+tYHn2xAAAAAOC7KGKhSWXnntNB/1pZAAAgAElEQVSSD/ZoYK+OeiC+t9HhAC5tQK+LfbF2HTptcCQAAAAA4HooYqHJVFTWaOGy7fLz9dCz9MECrisowEfhoe2061Ch0aEAAAAAgMuhiIUmYbfb9fp7u5V35ryeeyhW7dt5GR0S0CIM6t1R+7KLVV1jNToUAAAAAHApFLHQJNb9+7g27TylqWP7Kjoi2OhwgBZjQO+Oqq6x6sDxYqNDAQAAAACX4hJFrOzsbE2ePFkJCQmaPHmyjh07dtkcq9Wq5ORkxcfHa/To0UpJSXFqbMuWLbrvvvsUFRWlhQsXNsdy2rwjJ0u09MO9GtwnRJNG0QcLqI+onkFyczPRFwsAAAAAvsPd6AAkaf78+Zo6daqSkpK0atUqzZs3T8uWLaszZ/Xq1crJydH69etVUlKiiRMnatiwYerSpcs1x8LDw/Xiiy/qk08+UXV1tUErbDsqKmu08B/p8r/JU89MHSw3+mAB9eLr7aE+XTto92GKWAAAAADwbYbvxCoqKlJWVpYSExMlSYmJicrKylJxcd2jNGvWrNGkSZPk5uamwMBAxcfHa926ddcd69atm2655Ra5u7tEva5Vs9vt+uP/7VJBcYWefyhWAX70wQIaYmDvjvrmRInKKyi8AwAAAMAlhhex8vLyFBoaKrPZLEkym80KCQlRXl7eZfPCwsIcny0Wi/Lz8687huaz5qtj+nJ3rqbdHal+PYOMDgdosQb27iibXdp75IzRoQAAAACAy2B7khMyMzONDqHRZGRkNMlzc4ur9eb6QvUK81Y3/3NN9h40H34PjWO12eXpbtL6LVnyrM67/g0ugHyBs8gV1Af5AmeRK3AWuYL6IF+aVkxMTL3vMbyIZbFYVFBQIKvVKrPZLKvVqsLCQlkslsvm5ebmqn///pLq7r661lhjiIqKkpdXyz8al5GR0aAkuZ7zF2r0l1c3qkM7b81/9A6OEbYCTZUrcN7gzK06cuqcBg8eLJPJtXvLkS9wFrmC+iBf4CxyBc4iV1Af5ItrMvw4YVBQkCIjI5WamipJSk1NVWRkpAIDA+vMGzt2rFJSUmSz2VRcXKy0tDQlJCRcdwxNy2636w8rdur02Qv62bQ4ClhAI4mN7KTTZy8op6DM6FAAAAAAwCUYvhNLkhYsWKC5c+dq8eLF8vf318KFCyVJM2bM0OzZsxUdHa2kpCTt3r1bY8aMkSTNmjVL4eHhknTNsfT0dD3zzDMqLy+X3W7Xxx9/rP/93//ViBEjDFhp67N6y1F9vTdPP0rsp8gegde/AYBTYiNDJEnpWQXq1snf4GgAAAAAwHguUcSKiIhQSkrKZdeXLl3q+NpsNis5OfmK919rLDY2Vps2bWqcQFHHoZyz+tvqfRpySyfde0eE0eEArUpQgI96hgVo+/4C3T+ql9HhAAAAAIDhDD9OiJapvKJaC/+Rrg7+3nrqwUEu37MHaIliIkO0/1ixyi/UGB0KAAAAABiOIhbqzW6367XlO1VUckE/mxardr6eRocEtEpxkZ1ks9m182Ch0aEAAAAAgOEoYqHeVm06qq378vXDxH7q240+WEBT6d2tg9r5eih9f4HRoQAAAACA4ShioV4OHC/W26n79L2oTkq6vafR4QCtmtnNpJi+odqeVSCr1WZ0OAAAAABgKIpYcFpZRbVe+Ue6gtr76MnJ9MECmsOwaIvKKqqVeaTI6FAAAAAAwFAUseAUu92uV/+1Q2dLKzVnWqz86IMFNIvBfUPk5WnWl3tzjQ4FAAAAAAxFEQtO+WDjEW3PKtCPxvdT764djA4HaDO8Pd0V2zdU/96bJ6vNbnQ4AAAAAGAYili4rv3Zxfr7mizd2t+i8cPpgwU0t1v7W3S2rEoHjhUbHQoAAAAAGIYiFq7pXHmVXvnHdoV08NHsB+iDBRghNjJUHu5u+moPRwoBAAAAtF0UsXBVNtvFPlgl5dWaMz1ON/l4GB0S0Cb5entocJ8QfbUnVzaOFAIAAABooyhi4apWbvxGGQcK9UhSlG7u0t7ocIA27bYBYTpzrlL7OVIIAAAAoI2iiIUr2ne0SP9Yu1/DB4Tpnlu7Gx0O0OYNi7LI29Osz7bnGB0KAAAAABiCIhYuc7EPVrpCA3310wcG0gcLcAHeXu66tX+YtuzOVWV1rdHhAAAAAECzo4iFOmw2u37/zx0qq6jW3Olx8vWmDxbgKu6KC9eFqlptzcw3OhQAAAAAaHYUsVBHyueHtONgoWZMjFbPzgFGhwPgW6J6BqtjBx+lbeNIIQAAAIC2hyIWHPZ+c0b/XHdAtw/qrLHf62Z0OAC+w83NpDFDu2nX4dM6WVhmdDgAAAAA0KwoYkGSdLasUr95J12W4Js0638G0AcLcFEJ3+smd7NJa746ZnQoAAAAANCsKGJBVptdv393h85fqNEc+mABLq1DO28NH9BZn23PUUVljdHhAAAAAECzoYgF/V/aIe06fFoz7+2vHmH0wQJc3bjhPVRRWau07fTGAgAAANB2UMRq43YfPq1/rT+gO2K6aMzQrkaHA8AJfbp2UL+eQXr/88OqrrEaHQ4AAAAANAuKWG3Y2dJK/fbdDHXu6Kef3E8fLKClMJlMmprQR8WlVfrk38eNDgcAAAAAmgVFrDbKarPrt+9mqKKyVnOnx8nHy93okADUQ3REsPr1DNJ7nx9SZVWt0eEAAAAAQJOjiNVGLV9/UHu+OaPH74tWN4u/0eEAqCeTyaRpd0equLRKyz89aHQ4AAAAANDkKGK1QTsPFmpF2kGNig1X/JBuRocDoIH69QxSfFxXffjFER3PKzU6HAAAAABoUhSx2piicxf0u39mqEtIOz1+X3+jwwFwg36YeIt8vT302oqdqqmlyTsAAACA1ssliljZ2dmaPHmyEhISNHnyZB07duyyOVarVcnJyYqPj9fo0aOVkpJyw2NtjdVm12/eyVBltVVzp8fKmz5YQIsX4Oelnz4wUN+cKNGf398ju91udEgAAAAA0CRcoooxf/58TZ06VUlJSVq1apXmzZunZcuW1ZmzevVq5eTkaP369SopKdHEiRM1bNgwdenSpcFjbc3GvaXad7RMTz84WF070QcLaC2GRVs0Ob63VqQdkrvZTTPvjZa72SX+GwUAAAAANBrD/5ZTVFSkrKwsJSYmSpISExOVlZWl4uLiOvPWrFmjSZMmyc3NTYGBgYqPj9e6detuaKwt2XGgUJv3lWn0kK4aFRtudDgAGtn3x/bV/XferLVfH9Pzizbr6725qqrheCEAAACA1sPwIlZeXp5CQ0NlNpslSWazWSEhIcrLy7tsXlhYmOOzxWJRfn7+DY21JX9L3aeQAHfNvDfa6FAANAGTyaQfJvbTnOmxKimr0ktvb9cbK/cYHRYAAAAANBqXOE7o6jIzM40O4YaN7u+t9jfdpH17dxsdClqIjIwMo0NAA/hIenxsoI4XVqmDX3Wz/T6SL3AWuYL6IF/gLHIFziJXUB/kS9OKiYmp9z2GF7EsFosKCgpktVplNptltVpVWFgoi8Vy2bzc3Fz173/xJ+p9e4dVQ8ecFRUVJS8vrxtap9FidPEfwIYkCdoecqXlG9KM7yJf4CxyBfVBvsBZ5AqcRa6gPsgX12T4ccKgoCBFRkYqNTVVkpSamqrIyEgFBgbWmTd27FilpKTIZrOpuLhYaWlpSkhIuKExAAAAAAAAtAyG78SSpAULFmju3LlavHix/P39tXDhQknSjBkzNHv2bEVHRyspKUm7d+/WmDFjJEmzZs1SePjFBuUNHQMAAAAAAEDL4BJFrIiICKWkpFx2fenSpY6vzWazkpOTr3h/Q8cAAAAAAADQMrhEEctV2e12SVJ1dbXBkTSeqqoqo0NAC0GuoD7IFziLXEF9kC9wFrkCZ5ErqA/ypel5enrKZDI5Pd9kv1SpwWXKysp06NAho8MAAAAAAABoder7g/QoYl2DzWbT+fPn5eHhUa/KIAAAAAAAAK6NnVgAAAAAAABoddyMDgAAAAAAAAC4HopYAAAAAAAAcHkUsQAAAAAAAODyKGIBAAAAAADA5VHEAgAAAAAAgMujiAUAAAAAAACXRxELAAAAAAAALo8iFgAAAAAAAFweRSwAAAAAAAC4PIpYAAAAAAAAcHkUsQAAAAAAAODyKGIBAIBWITc3V4MGDZLVajU6lEZ18uRJ9enTR7W1tc3+7vT0dCUkJDT7ewEAAK6EIhYAAGhRRo0apf79+2vQoEGOXwUFBQoLC9POnTtlNpsb/Z0rV67Ugw8+2OjPdXWxsbH65JNPjA5DkrR161bdfvvtRocBAAAM5G50AAAAAPX1l7/8RbfeeqvRYbRotbW1cnd3jT8K2u122e12ubnx31cBAMDV8ScFAADQKnz32N20adP02muvacqUKRo0aJAefvhhFRcXO+bv2rVLU6ZMUWxsrCZMmKCtW7de8blHjhzR/PnztWvXLg0aNEixsbGO56ekpDjmfXe3Vp8+ffSvf/1LY8aMUVxcnJKTk2W32x3j7733nu6++27FxcXpxz/+sU6dOuXUOsvKyvTzn/9cw4cP14gRI/Tqq686jlDm5ORo+vTpGjp0qIYOHapnn31WpaWljntHjRqlJUuWaPz48Ro4cKBqa2s1atQovfnmmxo/frxiYmL01FNPqaqqStLlu5+uNVeSli5dquHDh2v48OFKSUlRnz59dPz48SuuY9q0aXr11Vc1ZcoUDRgwQCdOnND777+vu+++W4MGDdJdd92l5cuXS5IqKio0Y8YMFRYW1tl9Z7PZtGTJEsXHx2vo0KF68sknVVJSIkmqqqrSc889p6FDhyo2Nlb333+/zpw549T3GAAAuCaKWAAAoNVKTU3Vyy+/rK+//lo1NTV66623JEkFBQV69NFH9fjjj2vbtm2aM2eOZs+eXafIdUlERISSk5M1cOBA7dy5U+np6U6/f+PGjXrvvfe0atUqrV27Vps3b5YkpaWl6Y033tCf/vQnff3114qJidGzzz7r1DPnzJkjd3d3rV+/Xh9++KG+/PJLRzHNbrfr0Ucf1ebNm7V27Vrl5+dr0aJFde7/+OOPtWTJEqWnpzt2Yq1du1Z//etf9dlnn+ngwYNauXLlVd9/tbmbNm3S22+/rb/97W/69NNPtW3btuuuZdWqVXrhhRe0Y8cOhYWFKSgoSG+88YZ27Nihl19+WS+//LL27dsnX19fLV26VCEhIdq5c6d27typ0NBQLVu2TGlpaXrnnXe0efNmBQQE6Fe/+pUk6YMPPlB5ebk2btyorVu3Kjk5Wd7e3k59jwEAgGuiiAUAAFqcWbNmKTY2VrGxsfrJT35y1Xn33XefevToIW9vb40dO1b79++XdLF4cvvtt2vkyJFyc3PTbbfdpqioKH3xxReNGueMGTPk7++vsLAwDR06VAcOHJAkLV++XDNnzlRERITc3d312GOPaf/+/dfdjXXmzBlt2rRJP//5z+Xr66ugoCD98Ic/1McffyxJ6tatm2677TZ5enoqMDBQP/rRj7R9+/Y6z5g2bZosFkudgs60adMUGhqq9u3b684773R8n67kanPXrl2r++67T7169ZKPj4+eeOKJ635/7r33XvXq1Uvu7u7y8PDQHXfcoa5du8pkMmnIkCG67bbbrlk0XLFihZ5++ml16tRJnp6eeuKJJ/TJJ584jkqWlJTo+PHjMpvNioqKkp+f33VjAgAArss1GiEAAADUw+uvv+5UT6yOHTs6vvbx8VFFRYWkiz/JcN26ddqwYYNjvLa2VkOHDlV6erpmzJghSQoLC3MUiBriu+8/f/684/0vvfSSFi5c6Bi32+0qKChQ586dr/q83Nxc1dbWavjw4Y5rNptNFotFklRUVKQXX3xR6enpOn/+vOx2u/z9/es849Lca8VZWFjo9JouzS0sLFRUVNQ13/Nd353zxRdf6PXXX9exY8dks9lUWVmp3r17X/X+3NxczZo1q04vLTc3NxUVFSkpKUn5+fl65plnVFpaqgkTJujpp5+Wh4fHdeMCAACuiSIWAABocywWi5KSkvTiiy9ecXznzp11PptMpsvm+Pj46MKFC47P9em3ZLFY9Nhjj2nChAlO3yPJsePo3//+9xWbsv/ud7+TyWTSRx99pA4dOigtLc1xvO6SK62lMYSEhKigoMDxOS8v77r3fDuW6upqzZ49WwsXLtRdd90lDw8P/eQnP3H0EbtS3J06ddJLL72kmJiYKz7/iSee0BNPPKGTJ09q5syZ6tGjhyZNmlTfpQEAABfBcUIAANDmTJgwQRs2bNDmzZtltVpVVVWlrVu3Kj8//4rzg4KCVFBQoOrqase1yMhIffrpp7pw4YKOHz+u9957z+n3T5kyRUuWLNHhw4clXWzWvnbt2uveFxISottuu02//vWvVV5eLpvNppycHEf/qfPnz8vX11f+/v4qKCjQX//6V6djulFjx47VypUrdeTIEV24cEGvv/56ve6vrq5WdXW1AgMD5e7uri+++EJffvmlYzwoKEglJSUqKytzXHvwwQf12muvOY5hFhcXKy0tTZL073//WwcPHpTVapWfn5/c3d1lNpsbYaUAAMAoFLEAAECbY7FYtHjxYr3xxhsaNmyYRo4cqTfffFM2m+2K87/3ve/p5ptv1vDhwzV06FBJ0g9+8AN5eHjo1ltv1Zw5czR+/Hin3z969Gg98sgjeuaZZzR48GAlJiZq06ZNTt37yiuvqKamRvfcc4/i4uI0e/ZsnT59WtLFnUdZWVmKjY3VzJkzNWbMGKdjulEjR47UtGnTNH36dI0ePVoDBw6UJHl6ejp1v5+fn/7f//t/euqppxQXF6fU1FSNGjXKMR4REaFx48YpPj5esbGxKigo0PTp0zVq1Cg9/PDDGjRokB544AHt2bNH0sWdcbNnz1ZMTIzuueceDRkypN473wAAgGsx2b/9s54BAACARnDkyBElJiZq7969Vzz6CAAAUF/sxAIAAECj+PTTT1VdXa1z587pN7/5je68804KWAAAoNFQxAIAAECjWL58uYYNG6bRo0fLbDZrwYIFRocEAABaEY4TAgAAAAAAwOWxEwsAAAAAAAAujyLWNdjtdlVVVYnNagAAAAAAAMaiiHUN1dXVyszMVHV1tdGhNIp9+/YZHQJaCHIF9UG+wFnkCuqDfIGzyBU4i1xBfZAvrokiVhtSWVlpdAhoIcgV1Af5AmeRK6gP8gXOIlfgLHIF9UG+uCaKWAAAAAAAAHB5zVbEys7O1uTJk5WQkKDJkyfr2LFjl82xWq1KTk5WfHy8Ro8erZSUlBseu+To0aMaMGCAFi5c2CTrAwAAAAAAQNNxb64XzZ8/X1OnTlVSUpJWrVqlefPmadmyZXXmrF69Wjk5OVq/fr1KSko0ceJEDRs2TF26dGnwmHSxyDV//nzFx8c313IBAAAAAADQiJplJ1ZRUZGysrKUmJgoSUpMTFRWVpaKi4vrzFuzZo0mTZokNzc3BQYGKj4+XuvWrbuhMUlasmSJ7rjjDnXv3r05lgsAAAAAAIBG1iw7sfLy8hQaGiqz2SxJMpvNCgkJUV5engIDA+vMCwsLc3y2WCzKz8+/obEDBw5oy5YtWrZsmRYvXtx0iwQAF1FRWaNVXxxR6pfZqqyqvfIkk+mq919t6Gp32Gw2md/Pq8cd9X/H1W64+huutcSrPOsaD7t6vPVbSHPEe613XPX72Ei/H1e7x2SSPNzdVFNdpYAvN8nD3U0eZjd5epjl7u4mD3c3ebqbL153d5O3p7t8vNzl42WWj5e7vL0uff7vL28vd/l4mmU2094TAACgrWi244RGqKmp0S9/+Uu9/PLLjgJaQ2RmZjZiVMbKyMgwOgS0EORKy1NrtSv9cLk27StTRZVNfTp7K9jf94pz7fV8tr2+NzTgnqtOv8Zzrj505ZEGraOeAw14RQO+V/V/uZG/53ZdPNpf625WbVWFKi/YVWu9mLNWm121VrtqbXZZ/3Otxur8yz3dTfL2dJPPpV9ebvL2NP33s+Oam3y93OTnbZavl5vMbtcs98FF8P9FcBa5AmeRK6gP8qVpxcTE1PueZiliWSwWFRQUyGq1ymw2y2q1qrCwUBaL5bJ5ubm56t+/v6S6O6waMnb69Gnl5ORo5syZkqTS0lLZ7XaVl5frhRdecDr+qKgoeXl53dg3wQVkZGQ0KEnQ9pArLYvVZtfGjBN6d/0BnT57Qf1vDtYPxt2i3l07NMv7yRc4y9lcsdnsqqyuVWW1VReqah2/Kh1f//f6+Qs1Kr9QrfKKGpVfqFFZRbUKzlWrrKJSNbW2q76jna+n2rfzUns/L7Vv56UAv2999vNSB39vBQV4q307bwpeBuHfLXAWuQJnkSuoD/LFNTVLESsoKEiRkZFKTU1VUlKSUlNTFRkZWecooSSNHTtWKSkpGjNmjEpKSpSWlqZ33323wWNhYWHaunWr4/mLFi1SRUWF5syZ0xzLBoAmZbfbtXVfvpat2a8TBWW6uUuAZj8wUAN7hxgdGnBD3NxM8vX2kK+3xw09p6rGqvKKiwWusopqlZ6vVkl5lUrKqhz/e668SkdOluhceZXOV15+/NbNzaTAdl4KCvBRYIC3gtv7KOg/Ba6g9j4KCvBWcICPPD0avuMbAAAAzmm244QLFizQ3LlztXjxYvn7+2vhwoWSpBkzZmj27NmKjo5WUlKSdu/erTFjxkiSZs2apfDwcElq8BgAtEZ7j5zR3z/O0sHjZ9W5402aOz1Ot/a3yHStxk5AG+PlYZZXgI+CAnycml9dY9W58mqVlFfqbGmVis5d0JlzlSo6d0FFJZU6WVimXYdO68IVes11aOel0EBfhQT6KvQ/v0I6+Co0yFcd2/vIw50iFwAAwI1qtiJWRESEUlJSLru+dOlSx9dms1nJyclXvL+hY9/205/+1MloAcA1HTlZomVr92vHgUIFBXjriUkDFR8XTnNroBF4epjVsYOPOna4dtGrorJGRf8pbp0pqdSZcxdUWFyhguIKHTx+Vlt258pm+29fL5NJCvT3dhS5LEE3KSz4JoV19FNYRz/5+dzYjjMAAIC2olU3dgeA1iL3TLneXXtAm3adkp+Ph36U2E/jhveQF0eYgGZ36ahjeGi7K45brTYVnatUwdkKR3Hr0q/MI0X6YsfJOk3w/W/yVOeOfrIE36Swjjepc0c/hQVf/OzjxR/VAAAALuFPRgDgworOXdCKTw9p/dbjcnd30wPxvXXvHTezcwNwYWazm0L+s+tKEZePV9dYlV90XqdOn1femXLlnjmv3NPntevQaX2efqLO3EB/b4WH+ik8tJ26hrZT+H9+Bfi1/B84AwAAUF8UsQDABZVXVOv9Dd/oo81HZbXaNHZYd02O760O/t5GhwbgBnl6mNW1k7+6dvK/bKyyqlZ5RReLWqdOl+vU6XKdLCzTZ9tzdKHK6pgX4OeprqH+Cg/1u1jc6nSxuNXez4veeAAAoNWiiAUALqSyulapW7L13ueHVVFZo5GDuuj7Y/uqU9BNRocGoBl4e7mrR1iAeoQF1Llut9t1pqRSJwrKlFNQphP/+fXFjpN1fqpiO19P9QjzV/cwf/WwBKhHmL+6dmpHY3kAANAqUMQCABdQa7Xp063HtfzTgyourVJsZKim3xN52V9kAbRNJpPJ0XR+cN8Qx3W73a7i0v8Wt3Lyy5Sde07rvj6u6pqLO7fc3EzqEuLnKGpdLJL5s7MTAAC0OBSxAMBANptdW3af0jvrDijvzHlFdg/Uz6bFqV/PIKNDA9ACmEwmBQX4KCjARwN7/7e4ZbXZlXemXNm5pTqWV6rs3HPal12kL3aedMxp7+el7mH+iugcoF7hHRTRJUChgb4cRwQAAC6LIhYAGMBut2vHwUIt+3i/juaeU3eLv+b9eKhiI0P5CySAG2Z2M6lLSDt1CWmnEQM7O66XVVTrWO7FolZ2bqmO5p7Tqk1HVGu9+OMS2/l66OYu7XVzePuL/9ulvTp28OHfSwAAwCVQxAKAZnbgWLH+viZLmUeKFBroq2enDtaIQV1kduMviQCaVjtfT0XfHKzom4Md12pqrTqWV6pvTp7TNydK9M3JEq3c8I2stouFLf//z96dx0dZ3/vff81M9j2TdbKQkITsCYGwb27IVixQi1IEPW3VntZf7elpe36cem5x6/kVe5/TxVu78DvagqgVUSgIiAjKKrImhGwEAgnJJCEbSQhZmMz9RzCVqjgBwmR5Px+PPCa5vtdkPtEPM9e853t9L2+3q0KtxGEBBPl7OutPEBERkSFMIZaIyC1ytqqJ1ZsLOHCiigAfd/55QQYzJsTi6mJ0dmkiMoS5upgYER3IiOhAmNi9raPz02CrsSfYemvHSbquBFvBAZ4kxQSSHBNI0jAz8VH+uLlq8XgRERHpWwqxRET6WHV9K6+9V8jOw+V4uruwZHYyX58aj6e7noJFpH9yczWROCyQxGGBPdvaO22UVl6guKyBorMNFJ5tYG9OJQAuJgNxkf4kxZhJGhZIUkyg1tcSERGRm07voERE+khjcztrPyhm874zGAww/7YEvnnnCPy83ZxdmohIr7m7mkiOMZMcY4ap3dsamtoouhJqFZ1tYNuBs2zcfRroXjg+KaY70EqJNZM4LFCztUREROSGKMQSEbnJWts6eefDU2zYVUJ7h43p42JYdHcSIYFaQ0ZEBpdAPw8mpFuYkG4BwGbr4mxVM0Vn6yk820DR2XoOnKgCwMVkZER0AKnDzaTFBZEyPAgfT1dnli8iIiIDjEIsEZGbpKPTxuZ9Z3hzezHNrR1MzozggVnJRIf5Ors0EZFbwmQyEhfpT1ykP7MnDQeg6WIHhWfqOXG6jhOldaz/6BTrdpZgMEBMuF9PqJU6PIjgAIX9IiIi8uUUYomI3CCbrYudh8tZ814RtY2XyBoRwtI5KVetJSMiMlT5ebsxLi2ccWnhALR1XOZkWSMnSus4cbqOneVTwCkAACAASURBVIfL2bzvDAChZi/ShptJHR4Elzqx2+1aV0tERER6KMQSEblOdrudj/OsrN5SQHl1CwnRAfzo/iyyEkOdXZqISL/l4eZCRkIwGQnBQPcHAaWVTZworSO/tI6jRefZefgcAK/vfo/0+GAyr+xvCfJWqCUiIjKEKcQSEbkOuSXn+cu7+RSXNRIZ4sOyh8YyKcOiN1ciIr1kMhlJiA4gITqAedPisdvtVNZe5N0dh7nQ6c3xklp2Ha0AINjfg8wRIWRcCbZCzV5Orl5ERERuJYVYIiK9UFLeyKrN+RwtPk+wvwc/vC+Lu8ZEYzIZnV2aiMigYDAYiAzxITvBh+zsbOx2O+dqWjh+qpbckloOF1az41A5AGFmr55ZWpkJwQT5a00tERGRwUwhloiIAyrOt/DqlgL25FTi6+XKd+5JY87k4bjrcvEiIn3KYDAQHeZLdJgvcyYNp6vLTnl1Mzkl5zleUsv+41be/6QMgIhgb7ISQ8hKDCUzIRhvXf1QRERkUFGIJSJyDXUXLvH6tiLe/6QMNxcj909PZMHtCXpjJCLiJEajgRiLHzEWP74+NZ6uLjullRc4fqqWnJO17DjUvVC80WggMTqArMRQshJDSIoJxEWzZkVERAY0hVgiIl+gubWDdTtOsnH3abrsduZMiuW+6YkE+no4uzQREfkMo9FAfFQA8VEBzL8tgc7LXRSdredY8XmOFZ/nze1FvPF+EZ7uLmQmBF+ZqRVCZIiP1jEUEREZYBRiiYh8Rlv7ZTbuOc26HSdpbb/M7aOjWDwzmfAgb2eXJiIiDnB1MZIeH0x6fDBLZqfQ0tpBTkntlVCrhgMnqgAIDvBk1JVAa+SIEPx93J1cuYiIiHwVhVgiIsBlWxfbDpzljW1FNDS3My41nKVzUoi1+Dm7NBERuQE+Xm5MzoxgcmYEAFV1FzlafJ6jRTXsu7KelsEACVEBZCeHkZ0SyojoQExGzdISERHpbxRiiciQ1tVlZ/exCtZsLcRad5HU4WaWPTSW1OFBzi5NRET6QHiQN7MnejN7Yiy2Ljsl5Q0cLT7P4YLqnlMPfb3cGJ0USnZKKKOTQjVLS0REpJ9wOMQ6deoUW7dupba2luXLl3Pq1Ck6OztJTk7uy/pERPqE3W7ncGENqzbnU1rZRKzFj+UPTyA7OVRrpIiIDBEmo4GkGDNJMWYW3Z1E08UOjhbVcLiwmiNFNXx09JxmaYmIiPQjDoVYW7Zs4ZlnnuHuu+9m06ZNLF++nNbWVv7rv/6LP//5z31coojIzVVQWs9fNudz4nQd4UFe/OSBbKZlRWLUmxIRkSHNz9uN20ZHcdvoKLq67JyqaORwYY1maYmIiPQTDoVYv/vd73j55ZdJSUlhy5YtACQnJ1NYWOjwA5WWlrJs2TIaGxsJCAhgxYoVxMbGXrWPzWbjueeeY/fu3RgMBh599FEWLlx4Q2Pr1q3jz3/+M0ajka6uLhYuXMiDDz7ocN0iMnictTaxeksBB05UEeDrzj9/I5MZ42NwddEl10VE5GpGo4ER0YGMiA7smaV1rLimO9QqrL5qltbY1HDGpYYRF+mv2bwiIiJ9yKEQq76+vue0wU9fmA0GQ69epJcvX87ixYuZN28eGzZs4Mknn2TVqlVX7bNx40bKysrYtm0bjY2NzJ8/n4kTJxIVFXXdYzNnzuQb3/gGBoOBlpYW7rnnHsaNG6fTIEWGkOr6Vl57r5Cdh8vxdHdh6ewUvj41Dg93LQsoIiKO8fN2Y9qoKKaNunqW1qGCal7fVshr7xUS7O/RHWilhZOZEIybq8nZZYuIiAwqDr2DS0tLY8OGDcyfP79n27vvvktmZqZDD1JXV0d+fj6vvPIKAHPnzuXZZ5+lvr4es9ncs9/mzZtZuHAhRqMRs9nM9OnT2bp1Kw8//PB1j/n4+PT8/ra2Njo7O/UJmcgQ0dDcxpvbi9m6/wxGg4EFtyVw750j8PN2c3ZpIiIygP3jLK3G5nYOFVTxSX41Ow+Xs2X/GdzdTGSNCOmZpRXo5+HsskVERAY8h0KsJ554gu9+97u89dZbtLa28t3vfpfS0lJefvllhx7EarUSFhaGydT9aZTJZCI0NBSr1XpViGW1WomIiOj52WKxUFVVdUNjAB988AH//d//TVlZGT/5yU9ISkpyqO5P5eXl9Wr//uzw4cPOLkEGiIHcK22dXewraGZ/YQuXbXZGxXlzW4Yv/l5tnCw87uzyBqWB3C9ya6lXpDcGUr8EmmBmhok7U8M5W91OUcUlCkvPc+BE9zFphNmVpEhPEqM8CA9w1YeqN9lA6hVxLvWK9Ib6pW9lZ2f3+j4OhVjx8fFs2bKFnTt3cvvtt2OxWLj99tvx9vbu9QM6w1133cVdd91FZWUljz32GNOmTSMuLs7h+6enp+PuPvAX7Tx8+PB1NYkMPQO1Vzo6bWzeV8qb20/S3NrBlJERLJmdQmSIz1ffWa7bQO0XufXUK9IbA7lfJly5tdvtnLE28Ul+FQdPVPNhXgM7jzcRHODJ2NQwxqXqtMObYSD3itxa6hXpDfVL/+RQiPXcc8/xH//xH8yZM+eq7b/4xS944oknvvL+FouF6upqbDYbJpMJm81GTU0NFovlc/tVVlb2nKb42RlW1zv2WREREWRkZPDhhx/2KsQSkf7NZutix6FyXttWRG3jJUYlhvDgnFQSogOcXZqIiAxhBoOB4RH+DI/w5/7pSTQ0t3Eov5pP8qvYcaicLfvO4OFmIjs5jAnp4YxJDcfH09XZZYuIiPRbDl2S6+233/7C7X/7298cepCgoCBSUlLYtGkTAJs2bSIlJeWqUwkBZs2axdq1a+nq6qK+vp7t27czc+bMGxo7depUz++vr6/nwIEDJCYmOlS3iPRvdrudfbmV/K//dye/e/MYZj93nvvnSTzzvUkKsEREpN8J9PXg7vExPPHt8bz2zGyWPzyB27OjyS+t479eO8KSJ7fw//xxH5v3lVJ34ZKzyxUREel3rjkT66233gLAZrP1fP+p8vJyAgIcf5P41FNPsWzZMl566SX8/PxYsWIFAI888giPP/44GRkZzJs3j5ycHGbMmAHAY489RnR0NMB1j/31r39l7969uLi4YLfbWbJkCVOmTHG4bhHpn3JOnmfV5nyKyxqJCvXh5/80lgnpFq0xIiIiA4Kbq4kxKWGMSQnj+9/IpLisgY/zrOw/buX363L5/bpckoYFMj49nAnpFqLDfJ1dsoiIiNMZ7Ha7/csGly5dCnz+XFCDwUBwcDAPPvggWVlZfV+lk7S3t5OXl6c1sWTI6c+9UlLeyF8253Os+DzBAZ48MDOJO7KjMZkcmlgqfaA/94v0L+oV6Y2h2i92u53y6mY+zqtif56VkvJGAKJCfZiQbmFihoWEqACMRn1o86mh2ivSe+oV6Q31S/90zZlYq1evBuDXv/41P/7xj29JQSIiX+RcTTOvbi1kb04lvl5ufPfr6cyZFKvFcEVEZFAxGAwMC/djWLgf901P5HzDJT45YWV/npW3PyzhrR0nMft5MD49nInpFtLjg3F10Qc5IiIyNDi0sPtnAyy73c5nJ28ZjXrRFJG+U9t4iTfeL+L9T8pwczGy6O4kFtwej5eHFr4VEZHBLyTQk69NieNrU+Jobu3gYH41H+dZexaG9/ZwYWxqOJMyLYxODsNdH+6IiMgg5lCIVV1dzTPPPMOhQ4doamq6aqygoKBPChORoa3pYgfrdpxk057TdNntfG3ycO67K5EA34F/aq+IiMj18PVy484x0dw5Jpr2ThvHimr4OK+KAyesfHjkXPeVDlPCmJwZwZiUMDzdHTrUFxERGTAcemVbvnw5Hh4e/PnPf2bJkiWsWbOGF154gdtuu62v6xORIaat/TIbdp/i7Z0lXGq/zB3Z0SyemUyY2cvZpYmIiPQb7q4mxqdbGJ9u4bJtJHmnatmX270w/N6cStxcjIxODmVyZgRjU8Px9tQMZhERGfgcCrGOHj3Kzp078fLywmAwkJyczC9+8QsWLVrEfffd19c1isgQ0Hm5i20fn+GN7cU0NrczPi2cpbNTiLH4Obs0ERGRfs3FZCQrMZSsxFC+941MCkrr2Jtbyf7jVj7Oq7oyHsLkzO7Qy9fLzdkli4iIXBeHQiyj0YiLS/eufn5+1NfX4+PjQ3V1dZ8WJyKDX1eXnV1Hz/Hq1kKq61tJiwvi5w+NI2W42dmliYiIDDgmo4H0+GDS44N5ZF4GxWUN7M2tZF9uJYcKqjGtzSEjIZjJmRFMSLfoNH0RERlQHAqxRo4cyUcffcTdd9/NlClT+Jd/+Rc8PDxIT0/v6/pEZJCy2+0cKqhm1eYCzlibGB7hx/KHJ5CdHIrBoMuGi4iI3Cij0UByrJnkWDPfuSeNknON7Mu1sje3khffyuH363JIiwtmcqaFCRkWgvw9nV2yiIjINTkUYj3//PN0dXUB8POf/5yXX36Zixcv8tBDD/VpcSIyOJ04Xceqzfnkl9YTHuTFTx/IZmpWJEajwisREZG+YDAYGBEdyIjoQB6ck8IZa9OVGVpW/vDOcf64/jjJMWYmj4xgcmYEwQEKtEREpP/5yhDLZrPxi1/8gmeffRYADw8PfvCDH/R5YSIy+JRWXmD1lgIO5lcT6OvO9+/N5O5xMbi6GJ1dmoiIyJBhMBgYHuHP8Ah/lsxKoby6mX25lezNreT/bsjj/27IIyXWzNSsSCZlaoaWiIj0H18ZYplMJvbu3avTe0TkulXVXWTNe4V8dOQcXu4uPDgnhXumxOGhS3+LiIg4XXSYL/ffncT9dydReb6F3TkV7DlWyZ/WH2flhuOkDg/qCbQCfT2cXa6IiAxhDr2DfOihh3jhhRf44Q9/iKurLs8rIo5paG7jzfeL2frxGYwGA9+4PYF77xyhqyKJiIj0UxEhPtw/PYn7pydRXt3MnpxKdh+r4A9v5/Knd3JJjw9mSlYkkzIs+PtoUXgREbm1HAqxXn31VWpra3nllVcwm81Xzcr68MMP+6o2ERmgLl7q5J0PS9iw6xQdl7uYMT6GRXcn6nQEERGRASQ6zJdvzUjiWzOSOFvVxJ5j3YHWS2/l8Ie3c8lMCGbKyEgmZljw89YHVCIi0vccCrF+9atf9XUdIjIItHfa2Ly3lLUfFNPc2snUrEiWzEomIsTH2aWJiIjIDYgJ9yNmlh+LZyZxxtrE7mMV7Mmp5P9be4zfr8thZGIIU0dGMCHdgo9mXIuISB9xKMQaN25cX9chIgOYzdbFB4fKef29QmovtDE6KZSlc1JIiApwdmkiIiJyE312Ufils1M4XXGhJ9D67V+P8eJbOWQlhjI1K4LxaRa8PbUUiYiI3DxaVVlErpvdbmffcSurNxdQcb6FpGGB/HjxaDITQpxdmoiIiPQxg8FAfFQA8VEBPPS1VE6WN7Inp5I9ORUcer0aF1MO2cmhTBkZwbi0cLw8FGiJiMiNUYglItclp/g8f96cT0l5I9FhPvz8n8YxIT1cVzIVEREZggwGA4nDAkkcFsi356ZSVNbAnmPdgdaBE1W4uhgZkxLG1KxIxqaG4eGmtyEiItJ7evUQkV4pLmtg9eYCjp08T0igJz+6fxR3jInGZFR4JSIiIt2BVnKMmeQYM9+5J43Cs/XsPlbB3pxK9h+34uluYnyahamjIhmVGIqri9HZJYuIyAChEEtEHFJe3cyrWwvYl2vFz9uNh+elM3tiLG6uJmeXJiIiIv2U0WggdXgQqcODeHheBidO17LraHeg9eGRc/h4ujIpM4JpWZGkJwTrQzEREbkmh0Ksn/3sZ194ipCbmxvh4eFMnz6d5OTkm16ciDjf+YZLvL6tkA8OluHuZuJbM5KYf1u81rUQERGRXjEZDWQmhJCZEML3FmRyrLiGXccq2H3sHNsOnCXA150pIyO4bVQUSTGBWqJAREQ+x6EQy9fXlw0bNnDnnXdisViwWq3s3LmTOXPmcOrUKVauXMnTTz/N/Pnz+7peEblFmi52sPaDYt7dW4rdDnOnxnHfXYn4+7g7uzQREREZ4FxdjIxNDWdsajhtHZc5XFDDR0fP8d7HZ9m0p5TQQE+mZkUybVQUwyP8FGiJiAjgYIh15swZ/vSnP5Gdnd2z7ejRo/zud7/jlVdeYdeuXfznf/6nQiyRQeBS+2U+ymvi+bffp639MneMiWbxjGRCzV7OLk1EREQGIQ83FyaPjGDyyAha2zr5OM/KrqMVvPPRKdbtLCEq1IdpWZFMGx1FZIiPs8sVEREncijEysnJYeTIkVdtS09PJzc3F4CpU6dSXV1986sTkVum83IX7318hr++X0xjSzsT0sNZMjuFmHA/Z5cmIiIiQ4SXhyt3jhnGnWOGcaGlnX3Hrew+WsHr7xfx2rYi4iL9uW1UJFOyIgkN1AdsIiJDjUMhVkpKCr/+9a95/PHHcXd3p729nRdeeKFnHaxz587h7+/fp4WKSN+wddnZdfQca7YWUl3fSnp8EPdO8mP+zPHOLk1ERESGMH8fd2ZPjGX2xFjqLlxiT04lu46e45VN+byyKZ+UWDPTRkXii83ZpYqIyC3iUIj1y1/+kp/+9KdkZ2fj7+/PhQsXSE9P51e/+hUAjY2NLF++vE8LFZGby263c7CgmtWbCzhjbSIu0p+nH5nIqKQQjhw54uzyRERERHoE+Xsyb1o886bFU1V3kV1HK9h9rII/vnMcgwG25+1j6qhIJmVY8PFyc3a5IiLSRxwKsaKionjjjTewWq3U1NQQEhJCREREz3hGRsZX/o7S0lKWLVtGY2MjAQEBrFixgtjY2Kv2sdlsPPfcc+zevRuDwcCjjz7KwoULb2jsxRdfZPPmzZhMJlxcXPjxj3/M1KlTHfqPIzJYnThdx1/ezafgTD2WYG/+bckYJo+MwKjLWouIiEg/Fx7kzX3TE7lveiJnq5p4c/MhTla18sKbx/j9uhxGJ4UxbVQk49LC8XR36O2OiIgMEL16Vndzc8NsNmOz2SgvLwcgOjraofsuX76cxYsXM2/ePDZs2MCTTz7JqlWrrtpn48aNlJWVsW3bNhobG5k/fz4TJ04kKirquscyMzP5zne+g6enJ4WFhSxZsoQ9e/bg4eHRmz9dZFAorbzAqs0FHCqoxuznzg++OZK7xw3DxWR0dmkiIiIivRYT7sedI/356bdHU3KusWeG1if5Vbi7mRiXGs7UrEjGpITi6mJydrkiInKDHAqxdu3axRNPPMH58+ev2m4wGCgoKPjK+9fV1ZGfn88rr7wCwNy5c3n22Wepr6/HbDb37Ld582YWLlyI0WjEbDYzffp0tm7dysMPP3zdY5+ddZWUlITdbqexsZHw8HCH/gOJDAZVdRdZs7WQj46ew8vDlYe+lsrcKcPxcNOnkyIiIjLwGQwGRkQHMiI6kG/PTSO/tI5dxyrYm1PJ7mMVeHu4MCHDwrSsKEaOCMakD/BERAYkh97BPvPMM/zgBz9gwYIF1zWDyWq1EhYWhsnU/emHyWQiNDQUq9V6VYhltVqvOk3RYrFQVVV1Q2OftX79eoYNG6YAS4aMhqY2/rq9mK37z2AyGbn3jhHce0eC1ooQERGRQctoNJAeH0x6fDCPzs8g92Qtu46dY/9xKx8cLMffx41JmRFMy4okdXiQllMQERlAHAqxmpqaWLRoEQbDwH2C/+STT/jtb3/Lyy+/3Ov75uXl9UFFznH48GFnlyC3QFtHF3sLmvm4sIXLXXay472Zlu6Hn9cligqOO/Q71CvSG+oXcZR6RXpD/SKO+qpemToCJsSFUVLZRt7ZVt4/cJYt+87g62kiPcaT9BgvIsyuA/r9jjhGzyvSG+qXvpWdnd3r+zgUYt17772sW7eOb37zm71+AOieGVVdXY3NZsNkMmGz2aipqcFisXxuv8rKSjIzM4GrZ1hd7xjA0aNH+dnPfsZLL71EXFxcr+tPT0/H3d299394P3P48OHrahIZONo7bby7p5S3dhTT3NrJtKxIHpiVTESIT69+j3pFekP9Io5Sr0hvqF/EUb3plQlXbi+1X+aTE1XsPlbBwcJq9he2YAnyZuqoSKZlRRJj8eu7gsVp9LwivaF+6Z8cCrFycnJYvXo1K1euJDg4+KqxNWvWfOX9g4KCSElJYdOmTcybN49NmzaRkpJy1amEALNmzWLt2rXMmDGDxsZGtm/f3vP7r3csNzeXH//4x/zud78jLS3NkT9XZMCx2brYfrCc17cVUnehjdHJoTw4O4X4qABnlyYiIiLS73i6u3Db6ChuGx1FS2sH+49b2XWsgrc+KObN7cXEhPsydVQkU7MiiQju3YeBIiLSdxwKsRYuXMjChQtv6IGeeuopli1bxksvvYSfnx8rVqwA4JFHHuHxxx8nIyODefPmkZOTw4wZMwB47LHHeq5+eL1jTz/9NG1tbTz55JM9tTz//PMkJSXd0N8j0h/Y7Xb25VpZvSWfivMXSYoJ5CeLs8lICP7qO4uIiIgIPl5u3D0+hrvHx9DQ3Ma+nEp2Havg1S2FvLqlkBHRAUwbFcmUkZEEB3g6u1wRkSHNYLfb7c4uor9qb28nLy9PpxNKv3SsuIa/vJtPybkLDAv3ZensFManhd+UtRzUK9Ib6hdxlHpFekP9Io7qq16paWhlz7FKdh87R8m5CwCkxQUxbVQkkzMj8PcZ+O8Phho9r0hvqF/6py+dibV+/Xrmz58PwFtvvfWlv+B618kSketTXNbAX97NJ7ekltBAT378rVHcNjoak66sIyIiInLThAZ68Y07EvjGHQlUnG9h97EKdh09x+/X5fLHd44zMiGYaaOimJBhwcfT1dnliogMCV8aYr377rs9IdaGDRu+cB+DwaAQS+QWKa9uZvWWAvYft+Lv48Yj89OZPTEWVxeTs0sTERERGdQiQ3xYdHcS909P5Iy16UqgVcFv/3qUF9/KITs5lNtGRTE2NQwPd4dWbBERkevwpc+wK1eu7Pl+9erVt6QYEfm8moZW3thWxAcHy3B3c2HxzGTmTYvDy0Of+ImIiIjcSgaDgeER/gyP8Gfp7BSKyxrYdayCPccqOHCiCnc3E+NTw5k6KpLs5FB92CgicpP16mOCuro6Wltbr9r26QLqInJzXWhp560dJ3l3byl2O9wzNZ6Fd43Q+gsiIiIi/YDBYCApxkxSjJnv3JNOfmkdu45WsPfKwvDeHi5MzIhg6qhIRiYEYzIZnV2yiMiA51CItWvXLp544gnOnz9/1XaDwUBBQUGfFCYyVF1qv8yGXad4e2cJ7R2XuXPMML41M4nQQC9nlyYiIiIiX8BkNJARH0xGfDDfW5BBzsnz7Dpawb7jlWw/WIa/jxuTMyOYNiqKlFgzRq1lKiJyXRwKsZ555hl+8IMfsGDBAjw8PPq6JpEhqfOyja37z/LX7UVcaOlgYoaFJbOSGRbu5+zSRERERMRBLiYj2clhZCeH0dFp43BhNbuOVrD9YDmb950h2N+DKVmRTBsVSUJUwE25srSIyFDhUIjV1NTEokWL9AQr0gdsXXY+OnKONe8VUlPfSkZ8MA99J4WkGLOzSxMRERGRG+DmamJiRgQTMyJobevkk/xqdh+tYNOe06z/6BSWYG+mZUUydVQkMfrgUkTkKzkUYt17772sW7dOVyIUuYnsdjufnKhi9ZYCzlY1Ex/lz2PfnMioxBAFxiIiIiKDjJeHK7ePjuL20VE0t3aw/7iV3UcrWPtBMX/dXkysxY+pWZFMzYrEEuzt7HJFRPolh0KsnJwcVq9ezcqVKwkODr5qbM2aNX1SmMhglneqlr+8m0/h2QYigr35t6VjmJwZofURRERERIYAXy83ZoyPYcb4GBqa2tibW8muoxWs3lLA6i0FJA4LYGpWFFOzIgjy93R2uSIi/YZDIdbChQtZuHBhX9ciMuidrrjAqs35HC6sweznwf9aOJK7xg7DRVerERERERmSAv08mDsljrlT4qipb2VPTgW7jlXwP3/L4+WNeaQOD2JyZgSTMi0KtERkyPvKEMtms1FWVsb3v/993NzcbkVNIoNOZW0La7YWsutoBT6ernx7bipfmxKHu6vJ2aWJiIiISD8RavbiG3eM4Bt3jKDifAu7jlawN6eCP60/zsoNx0kdHsSUkRFMyozA7KcLbonI0POVIZbJZOK1117jhz/84a2oR2RQqW9q4433i9j28VlMJiML7+o+KPHxdHV2aSIiIiLSj0WG+PCtGUl8a0YS5dXN7MmpZE9OBX985zh/Wt8daE29EmgFKtASkSHCodMJ58+fz+uvv84DDzzQ1/WIDAotlzp5e+dJNuw6jc3WxcwJMdx/d5I+MRMRERGRXosO8+0JtMqqmtibU8me3Er+8M5x/rj+OGlxQUwZGcmkDIsCLREZ1BwKsXJzc3n11Vf5n//5H8LDw6+6cpoWdhf5u7aOy7y7p5S3dpyk5VInt42K4oFZybrCjIiIiIjcFMPC/RgW7se3Zib3BFq7cyr5w9u5/PGdXNLjgpk8snsNrUBfBVoiMrg4FGLdd9993HfffX1di8iAddnWxfZPynh9WxH1TW2MSQlj6ewU4iL9nV2aiIiIiAxSnw20zn46Qyungj+8ncuf3sklLS6YKVkRTMxQoCUig4NDIdaCBQv6ug6RAamry87e3Epe3VJAZe1FUmLN/GxJNunxwc4uTURERESGkJhwP2LC/Vh8JdDac6ySvbkV/H5dLn98O5f0+GCmjIxgYkYEAb7uzi5XROS6OBRiAdTW1pKbm0tDQwN2u71n+ze/+c0+KUykP7Pb7RwtPs+qzfmcOneBmHBf/p/vjGdsathVp9uKiIiIiNxqMeF+xMzyY/HMJMqq/r4o/EvrcvnDZwKtCZqhJSIDjEMh1vbt2/nZz35GTEwMJSUlJCQkcPLkSUaPHq0QS4acorP1rNpcQG5JLaFmL378rdHcNjoKk1HhlYiIiIj0HwaDgRiLtjoFfQAAIABJREFUHzGWvwdau3Mq2JtTyUvrcvn927mkDg9iUoaFCRkWQgO9nF2yiMg1ORRi/eY3v+E///M/mT17NmPHjmX9+vWsW7eOkpKSvq5PpN8oq2ri1a2F7D9uxd/HjUfnZzBrYgyuLiZnlyYiIiIick2fDbQemJlMWVUz+3Ir2XfcysoNeazckEdCdACTMixMyowgMsTH2SWLiHyOQyFWZWUls2fPvmrbggULmDx5Mv/7f//vPilMpL+oaWjl9feK2HGoDHc3Fx6YlczXp8bh5eHq7NJERERERHrts4HWt2YmU1nbwv5cK/uPW1m1uYBVmwuICfdlYkb3VQ5jLX5aMkNE+gWHQqygoCBqa2sJDg4mMjKSo0ePEhgYSFdXV1/XJ+I0F1raWfvBSd7dW4rBAF+fFs837xyBv48WwhQRERGRwSMi2Id77xzBvXeO4HzDJT7Os7LveCVvbi/ijfeLsAR5MzHDwqRMCyOiAzFqGQ0RcRKHQqyFCxdy+PBhZs6cyT/90z/x4IMPYjQa+fa3v93X9Ynccq1tnWzYdZp3PiyhveMyd40dxqIZSVojQEREREQGvZBAT+6ZGsc9U+NobG7nwAkr+45b+dvuU7z9YQlB/h5MTO8+5TB1uBmTyejskkVkCHEoxHr00Ud7vp8/fz7jxo3j0qVLxMfH91lhIrda52UbW/af4c3txVxo6WBihoWls1OIDvN1dmkiIiIiIrdcgK87MyfEMnNCLC2XOjmYX8X+41a2fVLGpr2l+Hm7MSHdwsQMCyNHBGutWBHpcw6FWACdnZ3k5ORQU1PDnDlzaG1tpbW1FS8vzU6Rgc3WZefDw+W89l4hNQ2XyEwI5qGvpZI4LNDZpYmIiIiI9As+nq7ckR3NHdnRtLVf5nBRDftyK9l9rIJtB87i6e7C6ORQJqSFMyYlDB8vN2eXLCKDkEMhVlFREd///vdxc3OjurqaOXPmcPDgQd555x1+85vf9HWNIn3Cbrdz4EQVq7cUUFbVTEKUPz+8L4usxFBnlyYiIiIi0m95uLswOTOCyZkRdF62kXOylo/zrHxyooq9OZUYjQbS44IYnx7O+DQLYWZNfBCRm8OhEOupp57i8ccfZ/78+YwdOxaAsWPH8h//8R8OP1BpaSnLli2jsbGRgIAAVqxYQWxs7FX72Gw2nnvuOXbv3o3BYODRRx9l4cKFNzS2Z88e/vu//5vi4mKWLl2qqykKAMdP1fKXd/MpOttAZIg3yx4cy6RMi666IiIiIiLSC64uJsakhDEmJYyue+2cLG/gwIkqPs6rYuX6PFauzyPW4sf49HAmpFmIj/LXMbeIXDeHQqySkhLmzZsH0POE4+XlRXt7u8MPtHz5chYvXsy8efPYsGEDTz75JKtWrbpqn40bN1JWVsa2bdtobGxk/vz5TJw4kaioqOsei46O5rnnnuO9996jo6PD4XplcDp1rpFVWwo4UlhDkL8H/2thFtPHRmtBShERERGRG2Q0GkiKMZMUY+bBOalU1rZwIK+KAyeqWLu9mL++X0ywvwfj0sIZn24hIz4YVxcdh4uI4xx6xoiMjCQvL++qbbm5uQwbNsyhB6mrqyM/P5+5c+cCMHfuXPLz86mvr79qv82bN7Nw4UKMRiNms5np06ezdevWGxqLiYkhNTUVFxeHl/+SQaiytoVfrT7Ev/z6I4rPNvDtuWn88d+nM3NCjAIsEREREZE+EBHsw4LbE/jlY1NY9dQsfnT/KBKiA9h+sJzlf9rPkuVbeH71IT46co6WS53OLldEBgCHkp0f/ehHfO9732PRokV0dnbyxz/+kTfeeINnn33WoQexWq2EhYVhMnVfrcJkMhEaGorVasVsNl+1X0RERM/PFouFqqqqGxqToa3uwiX++n4x2w6cxcXFyH3TE1lwewI+nq7OLk1EREREZMjw93Fn+rhhTB83jPZOGznF5/k4z8rB/Gp2H6vAZDSQFhfE2NQwxqaGExni4+ySRaQfcijEuuOOO1i5ciVr165l7NixVFRU8MILL5Cent7X9fUL/zgLbSA7fPiws0u4JS51dLE3v5mPi1ro6rKTneDNtHQ/fD1bKcrPdXZ5A8JQ6RW5OdQv4ij1ivSG+kUcpV4ZeEzA5ASYGBfMuboOis5doriykf/5Wy3/87cTmH1cGBHpQWKkBzEh7riYbs46WuoV6Q31S9/Kzs7u9X0cPscuLS2NtLS0np9tNhu//e1v+dGPfvSV97VYLFRXV2Oz2TCZTNhsNmpqarBYLJ/br7KykszMTODqGVbXO3YzpKen4+7uftN+n7McPnz4uppkIGnruMymPaW8teMkrW2d3DYqigdmJRMe5O3s0gaUodArcvOoX8RR6hXpDfWLOEq9MvCN/cz31fWtHMqv4mBBNUdKajlQ1IKnu4msxNCeBeTNfh7X9TjqFekN9Uv/dN0LRdlsNv7whz84FGIFBQWRkpLCpk2bmDdvHps2bSIlJeWqUwkBZs2axdq1a5kxYwaNjY1s376dNWvW3NCYDA2XbV28/0kZb2wrpL6pnTEpYTw4J4XhEf7OLk1ERERERBwUZvbia1Pi+NqUONraL5NbUsvBgmoO5Vex/7gVgPgof8amhDM2NYyEqACMRl3tUGSouKHVzu12u8P7PvXUUyxbtoyXXnoJPz8/VqxYAcAjjzzC448/TkZGBvPmzSMnJ4cZM2YA8NhjjxEdHQ1w3WOHDh3iX//1X2lpacFut/Puu+/yi1/8gqlTp97Iny79RFeXnT05Fby6tRBr7UVSYs3829KxpMUFObs0ERERERG5AR7uLoxLC2dcWjh2eyZnrE0cKqjmYH41b24v4o33iwjwcWd0cihjU8PISgzV2rcig9wNhVgGg+OJd3x8PGvXrv3c9pUrV/Z8bzKZePrpp7/w/tc7NmbMGHbt2uVwnTIw2O12jhTVsOrdAk5XXiDW4seT3x3PmJSwXvWliIiIiIj0fwaDgeER/gyP8GfhXYk0XezgSGE1Bwuq+eREFTsOlWM0GkgaFsiopFBGJ4WQEB2ISbO0RAaVa4ZY+/fv/9Kxzk5dAlWco/BMPX/ZnE/eqTrCzF78ZPFopo6K0guUiIiIiMgQ4eftxu3Z0dyeHY3N1kXh2QaOFtVwpKiG17cV8tp7hfh6uTJyRAjZyaGMSgp1dskichNcM8R64oknrnnnf1yYXaQvna1qYvXmAg6cqCLAx51/XpDBjAmxuLoYnV2aiIiIiIg4iclkJC0uiLS4IJbMTuFCSzs5J89zpKiGo0U17MmpBCDU34VJFXmMTgolLS4IN1eTkysXkd66Zoi1Y8eOW1WHyJeqqW9lzXuF7Dxcjqe7C0tmJ/P1qfF4ut/Q2bAiIiIiIjII+fu4M21UFNNGRWG32zljbeJoUQ0fHjzFpj2lrP/oFG6uJjLigxid1D1LKyrUR8uSiAwASgGk32psbmftB8Vs3ncGgwHm35bAN+8cgZ+3m7NLExERERGRAeCza2nF+DWRlj6S46dqe2ZprdyQB0CQvwcjR4QwckQwI0eEEOTv6eTKReSLKMSSfqe1rZP1H51i/UcltHfYmD4uhkV3JxESqBcSERERERG5fh7uLoxNDWdsajgA1fWtHCmqIefkeQ7mV7PjUDkAkSE+PYFWZkIwPl76IF2kP1CIJf1GR6eNLfvP8Ob2YpoudjA5M4IHZiUTHebr7NJERERERGQQCjN7MXtiLLMnxtLV1X3qYc7J8+ScPM+OQ+U9Z4XER/p3B1ojQkgdbsbDTW+lRZxB//LE6WxddnYeKue1bYWcb7hE1ogQls5JIXFYoLNLExERERGRIcJoNBAX6U9cpD8Lbk+g83IXxWUN5J48T05JLRt2nWLdzhJcTEaSYwMZOSKEjPhgEocF4OqiReJFbgWFWOI0drudj/OqWL2lgPLqZhKiA3j8viyyEnX5WxERERERcS5Xl79f9fBbM6Gt/TInSuvIOVlLzsnzvPZeIXY7uLkYSYoxkx7fvW9STKBmaon0Ef3LEqc4XlLLX97Np6isgcgQH5Y9NJZJGRZdEURERERERPolD3cXspPDyE4OA6C5tYMTp+vIO1XHidO1/PX9Irrs4GIyMCI6kPT4INLjgkmODcTLw9XJ1YsMDgqx5JYqOdfI6s0FHCmqIdjfgx/el8VdY6IxmYzOLk1ERERERMRhvl5uTEi3MCHdAsDFS50UnKkn71QteafreHtnCWs/OInRaCAhyp+0uGDS44NIjTVroXiR66QQS26JyvMtvLq1kN3HKvD1cuU796QxZ/Jw3F117riIiIiIiAx83p6ujEkJY0xK90yttvbLFJ6tJ+9UHXmn69i4+zTvfFgCQHSYL6nDzSTHmEkZbiYi2FtnpYg4QCGW9Km6C5d44/1ith04i6uLkfunJ7Lg9gS8PTWdVkREREREBi8PdxeyEkN71vxt77RRfLaBgjP1FJypZ29OJe99fBYAP283UmLNJMeaSYk1kxAdoA/8Rb6AQizpEy2tHby14yQbd5+my25nzqRY7pueSKCvh7NLExERERERueXcXU1kJASTkRAMQFeXnYrzLeSX1lN4Jdg6cKIK6F5XKz4yoCfUSooJJMjfQ7O1ZMhTiCU3VVv7ZTbuOc26HSdpbb/M7aOjWDwzmfAgb2eXJiIiIiIi0m8YjQaiw3yJDvNl5oQYAC60tFP0mdlaW/aVsmHXKQDMfu6MiA5kxLAAEqMDGREdoLW1ZMhRiCU3xWVbF9sOnOWNbUU0NLczLjWcpXNSiLX4Obs0ERERERGRAcHfx51xaeGMSwsHoPNyF6WVFygua7jy1dgzWwsgItibxGFXgq1hgcRF+OOm0xBlEFOIJTekq8vO7mMVrNlaiLXuIqnDzSx7aCypw4OcXZqIiIiIiMiA5upiJHFYIInDAnu2tVzq5FR5I8Xl3cFWbkktHx45B4DJaCA2wo8R0YEkRPkTF+lPTLifgi0ZNBRiyXWx2+0cLqxh1eZ8SiubiLX4sfzhCWQnh+o8bRERERERkT7i4+nKyMQQRiaG9Gyru3CJ4rJGTpY3cLKskd1Hz7F1/xmg+7TFYWG+xEX6//0rwl8X25IBSSGW9FpBaT1/2ZzPidN1hAd58ZMHspmWFYnRqPBKRERERETkVgvy92RihicTMyxA96SD6vpWTlVc4PSVr2PFNew4VN5zn/Agr55QKz4ygOERfpj9tHi89G8KscRhZ61NrN5SwIETVQT4uvPP38hkxvgYXF2Mzi5NRERERERErjAYDIQHeRMe5M3kzIie7Q1NbVcFW6crLrAv19oz7u3pSky4LzEWP2LC/Xq+99UC8tJPKMSSr1Rd38pr7xWy83A5nu4uLJ2dwtenxuHhrvYREREREREZKAL9PBjj58GYlLCebRcvdVJaeYGz1ibOVjVztqqJXUfOcbHtcs8+Zj+Pq8Mtiy/Rob56Tyi3nDpOvlRjcztvflDMln2lGA0GFtyWwL13jsDPWym8iIiIiIjIYODt6Up6fDDp8cE92+x2O3UX2jhb1XRVuLV5bykdl7t69gsO8CQq1IeoEB8iQ32ICvUhMsSX4ACdlih9QyGWfE5rWyfvfHiK9R+V0HG5i7vHDWPR3UkEB3g6uzQRERERERHpYwaDgeAAT4IDPMlO/vusLVuXnaq6i5yxNnGuuplz51s4V9PCB4fKudT+95lbHm4mIkK6w62o0O6AKyLYh/AgL3x0aqLcAIVY0qOj08bmfWd4c3sxza0dTBkZwZLZKUSG+Di7NBEREREREXEyk9FAZIjP594j2u12GprbOVfTTEVNd7B17nwLRWUN7M6pwG7/+74+nq6EB3n1rNnV/eWFJciboABPTLpgmFyDQizBZuti5+Fy1rxXRG3jJUYlhvDgnFQSogOcXZqIiIiIiIj0cwaDAbOfB2Y/DzITQq4aa++0Ya29iLW2haq6Vqx1F6mu675y4v7jVmxdf0+4XEwGQgO7A66QQE9CAjwJCeyeERYS4EVwgAeuLqZb/edJP6IQawiz2+18nGdl9ZYCyqtbSBwWwL8sGsXIESFffWcRERERERGRr+DuaiLW4kesxe9zYzZbF7UX2qiqu3jl69OQ6yKnKhq50NLxufsE+LpfCbX+HnIF+Xti9vMg0NedQD8PPLXg/KB1y/7PlpaWsmzZMhobGwkICGDFihXExsZetY/NZuO5555j9+7dGAwGHn30URYuXNhnY0NZbsl5/vJuPsVljUSF+vDzfxrLhHSLFt8TERERERGRW8JkMhJm9iLM7PWFkynaO23UNV7ifOMlzjdcovbCldvGS5yraeZoUQ1tHbbP3c/DzUTgZ0KtQF/3npArwNcDP2+3ni9Pdxe9Dx5AblmItXz5chYvXsy8efPYsGEDTz75JKtWrbpqn40bN1JWVsa2bdtobGxk/vz5TJw4kaioqD4ZG4pKyhtZtTmfo8XnCQ7w5Ef3Z3FHdjQmk9HZpYmIiIiIiIj0cHftXiA+4kvWabbb7Vy81EnthTYamtpoaG7/3O2ZyiaONbdxse3yF/4OF5MBX6/uQMv3SrDl6+VGa/MFyltK8PJwxdvDFU8PF7w9XPDycMXryq2Hm0kB2C12S0Ksuro68vPzeeWVVwCYO3cuzz77LPX19ZjN5p79Nm/ezMKFCzEajZjNZqZPn87WrVt5+OGH+2RsKKmsbWHtnjpOlJ3D18uN7349nTmTYnFz1fnEIiIiIiIiMvAYDAZ8vNzw8XL7wtMVP6u900ZDUxuNze00tXbQ1NJBc2sHTRf/ftt0sYPy6haaL3Zw4WI7u0+cuObvNBrA08O1J9xydzPh7mrCzbX71t3t79+7uRp7xt1dTbiYjJhMBkzGT28NmEzG7tt/+N5oNGDAAAZ61h4bqm5JiGW1WgkLC8Nk6g5MTCYToaGhWK3Wq0Isq9VKREREz88Wi4Wqqqo+GxtKnl99iLKqNhbdncSC2+Px8nB1dkkiIiIiIiIit4S7q6nnaoiOOHToEMlpI2lt66S17fJVtxfbLnPpyu2n2y9e6qS900ZHp42LbZ10dNpo77DR0dlFe+dl2jtsfGYN++vm5mJk3Yp7bvwXDVBa7cwBeXl5zi7hhn1ttCfuLl54e1yk4ESus8uRAeDw4cPOLkEGEPWLOEq9Ir2hfhFHqVfEUeoVcZTBYKAo/+r3zgbAG/B2A9yAqyZ/uXCtiMVut2Prgss2O502O7YuO11d0GXvvrXZr/65Z3uXHbsdPs2//LxMg6aPs7Oze32fWxJiWSwWqqursdlsmEwmbDYbNTU1WCyWz+1XWVlJZmYmcPUsqr4Yc1R6ejru7u7X+df3H4cPH76uJpGhR70ivaF+EUepV6Q31C/iKPWKOEq9Ir2hfumfbslq3kFBQaSkpLBp0yYANm3aREpKylWnEgLMmjWLtWvX0tXVRX19Pdu3b2fmzJl9NiYiIiIiIiIiIgPDLTud8KmnnmLZsmW89NJL+Pn5sWLFCgAeeeQRHn/8cTIyMpg3bx45OTnMmDEDgMcee4zo6GiAPhkTEREREREREZGB4ZaFWPHx8axdu/Zz21euXNnzvclk4umnn/7C+/fFmIiIiIiIiIiIDAxa2P0a7PbupdM6OjqcXMnN097e7uwSZIBQr0hvqF/EUeoV6Q31izhKvSKOUq9Ib6hf+p6bmxsGg8Hh/Q32T5Ma+Zzm5maKi4udXYaIiIiIiIiIyKDT2wvpKcS6hq6uLi5evIirq2uvkkEREREREREREbk2zcQSEREREREREZFBx+jsAkRERERERERERL6KQiwREREREREREen3FGKJiIiIiIiIiEi/pxBLRERERERERET6PYVYIiIiIiIiIiLS7ynEEhERERERERGRfk8hloiIiIiIiIiI9Hsuzi5A+l5paSnLli2jsbGRgIAAVqxYQWxsrLPLEidpaGjg3/7t3ygrK8PNzY2YmBieeeYZzGYzSUlJJCYmYjR259vPP/88SUlJAOzYsYPnn38em81GWloa/+f//B88PT2d+afILXDnnXfi5uaGu7s7AD/96U+ZOnUqx44d48knn6S9vZ3IyEh+9atfERQUBHDNMRm8zp07x2OPPdbzc3NzMy0tLXzyySdf2kegfhkqVqxYwXvvvUdFRQUbN24kMTERuPYxyvWOycD2Rb1yrWMXQMcvQ9iXPbdc7+uOXpMGry/qlWsdu8D195H0MbsMekuXLrWvX7/ebrfb7evXr7cvXbrUyRWJMzU0NNg//vjjnp9/+ctf2v/93//dbrfb7YmJifaWlpbP3aelpcU+adIke2lpqd1ut9t//vOf21944YVbUq841x133GEvKiq6altXV5d9+vTp9oMHD9rtdrv9xRdftC9btuwrx2Roee655+xPP/203W7/4j6y29UvQ8nBgwftlZWVn+uFax2jXO+YDGxf1CvXOnax23X8MpR92XPL9bzu6DVpcPuyXvmszx672O06fumvdDrhIFdXV0d+fj5z584FYO7cueTn51NfX+/kysRZAgICGD9+fM/PWVlZVFZWXvM+u3btIj09vedT7kWLFrFly5a+LFP6sePHj+Pu7s6YMWOA7n7YunXrV47J0NHR0cHGjRu59957r7mf+mXoGDNmDBaL5apt1zpGud4xGfi+qFeu59gFdPwyFHxRv1yLjmGGrq/qFUePXUC94mw6nXCQs1qthIWFYTKZADCZTISGhmK1WnumYMvQ1dXVxeuvv86dd97Zs23p0qXYbDamTZvGD3/4Q9zc3LBarURERPTsExERgdVqdUbJ4gQ//elPsdvtZGdn86//+q+f6wez2UxXVxeNjY3XHAsICHBG+eIEO3bsICwsjLS0tJ5t/9hHfn5+6pch7lrHKHa7/brGdGwz+H3RsQvo+EU+r7evO3pNGtq+6NgFdPzSH2kmlsgQ9uyzz+Ll5cWSJUsA+PDDD3n77bdZs2YNJSUlvPjii06uUJxtzZo1/O1vf2PdunXY7XaeeeYZZ5ckA8C6deuu+iRTfSQiN8s/HruAjl/k8/S6I731j8cuoD7qrxRiDXIWi4Xq6mpsNhsANpuNmpqaXk27lcFpxYoVnD17lt/85jc9C6F+2hc+Pj4sXLiQI0eO9Gz/7LT9yspK9dAQ8en/Zzc3NxYvXsyRI0c+1w/19fUYDAYCAgKuOSZDQ3V1NQcPHuSee+7p2fZFffTpdvXL0HWtY5TrHZPB7YuOXUDHL/J51/O6o9ekoeuLjl1Axy/9lUKsQS4oKIiUlBQ2bdoEwKZNm0hJSdF0+yHu17/+NXl5ebz44ou4ubkBcOHCBf7/9u4lJMq2j+P41ywxNVOShGiRTRFSVENhBzxUmzJcCYXug9xEQVihpXZANAmiEN1Uq6IIOogWSYvMlTBkh2WZQYsyrEjFZEznXTy8gm89Qj1Pzf3W97O757pn5n/BBdefH9c9MzY2BsCXL1+4d+8eubm5ABQUFPDs2TNevXoFwNWrVykuLo5L7fp1RkdHGR4eBiAWi3Hnzh1yc3NZtWoVY2NjRCIRYPp6mGlMf4abN29SVFREZmYm8PfrCFwvf7qZepQfHdPv61u9C9i/6Gs/uu+4J/25/rd3AfuXIEuIxWKxeBehn6uvr48jR44wNDREeno6jY2NLF26NN5lKU6eP39OSUkJS5YsITk5GYDFixezZ88eampqSEhI4MuXL4TDYaqqqkhNTQXg/v37NDU1MTk5SW5uLg0NDaSkpMRzKvrJXr9+zb59+5iYmGBycpJQKMTRo0dZuHAhjx49ora2dtrfCmdlZQHMOKbf3/bt26murqawsBCYeR2B6+VPcerUKTo7OxkcHCQzM5OMjAw6Ojpm7FF+dEz/3761Vs6ePfvN3qW5uZne3l77lz/Yt9ZLa2vrD+877km/r7/bh+Dr3gXsX4LMEEuSJEmSJEmB5+OEkiRJkiRJCjxDLEmSJEmSJAWeIZYkSZIkSZICzxBLkiRJkiRJgWeIJUmSJEmSpMAzxJIkSZIkSVLgGWJJkiQFSCQSoaysjHXr1pGXl0dZWRlPnz7lxo0blJeXx7s8SZKkuJkd7wIkSZL0l5GRESoqKqirq6O4uJjx8XEikQhJSUnxLk2SJCnuPIklSZIUEP39/QCUlJSQmJhIcnIy+fn5zJkzh9raWh4/fkw4HGb9+vUARKNRGhsb2bJlC5s3b6ampoaxsTEAenp6KCwspLW1lQ0bNrBt2zba2tqmvqurq4udO3cSDocpKCjgwoULv37CkiRJ38EQS5IkKSBycnJITEzk8OHDdHV18enTJwBCoRDHjx9n7dq19Pb2EolEAGhqaqK/v59bt27R2dnJu3fvaG5unvq8wcFBPn78SHd3Nw0NDdTU1PDy5UsAqqurOXHiBL29vbS3t7Nx48ZfP2FJkqTvYIglSZIUEGlpaVy5coWEhASOHTvGpk2bqKioYHBw8Kt7Y7EY169fp6qqioyMDNLS0ti7dy8dHR3T7tu/fz9JSUnk5eVRVFTE3bt3AZg9ezYvXrxgZGSE+fPns3Llyl8yR0mSpB/lb2JJkiQFSCgUoqGhAYC+vj4qKyupr68nPz9/2n0fPnzg8+fPlJaWTr0Wi8WYnJycuk5PTyclJWXqetGiRbx79w6Ac+fO0dLSwpkzZ1ixYgUHDx4kHA7/zKlJkiT9I4ZYkiRJARUKhSgtLeXatWsUFBRMG8vMzCQ5OZmOjg6ys7O/+f6hoSFGR0engqw3b96wfPlyAFavXk1LSwvj4+NcvnyZAwcO0NXV9XMnJEmS9A/4OKEkSVJA9PX1cfHiRd6+fQv8FTq1t7ezZs0aFixYwMDAANFoFIBZs2axa9cu6uvref/+PQADAwN0d3dP+8zz588TjUaJRCI8ePCAHTt2EI1GaWtrY3h4mDlz5pCamkpiYuKvnawkSdJ38iSWJElSQKSlpfHkyRMuXbrE8PAw8+bNY+vWrRw6dIikpCSWLVtGfn5+jP3RAAAAyElEQVQ+CQkJ9PT0UFlZSXNzM7t37+bjx49kZ2dTXl4+dWorKyuL9PR0CgoKmDt3LnV1dYRCIaLRKLdv3+bkyZNMTEyQk5PD6dOn4zx7SZKkmSXEYrFYvIuQJEnSv+u/IdfDhw/jXYokSdK/wscJJUmSJEmSFHiGWJIkSZIkSQo8HyeUJEmSJElS4HkSS5IkSZIkSYFniCVJkiRJkqTAM8SSJEmSJElS4BliSZIkSZIkKfAMsSRJkiRJkhR4hliSJEmSJEkKvP8AJJV1UdteNy8AAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(2, 1, sharex='col', figsize=(20, 14))\n\nax1.plot(history['loss'], label='Train loss')\nax1.plot(history['val_loss'], label='Validation loss')\nax1.legend(loc='best')\nax1.set_title('Loss')\n\nax2.plot(history['acc'], label='Train accuracy')\nax2.plot(history['val_acc'], label='Validation accuracy')\nax2.legend(loc='best')\nax2.set_title('Accuracy')\n\nplt.xlabel('Epochs')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:54:22.807368Z","iopub.execute_input":"2024-05-03T05:54:22.808197Z","iopub.status.idle":"2024-05-03T05:54:23.496603Z","shell.execute_reply.started":"2024-05-03T05:54:22.808113Z","shell.execute_reply":"2024-05-03T05:54:23.495898Z"},"trusted":true},"execution_count":57,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"# Create empty arays to keep the predictions and labels\ndf_preds = pd.DataFrame(columns=['label', 'pred', 'set'])\ntrain_generator.reset()\nvalid_generator.reset()\n\n# Add train predictions and labels\nfor i in range(STEP_SIZE_TRAIN + 1):\n im, lbl = next(train_generator)\n preds = model.predict(im, batch_size=train_generator.batch_size)\n for index in range(len(preds)):\n df_preds.loc[len(df_preds)] = [lbl[index], preds[index][0], 'train']\n\n# Add validation predictions and labels\nfor i in range(STEP_SIZE_VALID + 1):\n im, lbl = next(valid_generator)\n preds = model.predict(im, batch_size=valid_generator.batch_size)\n for index in range(len(preds)):\n df_preds.loc[len(df_preds)] = [lbl[index], preds[index][0], 'validation']\n\ndf_preds['label'] = df_preds['label'].astype('int')","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:54:23.497852Z","iopub.execute_input":"2024-05-03T05:54:23.498133Z","iopub.status.idle":"2024-05-03T05:55:59.421808Z","shell.execute_reply.started":"2024-05-03T05:54:23.498083Z","shell.execute_reply":"2024-05-03T05:55:59.421089Z"},"trusted":true},"execution_count":58,"outputs":[]},{"cell_type":"code","source":"def classify(x):\n if x < 0.5:\n return 0\n elif x < 1.5:\n return 1\n elif x < 2.5:\n return 2\n elif x < 3.5:\n return 3\n return 4\n\n# Classify predictions\ndf_preds['predictions'] = df_preds['pred'].apply(lambda x: classify(x))\n\ntrain_preds = df_preds[df_preds['set'] == 'train']\nvalidation_preds = df_preds[df_preds['set'] == 'validation']","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:55:59.423214Z","iopub.execute_input":"2024-05-03T05:55:59.423454Z","iopub.status.idle":"2024-05-03T05:55:59.438793Z","shell.execute_reply.started":"2024-05-03T05:55:59.423413Z","shell.execute_reply":"2024-05-03T05:55:59.438069Z"},"trusted":true},"execution_count":59,"outputs":[]},{"cell_type":"code","source":"labels = ['0 - No DR', '1 - Mild', '2 - Moderate', '3 - Severe', '4 - Proliferative DR']\ndef plot_confusion_matrix(train, validation, labels=labels):\n train_labels, train_preds = train\n validation_labels, validation_preds = validation\n fig, (ax1, ax2) = plt.subplots(1, 2, sharex='col', figsize=(24, 7))\n train_cnf_matrix = confusion_matrix(train_labels, train_preds)\n validation_cnf_matrix = confusion_matrix(validation_labels, validation_preds)\n\n train_cnf_matrix_norm = train_cnf_matrix.astype('float') / train_cnf_matrix.sum(axis=1)[:, np.newaxis]\n validation_cnf_matrix_norm = validation_cnf_matrix.astype('float') / validation_cnf_matrix.sum(axis=1)[:, np.newaxis]\n\n train_df_cm = pd.DataFrame(train_cnf_matrix_norm, index=labels, columns=labels)\n validation_df_cm = pd.DataFrame(validation_cnf_matrix_norm, index=labels, columns=labels)\n\n sns.heatmap(train_df_cm, annot=True, fmt='.2f', cmap=\"Blues\",ax=ax1).set_title('Train')\n sns.heatmap(validation_df_cm, annot=True, fmt='.2f', cmap=sns.cubehelix_palette(8),ax=ax2).set_title('Validation')\n plt.show()\n\nplot_confusion_matrix((train_preds['label'], train_preds['predictions']), (validation_preds['label'], validation_preds['predictions']))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:55:59.440627Z","iopub.execute_input":"2024-05-03T05:55:59.441084Z","iopub.status.idle":"2024-05-03T05:56:00.4337Z","shell.execute_reply.started":"2024-05-03T05:55:59.44092Z","shell.execute_reply":"2024-05-03T05:56:00.432639Z"},"trusted":true},"execution_count":60,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"def evaluate_model(train, validation):\n train_labels, train_preds = train\n validation_labels, validation_preds = validation\n print(\"Train Cohen Kappa score: %.3f\" % cohen_kappa_score(train_preds, train_labels, weights='quadratic'))\n print(\"Validation Cohen Kappa score: %.3f\" % cohen_kappa_score(validation_preds, validation_labels, weights='quadratic'))\n print(\"Complete set Cohen Kappa score: %.3f\" % cohen_kappa_score(np.append(train_preds, validation_preds), np.append(train_labels, validation_labels), weights='quadratic'))\n \nevaluate_model((train_preds['label'], train_preds['predictions']), (validation_preds['label'], validation_preds['predictions']))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:56:00.435544Z","iopub.execute_input":"2024-05-03T05:56:00.436161Z","iopub.status.idle":"2024-05-03T05:56:00.472124Z","shell.execute_reply.started":"2024-05-03T05:56:00.436093Z","shell.execute_reply":"2024-05-03T05:56:00.471384Z"},"trusted":true},"execution_count":61,"outputs":[{"name":"stdout","text":"Train Cohen Kappa score: 0.976\nValidation Cohen Kappa score: 0.901\nComplete set Cohen Kappa score: 0.962\n","output_type":"stream"}]},{"cell_type":"code","source":"def apply_tta(model, generator, steps=10):\n step_size = generator.n//generator.batch_size\n preds_tta = []\n for i in range(steps):\n generator.reset()\n preds = model.predict_generator(generator, steps=step_size)\n preds_tta.append(preds)\n\n return np.mean(preds_tta, axis=0)\n\npreds = apply_tta(model, test_generator)\npredictions = [classify(x) for x in preds]\n\nresults = pd.DataFrame({'id_code':test['id_code'], 'diagnosis':predictions})\nresults['id_code'] = results['id_code'].map(lambda x: str(x)[:-4])","metadata":{"execution":{"iopub.status.busy":"2024-05-03T05:56:00.47341Z","iopub.execute_input":"2024-05-03T05:56:00.473648Z","iopub.status.idle":"2024-05-03T06:06:57.143157Z","shell.execute_reply.started":"2024-05-03T05:56:00.473601Z","shell.execute_reply":"2024-05-03T06:06:57.142162Z"},"trusted":true},"execution_count":62,"outputs":[]},{"cell_type":"code","source":"# Cleaning created directories\nif os.path.exists(train_dest_path):\n shutil.rmtree(train_dest_path)\nif os.path.exists(validation_dest_path):\n shutil.rmtree(validation_dest_path)\nif os.path.exists(test_dest_path):\n shutil.rmtree(test_dest_path)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T06:06:57.144495Z","iopub.execute_input":"2024-05-03T06:06:57.144766Z","iopub.status.idle":"2024-05-03T06:06:57.424475Z","shell.execute_reply.started":"2024-05-03T06:06:57.144715Z","shell.execute_reply":"2024-05-03T06:06:57.423817Z"},"trusted":true},"execution_count":63,"outputs":[]},{"cell_type":"code","source":"fig = plt.subplots(sharex='col', figsize=(24, 8.7))\nsns.countplot(x=\"diagnosis\", data=results).set_title('Test')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T06:06:57.42588Z","iopub.execute_input":"2024-05-03T06:06:57.426136Z","iopub.status.idle":"2024-05-03T06:06:57.802133Z","shell.execute_reply.started":"2024-05-03T06:06:57.426092Z","shell.execute_reply":"2024-05-03T06:06:57.801258Z"},"trusted":true},"execution_count":64,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"results.to_csv('submission_3.csv', index=False)\ndisplay(results.head())","metadata":{"execution":{"iopub.status.busy":"2024-05-03T06:06:57.803543Z","iopub.execute_input":"2024-05-03T06:06:57.80379Z","iopub.status.idle":"2024-05-03T06:06:57.824419Z","shell.execute_reply.started":"2024-05-03T06:06:57.803749Z","shell.execute_reply":"2024-05-03T06:06:57.823468Z"},"trusted":true},"execution_count":65,"outputs":[{"output_type":"display_data","data":{"text/plain":" id_code diagnosis\n0 0005cfc8afb6 2\n1 003f0afdcd15 3\n2 006efc72b638 2\n3 00836aaacf06 2\n4 009245722fa4 3","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosis
00005cfc8afb62
1003f0afdcd153
2006efc72b6382
300836aaacf062
4009245722fa43
\n
"},"metadata":{}}]},{"cell_type":"code","source":"# model.save_weights('../working/effNetB5_bs32_img224_fold3.h5')","metadata":{"execution":{"iopub.status.busy":"2024-05-03T06:06:57.826104Z","iopub.execute_input":"2024-05-03T06:06:57.826404Z","iopub.status.idle":"2024-05-03T06:06:57.82969Z","shell.execute_reply.started":"2024-05-03T06:06:57.826346Z","shell.execute_reply":"2024-05-03T06:06:57.828898Z"},"trusted":true},"execution_count":66,"outputs":[]},{"cell_type":"markdown","source":"FOLD_4","metadata":{}},{"cell_type":"code","source":"import os\nimport sys\nimport cv2\nimport shutil\nimport random\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom tensorflow import set_random_seed\nfrom sklearn.utils import class_weight\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, cohen_kappa_score\nfrom keras import backend as K\nfrom keras.models import Model\nfrom keras.utils import to_categorical\nfrom keras import optimizers, applications\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.layers import Dense, Dropout, GlobalAveragePooling2D, Input\nfrom keras.callbacks import EarlyStopping, ReduceLROnPlateau, Callback, LearningRateScheduler\n\ndef seed_everything(seed=0):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n set_random_seed(0)\n\nseed = 0\nseed_everything(seed)\n%matplotlib inline\nsns.set(style=\"whitegrid\")\nwarnings.filterwarnings(\"ignore\")\nsys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))\nfrom efficientnet import *","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:01:08.458237Z","iopub.execute_input":"2024-05-03T16:01:08.458578Z","iopub.status.idle":"2024-05-03T16:01:15.081597Z","shell.execute_reply.started":"2024-05-03T16:01:08.458531Z","shell.execute_reply":"2024-05-03T16:01:15.080833Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\nUsing TensorFlow backend.\n","output_type":"stream"}]},{"cell_type":"code","source":"fold_set = pd.read_csv('/kaggle/input/main-data/5-fold.csv')\nX_train = fold_set[fold_set['fold_3'] == 'train']\nX_val = fold_set[fold_set['fold_3'] == 'validation']\ntest = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')\nprint('Number of train samples: ', X_train.shape[0])\nprint('Number of validation samples: ', X_val.shape[0])\nprint('Number of test samples: ', test.shape[0])\n\n# Preprocecss data\nX_train[\"id_code\"] = X_train[\"id_code\"].apply(lambda x: x + \".png\")\nX_val[\"id_code\"] = X_val[\"id_code\"].apply(lambda x: x + \".png\")\ntest[\"id_code\"] = test[\"id_code\"].apply(lambda x: x + \".png\")\ndisplay(X_train.head())","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:01:15.083683Z","iopub.execute_input":"2024-05-03T16:01:15.083937Z","iopub.status.idle":"2024-05-03T16:01:15.336894Z","shell.execute_reply.started":"2024-05-03T16:01:15.083896Z","shell.execute_reply":"2024-05-03T16:01:15.336125Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Number of train samples: 2930\nNumber of validation samples: 732\nNumber of test samples: 1928\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" id_code diagnosis height width fold_0 fold_1 fold_2 \\\n1 001639a390f0.png 4 2136.0 3216.0 train train train \n2 0024cdab0c1e.png 1 1736.0 2416.0 validation train train \n4 005b95c28852.png 0 1536.0 2048.0 validation train train \n5 0083ee8054ee.png 4 2588.0 3388.0 train train validation \n6 0097f532ac9f.png 0 1958.0 2588.0 validation train train \n\n fold_3 fold_4 \n1 train validation \n2 train train \n4 train train \n5 train train \n6 train train ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosisheightwidthfold_0fold_1fold_2fold_3fold_4
1001639a390f0.png42136.03216.0traintraintraintrainvalidation
20024cdab0c1e.png11736.02416.0validationtraintraintraintrain
4005b95c28852.png01536.02048.0validationtraintraintraintrain
50083ee8054ee.png42588.03388.0traintrainvalidationtraintrain
60097f532ac9f.png01958.02588.0validationtraintraintraintrain
\n
"},"metadata":{}}]},{"cell_type":"code","source":"# Model parameters\nFACTOR = 4\nBATCH_SIZE = 8 * FACTOR\nEPOCHS = 20\nWARMUP_EPOCHS = 5\nLEARNING_RATE = 1e-4 * FACTOR\nWARMUP_LEARNING_RATE = 1e-3 * FACTOR\nHEIGHT = 224\nWIDTH = 224\nCHANNELS = 3\nES_PATIENCE = 5\nRLROP_PATIENCE = 3\nDECAY_DROP = 0.5\nLR_WARMUP_EPOCHS_1st = 2\nLR_WARMUP_EPOCHS_2nd = 5\nSTEP_SIZE = len(X_train) // BATCH_SIZE\nTOTAL_STEPS_1st = WARMUP_EPOCHS * STEP_SIZE\nTOTAL_STEPS_2nd = EPOCHS * STEP_SIZE\nWARMUP_STEPS_1st = LR_WARMUP_EPOCHS_1st * STEP_SIZE\nWARMUP_STEPS_2nd = LR_WARMUP_EPOCHS_2nd * STEP_SIZE","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:01:15.338038Z","iopub.execute_input":"2024-05-03T16:01:15.338261Z","iopub.status.idle":"2024-05-03T16:01:15.345022Z","shell.execute_reply.started":"2024-05-03T16:01:15.338224Z","shell.execute_reply":"2024-05-03T16:01:15.344303Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"train_base_path = '../input/aptos2019-blindness-detection/train_images/'\ntest_base_path = '../input/aptos2019-blindness-detection/test_images/'\ntrain_dest_path = 'base_dir/train_images/'\nvalidation_dest_path = 'base_dir/validation_images/'\ntest_dest_path = 'base_dir/test_images/'\n\n# Making sure directories don't exist\nif os.path.exists(train_dest_path):\n shutil.rmtree(train_dest_path)\nif os.path.exists(validation_dest_path):\n shutil.rmtree(validation_dest_path)\nif os.path.exists(test_dest_path):\n shutil.rmtree(test_dest_path)\n \n# Creating train, validation and test directories\nos.makedirs(train_dest_path)\nos.makedirs(validation_dest_path)\nos.makedirs(test_dest_path)\n\ndef crop_image(img, tol=7):\n if img.ndim ==2:\n mask = img>tol\n return img[np.ix_(mask.any(1),mask.any(0))]\n elif img.ndim==3:\n gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n mask = gray_img>tol\n check_shape = img[:,:,0][np.ix_(mask.any(1),mask.any(0))].shape[0]\n if (check_shape == 0): # image is too dark so that we crop out everything,\n return img # return original image\n else:\n img1=img[:,:,0][np.ix_(mask.any(1),mask.any(0))]\n img2=img[:,:,1][np.ix_(mask.any(1),mask.any(0))]\n img3=img[:,:,2][np.ix_(mask.any(1),mask.any(0))]\n img = np.stack([img1,img2,img3],axis=-1)\n \n return img\n\ndef circle_crop(img):\n img = crop_image(img)\n\n height, width, depth = img.shape\n largest_side = np.max((height, width))\n img = cv2.resize(img, (largest_side, largest_side))\n\n height, width, depth = img.shape\n\n x = width//2\n y = height//2\n r = np.amin((x, y))\n\n circle_img = np.zeros((height, width), np.uint8)\n cv2.circle(circle_img, (x, y), int(r), 1, thickness=-1)\n img = cv2.bitwise_and(img, img, mask=circle_img)\n img = crop_image(img)\n\n return img\n \ndef preprocess_image(base_path, save_path, image_id, HEIGHT, WIDTH, sigmaX=10):\n image = cv2.imread(base_path + image_id)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image = circle_crop(image)\n image = cv2.resize(image, (HEIGHT, WIDTH))\n image = cv2.addWeighted(image, 4, cv2.GaussianBlur(image, (0,0), sigmaX), -4 , 128)\n cv2.imwrite(save_path + image_id, image)\n \n# Pre-procecss train set\nfor i, image_id in enumerate(X_train['id_code']):\n preprocess_image(train_base_path, train_dest_path, image_id, HEIGHT, WIDTH)\n \n# Pre-procecss validation set\nfor i, image_id in enumerate(X_val['id_code']):\n preprocess_image(train_base_path, validation_dest_path, image_id, HEIGHT, WIDTH)\n \n# Pre-procecss test set\nfor i, image_id in enumerate(test['id_code']):\n preprocess_image(test_base_path, test_dest_path, image_id, HEIGHT, WIDTH)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:01:15.346713Z","iopub.execute_input":"2024-05-03T16:01:15.34696Z","iopub.status.idle":"2024-05-03T16:25:08.462965Z","shell.execute_reply.started":"2024-05-03T16:01:15.346918Z","shell.execute_reply":"2024-05-03T16:25:08.4622Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"datagen=ImageDataGenerator(rescale=1./255, \n rotation_range=360,\n horizontal_flip=True,\n vertical_flip=True)\n\ntrain_generator=datagen.flow_from_dataframe(\n dataframe=X_train,\n directory=train_dest_path,\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n class_mode=\"raw\",\n batch_size=BATCH_SIZE,\n target_size=(HEIGHT, WIDTH),\n seed=seed)\n\nvalid_generator=datagen.flow_from_dataframe(\n dataframe=X_val,\n directory=validation_dest_path,\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n class_mode=\"raw\",\n batch_size=BATCH_SIZE,\n target_size=(HEIGHT, WIDTH),\n seed=seed)\n\ntest_generator=datagen.flow_from_dataframe( \n dataframe=test,\n directory=test_dest_path,\n x_col=\"id_code\",\n batch_size=1,\n class_mode=None,\n shuffle=False,\n target_size=(HEIGHT, WIDTH),\n seed=seed)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:25:08.466489Z","iopub.execute_input":"2024-05-03T16:25:08.466832Z","iopub.status.idle":"2024-05-03T16:25:08.554462Z","shell.execute_reply.started":"2024-05-03T16:25:08.466774Z","shell.execute_reply":"2024-05-03T16:25:08.553599Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Found 2930 validated image filenames.\nFound 732 validated image filenames.\nFound 1928 validated image filenames.\n","output_type":"stream"}]},{"cell_type":"code","source":"def cosine_decay_with_warmup(global_step,\n learning_rate_base,\n total_steps,\n warmup_learning_rate=0.0,\n warmup_steps=0,\n hold_base_rate_steps=0):\n\n if total_steps < warmup_steps:\n raise ValueError('total_steps must be larger or equal to warmup_steps.')\n learning_rate = 0.5 * learning_rate_base * (1 + np.cos(\n np.pi *\n (global_step - warmup_steps - hold_base_rate_steps\n ) / float(total_steps - warmup_steps - hold_base_rate_steps)))\n if hold_base_rate_steps > 0:\n learning_rate = np.where(global_step > warmup_steps + hold_base_rate_steps,\n learning_rate, learning_rate_base)\n if warmup_steps > 0:\n if learning_rate_base < warmup_learning_rate:\n raise ValueError('learning_rate_base must be larger or equal to warmup_learning_rate.')\n slope = (learning_rate_base - warmup_learning_rate) / warmup_steps\n warmup_rate = slope * global_step + warmup_learning_rate\n learning_rate = np.where(global_step < warmup_steps, warmup_rate,\n learning_rate)\n return np.where(global_step > total_steps, 0.0, learning_rate)\n\n\nclass WarmUpCosineDecayScheduler(Callback):\n\n def __init__(self,\n learning_rate_base,\n total_steps,\n global_step_init=0,\n warmup_learning_rate=0.0,\n warmup_steps=0,\n hold_base_rate_steps=0,\n verbose=0):\n\n super(WarmUpCosineDecayScheduler, self).__init__()\n self.learning_rate_base = learning_rate_base\n self.total_steps = total_steps\n self.global_step = global_step_init\n self.warmup_learning_rate = warmup_learning_rate\n self.warmup_steps = warmup_steps\n self.hold_base_rate_steps = hold_base_rate_steps\n self.verbose = verbose\n self.learning_rates = []\n\n def on_batch_end(self, batch, logs=None):\n self.global_step = self.global_step + 1\n lr = K.get_value(self.model.optimizer.lr)\n self.learning_rates.append(lr)\n\n def on_batch_begin(self, batch, logs=None):\n lr = cosine_decay_with_warmup(global_step=self.global_step,\n learning_rate_base=self.learning_rate_base,\n total_steps=self.total_steps,\n warmup_learning_rate=self.warmup_learning_rate,\n warmup_steps=self.warmup_steps,\n hold_base_rate_steps=self.hold_base_rate_steps)\n K.set_value(self.model.optimizer.lr, lr)\n if self.verbose > 0:\n print('\\nBatch %02d: setting learning rate to %s.' % (self.global_step + 1, lr))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:25:08.556867Z","iopub.execute_input":"2024-05-03T16:25:08.557158Z","iopub.status.idle":"2024-05-03T16:25:08.574844Z","shell.execute_reply.started":"2024-05-03T16:25:08.557106Z","shell.execute_reply":"2024-05-03T16:25:08.574099Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"def create_model(input_shape):\n input_tensor = Input(shape=input_shape)\n base_model = EfficientNetB5(weights=None, \n include_top=False,\n input_tensor=input_tensor)\n base_model.load_weights('../input/efficientnet-keras-weights-b0b5/efficientnet-b5_imagenet_1000_notop.h5')\n\n x = GlobalAveragePooling2D()(base_model.output)\n final_output = Dense(1, activation='linear', name='final_output')(x)\n model = Model(input_tensor, final_output)\n \n return model","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:25:08.576455Z","iopub.execute_input":"2024-05-03T16:25:08.576754Z","iopub.status.idle":"2024-05-03T16:25:08.593638Z","shell.execute_reply.started":"2024-05-03T16:25:08.576701Z","shell.execute_reply":"2024-05-03T16:25:08.592999Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"model = create_model(input_shape=(HEIGHT, WIDTH, CHANNELS))\n\nfor layer in model.layers:\n layer.trainable = False\n\nfor i in range(-2, 0):\n model.layers[i].trainable = True\n\ncosine_lr_1st = WarmUpCosineDecayScheduler(learning_rate_base=WARMUP_LEARNING_RATE,\n total_steps=TOTAL_STEPS_1st,\n warmup_learning_rate=0.0,\n warmup_steps=WARMUP_STEPS_1st,\n hold_base_rate_steps=(2 * STEP_SIZE))\n\nmetric_list = [\"accuracy\"]\ncallback_list = [cosine_lr_1st]\noptimizer = optimizers.Adam(lr=WARMUP_LEARNING_RATE)\nmodel.compile(optimizer=optimizer, loss='mean_squared_error', metrics=metric_list)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:25:08.595175Z","iopub.execute_input":"2024-05-03T16:25:08.595501Z","iopub.status.idle":"2024-05-03T16:25:41.476404Z","shell.execute_reply.started":"2024-05-03T16:25:08.595433Z","shell.execute_reply":"2024-05-03T16:25:41.475548Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_1 (InputLayer) (None, 224, 224, 3) 0 \n__________________________________________________________________________________________________\nconv2d_1 (Conv2D) (None, 112, 112, 48) 1296 input_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_1 (BatchNor (None, 112, 112, 48) 192 conv2d_1[0][0] \n__________________________________________________________________________________________________\nswish_1 (Swish) (None, 112, 112, 48) 0 batch_normalization_1[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_1 (DepthwiseCo (None, 112, 112, 48) 432 swish_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_2 (BatchNor (None, 112, 112, 48) 192 depthwise_conv2d_1[0][0] \n__________________________________________________________________________________________________\nswish_2 (Swish) (None, 112, 112, 48) 0 batch_normalization_2[0][0] \n__________________________________________________________________________________________________\nlambda_1 (Lambda) (None, 1, 1, 48) 0 swish_2[0][0] \n__________________________________________________________________________________________________\nconv2d_2 (Conv2D) (None, 1, 1, 12) 588 lambda_1[0][0] \n__________________________________________________________________________________________________\nswish_3 (Swish) (None, 1, 1, 12) 0 conv2d_2[0][0] \n__________________________________________________________________________________________________\nconv2d_3 (Conv2D) (None, 1, 1, 48) 624 swish_3[0][0] \n__________________________________________________________________________________________________\nactivation_1 (Activation) (None, 1, 1, 48) 0 conv2d_3[0][0] \n__________________________________________________________________________________________________\nmultiply_1 (Multiply) (None, 112, 112, 48) 0 activation_1[0][0] \n swish_2[0][0] \n__________________________________________________________________________________________________\nconv2d_4 (Conv2D) (None, 112, 112, 24) 1152 multiply_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_3 (BatchNor (None, 112, 112, 24) 96 conv2d_4[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_2 (DepthwiseCo (None, 112, 112, 24) 216 batch_normalization_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_4 (BatchNor (None, 112, 112, 24) 96 depthwise_conv2d_2[0][0] \n__________________________________________________________________________________________________\nswish_4 (Swish) (None, 112, 112, 24) 0 batch_normalization_4[0][0] \n__________________________________________________________________________________________________\nlambda_2 (Lambda) (None, 1, 1, 24) 0 swish_4[0][0] \n__________________________________________________________________________________________________\nconv2d_5 (Conv2D) (None, 1, 1, 6) 150 lambda_2[0][0] \n__________________________________________________________________________________________________\nswish_5 (Swish) (None, 1, 1, 6) 0 conv2d_5[0][0] \n__________________________________________________________________________________________________\nconv2d_6 (Conv2D) (None, 1, 1, 24) 168 swish_5[0][0] \n__________________________________________________________________________________________________\nactivation_2 (Activation) (None, 1, 1, 24) 0 conv2d_6[0][0] \n__________________________________________________________________________________________________\nmultiply_2 (Multiply) (None, 112, 112, 24) 0 activation_2[0][0] \n swish_4[0][0] \n__________________________________________________________________________________________________\nconv2d_7 (Conv2D) (None, 112, 112, 24) 576 multiply_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_5 (BatchNor (None, 112, 112, 24) 96 conv2d_7[0][0] \n__________________________________________________________________________________________________\ndrop_connect_1 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_5[0][0] \n__________________________________________________________________________________________________\nadd_1 (Add) (None, 112, 112, 24) 0 drop_connect_1[0][0] \n batch_normalization_3[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_3 (DepthwiseCo (None, 112, 112, 24) 216 add_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_6 (BatchNor (None, 112, 112, 24) 96 depthwise_conv2d_3[0][0] \n__________________________________________________________________________________________________\nswish_6 (Swish) (None, 112, 112, 24) 0 batch_normalization_6[0][0] \n__________________________________________________________________________________________________\nlambda_3 (Lambda) (None, 1, 1, 24) 0 swish_6[0][0] \n__________________________________________________________________________________________________\nconv2d_8 (Conv2D) (None, 1, 1, 6) 150 lambda_3[0][0] \n__________________________________________________________________________________________________\nswish_7 (Swish) (None, 1, 1, 6) 0 conv2d_8[0][0] \n__________________________________________________________________________________________________\nconv2d_9 (Conv2D) (None, 1, 1, 24) 168 swish_7[0][0] \n__________________________________________________________________________________________________\nactivation_3 (Activation) (None, 1, 1, 24) 0 conv2d_9[0][0] \n__________________________________________________________________________________________________\nmultiply_3 (Multiply) (None, 112, 112, 24) 0 activation_3[0][0] \n swish_6[0][0] \n__________________________________________________________________________________________________\nconv2d_10 (Conv2D) (None, 112, 112, 24) 576 multiply_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_7 (BatchNor (None, 112, 112, 24) 96 conv2d_10[0][0] \n__________________________________________________________________________________________________\ndrop_connect_2 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_7[0][0] \n__________________________________________________________________________________________________\nadd_2 (Add) (None, 112, 112, 24) 0 drop_connect_2[0][0] \n add_1[0][0] \n__________________________________________________________________________________________________\nconv2d_11 (Conv2D) (None, 112, 112, 144 3456 add_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_8 (BatchNor (None, 112, 112, 144 576 conv2d_11[0][0] \n__________________________________________________________________________________________________\nswish_8 (Swish) (None, 112, 112, 144 0 batch_normalization_8[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_4 (DepthwiseCo (None, 56, 56, 144) 1296 swish_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_9 (BatchNor (None, 56, 56, 144) 576 depthwise_conv2d_4[0][0] \n__________________________________________________________________________________________________\nswish_9 (Swish) (None, 56, 56, 144) 0 batch_normalization_9[0][0] \n__________________________________________________________________________________________________\nlambda_4 (Lambda) (None, 1, 1, 144) 0 swish_9[0][0] \n__________________________________________________________________________________________________\nconv2d_12 (Conv2D) (None, 1, 1, 6) 870 lambda_4[0][0] \n__________________________________________________________________________________________________\nswish_10 (Swish) (None, 1, 1, 6) 0 conv2d_12[0][0] \n__________________________________________________________________________________________________\nconv2d_13 (Conv2D) (None, 1, 1, 144) 1008 swish_10[0][0] \n__________________________________________________________________________________________________\nactivation_4 (Activation) (None, 1, 1, 144) 0 conv2d_13[0][0] \n__________________________________________________________________________________________________\nmultiply_4 (Multiply) (None, 56, 56, 144) 0 activation_4[0][0] \n swish_9[0][0] \n__________________________________________________________________________________________________\nconv2d_14 (Conv2D) (None, 56, 56, 40) 5760 multiply_4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_10 (BatchNo (None, 56, 56, 40) 160 conv2d_14[0][0] \n__________________________________________________________________________________________________\nconv2d_15 (Conv2D) (None, 56, 56, 240) 9600 batch_normalization_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_11 (BatchNo (None, 56, 56, 240) 960 conv2d_15[0][0] \n__________________________________________________________________________________________________\nswish_11 (Swish) (None, 56, 56, 240) 0 batch_normalization_11[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_5 (DepthwiseCo (None, 56, 56, 240) 2160 swish_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_12 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_5[0][0] \n__________________________________________________________________________________________________\nswish_12 (Swish) (None, 56, 56, 240) 0 batch_normalization_12[0][0] \n__________________________________________________________________________________________________\nlambda_5 (Lambda) (None, 1, 1, 240) 0 swish_12[0][0] \n__________________________________________________________________________________________________\nconv2d_16 (Conv2D) (None, 1, 1, 10) 2410 lambda_5[0][0] \n__________________________________________________________________________________________________\nswish_13 (Swish) (None, 1, 1, 10) 0 conv2d_16[0][0] \n__________________________________________________________________________________________________\nconv2d_17 (Conv2D) (None, 1, 1, 240) 2640 swish_13[0][0] \n__________________________________________________________________________________________________\nactivation_5 (Activation) (None, 1, 1, 240) 0 conv2d_17[0][0] \n__________________________________________________________________________________________________\nmultiply_5 (Multiply) (None, 56, 56, 240) 0 activation_5[0][0] \n swish_12[0][0] \n__________________________________________________________________________________________________\nconv2d_18 (Conv2D) (None, 56, 56, 40) 9600 multiply_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_13 (BatchNo (None, 56, 56, 40) 160 conv2d_18[0][0] \n__________________________________________________________________________________________________\ndrop_connect_3 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_13[0][0] \n__________________________________________________________________________________________________\nadd_3 (Add) (None, 56, 56, 40) 0 drop_connect_3[0][0] \n batch_normalization_10[0][0] \n__________________________________________________________________________________________________\nconv2d_19 (Conv2D) (None, 56, 56, 240) 9600 add_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_14 (BatchNo (None, 56, 56, 240) 960 conv2d_19[0][0] \n__________________________________________________________________________________________________\nswish_14 (Swish) (None, 56, 56, 240) 0 batch_normalization_14[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_6 (DepthwiseCo (None, 56, 56, 240) 2160 swish_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_15 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_6[0][0] \n__________________________________________________________________________________________________\nswish_15 (Swish) (None, 56, 56, 240) 0 batch_normalization_15[0][0] \n__________________________________________________________________________________________________\nlambda_6 (Lambda) (None, 1, 1, 240) 0 swish_15[0][0] \n__________________________________________________________________________________________________\nconv2d_20 (Conv2D) (None, 1, 1, 10) 2410 lambda_6[0][0] \n__________________________________________________________________________________________________\nswish_16 (Swish) (None, 1, 1, 10) 0 conv2d_20[0][0] \n__________________________________________________________________________________________________\nconv2d_21 (Conv2D) (None, 1, 1, 240) 2640 swish_16[0][0] \n__________________________________________________________________________________________________\nactivation_6 (Activation) (None, 1, 1, 240) 0 conv2d_21[0][0] \n__________________________________________________________________________________________________\nmultiply_6 (Multiply) (None, 56, 56, 240) 0 activation_6[0][0] \n swish_15[0][0] \n__________________________________________________________________________________________________\nconv2d_22 (Conv2D) (None, 56, 56, 40) 9600 multiply_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_16 (BatchNo (None, 56, 56, 40) 160 conv2d_22[0][0] \n__________________________________________________________________________________________________\ndrop_connect_4 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_16[0][0] \n__________________________________________________________________________________________________\nadd_4 (Add) (None, 56, 56, 40) 0 drop_connect_4[0][0] \n add_3[0][0] \n__________________________________________________________________________________________________\nconv2d_23 (Conv2D) (None, 56, 56, 240) 9600 add_4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_17 (BatchNo (None, 56, 56, 240) 960 conv2d_23[0][0] \n__________________________________________________________________________________________________\nswish_17 (Swish) (None, 56, 56, 240) 0 batch_normalization_17[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_7 (DepthwiseCo (None, 56, 56, 240) 2160 swish_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_18 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_7[0][0] \n__________________________________________________________________________________________________\nswish_18 (Swish) (None, 56, 56, 240) 0 batch_normalization_18[0][0] \n__________________________________________________________________________________________________\nlambda_7 (Lambda) (None, 1, 1, 240) 0 swish_18[0][0] \n__________________________________________________________________________________________________\nconv2d_24 (Conv2D) (None, 1, 1, 10) 2410 lambda_7[0][0] \n__________________________________________________________________________________________________\nswish_19 (Swish) (None, 1, 1, 10) 0 conv2d_24[0][0] \n__________________________________________________________________________________________________\nconv2d_25 (Conv2D) (None, 1, 1, 240) 2640 swish_19[0][0] \n__________________________________________________________________________________________________\nactivation_7 (Activation) (None, 1, 1, 240) 0 conv2d_25[0][0] \n__________________________________________________________________________________________________\nmultiply_7 (Multiply) (None, 56, 56, 240) 0 activation_7[0][0] \n swish_18[0][0] \n__________________________________________________________________________________________________\nconv2d_26 (Conv2D) (None, 56, 56, 40) 9600 multiply_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_19 (BatchNo (None, 56, 56, 40) 160 conv2d_26[0][0] \n__________________________________________________________________________________________________\ndrop_connect_5 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_19[0][0] \n__________________________________________________________________________________________________\nadd_5 (Add) (None, 56, 56, 40) 0 drop_connect_5[0][0] \n add_4[0][0] \n__________________________________________________________________________________________________\nconv2d_27 (Conv2D) (None, 56, 56, 240) 9600 add_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_20 (BatchNo (None, 56, 56, 240) 960 conv2d_27[0][0] \n__________________________________________________________________________________________________\nswish_20 (Swish) (None, 56, 56, 240) 0 batch_normalization_20[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_8 (DepthwiseCo (None, 56, 56, 240) 2160 swish_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_21 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_8[0][0] \n__________________________________________________________________________________________________\nswish_21 (Swish) (None, 56, 56, 240) 0 batch_normalization_21[0][0] \n__________________________________________________________________________________________________\nlambda_8 (Lambda) (None, 1, 1, 240) 0 swish_21[0][0] \n__________________________________________________________________________________________________\nconv2d_28 (Conv2D) (None, 1, 1, 10) 2410 lambda_8[0][0] \n__________________________________________________________________________________________________\nswish_22 (Swish) (None, 1, 1, 10) 0 conv2d_28[0][0] \n__________________________________________________________________________________________________\nconv2d_29 (Conv2D) (None, 1, 1, 240) 2640 swish_22[0][0] \n__________________________________________________________________________________________________\nactivation_8 (Activation) (None, 1, 1, 240) 0 conv2d_29[0][0] \n__________________________________________________________________________________________________\nmultiply_8 (Multiply) (None, 56, 56, 240) 0 activation_8[0][0] \n swish_21[0][0] \n__________________________________________________________________________________________________\nconv2d_30 (Conv2D) (None, 56, 56, 40) 9600 multiply_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_22 (BatchNo (None, 56, 56, 40) 160 conv2d_30[0][0] \n__________________________________________________________________________________________________\ndrop_connect_6 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_22[0][0] \n__________________________________________________________________________________________________\nadd_6 (Add) (None, 56, 56, 40) 0 drop_connect_6[0][0] \n add_5[0][0] \n__________________________________________________________________________________________________\nconv2d_31 (Conv2D) (None, 56, 56, 240) 9600 add_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_23 (BatchNo (None, 56, 56, 240) 960 conv2d_31[0][0] \n__________________________________________________________________________________________________\nswish_23 (Swish) (None, 56, 56, 240) 0 batch_normalization_23[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_9 (DepthwiseCo (None, 28, 28, 240) 6000 swish_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_24 (BatchNo (None, 28, 28, 240) 960 depthwise_conv2d_9[0][0] \n__________________________________________________________________________________________________\nswish_24 (Swish) (None, 28, 28, 240) 0 batch_normalization_24[0][0] \n__________________________________________________________________________________________________\nlambda_9 (Lambda) (None, 1, 1, 240) 0 swish_24[0][0] \n__________________________________________________________________________________________________\nconv2d_32 (Conv2D) (None, 1, 1, 10) 2410 lambda_9[0][0] \n__________________________________________________________________________________________________\nswish_25 (Swish) (None, 1, 1, 10) 0 conv2d_32[0][0] \n__________________________________________________________________________________________________\nconv2d_33 (Conv2D) (None, 1, 1, 240) 2640 swish_25[0][0] \n__________________________________________________________________________________________________\nactivation_9 (Activation) (None, 1, 1, 240) 0 conv2d_33[0][0] \n__________________________________________________________________________________________________\nmultiply_9 (Multiply) (None, 28, 28, 240) 0 activation_9[0][0] \n swish_24[0][0] \n__________________________________________________________________________________________________\nconv2d_34 (Conv2D) (None, 28, 28, 64) 15360 multiply_9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_25 (BatchNo (None, 28, 28, 64) 256 conv2d_34[0][0] \n__________________________________________________________________________________________________\nconv2d_35 (Conv2D) (None, 28, 28, 384) 24576 batch_normalization_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_26 (BatchNo (None, 28, 28, 384) 1536 conv2d_35[0][0] \n__________________________________________________________________________________________________\nswish_26 (Swish) (None, 28, 28, 384) 0 batch_normalization_26[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_10 (DepthwiseC (None, 28, 28, 384) 9600 swish_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_27 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_10[0][0] \n__________________________________________________________________________________________________\nswish_27 (Swish) (None, 28, 28, 384) 0 batch_normalization_27[0][0] \n__________________________________________________________________________________________________\nlambda_10 (Lambda) (None, 1, 1, 384) 0 swish_27[0][0] \n__________________________________________________________________________________________________\nconv2d_36 (Conv2D) (None, 1, 1, 16) 6160 lambda_10[0][0] \n__________________________________________________________________________________________________\nswish_28 (Swish) (None, 1, 1, 16) 0 conv2d_36[0][0] \n__________________________________________________________________________________________________\nconv2d_37 (Conv2D) (None, 1, 1, 384) 6528 swish_28[0][0] \n__________________________________________________________________________________________________\nactivation_10 (Activation) (None, 1, 1, 384) 0 conv2d_37[0][0] \n__________________________________________________________________________________________________\nmultiply_10 (Multiply) (None, 28, 28, 384) 0 activation_10[0][0] \n swish_27[0][0] \n__________________________________________________________________________________________________\nconv2d_38 (Conv2D) (None, 28, 28, 64) 24576 multiply_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_28 (BatchNo (None, 28, 28, 64) 256 conv2d_38[0][0] \n__________________________________________________________________________________________________\ndrop_connect_7 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_28[0][0] \n__________________________________________________________________________________________________\nadd_7 (Add) (None, 28, 28, 64) 0 drop_connect_7[0][0] \n batch_normalization_25[0][0] \n__________________________________________________________________________________________________\nconv2d_39 (Conv2D) (None, 28, 28, 384) 24576 add_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_29 (BatchNo (None, 28, 28, 384) 1536 conv2d_39[0][0] \n__________________________________________________________________________________________________\nswish_29 (Swish) (None, 28, 28, 384) 0 batch_normalization_29[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_11 (DepthwiseC (None, 28, 28, 384) 9600 swish_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_30 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_11[0][0] \n__________________________________________________________________________________________________\nswish_30 (Swish) (None, 28, 28, 384) 0 batch_normalization_30[0][0] \n__________________________________________________________________________________________________\nlambda_11 (Lambda) (None, 1, 1, 384) 0 swish_30[0][0] \n__________________________________________________________________________________________________\nconv2d_40 (Conv2D) (None, 1, 1, 16) 6160 lambda_11[0][0] \n__________________________________________________________________________________________________\nswish_31 (Swish) (None, 1, 1, 16) 0 conv2d_40[0][0] \n__________________________________________________________________________________________________\nconv2d_41 (Conv2D) (None, 1, 1, 384) 6528 swish_31[0][0] \n__________________________________________________________________________________________________\nactivation_11 (Activation) (None, 1, 1, 384) 0 conv2d_41[0][0] \n__________________________________________________________________________________________________\nmultiply_11 (Multiply) (None, 28, 28, 384) 0 activation_11[0][0] \n swish_30[0][0] \n__________________________________________________________________________________________________\nconv2d_42 (Conv2D) (None, 28, 28, 64) 24576 multiply_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_31 (BatchNo (None, 28, 28, 64) 256 conv2d_42[0][0] \n__________________________________________________________________________________________________\ndrop_connect_8 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_31[0][0] \n__________________________________________________________________________________________________\nadd_8 (Add) (None, 28, 28, 64) 0 drop_connect_8[0][0] \n add_7[0][0] \n__________________________________________________________________________________________________\nconv2d_43 (Conv2D) (None, 28, 28, 384) 24576 add_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_32 (BatchNo (None, 28, 28, 384) 1536 conv2d_43[0][0] \n__________________________________________________________________________________________________\nswish_32 (Swish) (None, 28, 28, 384) 0 batch_normalization_32[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_12 (DepthwiseC (None, 28, 28, 384) 9600 swish_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_33 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_12[0][0] \n__________________________________________________________________________________________________\nswish_33 (Swish) (None, 28, 28, 384) 0 batch_normalization_33[0][0] \n__________________________________________________________________________________________________\nlambda_12 (Lambda) (None, 1, 1, 384) 0 swish_33[0][0] \n__________________________________________________________________________________________________\nconv2d_44 (Conv2D) (None, 1, 1, 16) 6160 lambda_12[0][0] \n__________________________________________________________________________________________________\nswish_34 (Swish) (None, 1, 1, 16) 0 conv2d_44[0][0] \n__________________________________________________________________________________________________\nconv2d_45 (Conv2D) (None, 1, 1, 384) 6528 swish_34[0][0] \n__________________________________________________________________________________________________\nactivation_12 (Activation) (None, 1, 1, 384) 0 conv2d_45[0][0] \n__________________________________________________________________________________________________\nmultiply_12 (Multiply) (None, 28, 28, 384) 0 activation_12[0][0] \n swish_33[0][0] \n__________________________________________________________________________________________________\nconv2d_46 (Conv2D) (None, 28, 28, 64) 24576 multiply_12[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_34 (BatchNo (None, 28, 28, 64) 256 conv2d_46[0][0] \n__________________________________________________________________________________________________\ndrop_connect_9 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_34[0][0] \n__________________________________________________________________________________________________\nadd_9 (Add) (None, 28, 28, 64) 0 drop_connect_9[0][0] \n add_8[0][0] \n__________________________________________________________________________________________________\nconv2d_47 (Conv2D) (None, 28, 28, 384) 24576 add_9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_35 (BatchNo (None, 28, 28, 384) 1536 conv2d_47[0][0] \n__________________________________________________________________________________________________\nswish_35 (Swish) (None, 28, 28, 384) 0 batch_normalization_35[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_13 (DepthwiseC (None, 28, 28, 384) 9600 swish_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_36 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_13[0][0] \n__________________________________________________________________________________________________\nswish_36 (Swish) (None, 28, 28, 384) 0 batch_normalization_36[0][0] \n__________________________________________________________________________________________________\nlambda_13 (Lambda) (None, 1, 1, 384) 0 swish_36[0][0] \n__________________________________________________________________________________________________\nconv2d_48 (Conv2D) (None, 1, 1, 16) 6160 lambda_13[0][0] \n__________________________________________________________________________________________________\nswish_37 (Swish) (None, 1, 1, 16) 0 conv2d_48[0][0] \n__________________________________________________________________________________________________\nconv2d_49 (Conv2D) (None, 1, 1, 384) 6528 swish_37[0][0] \n__________________________________________________________________________________________________\nactivation_13 (Activation) (None, 1, 1, 384) 0 conv2d_49[0][0] \n__________________________________________________________________________________________________\nmultiply_13 (Multiply) (None, 28, 28, 384) 0 activation_13[0][0] \n swish_36[0][0] \n__________________________________________________________________________________________________\nconv2d_50 (Conv2D) (None, 28, 28, 64) 24576 multiply_13[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_37 (BatchNo (None, 28, 28, 64) 256 conv2d_50[0][0] \n__________________________________________________________________________________________________\ndrop_connect_10 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_37[0][0] \n__________________________________________________________________________________________________\nadd_10 (Add) (None, 28, 28, 64) 0 drop_connect_10[0][0] \n add_9[0][0] \n__________________________________________________________________________________________________\nconv2d_51 (Conv2D) (None, 28, 28, 384) 24576 add_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_38 (BatchNo (None, 28, 28, 384) 1536 conv2d_51[0][0] \n__________________________________________________________________________________________________\nswish_38 (Swish) (None, 28, 28, 384) 0 batch_normalization_38[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_14 (DepthwiseC (None, 14, 14, 384) 3456 swish_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_39 (BatchNo (None, 14, 14, 384) 1536 depthwise_conv2d_14[0][0] \n__________________________________________________________________________________________________\nswish_39 (Swish) (None, 14, 14, 384) 0 batch_normalization_39[0][0] \n__________________________________________________________________________________________________\nlambda_14 (Lambda) (None, 1, 1, 384) 0 swish_39[0][0] \n__________________________________________________________________________________________________\nconv2d_52 (Conv2D) (None, 1, 1, 16) 6160 lambda_14[0][0] \n__________________________________________________________________________________________________\nswish_40 (Swish) (None, 1, 1, 16) 0 conv2d_52[0][0] \n__________________________________________________________________________________________________\nconv2d_53 (Conv2D) (None, 1, 1, 384) 6528 swish_40[0][0] \n__________________________________________________________________________________________________\nactivation_14 (Activation) (None, 1, 1, 384) 0 conv2d_53[0][0] \n__________________________________________________________________________________________________\nmultiply_14 (Multiply) (None, 14, 14, 384) 0 activation_14[0][0] \n swish_39[0][0] \n__________________________________________________________________________________________________\nconv2d_54 (Conv2D) (None, 14, 14, 128) 49152 multiply_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_40 (BatchNo (None, 14, 14, 128) 512 conv2d_54[0][0] \n__________________________________________________________________________________________________\nconv2d_55 (Conv2D) (None, 14, 14, 768) 98304 batch_normalization_40[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_41 (BatchNo (None, 14, 14, 768) 3072 conv2d_55[0][0] \n__________________________________________________________________________________________________\nswish_41 (Swish) (None, 14, 14, 768) 0 batch_normalization_41[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_15 (DepthwiseC (None, 14, 14, 768) 6912 swish_41[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_42 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_15[0][0] \n__________________________________________________________________________________________________\nswish_42 (Swish) (None, 14, 14, 768) 0 batch_normalization_42[0][0] \n__________________________________________________________________________________________________\nlambda_15 (Lambda) (None, 1, 1, 768) 0 swish_42[0][0] \n__________________________________________________________________________________________________\nconv2d_56 (Conv2D) (None, 1, 1, 32) 24608 lambda_15[0][0] \n__________________________________________________________________________________________________\nswish_43 (Swish) (None, 1, 1, 32) 0 conv2d_56[0][0] \n__________________________________________________________________________________________________\nconv2d_57 (Conv2D) (None, 1, 1, 768) 25344 swish_43[0][0] \n__________________________________________________________________________________________________\nactivation_15 (Activation) (None, 1, 1, 768) 0 conv2d_57[0][0] \n__________________________________________________________________________________________________\nmultiply_15 (Multiply) (None, 14, 14, 768) 0 activation_15[0][0] \n swish_42[0][0] \n__________________________________________________________________________________________________\nconv2d_58 (Conv2D) (None, 14, 14, 128) 98304 multiply_15[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_43 (BatchNo (None, 14, 14, 128) 512 conv2d_58[0][0] \n__________________________________________________________________________________________________\ndrop_connect_11 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_43[0][0] \n__________________________________________________________________________________________________\nadd_11 (Add) (None, 14, 14, 128) 0 drop_connect_11[0][0] \n batch_normalization_40[0][0] \n__________________________________________________________________________________________________\nconv2d_59 (Conv2D) (None, 14, 14, 768) 98304 add_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_44 (BatchNo (None, 14, 14, 768) 3072 conv2d_59[0][0] \n__________________________________________________________________________________________________\nswish_44 (Swish) (None, 14, 14, 768) 0 batch_normalization_44[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_16 (DepthwiseC (None, 14, 14, 768) 6912 swish_44[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_45 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_16[0][0] \n__________________________________________________________________________________________________\nswish_45 (Swish) (None, 14, 14, 768) 0 batch_normalization_45[0][0] \n__________________________________________________________________________________________________\nlambda_16 (Lambda) (None, 1, 1, 768) 0 swish_45[0][0] \n__________________________________________________________________________________________________\nconv2d_60 (Conv2D) (None, 1, 1, 32) 24608 lambda_16[0][0] \n__________________________________________________________________________________________________\nswish_46 (Swish) (None, 1, 1, 32) 0 conv2d_60[0][0] \n__________________________________________________________________________________________________\nconv2d_61 (Conv2D) (None, 1, 1, 768) 25344 swish_46[0][0] \n__________________________________________________________________________________________________\nactivation_16 (Activation) (None, 1, 1, 768) 0 conv2d_61[0][0] \n__________________________________________________________________________________________________\nmultiply_16 (Multiply) (None, 14, 14, 768) 0 activation_16[0][0] \n swish_45[0][0] \n__________________________________________________________________________________________________\nconv2d_62 (Conv2D) (None, 14, 14, 128) 98304 multiply_16[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_46 (BatchNo (None, 14, 14, 128) 512 conv2d_62[0][0] \n__________________________________________________________________________________________________\ndrop_connect_12 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_46[0][0] \n__________________________________________________________________________________________________\nadd_12 (Add) (None, 14, 14, 128) 0 drop_connect_12[0][0] \n add_11[0][0] \n__________________________________________________________________________________________________\nconv2d_63 (Conv2D) (None, 14, 14, 768) 98304 add_12[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_47 (BatchNo (None, 14, 14, 768) 3072 conv2d_63[0][0] \n__________________________________________________________________________________________________\nswish_47 (Swish) (None, 14, 14, 768) 0 batch_normalization_47[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_17 (DepthwiseC (None, 14, 14, 768) 6912 swish_47[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_48 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_17[0][0] \n__________________________________________________________________________________________________\nswish_48 (Swish) (None, 14, 14, 768) 0 batch_normalization_48[0][0] \n__________________________________________________________________________________________________\nlambda_17 (Lambda) (None, 1, 1, 768) 0 swish_48[0][0] \n__________________________________________________________________________________________________\nconv2d_64 (Conv2D) (None, 1, 1, 32) 24608 lambda_17[0][0] \n__________________________________________________________________________________________________\nswish_49 (Swish) (None, 1, 1, 32) 0 conv2d_64[0][0] \n__________________________________________________________________________________________________\nconv2d_65 (Conv2D) (None, 1, 1, 768) 25344 swish_49[0][0] \n__________________________________________________________________________________________________\nactivation_17 (Activation) (None, 1, 1, 768) 0 conv2d_65[0][0] \n__________________________________________________________________________________________________\nmultiply_17 (Multiply) (None, 14, 14, 768) 0 activation_17[0][0] \n swish_48[0][0] \n__________________________________________________________________________________________________\nconv2d_66 (Conv2D) (None, 14, 14, 128) 98304 multiply_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_49 (BatchNo (None, 14, 14, 128) 512 conv2d_66[0][0] \n__________________________________________________________________________________________________\ndrop_connect_13 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_49[0][0] \n__________________________________________________________________________________________________\nadd_13 (Add) (None, 14, 14, 128) 0 drop_connect_13[0][0] \n add_12[0][0] \n__________________________________________________________________________________________________\nconv2d_67 (Conv2D) (None, 14, 14, 768) 98304 add_13[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_50 (BatchNo (None, 14, 14, 768) 3072 conv2d_67[0][0] \n__________________________________________________________________________________________________\nswish_50 (Swish) (None, 14, 14, 768) 0 batch_normalization_50[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_18 (DepthwiseC (None, 14, 14, 768) 6912 swish_50[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_51 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_18[0][0] \n__________________________________________________________________________________________________\nswish_51 (Swish) (None, 14, 14, 768) 0 batch_normalization_51[0][0] \n__________________________________________________________________________________________________\nlambda_18 (Lambda) (None, 1, 1, 768) 0 swish_51[0][0] \n__________________________________________________________________________________________________\nconv2d_68 (Conv2D) (None, 1, 1, 32) 24608 lambda_18[0][0] \n__________________________________________________________________________________________________\nswish_52 (Swish) (None, 1, 1, 32) 0 conv2d_68[0][0] \n__________________________________________________________________________________________________\nconv2d_69 (Conv2D) (None, 1, 1, 768) 25344 swish_52[0][0] \n__________________________________________________________________________________________________\nactivation_18 (Activation) (None, 1, 1, 768) 0 conv2d_69[0][0] \n__________________________________________________________________________________________________\nmultiply_18 (Multiply) (None, 14, 14, 768) 0 activation_18[0][0] \n swish_51[0][0] \n__________________________________________________________________________________________________\nconv2d_70 (Conv2D) (None, 14, 14, 128) 98304 multiply_18[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_52 (BatchNo (None, 14, 14, 128) 512 conv2d_70[0][0] \n__________________________________________________________________________________________________\ndrop_connect_14 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_52[0][0] \n__________________________________________________________________________________________________\nadd_14 (Add) (None, 14, 14, 128) 0 drop_connect_14[0][0] \n add_13[0][0] \n__________________________________________________________________________________________________\nconv2d_71 (Conv2D) (None, 14, 14, 768) 98304 add_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_53 (BatchNo (None, 14, 14, 768) 3072 conv2d_71[0][0] \n__________________________________________________________________________________________________\nswish_53 (Swish) (None, 14, 14, 768) 0 batch_normalization_53[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_19 (DepthwiseC (None, 14, 14, 768) 6912 swish_53[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_54 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_19[0][0] \n__________________________________________________________________________________________________\nswish_54 (Swish) (None, 14, 14, 768) 0 batch_normalization_54[0][0] \n__________________________________________________________________________________________________\nlambda_19 (Lambda) (None, 1, 1, 768) 0 swish_54[0][0] \n__________________________________________________________________________________________________\nconv2d_72 (Conv2D) (None, 1, 1, 32) 24608 lambda_19[0][0] \n__________________________________________________________________________________________________\nswish_55 (Swish) (None, 1, 1, 32) 0 conv2d_72[0][0] \n__________________________________________________________________________________________________\nconv2d_73 (Conv2D) (None, 1, 1, 768) 25344 swish_55[0][0] \n__________________________________________________________________________________________________\nactivation_19 (Activation) (None, 1, 1, 768) 0 conv2d_73[0][0] \n__________________________________________________________________________________________________\nmultiply_19 (Multiply) (None, 14, 14, 768) 0 activation_19[0][0] \n swish_54[0][0] \n__________________________________________________________________________________________________\nconv2d_74 (Conv2D) (None, 14, 14, 128) 98304 multiply_19[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_55 (BatchNo (None, 14, 14, 128) 512 conv2d_74[0][0] \n__________________________________________________________________________________________________\ndrop_connect_15 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_55[0][0] \n__________________________________________________________________________________________________\nadd_15 (Add) (None, 14, 14, 128) 0 drop_connect_15[0][0] \n add_14[0][0] \n__________________________________________________________________________________________________\nconv2d_75 (Conv2D) (None, 14, 14, 768) 98304 add_15[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_56 (BatchNo (None, 14, 14, 768) 3072 conv2d_75[0][0] \n__________________________________________________________________________________________________\nswish_56 (Swish) (None, 14, 14, 768) 0 batch_normalization_56[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_20 (DepthwiseC (None, 14, 14, 768) 6912 swish_56[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_57 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_20[0][0] \n__________________________________________________________________________________________________\nswish_57 (Swish) (None, 14, 14, 768) 0 batch_normalization_57[0][0] \n__________________________________________________________________________________________________\nlambda_20 (Lambda) (None, 1, 1, 768) 0 swish_57[0][0] \n__________________________________________________________________________________________________\nconv2d_76 (Conv2D) (None, 1, 1, 32) 24608 lambda_20[0][0] \n__________________________________________________________________________________________________\nswish_58 (Swish) (None, 1, 1, 32) 0 conv2d_76[0][0] \n__________________________________________________________________________________________________\nconv2d_77 (Conv2D) (None, 1, 1, 768) 25344 swish_58[0][0] \n__________________________________________________________________________________________________\nactivation_20 (Activation) (None, 1, 1, 768) 0 conv2d_77[0][0] \n__________________________________________________________________________________________________\nmultiply_20 (Multiply) (None, 14, 14, 768) 0 activation_20[0][0] \n swish_57[0][0] \n__________________________________________________________________________________________________\nconv2d_78 (Conv2D) (None, 14, 14, 128) 98304 multiply_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_58 (BatchNo (None, 14, 14, 128) 512 conv2d_78[0][0] \n__________________________________________________________________________________________________\ndrop_connect_16 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_58[0][0] \n__________________________________________________________________________________________________\nadd_16 (Add) (None, 14, 14, 128) 0 drop_connect_16[0][0] \n add_15[0][0] \n__________________________________________________________________________________________________\nconv2d_79 (Conv2D) (None, 14, 14, 768) 98304 add_16[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_59 (BatchNo (None, 14, 14, 768) 3072 conv2d_79[0][0] \n__________________________________________________________________________________________________\nswish_59 (Swish) (None, 14, 14, 768) 0 batch_normalization_59[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_21 (DepthwiseC (None, 14, 14, 768) 19200 swish_59[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_60 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_21[0][0] \n__________________________________________________________________________________________________\nswish_60 (Swish) (None, 14, 14, 768) 0 batch_normalization_60[0][0] \n__________________________________________________________________________________________________\nlambda_21 (Lambda) (None, 1, 1, 768) 0 swish_60[0][0] \n__________________________________________________________________________________________________\nconv2d_80 (Conv2D) (None, 1, 1, 32) 24608 lambda_21[0][0] \n__________________________________________________________________________________________________\nswish_61 (Swish) (None, 1, 1, 32) 0 conv2d_80[0][0] \n__________________________________________________________________________________________________\nconv2d_81 (Conv2D) (None, 1, 1, 768) 25344 swish_61[0][0] \n__________________________________________________________________________________________________\nactivation_21 (Activation) (None, 1, 1, 768) 0 conv2d_81[0][0] \n__________________________________________________________________________________________________\nmultiply_21 (Multiply) (None, 14, 14, 768) 0 activation_21[0][0] \n swish_60[0][0] \n__________________________________________________________________________________________________\nconv2d_82 (Conv2D) (None, 14, 14, 176) 135168 multiply_21[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_61 (BatchNo (None, 14, 14, 176) 704 conv2d_82[0][0] \n__________________________________________________________________________________________________\nconv2d_83 (Conv2D) (None, 14, 14, 1056) 185856 batch_normalization_61[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_62 (BatchNo (None, 14, 14, 1056) 4224 conv2d_83[0][0] \n__________________________________________________________________________________________________\nswish_62 (Swish) (None, 14, 14, 1056) 0 batch_normalization_62[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_22 (DepthwiseC (None, 14, 14, 1056) 26400 swish_62[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_63 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_22[0][0] \n__________________________________________________________________________________________________\nswish_63 (Swish) (None, 14, 14, 1056) 0 batch_normalization_63[0][0] \n__________________________________________________________________________________________________\nlambda_22 (Lambda) (None, 1, 1, 1056) 0 swish_63[0][0] \n__________________________________________________________________________________________________\nconv2d_84 (Conv2D) (None, 1, 1, 44) 46508 lambda_22[0][0] \n__________________________________________________________________________________________________\nswish_64 (Swish) (None, 1, 1, 44) 0 conv2d_84[0][0] \n__________________________________________________________________________________________________\nconv2d_85 (Conv2D) (None, 1, 1, 1056) 47520 swish_64[0][0] \n__________________________________________________________________________________________________\nactivation_22 (Activation) (None, 1, 1, 1056) 0 conv2d_85[0][0] \n__________________________________________________________________________________________________\nmultiply_22 (Multiply) (None, 14, 14, 1056) 0 activation_22[0][0] \n swish_63[0][0] \n__________________________________________________________________________________________________\nconv2d_86 (Conv2D) (None, 14, 14, 176) 185856 multiply_22[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_64 (BatchNo (None, 14, 14, 176) 704 conv2d_86[0][0] \n__________________________________________________________________________________________________\ndrop_connect_17 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_64[0][0] \n__________________________________________________________________________________________________\nadd_17 (Add) (None, 14, 14, 176) 0 drop_connect_17[0][0] \n batch_normalization_61[0][0] \n__________________________________________________________________________________________________\nconv2d_87 (Conv2D) (None, 14, 14, 1056) 185856 add_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_65 (BatchNo (None, 14, 14, 1056) 4224 conv2d_87[0][0] \n__________________________________________________________________________________________________\nswish_65 (Swish) (None, 14, 14, 1056) 0 batch_normalization_65[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_23 (DepthwiseC (None, 14, 14, 1056) 26400 swish_65[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_66 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_23[0][0] \n__________________________________________________________________________________________________\nswish_66 (Swish) (None, 14, 14, 1056) 0 batch_normalization_66[0][0] \n__________________________________________________________________________________________________\nlambda_23 (Lambda) (None, 1, 1, 1056) 0 swish_66[0][0] \n__________________________________________________________________________________________________\nconv2d_88 (Conv2D) (None, 1, 1, 44) 46508 lambda_23[0][0] \n__________________________________________________________________________________________________\nswish_67 (Swish) (None, 1, 1, 44) 0 conv2d_88[0][0] \n__________________________________________________________________________________________________\nconv2d_89 (Conv2D) (None, 1, 1, 1056) 47520 swish_67[0][0] \n__________________________________________________________________________________________________\nactivation_23 (Activation) (None, 1, 1, 1056) 0 conv2d_89[0][0] \n__________________________________________________________________________________________________\nmultiply_23 (Multiply) (None, 14, 14, 1056) 0 activation_23[0][0] \n swish_66[0][0] \n__________________________________________________________________________________________________\nconv2d_90 (Conv2D) (None, 14, 14, 176) 185856 multiply_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_67 (BatchNo (None, 14, 14, 176) 704 conv2d_90[0][0] \n__________________________________________________________________________________________________\ndrop_connect_18 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_67[0][0] \n__________________________________________________________________________________________________\nadd_18 (Add) (None, 14, 14, 176) 0 drop_connect_18[0][0] \n add_17[0][0] \n__________________________________________________________________________________________________\nconv2d_91 (Conv2D) (None, 14, 14, 1056) 185856 add_18[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_68 (BatchNo (None, 14, 14, 1056) 4224 conv2d_91[0][0] \n__________________________________________________________________________________________________\nswish_68 (Swish) (None, 14, 14, 1056) 0 batch_normalization_68[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_24 (DepthwiseC (None, 14, 14, 1056) 26400 swish_68[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_69 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_24[0][0] \n__________________________________________________________________________________________________\nswish_69 (Swish) (None, 14, 14, 1056) 0 batch_normalization_69[0][0] \n__________________________________________________________________________________________________\nlambda_24 (Lambda) (None, 1, 1, 1056) 0 swish_69[0][0] \n__________________________________________________________________________________________________\nconv2d_92 (Conv2D) (None, 1, 1, 44) 46508 lambda_24[0][0] \n__________________________________________________________________________________________________\nswish_70 (Swish) (None, 1, 1, 44) 0 conv2d_92[0][0] \n__________________________________________________________________________________________________\nconv2d_93 (Conv2D) (None, 1, 1, 1056) 47520 swish_70[0][0] \n__________________________________________________________________________________________________\nactivation_24 (Activation) (None, 1, 1, 1056) 0 conv2d_93[0][0] \n__________________________________________________________________________________________________\nmultiply_24 (Multiply) (None, 14, 14, 1056) 0 activation_24[0][0] \n swish_69[0][0] \n__________________________________________________________________________________________________\nconv2d_94 (Conv2D) (None, 14, 14, 176) 185856 multiply_24[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_70 (BatchNo (None, 14, 14, 176) 704 conv2d_94[0][0] \n__________________________________________________________________________________________________\ndrop_connect_19 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_70[0][0] \n__________________________________________________________________________________________________\nadd_19 (Add) (None, 14, 14, 176) 0 drop_connect_19[0][0] \n add_18[0][0] \n__________________________________________________________________________________________________\nconv2d_95 (Conv2D) (None, 14, 14, 1056) 185856 add_19[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_71 (BatchNo (None, 14, 14, 1056) 4224 conv2d_95[0][0] \n__________________________________________________________________________________________________\nswish_71 (Swish) (None, 14, 14, 1056) 0 batch_normalization_71[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_25 (DepthwiseC (None, 14, 14, 1056) 26400 swish_71[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_72 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_25[0][0] \n__________________________________________________________________________________________________\nswish_72 (Swish) (None, 14, 14, 1056) 0 batch_normalization_72[0][0] \n__________________________________________________________________________________________________\nlambda_25 (Lambda) (None, 1, 1, 1056) 0 swish_72[0][0] \n__________________________________________________________________________________________________\nconv2d_96 (Conv2D) (None, 1, 1, 44) 46508 lambda_25[0][0] \n__________________________________________________________________________________________________\nswish_73 (Swish) (None, 1, 1, 44) 0 conv2d_96[0][0] \n__________________________________________________________________________________________________\nconv2d_97 (Conv2D) (None, 1, 1, 1056) 47520 swish_73[0][0] \n__________________________________________________________________________________________________\nactivation_25 (Activation) (None, 1, 1, 1056) 0 conv2d_97[0][0] \n__________________________________________________________________________________________________\nmultiply_25 (Multiply) (None, 14, 14, 1056) 0 activation_25[0][0] \n swish_72[0][0] \n__________________________________________________________________________________________________\nconv2d_98 (Conv2D) (None, 14, 14, 176) 185856 multiply_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_73 (BatchNo (None, 14, 14, 176) 704 conv2d_98[0][0] \n__________________________________________________________________________________________________\ndrop_connect_20 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_73[0][0] \n__________________________________________________________________________________________________\nadd_20 (Add) (None, 14, 14, 176) 0 drop_connect_20[0][0] \n add_19[0][0] \n__________________________________________________________________________________________________\nconv2d_99 (Conv2D) (None, 14, 14, 1056) 185856 add_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_74 (BatchNo (None, 14, 14, 1056) 4224 conv2d_99[0][0] \n__________________________________________________________________________________________________\nswish_74 (Swish) (None, 14, 14, 1056) 0 batch_normalization_74[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_26 (DepthwiseC (None, 14, 14, 1056) 26400 swish_74[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_75 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_26[0][0] \n__________________________________________________________________________________________________\nswish_75 (Swish) (None, 14, 14, 1056) 0 batch_normalization_75[0][0] \n__________________________________________________________________________________________________\nlambda_26 (Lambda) (None, 1, 1, 1056) 0 swish_75[0][0] \n__________________________________________________________________________________________________\nconv2d_100 (Conv2D) (None, 1, 1, 44) 46508 lambda_26[0][0] \n__________________________________________________________________________________________________\nswish_76 (Swish) (None, 1, 1, 44) 0 conv2d_100[0][0] \n__________________________________________________________________________________________________\nconv2d_101 (Conv2D) (None, 1, 1, 1056) 47520 swish_76[0][0] \n__________________________________________________________________________________________________\nactivation_26 (Activation) (None, 1, 1, 1056) 0 conv2d_101[0][0] \n__________________________________________________________________________________________________\nmultiply_26 (Multiply) (None, 14, 14, 1056) 0 activation_26[0][0] \n swish_75[0][0] \n__________________________________________________________________________________________________\nconv2d_102 (Conv2D) (None, 14, 14, 176) 185856 multiply_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_76 (BatchNo (None, 14, 14, 176) 704 conv2d_102[0][0] \n__________________________________________________________________________________________________\ndrop_connect_21 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_76[0][0] \n__________________________________________________________________________________________________\nadd_21 (Add) (None, 14, 14, 176) 0 drop_connect_21[0][0] \n add_20[0][0] \n__________________________________________________________________________________________________\nconv2d_103 (Conv2D) (None, 14, 14, 1056) 185856 add_21[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_77 (BatchNo (None, 14, 14, 1056) 4224 conv2d_103[0][0] \n__________________________________________________________________________________________________\nswish_77 (Swish) (None, 14, 14, 1056) 0 batch_normalization_77[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_27 (DepthwiseC (None, 14, 14, 1056) 26400 swish_77[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_78 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_27[0][0] \n__________________________________________________________________________________________________\nswish_78 (Swish) (None, 14, 14, 1056) 0 batch_normalization_78[0][0] \n__________________________________________________________________________________________________\nlambda_27 (Lambda) (None, 1, 1, 1056) 0 swish_78[0][0] \n__________________________________________________________________________________________________\nconv2d_104 (Conv2D) (None, 1, 1, 44) 46508 lambda_27[0][0] \n__________________________________________________________________________________________________\nswish_79 (Swish) (None, 1, 1, 44) 0 conv2d_104[0][0] \n__________________________________________________________________________________________________\nconv2d_105 (Conv2D) (None, 1, 1, 1056) 47520 swish_79[0][0] \n__________________________________________________________________________________________________\nactivation_27 (Activation) (None, 1, 1, 1056) 0 conv2d_105[0][0] \n__________________________________________________________________________________________________\nmultiply_27 (Multiply) (None, 14, 14, 1056) 0 activation_27[0][0] \n swish_78[0][0] \n__________________________________________________________________________________________________\nconv2d_106 (Conv2D) (None, 14, 14, 176) 185856 multiply_27[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_79 (BatchNo (None, 14, 14, 176) 704 conv2d_106[0][0] \n__________________________________________________________________________________________________\ndrop_connect_22 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_79[0][0] \n__________________________________________________________________________________________________\nadd_22 (Add) (None, 14, 14, 176) 0 drop_connect_22[0][0] \n add_21[0][0] \n__________________________________________________________________________________________________\nconv2d_107 (Conv2D) (None, 14, 14, 1056) 185856 add_22[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_80 (BatchNo (None, 14, 14, 1056) 4224 conv2d_107[0][0] \n__________________________________________________________________________________________________\nswish_80 (Swish) (None, 14, 14, 1056) 0 batch_normalization_80[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_28 (DepthwiseC (None, 7, 7, 1056) 26400 swish_80[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_81 (BatchNo (None, 7, 7, 1056) 4224 depthwise_conv2d_28[0][0] \n__________________________________________________________________________________________________\nswish_81 (Swish) (None, 7, 7, 1056) 0 batch_normalization_81[0][0] \n__________________________________________________________________________________________________\nlambda_28 (Lambda) (None, 1, 1, 1056) 0 swish_81[0][0] \n__________________________________________________________________________________________________\nconv2d_108 (Conv2D) (None, 1, 1, 44) 46508 lambda_28[0][0] \n__________________________________________________________________________________________________\nswish_82 (Swish) (None, 1, 1, 44) 0 conv2d_108[0][0] \n__________________________________________________________________________________________________\nconv2d_109 (Conv2D) (None, 1, 1, 1056) 47520 swish_82[0][0] \n__________________________________________________________________________________________________\nactivation_28 (Activation) (None, 1, 1, 1056) 0 conv2d_109[0][0] \n__________________________________________________________________________________________________\nmultiply_28 (Multiply) (None, 7, 7, 1056) 0 activation_28[0][0] \n swish_81[0][0] \n__________________________________________________________________________________________________\nconv2d_110 (Conv2D) (None, 7, 7, 304) 321024 multiply_28[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_82 (BatchNo (None, 7, 7, 304) 1216 conv2d_110[0][0] \n__________________________________________________________________________________________________\nconv2d_111 (Conv2D) (None, 7, 7, 1824) 554496 batch_normalization_82[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_83 (BatchNo (None, 7, 7, 1824) 7296 conv2d_111[0][0] \n__________________________________________________________________________________________________\nswish_83 (Swish) (None, 7, 7, 1824) 0 batch_normalization_83[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_29 (DepthwiseC (None, 7, 7, 1824) 45600 swish_83[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_84 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_29[0][0] \n__________________________________________________________________________________________________\nswish_84 (Swish) (None, 7, 7, 1824) 0 batch_normalization_84[0][0] \n__________________________________________________________________________________________________\nlambda_29 (Lambda) (None, 1, 1, 1824) 0 swish_84[0][0] \n__________________________________________________________________________________________________\nconv2d_112 (Conv2D) (None, 1, 1, 76) 138700 lambda_29[0][0] \n__________________________________________________________________________________________________\nswish_85 (Swish) (None, 1, 1, 76) 0 conv2d_112[0][0] \n__________________________________________________________________________________________________\nconv2d_113 (Conv2D) (None, 1, 1, 1824) 140448 swish_85[0][0] \n__________________________________________________________________________________________________\nactivation_29 (Activation) (None, 1, 1, 1824) 0 conv2d_113[0][0] \n__________________________________________________________________________________________________\nmultiply_29 (Multiply) (None, 7, 7, 1824) 0 activation_29[0][0] \n swish_84[0][0] \n__________________________________________________________________________________________________\nconv2d_114 (Conv2D) (None, 7, 7, 304) 554496 multiply_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_85 (BatchNo (None, 7, 7, 304) 1216 conv2d_114[0][0] \n__________________________________________________________________________________________________\ndrop_connect_23 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_85[0][0] \n__________________________________________________________________________________________________\nadd_23 (Add) (None, 7, 7, 304) 0 drop_connect_23[0][0] \n batch_normalization_82[0][0] \n__________________________________________________________________________________________________\nconv2d_115 (Conv2D) (None, 7, 7, 1824) 554496 add_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_86 (BatchNo (None, 7, 7, 1824) 7296 conv2d_115[0][0] \n__________________________________________________________________________________________________\nswish_86 (Swish) (None, 7, 7, 1824) 0 batch_normalization_86[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_30 (DepthwiseC (None, 7, 7, 1824) 45600 swish_86[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_87 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_30[0][0] \n__________________________________________________________________________________________________\nswish_87 (Swish) (None, 7, 7, 1824) 0 batch_normalization_87[0][0] \n__________________________________________________________________________________________________\nlambda_30 (Lambda) (None, 1, 1, 1824) 0 swish_87[0][0] \n__________________________________________________________________________________________________\nconv2d_116 (Conv2D) (None, 1, 1, 76) 138700 lambda_30[0][0] \n__________________________________________________________________________________________________\nswish_88 (Swish) (None, 1, 1, 76) 0 conv2d_116[0][0] \n__________________________________________________________________________________________________\nconv2d_117 (Conv2D) (None, 1, 1, 1824) 140448 swish_88[0][0] \n__________________________________________________________________________________________________\nactivation_30 (Activation) (None, 1, 1, 1824) 0 conv2d_117[0][0] \n__________________________________________________________________________________________________\nmultiply_30 (Multiply) (None, 7, 7, 1824) 0 activation_30[0][0] \n swish_87[0][0] \n__________________________________________________________________________________________________\nconv2d_118 (Conv2D) (None, 7, 7, 304) 554496 multiply_30[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_88 (BatchNo (None, 7, 7, 304) 1216 conv2d_118[0][0] \n__________________________________________________________________________________________________\ndrop_connect_24 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_88[0][0] \n__________________________________________________________________________________________________\nadd_24 (Add) (None, 7, 7, 304) 0 drop_connect_24[0][0] \n add_23[0][0] \n__________________________________________________________________________________________________\nconv2d_119 (Conv2D) (None, 7, 7, 1824) 554496 add_24[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_89 (BatchNo (None, 7, 7, 1824) 7296 conv2d_119[0][0] \n__________________________________________________________________________________________________\nswish_89 (Swish) (None, 7, 7, 1824) 0 batch_normalization_89[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_31 (DepthwiseC (None, 7, 7, 1824) 45600 swish_89[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_90 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_31[0][0] \n__________________________________________________________________________________________________\nswish_90 (Swish) (None, 7, 7, 1824) 0 batch_normalization_90[0][0] \n__________________________________________________________________________________________________\nlambda_31 (Lambda) (None, 1, 1, 1824) 0 swish_90[0][0] \n__________________________________________________________________________________________________\nconv2d_120 (Conv2D) (None, 1, 1, 76) 138700 lambda_31[0][0] \n__________________________________________________________________________________________________\nswish_91 (Swish) (None, 1, 1, 76) 0 conv2d_120[0][0] \n__________________________________________________________________________________________________\nconv2d_121 (Conv2D) (None, 1, 1, 1824) 140448 swish_91[0][0] \n__________________________________________________________________________________________________\nactivation_31 (Activation) (None, 1, 1, 1824) 0 conv2d_121[0][0] \n__________________________________________________________________________________________________\nmultiply_31 (Multiply) (None, 7, 7, 1824) 0 activation_31[0][0] \n swish_90[0][0] \n__________________________________________________________________________________________________\nconv2d_122 (Conv2D) (None, 7, 7, 304) 554496 multiply_31[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_91 (BatchNo (None, 7, 7, 304) 1216 conv2d_122[0][0] \n__________________________________________________________________________________________________\ndrop_connect_25 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_91[0][0] \n__________________________________________________________________________________________________\nadd_25 (Add) (None, 7, 7, 304) 0 drop_connect_25[0][0] \n add_24[0][0] \n__________________________________________________________________________________________________\nconv2d_123 (Conv2D) (None, 7, 7, 1824) 554496 add_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_92 (BatchNo (None, 7, 7, 1824) 7296 conv2d_123[0][0] \n__________________________________________________________________________________________________\nswish_92 (Swish) (None, 7, 7, 1824) 0 batch_normalization_92[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_32 (DepthwiseC (None, 7, 7, 1824) 45600 swish_92[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_93 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_32[0][0] \n__________________________________________________________________________________________________\nswish_93 (Swish) (None, 7, 7, 1824) 0 batch_normalization_93[0][0] \n__________________________________________________________________________________________________\nlambda_32 (Lambda) (None, 1, 1, 1824) 0 swish_93[0][0] \n__________________________________________________________________________________________________\nconv2d_124 (Conv2D) (None, 1, 1, 76) 138700 lambda_32[0][0] \n__________________________________________________________________________________________________\nswish_94 (Swish) (None, 1, 1, 76) 0 conv2d_124[0][0] \n__________________________________________________________________________________________________\nconv2d_125 (Conv2D) (None, 1, 1, 1824) 140448 swish_94[0][0] \n__________________________________________________________________________________________________\nactivation_32 (Activation) (None, 1, 1, 1824) 0 conv2d_125[0][0] \n__________________________________________________________________________________________________\nmultiply_32 (Multiply) (None, 7, 7, 1824) 0 activation_32[0][0] \n swish_93[0][0] \n__________________________________________________________________________________________________\nconv2d_126 (Conv2D) (None, 7, 7, 304) 554496 multiply_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_94 (BatchNo (None, 7, 7, 304) 1216 conv2d_126[0][0] \n__________________________________________________________________________________________________\ndrop_connect_26 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_94[0][0] \n__________________________________________________________________________________________________\nadd_26 (Add) (None, 7, 7, 304) 0 drop_connect_26[0][0] \n add_25[0][0] \n__________________________________________________________________________________________________\nconv2d_127 (Conv2D) (None, 7, 7, 1824) 554496 add_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_95 (BatchNo (None, 7, 7, 1824) 7296 conv2d_127[0][0] \n__________________________________________________________________________________________________\nswish_95 (Swish) (None, 7, 7, 1824) 0 batch_normalization_95[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_33 (DepthwiseC (None, 7, 7, 1824) 45600 swish_95[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_96 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_33[0][0] \n__________________________________________________________________________________________________\nswish_96 (Swish) (None, 7, 7, 1824) 0 batch_normalization_96[0][0] \n__________________________________________________________________________________________________\nlambda_33 (Lambda) (None, 1, 1, 1824) 0 swish_96[0][0] \n__________________________________________________________________________________________________\nconv2d_128 (Conv2D) (None, 1, 1, 76) 138700 lambda_33[0][0] \n__________________________________________________________________________________________________\nswish_97 (Swish) (None, 1, 1, 76) 0 conv2d_128[0][0] \n__________________________________________________________________________________________________\nconv2d_129 (Conv2D) (None, 1, 1, 1824) 140448 swish_97[0][0] \n__________________________________________________________________________________________________\nactivation_33 (Activation) (None, 1, 1, 1824) 0 conv2d_129[0][0] \n__________________________________________________________________________________________________\nmultiply_33 (Multiply) (None, 7, 7, 1824) 0 activation_33[0][0] \n swish_96[0][0] \n__________________________________________________________________________________________________\nconv2d_130 (Conv2D) (None, 7, 7, 304) 554496 multiply_33[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_97 (BatchNo (None, 7, 7, 304) 1216 conv2d_130[0][0] \n__________________________________________________________________________________________________\ndrop_connect_27 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_97[0][0] \n__________________________________________________________________________________________________\nadd_27 (Add) (None, 7, 7, 304) 0 drop_connect_27[0][0] \n add_26[0][0] \n__________________________________________________________________________________________________\nconv2d_131 (Conv2D) (None, 7, 7, 1824) 554496 add_27[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_98 (BatchNo (None, 7, 7, 1824) 7296 conv2d_131[0][0] \n__________________________________________________________________________________________________\nswish_98 (Swish) (None, 7, 7, 1824) 0 batch_normalization_98[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_34 (DepthwiseC (None, 7, 7, 1824) 45600 swish_98[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_99 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_34[0][0] \n__________________________________________________________________________________________________\nswish_99 (Swish) (None, 7, 7, 1824) 0 batch_normalization_99[0][0] \n__________________________________________________________________________________________________\nlambda_34 (Lambda) (None, 1, 1, 1824) 0 swish_99[0][0] \n__________________________________________________________________________________________________\nconv2d_132 (Conv2D) (None, 1, 1, 76) 138700 lambda_34[0][0] \n__________________________________________________________________________________________________\nswish_100 (Swish) (None, 1, 1, 76) 0 conv2d_132[0][0] \n__________________________________________________________________________________________________\nconv2d_133 (Conv2D) (None, 1, 1, 1824) 140448 swish_100[0][0] \n__________________________________________________________________________________________________\nactivation_34 (Activation) (None, 1, 1, 1824) 0 conv2d_133[0][0] \n__________________________________________________________________________________________________\nmultiply_34 (Multiply) (None, 7, 7, 1824) 0 activation_34[0][0] \n swish_99[0][0] \n__________________________________________________________________________________________________\nconv2d_134 (Conv2D) (None, 7, 7, 304) 554496 multiply_34[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_100 (BatchN (None, 7, 7, 304) 1216 conv2d_134[0][0] \n__________________________________________________________________________________________________\ndrop_connect_28 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_100[0][0] \n__________________________________________________________________________________________________\nadd_28 (Add) (None, 7, 7, 304) 0 drop_connect_28[0][0] \n add_27[0][0] \n__________________________________________________________________________________________________\nconv2d_135 (Conv2D) (None, 7, 7, 1824) 554496 add_28[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_101 (BatchN (None, 7, 7, 1824) 7296 conv2d_135[0][0] \n__________________________________________________________________________________________________\nswish_101 (Swish) (None, 7, 7, 1824) 0 batch_normalization_101[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_35 (DepthwiseC (None, 7, 7, 1824) 45600 swish_101[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_102 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_35[0][0] \n__________________________________________________________________________________________________\nswish_102 (Swish) (None, 7, 7, 1824) 0 batch_normalization_102[0][0] \n__________________________________________________________________________________________________\nlambda_35 (Lambda) (None, 1, 1, 1824) 0 swish_102[0][0] \n__________________________________________________________________________________________________\nconv2d_136 (Conv2D) (None, 1, 1, 76) 138700 lambda_35[0][0] \n__________________________________________________________________________________________________\nswish_103 (Swish) (None, 1, 1, 76) 0 conv2d_136[0][0] \n__________________________________________________________________________________________________\nconv2d_137 (Conv2D) (None, 1, 1, 1824) 140448 swish_103[0][0] \n__________________________________________________________________________________________________\nactivation_35 (Activation) (None, 1, 1, 1824) 0 conv2d_137[0][0] \n__________________________________________________________________________________________________\nmultiply_35 (Multiply) (None, 7, 7, 1824) 0 activation_35[0][0] \n swish_102[0][0] \n__________________________________________________________________________________________________\nconv2d_138 (Conv2D) (None, 7, 7, 304) 554496 multiply_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_103 (BatchN (None, 7, 7, 304) 1216 conv2d_138[0][0] \n__________________________________________________________________________________________________\ndrop_connect_29 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_103[0][0] \n__________________________________________________________________________________________________\nadd_29 (Add) (None, 7, 7, 304) 0 drop_connect_29[0][0] \n add_28[0][0] \n__________________________________________________________________________________________________\nconv2d_139 (Conv2D) (None, 7, 7, 1824) 554496 add_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_104 (BatchN (None, 7, 7, 1824) 7296 conv2d_139[0][0] \n__________________________________________________________________________________________________\nswish_104 (Swish) (None, 7, 7, 1824) 0 batch_normalization_104[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_36 (DepthwiseC (None, 7, 7, 1824) 45600 swish_104[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_105 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_36[0][0] \n__________________________________________________________________________________________________\nswish_105 (Swish) (None, 7, 7, 1824) 0 batch_normalization_105[0][0] \n__________________________________________________________________________________________________\nlambda_36 (Lambda) (None, 1, 1, 1824) 0 swish_105[0][0] \n__________________________________________________________________________________________________\nconv2d_140 (Conv2D) (None, 1, 1, 76) 138700 lambda_36[0][0] \n__________________________________________________________________________________________________\nswish_106 (Swish) (None, 1, 1, 76) 0 conv2d_140[0][0] \n__________________________________________________________________________________________________\nconv2d_141 (Conv2D) (None, 1, 1, 1824) 140448 swish_106[0][0] \n__________________________________________________________________________________________________\nactivation_36 (Activation) (None, 1, 1, 1824) 0 conv2d_141[0][0] \n__________________________________________________________________________________________________\nmultiply_36 (Multiply) (None, 7, 7, 1824) 0 activation_36[0][0] \n swish_105[0][0] \n__________________________________________________________________________________________________\nconv2d_142 (Conv2D) (None, 7, 7, 304) 554496 multiply_36[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_106 (BatchN (None, 7, 7, 304) 1216 conv2d_142[0][0] \n__________________________________________________________________________________________________\ndrop_connect_30 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_106[0][0] \n__________________________________________________________________________________________________\nadd_30 (Add) (None, 7, 7, 304) 0 drop_connect_30[0][0] \n add_29[0][0] \n__________________________________________________________________________________________________\nconv2d_143 (Conv2D) (None, 7, 7, 1824) 554496 add_30[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_107 (BatchN (None, 7, 7, 1824) 7296 conv2d_143[0][0] \n__________________________________________________________________________________________________\nswish_107 (Swish) (None, 7, 7, 1824) 0 batch_normalization_107[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_37 (DepthwiseC (None, 7, 7, 1824) 16416 swish_107[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_108 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_37[0][0] \n__________________________________________________________________________________________________\nswish_108 (Swish) (None, 7, 7, 1824) 0 batch_normalization_108[0][0] \n__________________________________________________________________________________________________\nlambda_37 (Lambda) (None, 1, 1, 1824) 0 swish_108[0][0] \n__________________________________________________________________________________________________\nconv2d_144 (Conv2D) (None, 1, 1, 76) 138700 lambda_37[0][0] \n__________________________________________________________________________________________________\nswish_109 (Swish) (None, 1, 1, 76) 0 conv2d_144[0][0] \n__________________________________________________________________________________________________\nconv2d_145 (Conv2D) (None, 1, 1, 1824) 140448 swish_109[0][0] \n__________________________________________________________________________________________________\nactivation_37 (Activation) (None, 1, 1, 1824) 0 conv2d_145[0][0] \n__________________________________________________________________________________________________\nmultiply_37 (Multiply) (None, 7, 7, 1824) 0 activation_37[0][0] \n swish_108[0][0] \n__________________________________________________________________________________________________\nconv2d_146 (Conv2D) (None, 7, 7, 512) 933888 multiply_37[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_109 (BatchN (None, 7, 7, 512) 2048 conv2d_146[0][0] \n__________________________________________________________________________________________________\nconv2d_147 (Conv2D) (None, 7, 7, 3072) 1572864 batch_normalization_109[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_110 (BatchN (None, 7, 7, 3072) 12288 conv2d_147[0][0] \n__________________________________________________________________________________________________\nswish_110 (Swish) (None, 7, 7, 3072) 0 batch_normalization_110[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_38 (DepthwiseC (None, 7, 7, 3072) 27648 swish_110[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_111 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_38[0][0] \n__________________________________________________________________________________________________\nswish_111 (Swish) (None, 7, 7, 3072) 0 batch_normalization_111[0][0] \n__________________________________________________________________________________________________\nlambda_38 (Lambda) (None, 1, 1, 3072) 0 swish_111[0][0] \n__________________________________________________________________________________________________\nconv2d_148 (Conv2D) (None, 1, 1, 128) 393344 lambda_38[0][0] \n__________________________________________________________________________________________________\nswish_112 (Swish) (None, 1, 1, 128) 0 conv2d_148[0][0] \n__________________________________________________________________________________________________\nconv2d_149 (Conv2D) (None, 1, 1, 3072) 396288 swish_112[0][0] \n__________________________________________________________________________________________________\nactivation_38 (Activation) (None, 1, 1, 3072) 0 conv2d_149[0][0] \n__________________________________________________________________________________________________\nmultiply_38 (Multiply) (None, 7, 7, 3072) 0 activation_38[0][0] \n swish_111[0][0] \n__________________________________________________________________________________________________\nconv2d_150 (Conv2D) (None, 7, 7, 512) 1572864 multiply_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_112 (BatchN (None, 7, 7, 512) 2048 conv2d_150[0][0] \n__________________________________________________________________________________________________\ndrop_connect_31 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_112[0][0] \n__________________________________________________________________________________________________\nadd_31 (Add) (None, 7, 7, 512) 0 drop_connect_31[0][0] \n batch_normalization_109[0][0] \n__________________________________________________________________________________________________\nconv2d_151 (Conv2D) (None, 7, 7, 3072) 1572864 add_31[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_113 (BatchN (None, 7, 7, 3072) 12288 conv2d_151[0][0] \n__________________________________________________________________________________________________\nswish_113 (Swish) (None, 7, 7, 3072) 0 batch_normalization_113[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_39 (DepthwiseC (None, 7, 7, 3072) 27648 swish_113[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_114 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_39[0][0] \n__________________________________________________________________________________________________\nswish_114 (Swish) (None, 7, 7, 3072) 0 batch_normalization_114[0][0] \n__________________________________________________________________________________________________\nlambda_39 (Lambda) (None, 1, 1, 3072) 0 swish_114[0][0] \n__________________________________________________________________________________________________\nconv2d_152 (Conv2D) (None, 1, 1, 128) 393344 lambda_39[0][0] \n__________________________________________________________________________________________________\nswish_115 (Swish) (None, 1, 1, 128) 0 conv2d_152[0][0] \n__________________________________________________________________________________________________\nconv2d_153 (Conv2D) (None, 1, 1, 3072) 396288 swish_115[0][0] \n__________________________________________________________________________________________________\nactivation_39 (Activation) (None, 1, 1, 3072) 0 conv2d_153[0][0] \n__________________________________________________________________________________________________\nmultiply_39 (Multiply) (None, 7, 7, 3072) 0 activation_39[0][0] \n swish_114[0][0] \n__________________________________________________________________________________________________\nconv2d_154 (Conv2D) (None, 7, 7, 512) 1572864 multiply_39[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_115 (BatchN (None, 7, 7, 512) 2048 conv2d_154[0][0] \n__________________________________________________________________________________________________\ndrop_connect_32 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_115[0][0] \n__________________________________________________________________________________________________\nadd_32 (Add) (None, 7, 7, 512) 0 drop_connect_32[0][0] \n add_31[0][0] \n__________________________________________________________________________________________________\nconv2d_155 (Conv2D) (None, 7, 7, 2048) 1048576 add_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_116 (BatchN (None, 7, 7, 2048) 8192 conv2d_155[0][0] \n__________________________________________________________________________________________________\nswish_116 (Swish) (None, 7, 7, 2048) 0 batch_normalization_116[0][0] \n__________________________________________________________________________________________________\nglobal_average_pooling2d_1 (Glo (None, 2048) 0 swish_116[0][0] \n__________________________________________________________________________________________________\nfinal_output (Dense) (None, 1) 2049 global_average_pooling2d_1[0][0] \n==================================================================================================\nTotal params: 28,515,569\nTrainable params: 2,049\nNon-trainable params: 28,513,520\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"STEP_SIZE_TRAIN = train_generator.n//train_generator.batch_size\nSTEP_SIZE_VALID = valid_generator.n//valid_generator.batch_size\n\nhistory_warmup = model.fit_generator(generator=train_generator,\n steps_per_epoch=STEP_SIZE_TRAIN,\n validation_data=valid_generator,\n validation_steps=STEP_SIZE_VALID,\n epochs=WARMUP_EPOCHS,\n callbacks=callback_list,\n verbose=2).history","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:25:41.4782Z","iopub.execute_input":"2024-05-03T16:25:41.478585Z","iopub.status.idle":"2024-05-03T16:29:18.880056Z","shell.execute_reply.started":"2024-05-03T16:25:41.47852Z","shell.execute_reply":"2024-05-03T16:29:18.87925Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Epoch 1/5\n - 55s - loss: 2.0629 - acc: 0.4203 - val_loss: 1.3805 - val_acc: 0.2741\nEpoch 2/5\n - 41s - loss: 1.2143 - acc: 0.4299 - val_loss: 1.9613 - val_acc: 0.3829\nEpoch 3/5\n - 40s - loss: 0.9693 - acc: 0.4634 - val_loss: 1.5661 - val_acc: 0.3143\nEpoch 4/5\n - 41s - loss: 0.8568 - acc: 0.4740 - val_loss: 1.7323 - val_acc: 0.4371\nEpoch 5/5\n - 40s - loss: 0.8789 - acc: 0.4654 - val_loss: 1.7358 - val_acc: 0.3671\n","output_type":"stream"}]},{"cell_type":"code","source":"for layer in model.layers:\n layer.trainable = True\n\nes = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)\ncosine_lr_2nd = WarmUpCosineDecayScheduler(learning_rate_base=LEARNING_RATE,\n total_steps=TOTAL_STEPS_2nd,\n warmup_learning_rate=0.0,\n warmup_steps=WARMUP_STEPS_2nd,\n hold_base_rate_steps=(3 * STEP_SIZE))\n\ncallback_list = [es, cosine_lr_2nd]\noptimizer = optimizers.Adam(lr=LEARNING_RATE)\nmodel.compile(optimizer=optimizer, loss='mean_squared_error', metrics=metric_list)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:29:18.88157Z","iopub.execute_input":"2024-05-03T16:29:18.881846Z","iopub.status.idle":"2024-05-03T16:29:19.158086Z","shell.execute_reply.started":"2024-05-03T16:29:18.881793Z","shell.execute_reply":"2024-05-03T16:29:19.157212Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_1 (InputLayer) (None, 224, 224, 3) 0 \n__________________________________________________________________________________________________\nconv2d_1 (Conv2D) (None, 112, 112, 48) 1296 input_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_1 (BatchNor (None, 112, 112, 48) 192 conv2d_1[0][0] \n__________________________________________________________________________________________________\nswish_1 (Swish) (None, 112, 112, 48) 0 batch_normalization_1[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_1 (DepthwiseCo (None, 112, 112, 48) 432 swish_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_2 (BatchNor (None, 112, 112, 48) 192 depthwise_conv2d_1[0][0] \n__________________________________________________________________________________________________\nswish_2 (Swish) (None, 112, 112, 48) 0 batch_normalization_2[0][0] \n__________________________________________________________________________________________________\nlambda_1 (Lambda) (None, 1, 1, 48) 0 swish_2[0][0] \n__________________________________________________________________________________________________\nconv2d_2 (Conv2D) (None, 1, 1, 12) 588 lambda_1[0][0] \n__________________________________________________________________________________________________\nswish_3 (Swish) (None, 1, 1, 12) 0 conv2d_2[0][0] \n__________________________________________________________________________________________________\nconv2d_3 (Conv2D) (None, 1, 1, 48) 624 swish_3[0][0] \n__________________________________________________________________________________________________\nactivation_1 (Activation) (None, 1, 1, 48) 0 conv2d_3[0][0] \n__________________________________________________________________________________________________\nmultiply_1 (Multiply) (None, 112, 112, 48) 0 activation_1[0][0] \n swish_2[0][0] \n__________________________________________________________________________________________________\nconv2d_4 (Conv2D) (None, 112, 112, 24) 1152 multiply_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_3 (BatchNor (None, 112, 112, 24) 96 conv2d_4[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_2 (DepthwiseCo (None, 112, 112, 24) 216 batch_normalization_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_4 (BatchNor (None, 112, 112, 24) 96 depthwise_conv2d_2[0][0] \n__________________________________________________________________________________________________\nswish_4 (Swish) (None, 112, 112, 24) 0 batch_normalization_4[0][0] \n__________________________________________________________________________________________________\nlambda_2 (Lambda) (None, 1, 1, 24) 0 swish_4[0][0] \n__________________________________________________________________________________________________\nconv2d_5 (Conv2D) (None, 1, 1, 6) 150 lambda_2[0][0] \n__________________________________________________________________________________________________\nswish_5 (Swish) (None, 1, 1, 6) 0 conv2d_5[0][0] \n__________________________________________________________________________________________________\nconv2d_6 (Conv2D) (None, 1, 1, 24) 168 swish_5[0][0] \n__________________________________________________________________________________________________\nactivation_2 (Activation) (None, 1, 1, 24) 0 conv2d_6[0][0] \n__________________________________________________________________________________________________\nmultiply_2 (Multiply) (None, 112, 112, 24) 0 activation_2[0][0] \n swish_4[0][0] \n__________________________________________________________________________________________________\nconv2d_7 (Conv2D) (None, 112, 112, 24) 576 multiply_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_5 (BatchNor (None, 112, 112, 24) 96 conv2d_7[0][0] \n__________________________________________________________________________________________________\ndrop_connect_1 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_5[0][0] \n__________________________________________________________________________________________________\nadd_1 (Add) (None, 112, 112, 24) 0 drop_connect_1[0][0] \n batch_normalization_3[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_3 (DepthwiseCo (None, 112, 112, 24) 216 add_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_6 (BatchNor (None, 112, 112, 24) 96 depthwise_conv2d_3[0][0] \n__________________________________________________________________________________________________\nswish_6 (Swish) (None, 112, 112, 24) 0 batch_normalization_6[0][0] \n__________________________________________________________________________________________________\nlambda_3 (Lambda) (None, 1, 1, 24) 0 swish_6[0][0] \n__________________________________________________________________________________________________\nconv2d_8 (Conv2D) (None, 1, 1, 6) 150 lambda_3[0][0] \n__________________________________________________________________________________________________\nswish_7 (Swish) (None, 1, 1, 6) 0 conv2d_8[0][0] \n__________________________________________________________________________________________________\nconv2d_9 (Conv2D) (None, 1, 1, 24) 168 swish_7[0][0] \n__________________________________________________________________________________________________\nactivation_3 (Activation) (None, 1, 1, 24) 0 conv2d_9[0][0] \n__________________________________________________________________________________________________\nmultiply_3 (Multiply) (None, 112, 112, 24) 0 activation_3[0][0] \n swish_6[0][0] \n__________________________________________________________________________________________________\nconv2d_10 (Conv2D) (None, 112, 112, 24) 576 multiply_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_7 (BatchNor (None, 112, 112, 24) 96 conv2d_10[0][0] \n__________________________________________________________________________________________________\ndrop_connect_2 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_7[0][0] \n__________________________________________________________________________________________________\nadd_2 (Add) (None, 112, 112, 24) 0 drop_connect_2[0][0] \n add_1[0][0] \n__________________________________________________________________________________________________\nconv2d_11 (Conv2D) (None, 112, 112, 144 3456 add_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_8 (BatchNor (None, 112, 112, 144 576 conv2d_11[0][0] \n__________________________________________________________________________________________________\nswish_8 (Swish) (None, 112, 112, 144 0 batch_normalization_8[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_4 (DepthwiseCo (None, 56, 56, 144) 1296 swish_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_9 (BatchNor (None, 56, 56, 144) 576 depthwise_conv2d_4[0][0] \n__________________________________________________________________________________________________\nswish_9 (Swish) (None, 56, 56, 144) 0 batch_normalization_9[0][0] \n__________________________________________________________________________________________________\nlambda_4 (Lambda) (None, 1, 1, 144) 0 swish_9[0][0] \n__________________________________________________________________________________________________\nconv2d_12 (Conv2D) (None, 1, 1, 6) 870 lambda_4[0][0] \n__________________________________________________________________________________________________\nswish_10 (Swish) (None, 1, 1, 6) 0 conv2d_12[0][0] \n__________________________________________________________________________________________________\nconv2d_13 (Conv2D) (None, 1, 1, 144) 1008 swish_10[0][0] \n__________________________________________________________________________________________________\nactivation_4 (Activation) (None, 1, 1, 144) 0 conv2d_13[0][0] \n__________________________________________________________________________________________________\nmultiply_4 (Multiply) (None, 56, 56, 144) 0 activation_4[0][0] \n swish_9[0][0] \n__________________________________________________________________________________________________\nconv2d_14 (Conv2D) (None, 56, 56, 40) 5760 multiply_4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_10 (BatchNo (None, 56, 56, 40) 160 conv2d_14[0][0] \n__________________________________________________________________________________________________\nconv2d_15 (Conv2D) (None, 56, 56, 240) 9600 batch_normalization_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_11 (BatchNo (None, 56, 56, 240) 960 conv2d_15[0][0] \n__________________________________________________________________________________________________\nswish_11 (Swish) (None, 56, 56, 240) 0 batch_normalization_11[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_5 (DepthwiseCo (None, 56, 56, 240) 2160 swish_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_12 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_5[0][0] \n__________________________________________________________________________________________________\nswish_12 (Swish) (None, 56, 56, 240) 0 batch_normalization_12[0][0] \n__________________________________________________________________________________________________\nlambda_5 (Lambda) (None, 1, 1, 240) 0 swish_12[0][0] \n__________________________________________________________________________________________________\nconv2d_16 (Conv2D) (None, 1, 1, 10) 2410 lambda_5[0][0] \n__________________________________________________________________________________________________\nswish_13 (Swish) (None, 1, 1, 10) 0 conv2d_16[0][0] \n__________________________________________________________________________________________________\nconv2d_17 (Conv2D) (None, 1, 1, 240) 2640 swish_13[0][0] \n__________________________________________________________________________________________________\nactivation_5 (Activation) (None, 1, 1, 240) 0 conv2d_17[0][0] \n__________________________________________________________________________________________________\nmultiply_5 (Multiply) (None, 56, 56, 240) 0 activation_5[0][0] \n swish_12[0][0] \n__________________________________________________________________________________________________\nconv2d_18 (Conv2D) (None, 56, 56, 40) 9600 multiply_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_13 (BatchNo (None, 56, 56, 40) 160 conv2d_18[0][0] \n__________________________________________________________________________________________________\ndrop_connect_3 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_13[0][0] \n__________________________________________________________________________________________________\nadd_3 (Add) (None, 56, 56, 40) 0 drop_connect_3[0][0] \n batch_normalization_10[0][0] \n__________________________________________________________________________________________________\nconv2d_19 (Conv2D) (None, 56, 56, 240) 9600 add_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_14 (BatchNo (None, 56, 56, 240) 960 conv2d_19[0][0] \n__________________________________________________________________________________________________\nswish_14 (Swish) (None, 56, 56, 240) 0 batch_normalization_14[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_6 (DepthwiseCo (None, 56, 56, 240) 2160 swish_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_15 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_6[0][0] \n__________________________________________________________________________________________________\nswish_15 (Swish) (None, 56, 56, 240) 0 batch_normalization_15[0][0] \n__________________________________________________________________________________________________\nlambda_6 (Lambda) (None, 1, 1, 240) 0 swish_15[0][0] \n__________________________________________________________________________________________________\nconv2d_20 (Conv2D) (None, 1, 1, 10) 2410 lambda_6[0][0] \n__________________________________________________________________________________________________\nswish_16 (Swish) (None, 1, 1, 10) 0 conv2d_20[0][0] \n__________________________________________________________________________________________________\nconv2d_21 (Conv2D) (None, 1, 1, 240) 2640 swish_16[0][0] \n__________________________________________________________________________________________________\nactivation_6 (Activation) (None, 1, 1, 240) 0 conv2d_21[0][0] \n__________________________________________________________________________________________________\nmultiply_6 (Multiply) (None, 56, 56, 240) 0 activation_6[0][0] \n swish_15[0][0] \n__________________________________________________________________________________________________\nconv2d_22 (Conv2D) (None, 56, 56, 40) 9600 multiply_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_16 (BatchNo (None, 56, 56, 40) 160 conv2d_22[0][0] \n__________________________________________________________________________________________________\ndrop_connect_4 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_16[0][0] \n__________________________________________________________________________________________________\nadd_4 (Add) (None, 56, 56, 40) 0 drop_connect_4[0][0] \n add_3[0][0] \n__________________________________________________________________________________________________\nconv2d_23 (Conv2D) (None, 56, 56, 240) 9600 add_4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_17 (BatchNo (None, 56, 56, 240) 960 conv2d_23[0][0] \n__________________________________________________________________________________________________\nswish_17 (Swish) (None, 56, 56, 240) 0 batch_normalization_17[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_7 (DepthwiseCo (None, 56, 56, 240) 2160 swish_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_18 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_7[0][0] \n__________________________________________________________________________________________________\nswish_18 (Swish) (None, 56, 56, 240) 0 batch_normalization_18[0][0] \n__________________________________________________________________________________________________\nlambda_7 (Lambda) (None, 1, 1, 240) 0 swish_18[0][0] \n__________________________________________________________________________________________________\nconv2d_24 (Conv2D) (None, 1, 1, 10) 2410 lambda_7[0][0] \n__________________________________________________________________________________________________\nswish_19 (Swish) (None, 1, 1, 10) 0 conv2d_24[0][0] \n__________________________________________________________________________________________________\nconv2d_25 (Conv2D) (None, 1, 1, 240) 2640 swish_19[0][0] \n__________________________________________________________________________________________________\nactivation_7 (Activation) (None, 1, 1, 240) 0 conv2d_25[0][0] \n__________________________________________________________________________________________________\nmultiply_7 (Multiply) (None, 56, 56, 240) 0 activation_7[0][0] \n swish_18[0][0] \n__________________________________________________________________________________________________\nconv2d_26 (Conv2D) (None, 56, 56, 40) 9600 multiply_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_19 (BatchNo (None, 56, 56, 40) 160 conv2d_26[0][0] \n__________________________________________________________________________________________________\ndrop_connect_5 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_19[0][0] \n__________________________________________________________________________________________________\nadd_5 (Add) (None, 56, 56, 40) 0 drop_connect_5[0][0] \n add_4[0][0] \n__________________________________________________________________________________________________\nconv2d_27 (Conv2D) (None, 56, 56, 240) 9600 add_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_20 (BatchNo (None, 56, 56, 240) 960 conv2d_27[0][0] \n__________________________________________________________________________________________________\nswish_20 (Swish) (None, 56, 56, 240) 0 batch_normalization_20[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_8 (DepthwiseCo (None, 56, 56, 240) 2160 swish_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_21 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_8[0][0] \n__________________________________________________________________________________________________\nswish_21 (Swish) (None, 56, 56, 240) 0 batch_normalization_21[0][0] \n__________________________________________________________________________________________________\nlambda_8 (Lambda) (None, 1, 1, 240) 0 swish_21[0][0] \n__________________________________________________________________________________________________\nconv2d_28 (Conv2D) (None, 1, 1, 10) 2410 lambda_8[0][0] \n__________________________________________________________________________________________________\nswish_22 (Swish) (None, 1, 1, 10) 0 conv2d_28[0][0] \n__________________________________________________________________________________________________\nconv2d_29 (Conv2D) (None, 1, 1, 240) 2640 swish_22[0][0] \n__________________________________________________________________________________________________\nactivation_8 (Activation) (None, 1, 1, 240) 0 conv2d_29[0][0] \n__________________________________________________________________________________________________\nmultiply_8 (Multiply) (None, 56, 56, 240) 0 activation_8[0][0] \n swish_21[0][0] \n__________________________________________________________________________________________________\nconv2d_30 (Conv2D) (None, 56, 56, 40) 9600 multiply_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_22 (BatchNo (None, 56, 56, 40) 160 conv2d_30[0][0] \n__________________________________________________________________________________________________\ndrop_connect_6 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_22[0][0] \n__________________________________________________________________________________________________\nadd_6 (Add) (None, 56, 56, 40) 0 drop_connect_6[0][0] \n add_5[0][0] \n__________________________________________________________________________________________________\nconv2d_31 (Conv2D) (None, 56, 56, 240) 9600 add_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_23 (BatchNo (None, 56, 56, 240) 960 conv2d_31[0][0] \n__________________________________________________________________________________________________\nswish_23 (Swish) (None, 56, 56, 240) 0 batch_normalization_23[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_9 (DepthwiseCo (None, 28, 28, 240) 6000 swish_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_24 (BatchNo (None, 28, 28, 240) 960 depthwise_conv2d_9[0][0] \n__________________________________________________________________________________________________\nswish_24 (Swish) (None, 28, 28, 240) 0 batch_normalization_24[0][0] \n__________________________________________________________________________________________________\nlambda_9 (Lambda) (None, 1, 1, 240) 0 swish_24[0][0] \n__________________________________________________________________________________________________\nconv2d_32 (Conv2D) (None, 1, 1, 10) 2410 lambda_9[0][0] \n__________________________________________________________________________________________________\nswish_25 (Swish) (None, 1, 1, 10) 0 conv2d_32[0][0] \n__________________________________________________________________________________________________\nconv2d_33 (Conv2D) (None, 1, 1, 240) 2640 swish_25[0][0] \n__________________________________________________________________________________________________\nactivation_9 (Activation) (None, 1, 1, 240) 0 conv2d_33[0][0] \n__________________________________________________________________________________________________\nmultiply_9 (Multiply) (None, 28, 28, 240) 0 activation_9[0][0] \n swish_24[0][0] \n__________________________________________________________________________________________________\nconv2d_34 (Conv2D) (None, 28, 28, 64) 15360 multiply_9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_25 (BatchNo (None, 28, 28, 64) 256 conv2d_34[0][0] \n__________________________________________________________________________________________________\nconv2d_35 (Conv2D) (None, 28, 28, 384) 24576 batch_normalization_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_26 (BatchNo (None, 28, 28, 384) 1536 conv2d_35[0][0] \n__________________________________________________________________________________________________\nswish_26 (Swish) (None, 28, 28, 384) 0 batch_normalization_26[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_10 (DepthwiseC (None, 28, 28, 384) 9600 swish_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_27 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_10[0][0] \n__________________________________________________________________________________________________\nswish_27 (Swish) (None, 28, 28, 384) 0 batch_normalization_27[0][0] \n__________________________________________________________________________________________________\nlambda_10 (Lambda) (None, 1, 1, 384) 0 swish_27[0][0] \n__________________________________________________________________________________________________\nconv2d_36 (Conv2D) (None, 1, 1, 16) 6160 lambda_10[0][0] \n__________________________________________________________________________________________________\nswish_28 (Swish) (None, 1, 1, 16) 0 conv2d_36[0][0] \n__________________________________________________________________________________________________\nconv2d_37 (Conv2D) (None, 1, 1, 384) 6528 swish_28[0][0] \n__________________________________________________________________________________________________\nactivation_10 (Activation) (None, 1, 1, 384) 0 conv2d_37[0][0] \n__________________________________________________________________________________________________\nmultiply_10 (Multiply) (None, 28, 28, 384) 0 activation_10[0][0] \n swish_27[0][0] \n__________________________________________________________________________________________________\nconv2d_38 (Conv2D) (None, 28, 28, 64) 24576 multiply_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_28 (BatchNo (None, 28, 28, 64) 256 conv2d_38[0][0] \n__________________________________________________________________________________________________\ndrop_connect_7 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_28[0][0] \n__________________________________________________________________________________________________\nadd_7 (Add) (None, 28, 28, 64) 0 drop_connect_7[0][0] \n batch_normalization_25[0][0] \n__________________________________________________________________________________________________\nconv2d_39 (Conv2D) (None, 28, 28, 384) 24576 add_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_29 (BatchNo (None, 28, 28, 384) 1536 conv2d_39[0][0] \n__________________________________________________________________________________________________\nswish_29 (Swish) (None, 28, 28, 384) 0 batch_normalization_29[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_11 (DepthwiseC (None, 28, 28, 384) 9600 swish_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_30 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_11[0][0] \n__________________________________________________________________________________________________\nswish_30 (Swish) (None, 28, 28, 384) 0 batch_normalization_30[0][0] \n__________________________________________________________________________________________________\nlambda_11 (Lambda) (None, 1, 1, 384) 0 swish_30[0][0] \n__________________________________________________________________________________________________\nconv2d_40 (Conv2D) (None, 1, 1, 16) 6160 lambda_11[0][0] \n__________________________________________________________________________________________________\nswish_31 (Swish) (None, 1, 1, 16) 0 conv2d_40[0][0] \n__________________________________________________________________________________________________\nconv2d_41 (Conv2D) (None, 1, 1, 384) 6528 swish_31[0][0] \n__________________________________________________________________________________________________\nactivation_11 (Activation) (None, 1, 1, 384) 0 conv2d_41[0][0] \n__________________________________________________________________________________________________\nmultiply_11 (Multiply) (None, 28, 28, 384) 0 activation_11[0][0] \n swish_30[0][0] \n__________________________________________________________________________________________________\nconv2d_42 (Conv2D) (None, 28, 28, 64) 24576 multiply_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_31 (BatchNo (None, 28, 28, 64) 256 conv2d_42[0][0] \n__________________________________________________________________________________________________\ndrop_connect_8 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_31[0][0] \n__________________________________________________________________________________________________\nadd_8 (Add) (None, 28, 28, 64) 0 drop_connect_8[0][0] \n add_7[0][0] \n__________________________________________________________________________________________________\nconv2d_43 (Conv2D) (None, 28, 28, 384) 24576 add_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_32 (BatchNo (None, 28, 28, 384) 1536 conv2d_43[0][0] \n__________________________________________________________________________________________________\nswish_32 (Swish) (None, 28, 28, 384) 0 batch_normalization_32[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_12 (DepthwiseC (None, 28, 28, 384) 9600 swish_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_33 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_12[0][0] \n__________________________________________________________________________________________________\nswish_33 (Swish) (None, 28, 28, 384) 0 batch_normalization_33[0][0] \n__________________________________________________________________________________________________\nlambda_12 (Lambda) (None, 1, 1, 384) 0 swish_33[0][0] \n__________________________________________________________________________________________________\nconv2d_44 (Conv2D) (None, 1, 1, 16) 6160 lambda_12[0][0] \n__________________________________________________________________________________________________\nswish_34 (Swish) (None, 1, 1, 16) 0 conv2d_44[0][0] \n__________________________________________________________________________________________________\nconv2d_45 (Conv2D) (None, 1, 1, 384) 6528 swish_34[0][0] \n__________________________________________________________________________________________________\nactivation_12 (Activation) (None, 1, 1, 384) 0 conv2d_45[0][0] \n__________________________________________________________________________________________________\nmultiply_12 (Multiply) (None, 28, 28, 384) 0 activation_12[0][0] \n swish_33[0][0] \n__________________________________________________________________________________________________\nconv2d_46 (Conv2D) (None, 28, 28, 64) 24576 multiply_12[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_34 (BatchNo (None, 28, 28, 64) 256 conv2d_46[0][0] \n__________________________________________________________________________________________________\ndrop_connect_9 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_34[0][0] \n__________________________________________________________________________________________________\nadd_9 (Add) (None, 28, 28, 64) 0 drop_connect_9[0][0] \n add_8[0][0] \n__________________________________________________________________________________________________\nconv2d_47 (Conv2D) (None, 28, 28, 384) 24576 add_9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_35 (BatchNo (None, 28, 28, 384) 1536 conv2d_47[0][0] \n__________________________________________________________________________________________________\nswish_35 (Swish) (None, 28, 28, 384) 0 batch_normalization_35[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_13 (DepthwiseC (None, 28, 28, 384) 9600 swish_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_36 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_13[0][0] \n__________________________________________________________________________________________________\nswish_36 (Swish) (None, 28, 28, 384) 0 batch_normalization_36[0][0] \n__________________________________________________________________________________________________\nlambda_13 (Lambda) (None, 1, 1, 384) 0 swish_36[0][0] \n__________________________________________________________________________________________________\nconv2d_48 (Conv2D) (None, 1, 1, 16) 6160 lambda_13[0][0] \n__________________________________________________________________________________________________\nswish_37 (Swish) (None, 1, 1, 16) 0 conv2d_48[0][0] \n__________________________________________________________________________________________________\nconv2d_49 (Conv2D) (None, 1, 1, 384) 6528 swish_37[0][0] \n__________________________________________________________________________________________________\nactivation_13 (Activation) (None, 1, 1, 384) 0 conv2d_49[0][0] \n__________________________________________________________________________________________________\nmultiply_13 (Multiply) (None, 28, 28, 384) 0 activation_13[0][0] \n swish_36[0][0] \n__________________________________________________________________________________________________\nconv2d_50 (Conv2D) (None, 28, 28, 64) 24576 multiply_13[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_37 (BatchNo (None, 28, 28, 64) 256 conv2d_50[0][0] \n__________________________________________________________________________________________________\ndrop_connect_10 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_37[0][0] \n__________________________________________________________________________________________________\nadd_10 (Add) (None, 28, 28, 64) 0 drop_connect_10[0][0] \n add_9[0][0] \n__________________________________________________________________________________________________\nconv2d_51 (Conv2D) (None, 28, 28, 384) 24576 add_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_38 (BatchNo (None, 28, 28, 384) 1536 conv2d_51[0][0] \n__________________________________________________________________________________________________\nswish_38 (Swish) (None, 28, 28, 384) 0 batch_normalization_38[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_14 (DepthwiseC (None, 14, 14, 384) 3456 swish_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_39 (BatchNo (None, 14, 14, 384) 1536 depthwise_conv2d_14[0][0] \n__________________________________________________________________________________________________\nswish_39 (Swish) (None, 14, 14, 384) 0 batch_normalization_39[0][0] \n__________________________________________________________________________________________________\nlambda_14 (Lambda) (None, 1, 1, 384) 0 swish_39[0][0] \n__________________________________________________________________________________________________\nconv2d_52 (Conv2D) (None, 1, 1, 16) 6160 lambda_14[0][0] \n__________________________________________________________________________________________________\nswish_40 (Swish) (None, 1, 1, 16) 0 conv2d_52[0][0] \n__________________________________________________________________________________________________\nconv2d_53 (Conv2D) (None, 1, 1, 384) 6528 swish_40[0][0] \n__________________________________________________________________________________________________\nactivation_14 (Activation) (None, 1, 1, 384) 0 conv2d_53[0][0] \n__________________________________________________________________________________________________\nmultiply_14 (Multiply) (None, 14, 14, 384) 0 activation_14[0][0] \n swish_39[0][0] \n__________________________________________________________________________________________________\nconv2d_54 (Conv2D) (None, 14, 14, 128) 49152 multiply_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_40 (BatchNo (None, 14, 14, 128) 512 conv2d_54[0][0] \n__________________________________________________________________________________________________\nconv2d_55 (Conv2D) (None, 14, 14, 768) 98304 batch_normalization_40[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_41 (BatchNo (None, 14, 14, 768) 3072 conv2d_55[0][0] \n__________________________________________________________________________________________________\nswish_41 (Swish) (None, 14, 14, 768) 0 batch_normalization_41[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_15 (DepthwiseC (None, 14, 14, 768) 6912 swish_41[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_42 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_15[0][0] \n__________________________________________________________________________________________________\nswish_42 (Swish) (None, 14, 14, 768) 0 batch_normalization_42[0][0] \n__________________________________________________________________________________________________\nlambda_15 (Lambda) (None, 1, 1, 768) 0 swish_42[0][0] \n__________________________________________________________________________________________________\nconv2d_56 (Conv2D) (None, 1, 1, 32) 24608 lambda_15[0][0] \n__________________________________________________________________________________________________\nswish_43 (Swish) (None, 1, 1, 32) 0 conv2d_56[0][0] \n__________________________________________________________________________________________________\nconv2d_57 (Conv2D) (None, 1, 1, 768) 25344 swish_43[0][0] \n__________________________________________________________________________________________________\nactivation_15 (Activation) (None, 1, 1, 768) 0 conv2d_57[0][0] \n__________________________________________________________________________________________________\nmultiply_15 (Multiply) (None, 14, 14, 768) 0 activation_15[0][0] \n swish_42[0][0] \n__________________________________________________________________________________________________\nconv2d_58 (Conv2D) (None, 14, 14, 128) 98304 multiply_15[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_43 (BatchNo (None, 14, 14, 128) 512 conv2d_58[0][0] \n__________________________________________________________________________________________________\ndrop_connect_11 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_43[0][0] \n__________________________________________________________________________________________________\nadd_11 (Add) (None, 14, 14, 128) 0 drop_connect_11[0][0] \n batch_normalization_40[0][0] \n__________________________________________________________________________________________________\nconv2d_59 (Conv2D) (None, 14, 14, 768) 98304 add_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_44 (BatchNo (None, 14, 14, 768) 3072 conv2d_59[0][0] \n__________________________________________________________________________________________________\nswish_44 (Swish) (None, 14, 14, 768) 0 batch_normalization_44[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_16 (DepthwiseC (None, 14, 14, 768) 6912 swish_44[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_45 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_16[0][0] \n__________________________________________________________________________________________________\nswish_45 (Swish) (None, 14, 14, 768) 0 batch_normalization_45[0][0] \n__________________________________________________________________________________________________\nlambda_16 (Lambda) (None, 1, 1, 768) 0 swish_45[0][0] \n__________________________________________________________________________________________________\nconv2d_60 (Conv2D) (None, 1, 1, 32) 24608 lambda_16[0][0] \n__________________________________________________________________________________________________\nswish_46 (Swish) (None, 1, 1, 32) 0 conv2d_60[0][0] \n__________________________________________________________________________________________________\nconv2d_61 (Conv2D) (None, 1, 1, 768) 25344 swish_46[0][0] \n__________________________________________________________________________________________________\nactivation_16 (Activation) (None, 1, 1, 768) 0 conv2d_61[0][0] \n__________________________________________________________________________________________________\nmultiply_16 (Multiply) (None, 14, 14, 768) 0 activation_16[0][0] \n swish_45[0][0] \n__________________________________________________________________________________________________\nconv2d_62 (Conv2D) (None, 14, 14, 128) 98304 multiply_16[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_46 (BatchNo (None, 14, 14, 128) 512 conv2d_62[0][0] \n__________________________________________________________________________________________________\ndrop_connect_12 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_46[0][0] \n__________________________________________________________________________________________________\nadd_12 (Add) (None, 14, 14, 128) 0 drop_connect_12[0][0] \n add_11[0][0] \n__________________________________________________________________________________________________\nconv2d_63 (Conv2D) (None, 14, 14, 768) 98304 add_12[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_47 (BatchNo (None, 14, 14, 768) 3072 conv2d_63[0][0] \n__________________________________________________________________________________________________\nswish_47 (Swish) (None, 14, 14, 768) 0 batch_normalization_47[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_17 (DepthwiseC (None, 14, 14, 768) 6912 swish_47[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_48 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_17[0][0] \n__________________________________________________________________________________________________\nswish_48 (Swish) (None, 14, 14, 768) 0 batch_normalization_48[0][0] \n__________________________________________________________________________________________________\nlambda_17 (Lambda) (None, 1, 1, 768) 0 swish_48[0][0] \n__________________________________________________________________________________________________\nconv2d_64 (Conv2D) (None, 1, 1, 32) 24608 lambda_17[0][0] \n__________________________________________________________________________________________________\nswish_49 (Swish) (None, 1, 1, 32) 0 conv2d_64[0][0] \n__________________________________________________________________________________________________\nconv2d_65 (Conv2D) (None, 1, 1, 768) 25344 swish_49[0][0] \n__________________________________________________________________________________________________\nactivation_17 (Activation) (None, 1, 1, 768) 0 conv2d_65[0][0] \n__________________________________________________________________________________________________\nmultiply_17 (Multiply) (None, 14, 14, 768) 0 activation_17[0][0] \n swish_48[0][0] \n__________________________________________________________________________________________________\nconv2d_66 (Conv2D) (None, 14, 14, 128) 98304 multiply_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_49 (BatchNo (None, 14, 14, 128) 512 conv2d_66[0][0] \n__________________________________________________________________________________________________\ndrop_connect_13 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_49[0][0] \n__________________________________________________________________________________________________\nadd_13 (Add) (None, 14, 14, 128) 0 drop_connect_13[0][0] \n add_12[0][0] \n__________________________________________________________________________________________________\nconv2d_67 (Conv2D) (None, 14, 14, 768) 98304 add_13[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_50 (BatchNo (None, 14, 14, 768) 3072 conv2d_67[0][0] \n__________________________________________________________________________________________________\nswish_50 (Swish) (None, 14, 14, 768) 0 batch_normalization_50[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_18 (DepthwiseC (None, 14, 14, 768) 6912 swish_50[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_51 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_18[0][0] \n__________________________________________________________________________________________________\nswish_51 (Swish) (None, 14, 14, 768) 0 batch_normalization_51[0][0] \n__________________________________________________________________________________________________\nlambda_18 (Lambda) (None, 1, 1, 768) 0 swish_51[0][0] \n__________________________________________________________________________________________________\nconv2d_68 (Conv2D) (None, 1, 1, 32) 24608 lambda_18[0][0] \n__________________________________________________________________________________________________\nswish_52 (Swish) (None, 1, 1, 32) 0 conv2d_68[0][0] \n__________________________________________________________________________________________________\nconv2d_69 (Conv2D) (None, 1, 1, 768) 25344 swish_52[0][0] \n__________________________________________________________________________________________________\nactivation_18 (Activation) (None, 1, 1, 768) 0 conv2d_69[0][0] \n__________________________________________________________________________________________________\nmultiply_18 (Multiply) (None, 14, 14, 768) 0 activation_18[0][0] \n swish_51[0][0] \n__________________________________________________________________________________________________\nconv2d_70 (Conv2D) (None, 14, 14, 128) 98304 multiply_18[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_52 (BatchNo (None, 14, 14, 128) 512 conv2d_70[0][0] \n__________________________________________________________________________________________________\ndrop_connect_14 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_52[0][0] \n__________________________________________________________________________________________________\nadd_14 (Add) (None, 14, 14, 128) 0 drop_connect_14[0][0] \n add_13[0][0] \n__________________________________________________________________________________________________\nconv2d_71 (Conv2D) (None, 14, 14, 768) 98304 add_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_53 (BatchNo (None, 14, 14, 768) 3072 conv2d_71[0][0] \n__________________________________________________________________________________________________\nswish_53 (Swish) (None, 14, 14, 768) 0 batch_normalization_53[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_19 (DepthwiseC (None, 14, 14, 768) 6912 swish_53[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_54 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_19[0][0] \n__________________________________________________________________________________________________\nswish_54 (Swish) (None, 14, 14, 768) 0 batch_normalization_54[0][0] \n__________________________________________________________________________________________________\nlambda_19 (Lambda) (None, 1, 1, 768) 0 swish_54[0][0] \n__________________________________________________________________________________________________\nconv2d_72 (Conv2D) (None, 1, 1, 32) 24608 lambda_19[0][0] \n__________________________________________________________________________________________________\nswish_55 (Swish) (None, 1, 1, 32) 0 conv2d_72[0][0] \n__________________________________________________________________________________________________\nconv2d_73 (Conv2D) (None, 1, 1, 768) 25344 swish_55[0][0] \n__________________________________________________________________________________________________\nactivation_19 (Activation) (None, 1, 1, 768) 0 conv2d_73[0][0] \n__________________________________________________________________________________________________\nmultiply_19 (Multiply) (None, 14, 14, 768) 0 activation_19[0][0] \n swish_54[0][0] \n__________________________________________________________________________________________________\nconv2d_74 (Conv2D) (None, 14, 14, 128) 98304 multiply_19[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_55 (BatchNo (None, 14, 14, 128) 512 conv2d_74[0][0] \n__________________________________________________________________________________________________\ndrop_connect_15 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_55[0][0] \n__________________________________________________________________________________________________\nadd_15 (Add) (None, 14, 14, 128) 0 drop_connect_15[0][0] \n add_14[0][0] \n__________________________________________________________________________________________________\nconv2d_75 (Conv2D) (None, 14, 14, 768) 98304 add_15[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_56 (BatchNo (None, 14, 14, 768) 3072 conv2d_75[0][0] \n__________________________________________________________________________________________________\nswish_56 (Swish) (None, 14, 14, 768) 0 batch_normalization_56[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_20 (DepthwiseC (None, 14, 14, 768) 6912 swish_56[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_57 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_20[0][0] \n__________________________________________________________________________________________________\nswish_57 (Swish) (None, 14, 14, 768) 0 batch_normalization_57[0][0] \n__________________________________________________________________________________________________\nlambda_20 (Lambda) (None, 1, 1, 768) 0 swish_57[0][0] \n__________________________________________________________________________________________________\nconv2d_76 (Conv2D) (None, 1, 1, 32) 24608 lambda_20[0][0] \n__________________________________________________________________________________________________\nswish_58 (Swish) (None, 1, 1, 32) 0 conv2d_76[0][0] \n__________________________________________________________________________________________________\nconv2d_77 (Conv2D) (None, 1, 1, 768) 25344 swish_58[0][0] \n__________________________________________________________________________________________________\nactivation_20 (Activation) (None, 1, 1, 768) 0 conv2d_77[0][0] \n__________________________________________________________________________________________________\nmultiply_20 (Multiply) (None, 14, 14, 768) 0 activation_20[0][0] \n swish_57[0][0] \n__________________________________________________________________________________________________\nconv2d_78 (Conv2D) (None, 14, 14, 128) 98304 multiply_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_58 (BatchNo (None, 14, 14, 128) 512 conv2d_78[0][0] \n__________________________________________________________________________________________________\ndrop_connect_16 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_58[0][0] \n__________________________________________________________________________________________________\nadd_16 (Add) (None, 14, 14, 128) 0 drop_connect_16[0][0] \n add_15[0][0] \n__________________________________________________________________________________________________\nconv2d_79 (Conv2D) (None, 14, 14, 768) 98304 add_16[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_59 (BatchNo (None, 14, 14, 768) 3072 conv2d_79[0][0] \n__________________________________________________________________________________________________\nswish_59 (Swish) (None, 14, 14, 768) 0 batch_normalization_59[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_21 (DepthwiseC (None, 14, 14, 768) 19200 swish_59[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_60 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_21[0][0] \n__________________________________________________________________________________________________\nswish_60 (Swish) (None, 14, 14, 768) 0 batch_normalization_60[0][0] \n__________________________________________________________________________________________________\nlambda_21 (Lambda) (None, 1, 1, 768) 0 swish_60[0][0] \n__________________________________________________________________________________________________\nconv2d_80 (Conv2D) (None, 1, 1, 32) 24608 lambda_21[0][0] \n__________________________________________________________________________________________________\nswish_61 (Swish) (None, 1, 1, 32) 0 conv2d_80[0][0] \n__________________________________________________________________________________________________\nconv2d_81 (Conv2D) (None, 1, 1, 768) 25344 swish_61[0][0] \n__________________________________________________________________________________________________\nactivation_21 (Activation) (None, 1, 1, 768) 0 conv2d_81[0][0] \n__________________________________________________________________________________________________\nmultiply_21 (Multiply) (None, 14, 14, 768) 0 activation_21[0][0] \n swish_60[0][0] \n__________________________________________________________________________________________________\nconv2d_82 (Conv2D) (None, 14, 14, 176) 135168 multiply_21[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_61 (BatchNo (None, 14, 14, 176) 704 conv2d_82[0][0] \n__________________________________________________________________________________________________\nconv2d_83 (Conv2D) (None, 14, 14, 1056) 185856 batch_normalization_61[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_62 (BatchNo (None, 14, 14, 1056) 4224 conv2d_83[0][0] \n__________________________________________________________________________________________________\nswish_62 (Swish) (None, 14, 14, 1056) 0 batch_normalization_62[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_22 (DepthwiseC (None, 14, 14, 1056) 26400 swish_62[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_63 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_22[0][0] \n__________________________________________________________________________________________________\nswish_63 (Swish) (None, 14, 14, 1056) 0 batch_normalization_63[0][0] \n__________________________________________________________________________________________________\nlambda_22 (Lambda) (None, 1, 1, 1056) 0 swish_63[0][0] \n__________________________________________________________________________________________________\nconv2d_84 (Conv2D) (None, 1, 1, 44) 46508 lambda_22[0][0] \n__________________________________________________________________________________________________\nswish_64 (Swish) (None, 1, 1, 44) 0 conv2d_84[0][0] \n__________________________________________________________________________________________________\nconv2d_85 (Conv2D) (None, 1, 1, 1056) 47520 swish_64[0][0] \n__________________________________________________________________________________________________\nactivation_22 (Activation) (None, 1, 1, 1056) 0 conv2d_85[0][0] \n__________________________________________________________________________________________________\nmultiply_22 (Multiply) (None, 14, 14, 1056) 0 activation_22[0][0] \n swish_63[0][0] \n__________________________________________________________________________________________________\nconv2d_86 (Conv2D) (None, 14, 14, 176) 185856 multiply_22[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_64 (BatchNo (None, 14, 14, 176) 704 conv2d_86[0][0] \n__________________________________________________________________________________________________\ndrop_connect_17 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_64[0][0] \n__________________________________________________________________________________________________\nadd_17 (Add) (None, 14, 14, 176) 0 drop_connect_17[0][0] \n batch_normalization_61[0][0] \n__________________________________________________________________________________________________\nconv2d_87 (Conv2D) (None, 14, 14, 1056) 185856 add_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_65 (BatchNo (None, 14, 14, 1056) 4224 conv2d_87[0][0] \n__________________________________________________________________________________________________\nswish_65 (Swish) (None, 14, 14, 1056) 0 batch_normalization_65[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_23 (DepthwiseC (None, 14, 14, 1056) 26400 swish_65[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_66 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_23[0][0] \n__________________________________________________________________________________________________\nswish_66 (Swish) (None, 14, 14, 1056) 0 batch_normalization_66[0][0] \n__________________________________________________________________________________________________\nlambda_23 (Lambda) (None, 1, 1, 1056) 0 swish_66[0][0] \n__________________________________________________________________________________________________\nconv2d_88 (Conv2D) (None, 1, 1, 44) 46508 lambda_23[0][0] \n__________________________________________________________________________________________________\nswish_67 (Swish) (None, 1, 1, 44) 0 conv2d_88[0][0] \n__________________________________________________________________________________________________\nconv2d_89 (Conv2D) (None, 1, 1, 1056) 47520 swish_67[0][0] \n__________________________________________________________________________________________________\nactivation_23 (Activation) (None, 1, 1, 1056) 0 conv2d_89[0][0] \n__________________________________________________________________________________________________\nmultiply_23 (Multiply) (None, 14, 14, 1056) 0 activation_23[0][0] \n swish_66[0][0] \n__________________________________________________________________________________________________\nconv2d_90 (Conv2D) (None, 14, 14, 176) 185856 multiply_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_67 (BatchNo (None, 14, 14, 176) 704 conv2d_90[0][0] \n__________________________________________________________________________________________________\ndrop_connect_18 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_67[0][0] \n__________________________________________________________________________________________________\nadd_18 (Add) (None, 14, 14, 176) 0 drop_connect_18[0][0] \n add_17[0][0] \n__________________________________________________________________________________________________\nconv2d_91 (Conv2D) (None, 14, 14, 1056) 185856 add_18[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_68 (BatchNo (None, 14, 14, 1056) 4224 conv2d_91[0][0] \n__________________________________________________________________________________________________\nswish_68 (Swish) (None, 14, 14, 1056) 0 batch_normalization_68[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_24 (DepthwiseC (None, 14, 14, 1056) 26400 swish_68[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_69 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_24[0][0] \n__________________________________________________________________________________________________\nswish_69 (Swish) (None, 14, 14, 1056) 0 batch_normalization_69[0][0] \n__________________________________________________________________________________________________\nlambda_24 (Lambda) (None, 1, 1, 1056) 0 swish_69[0][0] \n__________________________________________________________________________________________________\nconv2d_92 (Conv2D) (None, 1, 1, 44) 46508 lambda_24[0][0] \n__________________________________________________________________________________________________\nswish_70 (Swish) (None, 1, 1, 44) 0 conv2d_92[0][0] \n__________________________________________________________________________________________________\nconv2d_93 (Conv2D) (None, 1, 1, 1056) 47520 swish_70[0][0] \n__________________________________________________________________________________________________\nactivation_24 (Activation) (None, 1, 1, 1056) 0 conv2d_93[0][0] \n__________________________________________________________________________________________________\nmultiply_24 (Multiply) (None, 14, 14, 1056) 0 activation_24[0][0] \n swish_69[0][0] \n__________________________________________________________________________________________________\nconv2d_94 (Conv2D) (None, 14, 14, 176) 185856 multiply_24[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_70 (BatchNo (None, 14, 14, 176) 704 conv2d_94[0][0] \n__________________________________________________________________________________________________\ndrop_connect_19 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_70[0][0] \n__________________________________________________________________________________________________\nadd_19 (Add) (None, 14, 14, 176) 0 drop_connect_19[0][0] \n add_18[0][0] \n__________________________________________________________________________________________________\nconv2d_95 (Conv2D) (None, 14, 14, 1056) 185856 add_19[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_71 (BatchNo (None, 14, 14, 1056) 4224 conv2d_95[0][0] \n__________________________________________________________________________________________________\nswish_71 (Swish) (None, 14, 14, 1056) 0 batch_normalization_71[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_25 (DepthwiseC (None, 14, 14, 1056) 26400 swish_71[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_72 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_25[0][0] \n__________________________________________________________________________________________________\nswish_72 (Swish) (None, 14, 14, 1056) 0 batch_normalization_72[0][0] \n__________________________________________________________________________________________________\nlambda_25 (Lambda) (None, 1, 1, 1056) 0 swish_72[0][0] \n__________________________________________________________________________________________________\nconv2d_96 (Conv2D) (None, 1, 1, 44) 46508 lambda_25[0][0] \n__________________________________________________________________________________________________\nswish_73 (Swish) (None, 1, 1, 44) 0 conv2d_96[0][0] \n__________________________________________________________________________________________________\nconv2d_97 (Conv2D) (None, 1, 1, 1056) 47520 swish_73[0][0] \n__________________________________________________________________________________________________\nactivation_25 (Activation) (None, 1, 1, 1056) 0 conv2d_97[0][0] \n__________________________________________________________________________________________________\nmultiply_25 (Multiply) (None, 14, 14, 1056) 0 activation_25[0][0] \n swish_72[0][0] \n__________________________________________________________________________________________________\nconv2d_98 (Conv2D) (None, 14, 14, 176) 185856 multiply_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_73 (BatchNo (None, 14, 14, 176) 704 conv2d_98[0][0] \n__________________________________________________________________________________________________\ndrop_connect_20 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_73[0][0] \n__________________________________________________________________________________________________\nadd_20 (Add) (None, 14, 14, 176) 0 drop_connect_20[0][0] \n add_19[0][0] \n__________________________________________________________________________________________________\nconv2d_99 (Conv2D) (None, 14, 14, 1056) 185856 add_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_74 (BatchNo (None, 14, 14, 1056) 4224 conv2d_99[0][0] \n__________________________________________________________________________________________________\nswish_74 (Swish) (None, 14, 14, 1056) 0 batch_normalization_74[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_26 (DepthwiseC (None, 14, 14, 1056) 26400 swish_74[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_75 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_26[0][0] \n__________________________________________________________________________________________________\nswish_75 (Swish) (None, 14, 14, 1056) 0 batch_normalization_75[0][0] \n__________________________________________________________________________________________________\nlambda_26 (Lambda) (None, 1, 1, 1056) 0 swish_75[0][0] \n__________________________________________________________________________________________________\nconv2d_100 (Conv2D) (None, 1, 1, 44) 46508 lambda_26[0][0] \n__________________________________________________________________________________________________\nswish_76 (Swish) (None, 1, 1, 44) 0 conv2d_100[0][0] \n__________________________________________________________________________________________________\nconv2d_101 (Conv2D) (None, 1, 1, 1056) 47520 swish_76[0][0] \n__________________________________________________________________________________________________\nactivation_26 (Activation) (None, 1, 1, 1056) 0 conv2d_101[0][0] \n__________________________________________________________________________________________________\nmultiply_26 (Multiply) (None, 14, 14, 1056) 0 activation_26[0][0] \n swish_75[0][0] \n__________________________________________________________________________________________________\nconv2d_102 (Conv2D) (None, 14, 14, 176) 185856 multiply_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_76 (BatchNo (None, 14, 14, 176) 704 conv2d_102[0][0] \n__________________________________________________________________________________________________\ndrop_connect_21 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_76[0][0] \n__________________________________________________________________________________________________\nadd_21 (Add) (None, 14, 14, 176) 0 drop_connect_21[0][0] \n add_20[0][0] \n__________________________________________________________________________________________________\nconv2d_103 (Conv2D) (None, 14, 14, 1056) 185856 add_21[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_77 (BatchNo (None, 14, 14, 1056) 4224 conv2d_103[0][0] \n__________________________________________________________________________________________________\nswish_77 (Swish) (None, 14, 14, 1056) 0 batch_normalization_77[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_27 (DepthwiseC (None, 14, 14, 1056) 26400 swish_77[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_78 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_27[0][0] \n__________________________________________________________________________________________________\nswish_78 (Swish) (None, 14, 14, 1056) 0 batch_normalization_78[0][0] \n__________________________________________________________________________________________________\nlambda_27 (Lambda) (None, 1, 1, 1056) 0 swish_78[0][0] \n__________________________________________________________________________________________________\nconv2d_104 (Conv2D) (None, 1, 1, 44) 46508 lambda_27[0][0] \n__________________________________________________________________________________________________\nswish_79 (Swish) (None, 1, 1, 44) 0 conv2d_104[0][0] \n__________________________________________________________________________________________________\nconv2d_105 (Conv2D) (None, 1, 1, 1056) 47520 swish_79[0][0] \n__________________________________________________________________________________________________\nactivation_27 (Activation) (None, 1, 1, 1056) 0 conv2d_105[0][0] \n__________________________________________________________________________________________________\nmultiply_27 (Multiply) (None, 14, 14, 1056) 0 activation_27[0][0] \n swish_78[0][0] \n__________________________________________________________________________________________________\nconv2d_106 (Conv2D) (None, 14, 14, 176) 185856 multiply_27[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_79 (BatchNo (None, 14, 14, 176) 704 conv2d_106[0][0] \n__________________________________________________________________________________________________\ndrop_connect_22 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_79[0][0] \n__________________________________________________________________________________________________\nadd_22 (Add) (None, 14, 14, 176) 0 drop_connect_22[0][0] \n add_21[0][0] \n__________________________________________________________________________________________________\nconv2d_107 (Conv2D) (None, 14, 14, 1056) 185856 add_22[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_80 (BatchNo (None, 14, 14, 1056) 4224 conv2d_107[0][0] \n__________________________________________________________________________________________________\nswish_80 (Swish) (None, 14, 14, 1056) 0 batch_normalization_80[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_28 (DepthwiseC (None, 7, 7, 1056) 26400 swish_80[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_81 (BatchNo (None, 7, 7, 1056) 4224 depthwise_conv2d_28[0][0] \n__________________________________________________________________________________________________\nswish_81 (Swish) (None, 7, 7, 1056) 0 batch_normalization_81[0][0] \n__________________________________________________________________________________________________\nlambda_28 (Lambda) (None, 1, 1, 1056) 0 swish_81[0][0] \n__________________________________________________________________________________________________\nconv2d_108 (Conv2D) (None, 1, 1, 44) 46508 lambda_28[0][0] \n__________________________________________________________________________________________________\nswish_82 (Swish) (None, 1, 1, 44) 0 conv2d_108[0][0] \n__________________________________________________________________________________________________\nconv2d_109 (Conv2D) (None, 1, 1, 1056) 47520 swish_82[0][0] \n__________________________________________________________________________________________________\nactivation_28 (Activation) (None, 1, 1, 1056) 0 conv2d_109[0][0] \n__________________________________________________________________________________________________\nmultiply_28 (Multiply) (None, 7, 7, 1056) 0 activation_28[0][0] \n swish_81[0][0] \n__________________________________________________________________________________________________\nconv2d_110 (Conv2D) (None, 7, 7, 304) 321024 multiply_28[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_82 (BatchNo (None, 7, 7, 304) 1216 conv2d_110[0][0] \n__________________________________________________________________________________________________\nconv2d_111 (Conv2D) (None, 7, 7, 1824) 554496 batch_normalization_82[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_83 (BatchNo (None, 7, 7, 1824) 7296 conv2d_111[0][0] \n__________________________________________________________________________________________________\nswish_83 (Swish) (None, 7, 7, 1824) 0 batch_normalization_83[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_29 (DepthwiseC (None, 7, 7, 1824) 45600 swish_83[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_84 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_29[0][0] \n__________________________________________________________________________________________________\nswish_84 (Swish) (None, 7, 7, 1824) 0 batch_normalization_84[0][0] \n__________________________________________________________________________________________________\nlambda_29 (Lambda) (None, 1, 1, 1824) 0 swish_84[0][0] \n__________________________________________________________________________________________________\nconv2d_112 (Conv2D) (None, 1, 1, 76) 138700 lambda_29[0][0] \n__________________________________________________________________________________________________\nswish_85 (Swish) (None, 1, 1, 76) 0 conv2d_112[0][0] \n__________________________________________________________________________________________________\nconv2d_113 (Conv2D) (None, 1, 1, 1824) 140448 swish_85[0][0] \n__________________________________________________________________________________________________\nactivation_29 (Activation) (None, 1, 1, 1824) 0 conv2d_113[0][0] \n__________________________________________________________________________________________________\nmultiply_29 (Multiply) (None, 7, 7, 1824) 0 activation_29[0][0] \n swish_84[0][0] \n__________________________________________________________________________________________________\nconv2d_114 (Conv2D) (None, 7, 7, 304) 554496 multiply_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_85 (BatchNo (None, 7, 7, 304) 1216 conv2d_114[0][0] \n__________________________________________________________________________________________________\ndrop_connect_23 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_85[0][0] \n__________________________________________________________________________________________________\nadd_23 (Add) (None, 7, 7, 304) 0 drop_connect_23[0][0] \n batch_normalization_82[0][0] \n__________________________________________________________________________________________________\nconv2d_115 (Conv2D) (None, 7, 7, 1824) 554496 add_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_86 (BatchNo (None, 7, 7, 1824) 7296 conv2d_115[0][0] \n__________________________________________________________________________________________________\nswish_86 (Swish) (None, 7, 7, 1824) 0 batch_normalization_86[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_30 (DepthwiseC (None, 7, 7, 1824) 45600 swish_86[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_87 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_30[0][0] \n__________________________________________________________________________________________________\nswish_87 (Swish) (None, 7, 7, 1824) 0 batch_normalization_87[0][0] \n__________________________________________________________________________________________________\nlambda_30 (Lambda) (None, 1, 1, 1824) 0 swish_87[0][0] \n__________________________________________________________________________________________________\nconv2d_116 (Conv2D) (None, 1, 1, 76) 138700 lambda_30[0][0] \n__________________________________________________________________________________________________\nswish_88 (Swish) (None, 1, 1, 76) 0 conv2d_116[0][0] \n__________________________________________________________________________________________________\nconv2d_117 (Conv2D) (None, 1, 1, 1824) 140448 swish_88[0][0] \n__________________________________________________________________________________________________\nactivation_30 (Activation) (None, 1, 1, 1824) 0 conv2d_117[0][0] \n__________________________________________________________________________________________________\nmultiply_30 (Multiply) (None, 7, 7, 1824) 0 activation_30[0][0] \n swish_87[0][0] \n__________________________________________________________________________________________________\nconv2d_118 (Conv2D) (None, 7, 7, 304) 554496 multiply_30[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_88 (BatchNo (None, 7, 7, 304) 1216 conv2d_118[0][0] \n__________________________________________________________________________________________________\ndrop_connect_24 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_88[0][0] \n__________________________________________________________________________________________________\nadd_24 (Add) (None, 7, 7, 304) 0 drop_connect_24[0][0] \n add_23[0][0] \n__________________________________________________________________________________________________\nconv2d_119 (Conv2D) (None, 7, 7, 1824) 554496 add_24[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_89 (BatchNo (None, 7, 7, 1824) 7296 conv2d_119[0][0] \n__________________________________________________________________________________________________\nswish_89 (Swish) (None, 7, 7, 1824) 0 batch_normalization_89[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_31 (DepthwiseC (None, 7, 7, 1824) 45600 swish_89[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_90 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_31[0][0] \n__________________________________________________________________________________________________\nswish_90 (Swish) (None, 7, 7, 1824) 0 batch_normalization_90[0][0] \n__________________________________________________________________________________________________\nlambda_31 (Lambda) (None, 1, 1, 1824) 0 swish_90[0][0] \n__________________________________________________________________________________________________\nconv2d_120 (Conv2D) (None, 1, 1, 76) 138700 lambda_31[0][0] \n__________________________________________________________________________________________________\nswish_91 (Swish) (None, 1, 1, 76) 0 conv2d_120[0][0] \n__________________________________________________________________________________________________\nconv2d_121 (Conv2D) (None, 1, 1, 1824) 140448 swish_91[0][0] \n__________________________________________________________________________________________________\nactivation_31 (Activation) (None, 1, 1, 1824) 0 conv2d_121[0][0] \n__________________________________________________________________________________________________\nmultiply_31 (Multiply) (None, 7, 7, 1824) 0 activation_31[0][0] \n swish_90[0][0] \n__________________________________________________________________________________________________\nconv2d_122 (Conv2D) (None, 7, 7, 304) 554496 multiply_31[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_91 (BatchNo (None, 7, 7, 304) 1216 conv2d_122[0][0] \n__________________________________________________________________________________________________\ndrop_connect_25 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_91[0][0] \n__________________________________________________________________________________________________\nadd_25 (Add) (None, 7, 7, 304) 0 drop_connect_25[0][0] \n add_24[0][0] \n__________________________________________________________________________________________________\nconv2d_123 (Conv2D) (None, 7, 7, 1824) 554496 add_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_92 (BatchNo (None, 7, 7, 1824) 7296 conv2d_123[0][0] \n__________________________________________________________________________________________________\nswish_92 (Swish) (None, 7, 7, 1824) 0 batch_normalization_92[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_32 (DepthwiseC (None, 7, 7, 1824) 45600 swish_92[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_93 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_32[0][0] \n__________________________________________________________________________________________________\nswish_93 (Swish) (None, 7, 7, 1824) 0 batch_normalization_93[0][0] \n__________________________________________________________________________________________________\nlambda_32 (Lambda) (None, 1, 1, 1824) 0 swish_93[0][0] \n__________________________________________________________________________________________________\nconv2d_124 (Conv2D) (None, 1, 1, 76) 138700 lambda_32[0][0] \n__________________________________________________________________________________________________\nswish_94 (Swish) (None, 1, 1, 76) 0 conv2d_124[0][0] \n__________________________________________________________________________________________________\nconv2d_125 (Conv2D) (None, 1, 1, 1824) 140448 swish_94[0][0] \n__________________________________________________________________________________________________\nactivation_32 (Activation) (None, 1, 1, 1824) 0 conv2d_125[0][0] \n__________________________________________________________________________________________________\nmultiply_32 (Multiply) (None, 7, 7, 1824) 0 activation_32[0][0] \n swish_93[0][0] \n__________________________________________________________________________________________________\nconv2d_126 (Conv2D) (None, 7, 7, 304) 554496 multiply_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_94 (BatchNo (None, 7, 7, 304) 1216 conv2d_126[0][0] \n__________________________________________________________________________________________________\ndrop_connect_26 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_94[0][0] \n__________________________________________________________________________________________________\nadd_26 (Add) (None, 7, 7, 304) 0 drop_connect_26[0][0] \n add_25[0][0] \n__________________________________________________________________________________________________\nconv2d_127 (Conv2D) (None, 7, 7, 1824) 554496 add_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_95 (BatchNo (None, 7, 7, 1824) 7296 conv2d_127[0][0] \n__________________________________________________________________________________________________\nswish_95 (Swish) (None, 7, 7, 1824) 0 batch_normalization_95[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_33 (DepthwiseC (None, 7, 7, 1824) 45600 swish_95[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_96 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_33[0][0] \n__________________________________________________________________________________________________\nswish_96 (Swish) (None, 7, 7, 1824) 0 batch_normalization_96[0][0] \n__________________________________________________________________________________________________\nlambda_33 (Lambda) (None, 1, 1, 1824) 0 swish_96[0][0] \n__________________________________________________________________________________________________\nconv2d_128 (Conv2D) (None, 1, 1, 76) 138700 lambda_33[0][0] \n__________________________________________________________________________________________________\nswish_97 (Swish) (None, 1, 1, 76) 0 conv2d_128[0][0] \n__________________________________________________________________________________________________\nconv2d_129 (Conv2D) (None, 1, 1, 1824) 140448 swish_97[0][0] \n__________________________________________________________________________________________________\nactivation_33 (Activation) (None, 1, 1, 1824) 0 conv2d_129[0][0] \n__________________________________________________________________________________________________\nmultiply_33 (Multiply) (None, 7, 7, 1824) 0 activation_33[0][0] \n swish_96[0][0] \n__________________________________________________________________________________________________\nconv2d_130 (Conv2D) (None, 7, 7, 304) 554496 multiply_33[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_97 (BatchNo (None, 7, 7, 304) 1216 conv2d_130[0][0] \n__________________________________________________________________________________________________\ndrop_connect_27 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_97[0][0] \n__________________________________________________________________________________________________\nadd_27 (Add) (None, 7, 7, 304) 0 drop_connect_27[0][0] \n add_26[0][0] \n__________________________________________________________________________________________________\nconv2d_131 (Conv2D) (None, 7, 7, 1824) 554496 add_27[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_98 (BatchNo (None, 7, 7, 1824) 7296 conv2d_131[0][0] \n__________________________________________________________________________________________________\nswish_98 (Swish) (None, 7, 7, 1824) 0 batch_normalization_98[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_34 (DepthwiseC (None, 7, 7, 1824) 45600 swish_98[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_99 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_34[0][0] \n__________________________________________________________________________________________________\nswish_99 (Swish) (None, 7, 7, 1824) 0 batch_normalization_99[0][0] \n__________________________________________________________________________________________________\nlambda_34 (Lambda) (None, 1, 1, 1824) 0 swish_99[0][0] \n__________________________________________________________________________________________________\nconv2d_132 (Conv2D) (None, 1, 1, 76) 138700 lambda_34[0][0] \n__________________________________________________________________________________________________\nswish_100 (Swish) (None, 1, 1, 76) 0 conv2d_132[0][0] \n__________________________________________________________________________________________________\nconv2d_133 (Conv2D) (None, 1, 1, 1824) 140448 swish_100[0][0] \n__________________________________________________________________________________________________\nactivation_34 (Activation) (None, 1, 1, 1824) 0 conv2d_133[0][0] \n__________________________________________________________________________________________________\nmultiply_34 (Multiply) (None, 7, 7, 1824) 0 activation_34[0][0] \n swish_99[0][0] \n__________________________________________________________________________________________________\nconv2d_134 (Conv2D) (None, 7, 7, 304) 554496 multiply_34[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_100 (BatchN (None, 7, 7, 304) 1216 conv2d_134[0][0] \n__________________________________________________________________________________________________\ndrop_connect_28 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_100[0][0] \n__________________________________________________________________________________________________\nadd_28 (Add) (None, 7, 7, 304) 0 drop_connect_28[0][0] \n add_27[0][0] \n__________________________________________________________________________________________________\nconv2d_135 (Conv2D) (None, 7, 7, 1824) 554496 add_28[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_101 (BatchN (None, 7, 7, 1824) 7296 conv2d_135[0][0] \n__________________________________________________________________________________________________\nswish_101 (Swish) (None, 7, 7, 1824) 0 batch_normalization_101[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_35 (DepthwiseC (None, 7, 7, 1824) 45600 swish_101[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_102 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_35[0][0] \n__________________________________________________________________________________________________\nswish_102 (Swish) (None, 7, 7, 1824) 0 batch_normalization_102[0][0] \n__________________________________________________________________________________________________\nlambda_35 (Lambda) (None, 1, 1, 1824) 0 swish_102[0][0] \n__________________________________________________________________________________________________\nconv2d_136 (Conv2D) (None, 1, 1, 76) 138700 lambda_35[0][0] \n__________________________________________________________________________________________________\nswish_103 (Swish) (None, 1, 1, 76) 0 conv2d_136[0][0] \n__________________________________________________________________________________________________\nconv2d_137 (Conv2D) (None, 1, 1, 1824) 140448 swish_103[0][0] \n__________________________________________________________________________________________________\nactivation_35 (Activation) (None, 1, 1, 1824) 0 conv2d_137[0][0] \n__________________________________________________________________________________________________\nmultiply_35 (Multiply) (None, 7, 7, 1824) 0 activation_35[0][0] \n swish_102[0][0] \n__________________________________________________________________________________________________\nconv2d_138 (Conv2D) (None, 7, 7, 304) 554496 multiply_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_103 (BatchN (None, 7, 7, 304) 1216 conv2d_138[0][0] \n__________________________________________________________________________________________________\ndrop_connect_29 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_103[0][0] \n__________________________________________________________________________________________________\nadd_29 (Add) (None, 7, 7, 304) 0 drop_connect_29[0][0] \n add_28[0][0] \n__________________________________________________________________________________________________\nconv2d_139 (Conv2D) (None, 7, 7, 1824) 554496 add_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_104 (BatchN (None, 7, 7, 1824) 7296 conv2d_139[0][0] \n__________________________________________________________________________________________________\nswish_104 (Swish) (None, 7, 7, 1824) 0 batch_normalization_104[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_36 (DepthwiseC (None, 7, 7, 1824) 45600 swish_104[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_105 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_36[0][0] \n__________________________________________________________________________________________________\nswish_105 (Swish) (None, 7, 7, 1824) 0 batch_normalization_105[0][0] \n__________________________________________________________________________________________________\nlambda_36 (Lambda) (None, 1, 1, 1824) 0 swish_105[0][0] \n__________________________________________________________________________________________________\nconv2d_140 (Conv2D) (None, 1, 1, 76) 138700 lambda_36[0][0] \n__________________________________________________________________________________________________\nswish_106 (Swish) (None, 1, 1, 76) 0 conv2d_140[0][0] \n__________________________________________________________________________________________________\nconv2d_141 (Conv2D) (None, 1, 1, 1824) 140448 swish_106[0][0] \n__________________________________________________________________________________________________\nactivation_36 (Activation) (None, 1, 1, 1824) 0 conv2d_141[0][0] \n__________________________________________________________________________________________________\nmultiply_36 (Multiply) (None, 7, 7, 1824) 0 activation_36[0][0] \n swish_105[0][0] \n__________________________________________________________________________________________________\nconv2d_142 (Conv2D) (None, 7, 7, 304) 554496 multiply_36[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_106 (BatchN (None, 7, 7, 304) 1216 conv2d_142[0][0] \n__________________________________________________________________________________________________\ndrop_connect_30 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_106[0][0] \n__________________________________________________________________________________________________\nadd_30 (Add) (None, 7, 7, 304) 0 drop_connect_30[0][0] \n add_29[0][0] \n__________________________________________________________________________________________________\nconv2d_143 (Conv2D) (None, 7, 7, 1824) 554496 add_30[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_107 (BatchN (None, 7, 7, 1824) 7296 conv2d_143[0][0] \n__________________________________________________________________________________________________\nswish_107 (Swish) (None, 7, 7, 1824) 0 batch_normalization_107[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_37 (DepthwiseC (None, 7, 7, 1824) 16416 swish_107[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_108 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_37[0][0] \n__________________________________________________________________________________________________\nswish_108 (Swish) (None, 7, 7, 1824) 0 batch_normalization_108[0][0] \n__________________________________________________________________________________________________\nlambda_37 (Lambda) (None, 1, 1, 1824) 0 swish_108[0][0] \n__________________________________________________________________________________________________\nconv2d_144 (Conv2D) (None, 1, 1, 76) 138700 lambda_37[0][0] \n__________________________________________________________________________________________________\nswish_109 (Swish) (None, 1, 1, 76) 0 conv2d_144[0][0] \n__________________________________________________________________________________________________\nconv2d_145 (Conv2D) (None, 1, 1, 1824) 140448 swish_109[0][0] \n__________________________________________________________________________________________________\nactivation_37 (Activation) (None, 1, 1, 1824) 0 conv2d_145[0][0] \n__________________________________________________________________________________________________\nmultiply_37 (Multiply) (None, 7, 7, 1824) 0 activation_37[0][0] \n swish_108[0][0] \n__________________________________________________________________________________________________\nconv2d_146 (Conv2D) (None, 7, 7, 512) 933888 multiply_37[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_109 (BatchN (None, 7, 7, 512) 2048 conv2d_146[0][0] \n__________________________________________________________________________________________________\nconv2d_147 (Conv2D) (None, 7, 7, 3072) 1572864 batch_normalization_109[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_110 (BatchN (None, 7, 7, 3072) 12288 conv2d_147[0][0] \n__________________________________________________________________________________________________\nswish_110 (Swish) (None, 7, 7, 3072) 0 batch_normalization_110[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_38 (DepthwiseC (None, 7, 7, 3072) 27648 swish_110[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_111 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_38[0][0] \n__________________________________________________________________________________________________\nswish_111 (Swish) (None, 7, 7, 3072) 0 batch_normalization_111[0][0] \n__________________________________________________________________________________________________\nlambda_38 (Lambda) (None, 1, 1, 3072) 0 swish_111[0][0] \n__________________________________________________________________________________________________\nconv2d_148 (Conv2D) (None, 1, 1, 128) 393344 lambda_38[0][0] \n__________________________________________________________________________________________________\nswish_112 (Swish) (None, 1, 1, 128) 0 conv2d_148[0][0] \n__________________________________________________________________________________________________\nconv2d_149 (Conv2D) (None, 1, 1, 3072) 396288 swish_112[0][0] \n__________________________________________________________________________________________________\nactivation_38 (Activation) (None, 1, 1, 3072) 0 conv2d_149[0][0] \n__________________________________________________________________________________________________\nmultiply_38 (Multiply) (None, 7, 7, 3072) 0 activation_38[0][0] \n swish_111[0][0] \n__________________________________________________________________________________________________\nconv2d_150 (Conv2D) (None, 7, 7, 512) 1572864 multiply_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_112 (BatchN (None, 7, 7, 512) 2048 conv2d_150[0][0] \n__________________________________________________________________________________________________\ndrop_connect_31 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_112[0][0] \n__________________________________________________________________________________________________\nadd_31 (Add) (None, 7, 7, 512) 0 drop_connect_31[0][0] \n batch_normalization_109[0][0] \n__________________________________________________________________________________________________\nconv2d_151 (Conv2D) (None, 7, 7, 3072) 1572864 add_31[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_113 (BatchN (None, 7, 7, 3072) 12288 conv2d_151[0][0] \n__________________________________________________________________________________________________\nswish_113 (Swish) (None, 7, 7, 3072) 0 batch_normalization_113[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_39 (DepthwiseC (None, 7, 7, 3072) 27648 swish_113[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_114 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_39[0][0] \n__________________________________________________________________________________________________\nswish_114 (Swish) (None, 7, 7, 3072) 0 batch_normalization_114[0][0] \n__________________________________________________________________________________________________\nlambda_39 (Lambda) (None, 1, 1, 3072) 0 swish_114[0][0] \n__________________________________________________________________________________________________\nconv2d_152 (Conv2D) (None, 1, 1, 128) 393344 lambda_39[0][0] \n__________________________________________________________________________________________________\nswish_115 (Swish) (None, 1, 1, 128) 0 conv2d_152[0][0] \n__________________________________________________________________________________________________\nconv2d_153 (Conv2D) (None, 1, 1, 3072) 396288 swish_115[0][0] \n__________________________________________________________________________________________________\nactivation_39 (Activation) (None, 1, 1, 3072) 0 conv2d_153[0][0] \n__________________________________________________________________________________________________\nmultiply_39 (Multiply) (None, 7, 7, 3072) 0 activation_39[0][0] \n swish_114[0][0] \n__________________________________________________________________________________________________\nconv2d_154 (Conv2D) (None, 7, 7, 512) 1572864 multiply_39[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_115 (BatchN (None, 7, 7, 512) 2048 conv2d_154[0][0] \n__________________________________________________________________________________________________\ndrop_connect_32 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_115[0][0] \n__________________________________________________________________________________________________\nadd_32 (Add) (None, 7, 7, 512) 0 drop_connect_32[0][0] \n add_31[0][0] \n__________________________________________________________________________________________________\nconv2d_155 (Conv2D) (None, 7, 7, 2048) 1048576 add_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_116 (BatchN (None, 7, 7, 2048) 8192 conv2d_155[0][0] \n__________________________________________________________________________________________________\nswish_116 (Swish) (None, 7, 7, 2048) 0 batch_normalization_116[0][0] \n__________________________________________________________________________________________________\nglobal_average_pooling2d_1 (Glo (None, 2048) 0 swish_116[0][0] \n__________________________________________________________________________________________________\nfinal_output (Dense) (None, 1) 2049 global_average_pooling2d_1[0][0] \n==================================================================================================\nTotal params: 28,515,569\nTrainable params: 28,342,833\nNon-trainable params: 172,736\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"history = model.fit_generator(generator=train_generator,\n steps_per_epoch=STEP_SIZE_TRAIN,\n validation_data=valid_generator,\n validation_steps=STEP_SIZE_VALID,\n epochs=EPOCHS,\n callbacks=callback_list,\n verbose=2).history","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:29:19.159703Z","iopub.execute_input":"2024-05-03T16:29:19.160007Z","iopub.status.idle":"2024-05-03T16:54:00.513814Z","shell.execute_reply.started":"2024-05-03T16:29:19.159953Z","shell.execute_reply":"2024-05-03T16:54:00.512956Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Epoch 1/20\n - 165s - loss: 0.8146 - acc: 0.4703 - val_loss: 0.6259 - val_acc: 0.6000\nEpoch 2/20\n - 117s - loss: 0.6609 - acc: 0.5306 - val_loss: 0.5551 - val_acc: 0.6300\nEpoch 3/20\n - 117s - loss: 0.5448 - acc: 0.5893 - val_loss: 0.3890 - val_acc: 0.6743\nEpoch 4/20\n - 117s - loss: 0.4949 - acc: 0.6047 - val_loss: 0.4425 - val_acc: 0.6957\nEpoch 5/20\n - 117s - loss: 0.4370 - acc: 0.6559 - val_loss: 0.4577 - val_acc: 0.6800\nEpoch 6/20\n - 117s - loss: 0.3639 - acc: 0.6856 - val_loss: 0.4201 - val_acc: 0.7014\nEpoch 7/20\n - 117s - loss: 0.3548 - acc: 0.7036 - val_loss: 0.2987 - val_acc: 0.7686\nEpoch 8/20\n - 117s - loss: 0.3049 - acc: 0.7427 - val_loss: 0.3070 - val_acc: 0.7671\nEpoch 9/20\n - 117s - loss: 0.2947 - acc: 0.7379 - val_loss: 0.2998 - val_acc: 0.7800\nEpoch 10/20\n - 117s - loss: 0.2623 - acc: 0.7702 - val_loss: 0.3614 - val_acc: 0.7643\nEpoch 11/20\n - 117s - loss: 0.2588 - acc: 0.7697 - val_loss: 0.3135 - val_acc: 0.8000\nEpoch 12/20\n - 117s - loss: 0.2269 - acc: 0.8004 - val_loss: 0.3110 - val_acc: 0.7657\nRestoring model weights from the end of the best epoch\nEpoch 00012: early stopping\n","output_type":"stream"}]},{"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(2, 1, sharex='col', figsize=(20, 6))\n\nax1.plot(cosine_lr_1st.learning_rates)\nax1.set_title('Warm up learning rates')\n\nax2.plot(cosine_lr_2nd.learning_rates)\nax2.set_title('Fine-tune learning rates')\n\nplt.xlabel('Steps')\nplt.ylabel('Learning rate')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:54:00.515281Z","iopub.execute_input":"2024-05-03T16:54:00.515569Z","iopub.status.idle":"2024-05-03T16:54:01.037803Z","shell.execute_reply.started":"2024-05-03T16:54:00.515525Z","shell.execute_reply":"2024-05-03T16:54:01.036615Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(2, 1, sharex='col', figsize=(20, 14))\n\nax1.plot(history['loss'], label='Train loss')\nax1.plot(history['val_loss'], label='Validation loss')\nax1.legend(loc='best')\nax1.set_title('Loss')\n\nax2.plot(history['acc'], label='Train accuracy')\nax2.plot(history['val_acc'], label='Validation accuracy')\nax2.legend(loc='best')\nax2.set_title('Accuracy')\n\nplt.xlabel('Epochs')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:54:01.039806Z","iopub.execute_input":"2024-05-03T16:54:01.040445Z","iopub.status.idle":"2024-05-03T16:54:01.685079Z","shell.execute_reply.started":"2024-05-03T16:54:01.040373Z","shell.execute_reply":"2024-05-03T16:54:01.684157Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"# Create empty arays to keep the predictions and labels\ndf_preds = pd.DataFrame(columns=['label', 'pred', 'set'])\ntrain_generator.reset()\nvalid_generator.reset()\n\n# Add train predictions and labels\nfor i in range(STEP_SIZE_TRAIN + 1):\n im, lbl = next(train_generator)\n preds = model.predict(im, batch_size=train_generator.batch_size)\n for index in range(len(preds)):\n df_preds.loc[len(df_preds)] = [lbl[index], preds[index][0], 'train']\n\n# Add validation predictions and labels\nfor i in range(STEP_SIZE_VALID + 1):\n im, lbl = next(valid_generator)\n preds = model.predict(im, batch_size=valid_generator.batch_size)\n for index in range(len(preds)):\n df_preds.loc[len(df_preds)] = [lbl[index], preds[index][0], 'validation']\n\ndf_preds['label'] = df_preds['label'].astype('int')","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:54:01.686468Z","iopub.execute_input":"2024-05-03T16:54:01.686748Z","iopub.status.idle":"2024-05-03T16:55:31.340919Z","shell.execute_reply.started":"2024-05-03T16:54:01.686695Z","shell.execute_reply":"2024-05-03T16:55:31.340186Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"def classify(x):\n if x < 0.5:\n return 0\n elif x < 1.5:\n return 1\n elif x < 2.5:\n return 2\n elif x < 3.5:\n return 3\n return 4\n\n# Classify predictions\ndf_preds['predictions'] = df_preds['pred'].apply(lambda x: classify(x))\n\ntrain_preds = df_preds[df_preds['set'] == 'train']\nvalidation_preds = df_preds[df_preds['set'] == 'validation']","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:55:31.342226Z","iopub.execute_input":"2024-05-03T16:55:31.342463Z","iopub.status.idle":"2024-05-03T16:55:31.365657Z","shell.execute_reply.started":"2024-05-03T16:55:31.342424Z","shell.execute_reply":"2024-05-03T16:55:31.364927Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"labels = ['0 - No DR', '1 - Mild', '2 - Moderate', '3 - Severe', '4 - Proliferative DR']\ndef plot_confusion_matrix(train, validation, labels=labels):\n train_labels, train_preds = train\n validation_labels, validation_preds = validation\n fig, (ax1, ax2) = plt.subplots(1, 2, sharex='col', figsize=(24, 7))\n train_cnf_matrix = confusion_matrix(train_labels, train_preds)\n validation_cnf_matrix = confusion_matrix(validation_labels, validation_preds)\n\n train_cnf_matrix_norm = train_cnf_matrix.astype('float') / train_cnf_matrix.sum(axis=1)[:, np.newaxis]\n validation_cnf_matrix_norm = validation_cnf_matrix.astype('float') / validation_cnf_matrix.sum(axis=1)[:, np.newaxis]\n\n train_df_cm = pd.DataFrame(train_cnf_matrix_norm, index=labels, columns=labels)\n validation_df_cm = pd.DataFrame(validation_cnf_matrix_norm, index=labels, columns=labels)\n\n sns.heatmap(train_df_cm, annot=True, fmt='.2f', cmap=\"Blues\",ax=ax1).set_title('Train')\n sns.heatmap(validation_df_cm, annot=True, fmt='.2f', cmap=sns.cubehelix_palette(8),ax=ax2).set_title('Validation')\n plt.show()\n\nplot_confusion_matrix((train_preds['label'], train_preds['predictions']), (validation_preds['label'], validation_preds['predictions']))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:55:31.367134Z","iopub.execute_input":"2024-05-03T16:55:31.367742Z","iopub.status.idle":"2024-05-03T16:55:32.392564Z","shell.execute_reply.started":"2024-05-03T16:55:31.367595Z","shell.execute_reply":"2024-05-03T16:55:32.391434Z"},"trusted":true},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"def evaluate_model(train, validation):\n train_labels, train_preds = train\n validation_labels, validation_preds = validation\n print(\"Train Cohen Kappa score: %.3f\" % cohen_kappa_score(train_preds, train_labels, weights='quadratic'))\n print(\"Validation Cohen Kappa score: %.3f\" % cohen_kappa_score(validation_preds, validation_labels, weights='quadratic'))\n print(\"Complete set Cohen Kappa score: %.3f\" % cohen_kappa_score(np.append(train_preds, validation_preds), np.append(train_labels, validation_labels), weights='quadratic'))\n \nevaluate_model((train_preds['label'], train_preds['predictions']), (validation_preds['label'], validation_preds['predictions']))","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:55:32.394808Z","iopub.execute_input":"2024-05-03T16:55:32.395247Z","iopub.status.idle":"2024-05-03T16:55:32.428238Z","shell.execute_reply.started":"2024-05-03T16:55:32.395184Z","shell.execute_reply":"2024-05-03T16:55:32.427712Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"Train Cohen Kappa score: 0.924\nValidation Cohen Kappa score: 0.895\nComplete set Cohen Kappa score: 0.918\n","output_type":"stream"}]},{"cell_type":"code","source":"def apply_tta(model, generator, steps=10):\n step_size = generator.n//generator.batch_size\n preds_tta = []\n for i in range(steps):\n generator.reset()\n preds = model.predict_generator(generator, steps=step_size)\n preds_tta.append(preds)\n\n return np.mean(preds_tta, axis=0)\n\npreds = apply_tta(model, test_generator)\npredictions = [classify(x) for x in preds]\n\nresults = pd.DataFrame({'id_code':test['id_code'], 'diagnosis':predictions})\nresults['id_code'] = results['id_code'].map(lambda x: str(x)[:-4])","metadata":{"execution":{"iopub.status.busy":"2024-05-03T16:55:32.429113Z","iopub.execute_input":"2024-05-03T16:55:32.429439Z","iopub.status.idle":"2024-05-03T17:04:49.048916Z","shell.execute_reply.started":"2024-05-03T16:55:32.429404Z","shell.execute_reply":"2024-05-03T17:04:49.048013Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"# Cleaning created directories\nif os.path.exists(train_dest_path):\n shutil.rmtree(train_dest_path)\nif os.path.exists(validation_dest_path):\n shutil.rmtree(validation_dest_path)\nif os.path.exists(test_dest_path):\n shutil.rmtree(test_dest_path)","metadata":{"execution":{"iopub.status.busy":"2024-05-03T17:04:49.050616Z","iopub.execute_input":"2024-05-03T17:04:49.050952Z","iopub.status.idle":"2024-05-03T17:04:49.343118Z","shell.execute_reply.started":"2024-05-03T17:04:49.050887Z","shell.execute_reply":"2024-05-03T17:04:49.342354Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"fig = plt.subplots(sharex='col', figsize=(24, 8.7))\nsns.countplot(x=\"diagnosis\", data=results).set_title('Test')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-03T17:04:49.345001Z","iopub.execute_input":"2024-05-03T17:04:49.345403Z","iopub.status.idle":"2024-05-03T17:04:49.719468Z","shell.execute_reply.started":"2024-05-03T17:04:49.345337Z","shell.execute_reply":"2024-05-03T17:04:49.718725Z"},"trusted":true},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"iVBORw0KGgoAAAANSUhEUgAABX8AAAIbCAYAAACzEO00AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3X101vV9//HXlQSCYpWiAkFd1dl6WJkTycrp5vQM6xAH6LZaOLS2Fq1zllPqvON4Q069qQLuqEdZ7Vqnp2dWz9YdtaSu2Ja6TkddZVqJeNQiOicBDjcObzDB5Pr9sd9y6lFiikmu+OHx+Cu5Pt/v9X0n5+QieebLJ5VqtVoNAAAAAABFqav1AAAAAAAA9D/xFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABSoodYDAADAUDFp0qSet3fu3Jnhw4envr4+SfK1r30ts2bN2qPn/cxnPpPPfvazOe200/plTgAA6AvxFwAA/r/HH3+85+2pU6fmmmuuyR/8wR/UcCIAANhztn0AAIA+6urqyrJly3LSSSdlypQpufDCC7Njx44kyRtvvJELLrggn/jEJ9Lc3Jwzzjgj//M//5Prr78+a9asyRVXXJFJkybl+uuvr/FHAQDA3sKdvwAA0Ee33357HnnkkXz3u9/NqFGj0tLSkuuuuy7XXXdd/umf/ildXV352c9+loaGhqxduzbDhg3LwoUL85//+Z+2fQAAYNC58xcAAPronnvuyYUXXpixY8emsbExX/7yl/PAAw+kWq2moaEh27Zty3/913+loaEhxxxzTPbdd99ajwwAwF7Mnb8AANAH1Wo1GzduzLnnnptKpdLzeHd3d7Zv354zzjgjW7ZsyVe+8pW88cYbOf3007NgwYKePxgHAACDTfwFAIA+qFQqGTt2bG655ZZMnDjxXY9ZsGBBFixYkJdeeilnn312jjrqqMyaNettsRgAAAaLbR8AAKCP5syZk7/5m79Je3t7kmTr1q1ZuXJlkuTf//3f86tf/Srd3d0ZOXJk6uvre+76PfDAA/PSSy/VbG4AAPZO4i8AAPTROeeck09+8pP5whe+kEmTJmXOnDlZu3ZtkmTTpk05//zzc9xxx2XmzJk58cQTM3369CTJWWedlfvvvz+///u/nyVLltTyQwAAYC9SqVar1VoPAQAAAABA/3LnLwAAAABAgcRfAAAAAIACib8AAAAAAAUqNv5Wq9V0dHTElsYAAAAAwN6o2Pjb2dmZtra2dHZ21noUAAAAAIBBV2z8BQAAAADYm4m/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAGAI6nxrV61HgOL4ugIA9jYNtR4AAIB3Gt4wLGfdsaDWY0BR7vzizbUeAQBgULnzFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQoEGLvz/96U9z+umn57TTTsvMmTPz4IMPJknWr1+f2bNnZ9q0aZk9e3ZeeOGFnnN6WwMAAAAAYPcGJf5Wq9VccsklWbJkSe6///4sXbo0l156abq7u9PS0pK5c+dmxYoVmTt3bhYtWtRzXm9rAAAAAADs3qDd+VtXV5dXX301SfLqq69mzJgx2b59e9auXZsZM2YkSWbMmJG1a9dm27Zt2bp1627XAAAAAADoXcNgXKRSqeSmm27K+eefn3333Tevv/56vvnNb6a9vT1jx45NfX19kqS+vj5jxoxJe3t7qtXqbtdGjx7d52u3tbUNyMcEADCQJk+eXOsRoEirV6+u9QgAAHtkT35GGJT4+9Zbb+Wb3/xm/vZv/zaTJ0/O6tWrc8EFF2TJkiUDfu2JEyemsbFxwK8DAAAMfX6xAgDsTQYl/j799NPZvHlzzzdakydPzj777JPGxsZs2rQpXV1dqa+vT1dXVzZv3pympqZUq9XdrgEAAAAA0LtB2fN33Lhx2bhxY55//vkkybp167Jly5Z85CMfyYQJE9La2pokaW1tzYQJEzJ69OgceOCBu10DAAAAAKB3lWq1Wh2MC33/+9/Pt771rVQqlSTJV77ylXzqU5/KunXrsnDhwuzYsSP7779/Fi9enCOPPDJJel17Lx0dHWlra7PtAwDwgXXWHQtqPQIU5c4v3lzrEQAABtWgxd/BJv4CAB904i/0L/EXANjbDMq2DwAAAAAADC7xFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAo0KDF346OjrS0tORP/uRPMnPmzFx55ZVJkvXr12f27NmZNm1aZs+enRdeeKHnnN7WAAAAAADYvUGLv0uXLk1jY2NWrFiR5cuXZ8GCBUmSlpaWzJ07NytWrMjcuXOzaNGinnN6WwMAAAAAYPcGJf6+/vrrue+++7JgwYJUKpUkyUEHHZStW7dm7dq1mTFjRpJkxowZWbt2bbZt29brGgAAAAAAvWsYjIu89NJLGTVqVG699dY8+uijGTlyZBYsWJARI0Zk7Nixqa+vT5LU19dnzJgxaW9vT7Va3e3a6NGj+3zttra2AfmYAAAG0uTJk2s9AhRp9erVtR4BAGCP7MnPCIMSf99666289NJL+Z3f+Z1ceuml+eUvf5nzzjsvN99884Bfe+LEiWlsbBzw6wAAAEOfX6wAAHuTQYm/48ePT0NDQ88WDr/3e7+XD3/4wxkxYkQ2bdqUrq6u1NfXp6urK5s3b05TU1Oq1epu1wAAAAAA6N2g7Pk7evToTJkyJY888kiSZP369dm6dWsOP/zwTJgwIa2trUmS1tbWTJgwIaNHj86BBx642zUAAAAAAHpXqVar1cG40EsvvZTLLrssr7zyShoaGvLVr341J554YtatW5eFCxdmx44d2X///bN48eIceeSRSdLr2nvp6OhIW1ubbR8AgA+ss+5YUOsRoCh3fnHgt50DABhKBi3+DjbxFwD4oBN/oX+JvwDA3mZQtn0AAAAAAGBwib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABSoz/H39ttvf9fH77jjjn4bBgAAAACA/tHn+Lts2bJ3ffwb3/hGvw0DAAAAAED/aHivA1atWpUk6e7uzs9//vNUq9Wetf/+7//OyJEjB246AAAAAAD2yHvG38svvzxJ0tHRkcsuu6zn8UqlkoMPPjhXXHHFwE0HAAAAAMAeec/4u3LlyiTJJZdckiVLlgz4QAAAAAAAvH/vGX//z6+H3+7u7ret1dX1eetgAAAAAAAGQZ/j71NPPZWrrroqzzzzTDo6OpIk1Wo1lUolTz/99IANCAAAAADAb67P8XfhwoX54z/+43z961/PiBEjBnImAAAAAADepz7H35dffjkXXHBBKpXKQM4DAAAAAEA/6PNmvSeffHIefvjhgZwFAAAAAIB+0uc7fzs6OjJ//vxMnjw5Bx100NvWfv2PwQEAAAAAUHt9jr9HHXVUjjrqqIGcBQAAAACAftLn+Dt//vyBnAMAAAAAgH7U5/i7atWq3a598pOf7JdhAAAAAADoH32Ov5dffvnb3t++fXt27dqVsWPH5ic/+Um/DwYAAAAAwJ7rc/xduXLl297v6urKN77xjYwcObLfhwIAAAAA4P2p29MT6+vrc9555+Xb3/52f84DAAAAAEA/2OP4mySPPPJIKpVKf80CAAAAAEA/6fO2DyeeeOLbQu/OnTvT2dmZlpaWARkMAAAAAIA91+f4u3Tp0re9v88+++SII47Ifvvt1+9DAQAAAADw/vQ5/n7iE59IknR3d2fLli056KCDUlf3vnaNAAAAAABggPS53r722mu55JJLcswxx+SEE07IMccck0svvTSvvvrqQM4HAAAAAMAe6HP8veaaa7Jz584sX748Tz75ZJYvX56dO3fmmmuuGcj5AAAAAADYA33e9uHf/u3f8uMf/zj77LNPkuSII47Iddddl5NPPnnAhgMAAAAAYM/0+c7fxsbGbNu27W2Pbd++PcOHD+/3oQAAAAAAeH/6fOfvpz/96cybNy9nnXVWxo8fnw0bNuTOO+/MGWecMZDzAQAAAACwB/ocf//qr/4qY8eOzfLly7N58+aMGTMm55xzjvgLAAAAADAE9Xnbh2uvvTZHHHFE7rzzzjzwwAO5884789u//du59tprB3I+AAAAAAD2QJ/jb2trayZOnPi2xyZOnJjW1tZ+HwoAAAAAgPenz/G3Uqmku7v7bY91dXW94zEAAAAAAGqvz/G3ubk5N998c0/s7e7uzi233JLm5uYBGw4AAAAAgD3T5z/4dvnll+cv//Ivc/zxx2f8+PFpb2/PwQcfnNtuu20g5wMAAAAAYA/0Of6OGzcu9957b5588sm0t7enqakpxxxzTOrq+nzzMAAAAAAAg6TP8TdJ6urqcuyxx+bYY48dqHkAAAAAAOgHbtsFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoECDHn9vvfXWHH300Xn22WeTJE888URmzZqVadOmZd68edm6dWvPsb2tAQAAAACwe4Maf5966qk88cQTGT9+fJKkWq3m4osvzqJFi7JixYo0NzfnhhtueM81AAAAAAB6N2jxt7OzM1dddVVaWlpSqVSSJGvWrEljY2Oam5uTJHPmzMkPf/jD91wDAAAAAKB3DYN1oZtvvjmzZs3KYYcd1vNYe3t7z13ASTJ69Oh0d3fnlVde6XVt1KhRfb5uW1tb/3wAAACDaPLkybUeAYq0evXqWo8AALBH9uRnhEGJv48//njWrFmTiy66aDAu9zYTJ05MY2PjoF8XAAAYevxiBQDYmwxK/P3FL36R559/PieddFKSZOPGjTn77LNz5plnZsOGDT3Hbdu2LZVKJaNGjUpTU9Nu1wAAAAAA6N2g7Pl77rnn5uGHH87KlSuzcuXKjBs3LrfffnvOOeecvPnmm3nssceSJPfcc0+mT5+e5H/v2N3dGgAAAAAAvRu0PX/fTV1dXZYsWZKWlpZ0dHTkkEMOydKlS99zDQAAAACA3lWq1Wq11kMMhI6OjrS1tdnzFwD4wDrrjgW1HgGKcucXb671CAAAg2pQtn0AAAAAAGBwib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAADAB1hX565ajwDF8XUFlKKh1gMAAACw5+qHD8sDn/9irceAopz6nTtqPQJAv3DnLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFGETdb+2q9QhQJF9bAAAA79RQ6wEA9iZ1DcOyesk5tR4DijP5km/XegQAAIAhx52/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxN9+0Lmrq9YjQJF8bQEAAADsuYZaD1CC4cPqM/eSu2o9BhTnu0s+W+sRAAAAAD6w3PkLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUaFDi7/bt2/OlL30p06ZNy8yZMzN//vxs27YtSfLEE09k1qxZmTZtWubNm5etW7f2nNfbGgAAAAAAuzco8bdSqeScc87JihUrsnz58hx22GG54YYbUq1Wc/HFF2fRokVZsWJFmpubc8MNNyRJr2sAAAAAAPRuUOLvqFGjMmXKlJ73jz322GzYsCFr1qxJY2NjmpubkyRz5szJD3/4wyTpdQ0AAAAAgN41DPYFu7u7c/fdd2fq1Klpb2/P+PHje9ZGjx6d7u7uvPLKK72ujRo1qs/Xa2tr69f5383kyZMH/Bqwt1q9enWtR+hXXi9g4Hi9APqitNeKxOsFDJQSXy+AD7Y9+Td/0OPv1VdfnX333Tef+9zn8qMf/WjArzdx4sQ0NjYO+HWAgeGHGaCvvF4AfeG1AugrrxdACQY1/i5evDgvvvhibrvtttTV1aWpqSkbNmzoWd+2bVsqlUpGjRrV6xoAAAAAAL0blD1/k+TGG29MW1tbli1bluHDhyf537ty33zzzTz22GNJknvuuSfTp09/zzUAAAAAAHo3KHf+Pvfcc7ntttty+OGHZ86cOUmSQw89NMuWLcuSJUvS0tKSjo6OHHLIIVm6dGmSpK6ubrdrAAAAAAD0blDi70c/+tE888wz77p23HHHZfny5b/xGgAAAAAAuzdo2z4AAAAAADB4xF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUCDxFwAAAACgQOIvAAAAAECBxF8AAAAAgAKJvwAAAAAABRJ/AQAAAAAKJP4CAAAAABRI/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAOwF3trVVesRoDhD/euqodYDAAAAADDwGobV5+uXf6/WY0BRLrv207UeoVfu/AUAAAAAKJD4CwAAAABQIPEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn8BAAAAAAok/gIAAAAAFEj8BQAAAAAokPgLAAAAAFAg8RcAAAAAoEDiLwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAAAAACiQ+AsAAAAAUKAhH3/Xr1+f2bNnZ9q0aZk9e3ZeeOGFWo8EAAAAADDkDfn429LSkrlz52bFihWZO3duFi1aVOuRAAAAAACGvIZaD9CbrVu3Zu3atbnjjjuSJDNmzMjVV1+dbdu2ZfTo0b2eW61WkySdnZ0DPmeS7L/vsEG5DuxNOjo6aj3CwBjxoVpPAMUp9fXiQ8NG1noEKEqprxVJUvch319Afyr59WLEvkM6BcEHzmC/XgwfPjyVSqXPx1eq/1dJh6C2trZceuml+cEPftDz2KmnnpqlS5fm4x//eK/nvvrqq3n22WcHekQAAAAAgEExceLENDY29vn4Yn/dM3LkyHzsYx/LsGHDfqMaDgAAAAAwFA0fPvw3On5Ix9+mpqZs2rQpXV1dqa+vT1dXVzZv3pympqb3PLeuri4f8l+fAAAAAIC91JD+g28HHnhgJkyYkNbW1iRJa2trJkyY8J77/QIAAAAA7O2G9J6/SbJu3bosXLgwO3bsyP7775/FixfnyCOPrPVYAAAAAABD2pCPvwAAAAAA/OaG9LYPAAAAAADsGfEXAAAAAKBA4i8AAAAAQIHEXwAAAACAAom/AAAAAAAFEn/Za6xfvz6zZ8/OtGnTMnv27Lzwwgu1HgkYghYvXpypU6fm6KOPzrPPPlvrcYAhbPv27fnSl76UadOmZebMmZk/f362bdtW67GAIej888/PrFmzcvrpp2fu3Ll5+umnaz0SMMTdeuutfiahX4i/7DVaWloyd+7crFixInPnzs2iRYtqPRIwBJ100km56667csghh9R6FGCIq1QqOeecc7JixYosX748hx12WG644YZajwUMQYsXL873v//93HfffZk3b14uu+yyWo8EDGFPPfVUnnjiiYwfP77Wo1AA8Ze9wtatW7N27drMmDEjSTJjxoysXbvW3TnAOzQ3N6epqanWYwAfAKNGjcqUKVN63j/22GOzYcOGGk4EDFUf+tCHet5+7bXXUqlUajgNMJR1dnbmqquuSktLi9cK+kVDrQeAwdDe3p6xY8emvr4+SVJfX58xY8akvb09o0ePrvF0AMAHXXd3d+6+++5MnTq11qMAQ9Tll1+eRx55JNVqNd/+9rdrPQ4wRN18882ZNWtWDjvssFqPQiGHoVzEAAAHXklEQVTc+QsAAO/T1VdfnX333Tef+9znaj0KMERde+21eeihh3LBBRdkyZIltR4HGIIef/zxrFmzJnPnzq31KBRE/GWv0NTUlE2bNqWrqytJ0tXVlc2bN/uv3QDA+7Z48eK8+OKLuemmm1JX59troHenn356Hn300Wzfvr3WowBDzC9+8Ys8//zzOemkkzJ16tRs3LgxZ599dh5++OFaj8YHmO9O2SsceOCBmTBhQlpbW5Mkra2tmTBhgi0fAID35cYbb0xbW1uWLVuW4cOH13ocYAh6/fXX097e3vP+ypUrc8ABB2TUqFE1nAoYis4999w8/PDDWblyZVauXJlx48bl9ttvz/HHH1/r0fgAq1Sr1Wqth4DBsG7duixcuDA7duzI/vvvn8WLF+fII4+s9VjAEHPNNdfkwQcfzJYtW/LhD384o0aNyg9+8INajwUMQc8991xmzJiRww8/PCNGjEiSHHrooVm2bFmNJwOGki1btuT888/Pzp07U1dXlwMOOCCXXnppPv7xj9d6NGCImzp1am677bZ87GMfq/UofICJvwAAAAAABbLtAwAAAABAgcRfAAAAAIACib8AAAAAAAUSfwEAAAAACiT+AgAAAAAUSPwFAKB4CxcuzI033pjHHnss06ZNq/U4u3Xbbbfl8ssvr/UYAAAUoqHWAwAAwGBpbm7OihUraj3Gbp133nm1HgEAgIK48xcAAAAAoEDiLwAAxVm7dm3+7M/+LJMmTcpXv/rVdHR0JEkeffTRnHDCCT3H/d3f/V0+9alPZdKkSTn11FPzox/9qGetq6sr119/faZMmZKpU6fmH/7hH3L00UfnrbfeSpKceeaZuemmmzJnzpxMmjQp8+bNy7Zt23rO/8lPfpI//dM/TXNzc84888ysW7fubdf9oz/6o0yaNCnTpk3LqlWrkiS33HJLLrrooiRJR0dHLrrookyZMiXNzc35i7/4i2zZsmXgPmkAABRH/AUAoCidnZ358pe/nNNOOy3/8R//kVNOOSUPPvjgux572GGH5a677srq1aszf/78XHzxxdm8eXOS5B//8R/zs5/9LPfff3/uvffe/PjHP37H+a2trbnuuuuyatWq7Nq1K3//93+fJFm/fn0uvPDCXHbZZVm1alVOOOGEnHfeeens7Mzzzz+fu+66K9/73vfy+OOP5/bbb88hhxzyjue+995789prr+Whhx7Ko48+mq997WsZMWJEP36mAAAonfgLAEBRfvnLX2bXrl35whe+kGHDhuWUU07J7/7u777rsdOnT8/YsWNTV1eXU089NR/5yEfy5JNPJkn+5V/+JZ///Oczbty4HHDAATn33HPfcf6f//mf54gjjsiIESNyyimn5Omnn06SPPDAAznxxBPzh3/4hxk2bFjOPvvsvPnmm3n88cdTX1+fzs7OrFu3Lrt27cqhhx6a3/qt33rHczc0NOSVV17Jiy++mPr6+kycODH77bdfP36mAAAonT/4BgBAUTZv3pyxY8emUqn0PDZ+/Ph3Pfa+++7LHXfckZdffjlJ8sYbb2T79u09z9PU1NRz7Lhx495x/sEHH9zz9j777JM33nij59xfv2ZdXV2ampqyadOmTJkyJZdddlluueWW/OpXv8rxxx+fhQsXZuzYsW977tNOOy0bN27MX//1X2fHjh2ZNWtWLrjgggwbNuw3/ZQAALCXcucvAABFOfjgg7Np06ZUq9WexzZs2PCO415++eVcccUVufLKK/Poo4/msccey0c/+tG3Pc/GjRt73v/1t9/LmDFj3nbNarWa9vb2nsA7c+bM3H333fnpT3+aSqWSG2644R3PMWzYsMyfPz8PPPBA7rnnnjz00EO57777+jwDAACIvwAAFOXYY49NQ0NDvvOd7+Stt97Kgw8+mDVr1rzjuJ07d6ZSqWT06NFJkn/+53/Oc88917M+ffr0fOc738mmTZuyY8eOfOtb3+rzDNOnT8+//uu/vm0v4OHDh2fSpEl5/vnns2rVqnR2dmb48OFpbGxMfX39O57j5z//eZ555pl0dXVlv/32S0NDw7seBwAAu2PbBwAAijJ8+PDccsstufLKK3PTTTflxBNPzMknn/yO44466qjMmzcvc+bMSaVSyemnn57jjjuuZ/0zn/lMXnjhhcyaNSsjR/6/du4YNaEgCsPon0IR4yrsLN2BhZWFKxA3YqFoKZaCu5HXugftB+wVEZI2EIsHKYSXcxYwXKb8mLmfmc/nOZ1OtQJsv9/PdrvNZrNJKSWDwSCHwyHtdjuPxyO73S7n8zmtVivD4TDr9frXGdfrNcvlMqWUdLvdTCaTTKfTv10OAAD/ysfXz/9wAADAS1VVZbVa5Xg8vnsUAACoxdoHAAB44X6/p6qqPJ/PlFKy3+8zHo/fPRYAANTm5S8AALxwu90ym81yuVzS6XQyGo2yWCzS6/XePRoAANQi/gIAAAAANJC1DwAAAAAADST+AgAAAAA0kPgLAAAAANBA4i8AAAAAQAOJvwAAAAAADfQNw1oK5t2BgzwAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":"results.to_csv('submission_4.csv', index=False)\ndisplay(results.head())","metadata":{"execution":{"iopub.status.busy":"2024-05-03T17:04:49.720936Z","iopub.execute_input":"2024-05-03T17:04:49.721171Z","iopub.status.idle":"2024-05-03T17:04:50.063981Z","shell.execute_reply.started":"2024-05-03T17:04:49.72113Z","shell.execute_reply":"2024-05-03T17:04:50.063214Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":" id_code diagnosis\n0 0005cfc8afb6 2\n1 003f0afdcd15 3\n2 006efc72b638 2\n3 00836aaacf06 2\n4 009245722fa4 3","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosis
00005cfc8afb62
1003f0afdcd153
2006efc72b6382
300836aaacf062
4009245722fa43
\n
"},"metadata":{}}]},{"cell_type":"code","source":"# model.save_weights('../working/effNetB5_bs32_img224_fold4.h5')","metadata":{"execution":{"iopub.status.busy":"2024-05-03T17:04:50.065442Z","iopub.execute_input":"2024-05-03T17:04:50.065703Z","iopub.status.idle":"2024-05-03T17:04:50.068842Z","shell.execute_reply.started":"2024-05-03T17:04:50.065652Z","shell.execute_reply":"2024-05-03T17:04:50.068212Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"markdown","source":"FOLD_5","metadata":{}},{"cell_type":"code","source":"import os\nimport sys\nimport cv2\nimport shutil\nimport random\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom tensorflow import set_random_seed\nfrom sklearn.utils import class_weight\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, cohen_kappa_score\nfrom keras import backend as K\nfrom keras.models import Model\nfrom keras.utils import to_categorical\nfrom keras import optimizers, applications\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.layers import Dense, Dropout, GlobalAveragePooling2D, Input\nfrom keras.callbacks import EarlyStopping, ReduceLROnPlateau, Callback, LearningRateScheduler\n\ndef seed_everything(seed=0):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n set_random_seed(0)\n\nseed = 0\nseed_everything(seed)\n%matplotlib inline\nsns.set(style=\"whitegrid\")\nwarnings.filterwarnings(\"ignore\")\nsys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))\nfrom efficientnet import *","metadata":{"execution":{"iopub.status.busy":"2024-05-04T01:55:34.272429Z","iopub.execute_input":"2024-05-04T01:55:34.27277Z","iopub.status.idle":"2024-05-04T01:55:39.406674Z","shell.execute_reply.started":"2024-05-04T01:55:34.272711Z","shell.execute_reply":"2024-05-04T01:55:39.405839Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\nUsing TensorFlow backend.\n","output_type":"stream"}]},{"cell_type":"code","source":"fold_set = pd.read_csv('/kaggle/input/main-data/5-fold.csv')\nX_train = fold_set[fold_set['fold_4'] == 'train']\nX_val = fold_set[fold_set['fold_4'] == 'validation']\ntest = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')\nprint('Number of train samples: ', X_train.shape[0])\nprint('Number of validation samples: ', X_val.shape[0])\nprint('Number of test samples: ', test.shape[0])\n\n# Preprocecss data\nX_train[\"id_code\"] = X_train[\"id_code\"].apply(lambda x: x + \".png\")\nX_val[\"id_code\"] = X_val[\"id_code\"].apply(lambda x: x + \".png\")\ntest[\"id_code\"] = test[\"id_code\"].apply(lambda x: x + \".png\")\ndisplay(X_train.head())","metadata":{"execution":{"iopub.status.busy":"2024-05-04T02:01:17.117436Z","iopub.execute_input":"2024-05-04T02:01:17.117796Z","iopub.status.idle":"2024-05-04T02:01:17.35752Z","shell.execute_reply.started":"2024-05-04T02:01:17.117742Z","shell.execute_reply":"2024-05-04T02:01:17.356738Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Number of train samples: 2930\nNumber of validation samples: 732\nNumber of test samples: 1928\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" id_code diagnosis height width fold_0 fold_1 fold_2 \\\n0 000c1434d8d7.png 2 2136.0 3216.0 train train train \n2 0024cdab0c1e.png 1 1736.0 2416.0 validation train train \n3 002c21358ce6.png 0 1050.0 1050.0 train train train \n4 005b95c28852.png 0 1536.0 2048.0 validation train train \n5 0083ee8054ee.png 4 2588.0 3388.0 train train validation \n\n fold_3 fold_4 \n0 validation train \n2 train train \n3 validation train \n4 train train \n5 train train ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosisheightwidthfold_0fold_1fold_2fold_3fold_4
0000c1434d8d7.png22136.03216.0traintraintrainvalidationtrain
20024cdab0c1e.png11736.02416.0validationtraintraintraintrain
3002c21358ce6.png01050.01050.0traintraintrainvalidationtrain
4005b95c28852.png01536.02048.0validationtraintraintraintrain
50083ee8054ee.png42588.03388.0traintrainvalidationtraintrain
\n
"},"metadata":{}}]},{"cell_type":"code","source":"FACTOR = 4\nBATCH_SIZE = 8 * FACTOR\nEPOCHS = 20\nWARMUP_EPOCHS = 5\nLEARNING_RATE = 1e-4 * FACTOR\nWARMUP_LEARNING_RATE = 1e-3 * FACTOR\nHEIGHT = 224\nWIDTH = 224\nCHANNELS = 3\nES_PATIENCE = 5\nRLROP_PATIENCE = 3\nDECAY_DROP = 0.5\nLR_WARMUP_EPOCHS_1st = 2\nLR_WARMUP_EPOCHS_2nd = 5\nSTEP_SIZE = len(X_train) // BATCH_SIZE\nTOTAL_STEPS_1st = WARMUP_EPOCHS * STEP_SIZE\nTOTAL_STEPS_2nd = EPOCHS * STEP_SIZE\nWARMUP_STEPS_1st = LR_WARMUP_EPOCHS_1st * STEP_SIZE\nWARMUP_STEPS_2nd = LR_WARMUP_EPOCHS_2nd * STEP_SIZE","metadata":{"execution":{"iopub.status.busy":"2024-05-04T02:01:19.60279Z","iopub.execute_input":"2024-05-04T02:01:19.603129Z","iopub.status.idle":"2024-05-04T02:01:19.610482Z","shell.execute_reply.started":"2024-05-04T02:01:19.603076Z","shell.execute_reply":"2024-05-04T02:01:19.609641Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"train_base_path = '../input/aptos2019-blindness-detection/train_images/'\ntest_base_path = '../input/aptos2019-blindness-detection/test_images/'\ntrain_dest_path = 'base_dir/train_images/'\nvalidation_dest_path = 'base_dir/validation_images/'\ntest_dest_path = 'base_dir/test_images/'\n\nif os.path.exists(train_dest_path):\n shutil.rmtree(train_dest_path)\nif os.path.exists(validation_dest_path):\n shutil.rmtree(validation_dest_path)\nif os.path.exists(test_dest_path):\n shutil.rmtree(test_dest_path)\n \nos.makedirs(train_dest_path)\nos.makedirs(validation_dest_path)\nos.makedirs(test_dest_path)\n\ndef crop_image(img, tol=7):\n if img.ndim ==2:\n mask = img>tol\n return img[np.ix_(mask.any(1),mask.any(0))]\n elif img.ndim==3:\n gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n mask = gray_img>tol\n check_shape = img[:,:,0][np.ix_(mask.any(1),mask.any(0))].shape[0]\n if (check_shape == 0): # image is too dark so that we crop out everything,\n return img # return original image\n else:\n img1=img[:,:,0][np.ix_(mask.any(1),mask.any(0))]\n img2=img[:,:,1][np.ix_(mask.any(1),mask.any(0))]\n img3=img[:,:,2][np.ix_(mask.any(1),mask.any(0))]\n img = np.stack([img1,img2,img3],axis=-1)\n \n return img\n\ndef circle_crop(img):\n img = crop_image(img)\n\n height, width, depth = img.shape\n largest_side = np.max((height, width))\n img = cv2.resize(img, (largest_side, largest_side))\n\n height, width, depth = img.shape\n\n x = width//2\n y = height//2\n r = np.amin((x, y))\n\n circle_img = np.zeros((height, width), np.uint8)\n cv2.circle(circle_img, (x, y), int(r), 1, thickness=-1)\n img = cv2.bitwise_and(img, img, mask=circle_img)\n img = crop_image(img)\n\n return img\n \ndef preprocess_image(base_path, save_path, image_id, HEIGHT, WIDTH, sigmaX=10):\n image = cv2.imread(base_path + image_id)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image = circle_crop(image)\n image = cv2.resize(image, (HEIGHT, WIDTH))\n image = cv2.addWeighted(image, 4, cv2.GaussianBlur(image, (0,0), sigmaX), -4 , 128)\n cv2.imwrite(save_path + image_id, image)\n \n# Pre-procecss train set\nfor i, image_id in enumerate(X_train['id_code']):\n preprocess_image(train_base_path, train_dest_path, image_id, HEIGHT, WIDTH)\n \n# Pre-procecss validation set\nfor i, image_id in enumerate(X_val['id_code']):\n preprocess_image(train_base_path, validation_dest_path, image_id, HEIGHT, WIDTH)\n \n# Pre-procecss test set\nfor i, image_id in enumerate(test['id_code']):\n preprocess_image(test_base_path, test_dest_path, image_id, HEIGHT, WIDTH)","metadata":{"execution":{"iopub.status.busy":"2024-05-04T02:01:22.347565Z","iopub.execute_input":"2024-05-04T02:01:22.347861Z","iopub.status.idle":"2024-05-04T02:25:45.312578Z","shell.execute_reply.started":"2024-05-04T02:01:22.347818Z","shell.execute_reply":"2024-05-04T02:25:45.311941Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"datagen=ImageDataGenerator(rescale=1./255, \n rotation_range=360,\n horizontal_flip=True,\n vertical_flip=True)\n\ntrain_generator=datagen.flow_from_dataframe(\n dataframe=X_train,\n directory=train_dest_path,\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n class_mode=\"raw\",\n batch_size=BATCH_SIZE,\n target_size=(HEIGHT, WIDTH),\n seed=seed)\n\nvalid_generator=datagen.flow_from_dataframe(\n dataframe=X_val,\n directory=validation_dest_path,\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n class_mode=\"raw\",\n batch_size=BATCH_SIZE,\n target_size=(HEIGHT, WIDTH),\n seed=seed)\n\ntest_generator=datagen.flow_from_dataframe( \n dataframe=test,\n directory=test_dest_path,\n x_col=\"id_code\",\n batch_size=1,\n class_mode=None,\n shuffle=False,\n target_size=(HEIGHT, WIDTH),\n seed=seed)","metadata":{"execution":{"iopub.status.busy":"2024-05-04T02:36:29.190991Z","iopub.execute_input":"2024-05-04T02:36:29.191377Z","iopub.status.idle":"2024-05-04T02:36:29.276843Z","shell.execute_reply.started":"2024-05-04T02:36:29.191311Z","shell.execute_reply":"2024-05-04T02:36:29.276132Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Found 2930 validated image filenames.\nFound 732 validated image filenames.\nFound 1928 validated image filenames.\n","output_type":"stream"}]},{"cell_type":"code","source":"def cosine_decay_with_warmup(global_step,\n learning_rate_base,\n total_steps,\n warmup_learning_rate=0.0,\n warmup_steps=0,\n hold_base_rate_steps=0):\n\n if total_steps < warmup_steps:\n raise ValueError('total_steps must be larger or equal to warmup_steps.')\n learning_rate = 0.5 * learning_rate_base * (1 + np.cos(\n np.pi *\n (global_step - warmup_steps - hold_base_rate_steps\n ) / float(total_steps - warmup_steps - hold_base_rate_steps)))\n if hold_base_rate_steps > 0:\n learning_rate = np.where(global_step > warmup_steps + hold_base_rate_steps,\n learning_rate, learning_rate_base)\n if warmup_steps > 0:\n if learning_rate_base < warmup_learning_rate:\n raise ValueError('learning_rate_base must be larger or equal to warmup_learning_rate.')\n slope = (learning_rate_base - warmup_learning_rate) / warmup_steps\n warmup_rate = slope * global_step + warmup_learning_rate\n learning_rate = np.where(global_step < warmup_steps, warmup_rate,\n learning_rate)\n return np.where(global_step > total_steps, 0.0, learning_rate)\n\n\nclass WarmUpCosineDecayScheduler(Callback):\n\n def __init__(self,\n learning_rate_base,\n total_steps,\n global_step_init=0,\n warmup_learning_rate=0.0,\n warmup_steps=0,\n hold_base_rate_steps=0,\n verbose=0):\n\n super(WarmUpCosineDecayScheduler, self).__init__()\n self.learning_rate_base = learning_rate_base\n self.total_steps = total_steps\n self.global_step = global_step_init\n self.warmup_learning_rate = warmup_learning_rate\n self.warmup_steps = warmup_steps\n self.hold_base_rate_steps = hold_base_rate_steps\n self.verbose = verbose\n self.learning_rates = []\n\n def on_batch_end(self, batch, logs=None):\n self.global_step = self.global_step + 1\n lr = K.get_value(self.model.optimizer.lr)\n self.learning_rates.append(lr)\n\n def on_batch_begin(self, batch, logs=None):\n lr = cosine_decay_with_warmup(global_step=self.global_step,\n learning_rate_base=self.learning_rate_base,\n total_steps=self.total_steps,\n warmup_learning_rate=self.warmup_learning_rate,\n warmup_steps=self.warmup_steps,\n hold_base_rate_steps=self.hold_base_rate_steps)\n K.set_value(self.model.optimizer.lr, lr)\n if self.verbose > 0:\n print('\\nBatch %02d: setting learning rate to %s.' % (self.global_step + 1, lr))","metadata":{"execution":{"iopub.status.busy":"2024-05-04T02:36:33.510738Z","iopub.execute_input":"2024-05-04T02:36:33.511074Z","iopub.status.idle":"2024-05-04T02:36:33.529598Z","shell.execute_reply.started":"2024-05-04T02:36:33.510999Z","shell.execute_reply":"2024-05-04T02:36:33.528465Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"def create_model(input_shape):\n input_tensor = Input(shape=input_shape)\n base_model = EfficientNetB5(weights=None, \n include_top=False,\n input_tensor=input_tensor)\n base_model.load_weights('../input/efficientnet-keras-weights-b0b5/efficientnet-b5_imagenet_1000_notop.h5')\n\n x = GlobalAveragePooling2D()(base_model.output)\n final_output = Dense(1, activation='linear', name='final_output')(x)\n model = Model(input_tensor, final_output)\n \n return model","metadata":{"execution":{"iopub.status.busy":"2024-05-04T02:36:37.084394Z","iopub.execute_input":"2024-05-04T02:36:37.084694Z","iopub.status.idle":"2024-05-04T02:36:37.09163Z","shell.execute_reply.started":"2024-05-04T02:36:37.08465Z","shell.execute_reply":"2024-05-04T02:36:37.090536Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"model = create_model(input_shape=(HEIGHT, WIDTH, CHANNELS))\n\nfor layer in model.layers:\n layer.trainable = False\n\nfor i in range(-2, 0):\n model.layers[i].trainable = True\n\ncosine_lr_1st = WarmUpCosineDecayScheduler(learning_rate_base=WARMUP_LEARNING_RATE,\n total_steps=TOTAL_STEPS_1st,\n warmup_learning_rate=0.0,\n warmup_steps=WARMUP_STEPS_1st,\n hold_base_rate_steps=(2 * STEP_SIZE))\n\nmetric_list = [\"accuracy\"]\ncallback_list = [cosine_lr_1st]\noptimizer = optimizers.Adam(lr=WARMUP_LEARNING_RATE)\nmodel.compile(optimizer=optimizer, loss='mean_squared_error', metrics=metric_list)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-04T02:36:39.902445Z","iopub.execute_input":"2024-05-04T02:36:39.902747Z","iopub.status.idle":"2024-05-04T02:37:08.644214Z","shell.execute_reply.started":"2024-05-04T02:36:39.902704Z","shell.execute_reply":"2024-05-04T02:37:08.643432Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_1 (InputLayer) (None, 224, 224, 3) 0 \n__________________________________________________________________________________________________\nconv2d_1 (Conv2D) (None, 112, 112, 48) 1296 input_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_1 (BatchNor (None, 112, 112, 48) 192 conv2d_1[0][0] \n__________________________________________________________________________________________________\nswish_1 (Swish) (None, 112, 112, 48) 0 batch_normalization_1[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_1 (DepthwiseCo (None, 112, 112, 48) 432 swish_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_2 (BatchNor (None, 112, 112, 48) 192 depthwise_conv2d_1[0][0] \n__________________________________________________________________________________________________\nswish_2 (Swish) (None, 112, 112, 48) 0 batch_normalization_2[0][0] \n__________________________________________________________________________________________________\nlambda_1 (Lambda) (None, 1, 1, 48) 0 swish_2[0][0] \n__________________________________________________________________________________________________\nconv2d_2 (Conv2D) (None, 1, 1, 12) 588 lambda_1[0][0] \n__________________________________________________________________________________________________\nswish_3 (Swish) (None, 1, 1, 12) 0 conv2d_2[0][0] \n__________________________________________________________________________________________________\nconv2d_3 (Conv2D) (None, 1, 1, 48) 624 swish_3[0][0] \n__________________________________________________________________________________________________\nactivation_1 (Activation) (None, 1, 1, 48) 0 conv2d_3[0][0] \n__________________________________________________________________________________________________\nmultiply_1 (Multiply) (None, 112, 112, 48) 0 activation_1[0][0] \n swish_2[0][0] \n__________________________________________________________________________________________________\nconv2d_4 (Conv2D) (None, 112, 112, 24) 1152 multiply_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_3 (BatchNor (None, 112, 112, 24) 96 conv2d_4[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_2 (DepthwiseCo (None, 112, 112, 24) 216 batch_normalization_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_4 (BatchNor (None, 112, 112, 24) 96 depthwise_conv2d_2[0][0] \n__________________________________________________________________________________________________\nswish_4 (Swish) (None, 112, 112, 24) 0 batch_normalization_4[0][0] \n__________________________________________________________________________________________________\nlambda_2 (Lambda) (None, 1, 1, 24) 0 swish_4[0][0] \n__________________________________________________________________________________________________\nconv2d_5 (Conv2D) (None, 1, 1, 6) 150 lambda_2[0][0] \n__________________________________________________________________________________________________\nswish_5 (Swish) (None, 1, 1, 6) 0 conv2d_5[0][0] \n__________________________________________________________________________________________________\nconv2d_6 (Conv2D) (None, 1, 1, 24) 168 swish_5[0][0] \n__________________________________________________________________________________________________\nactivation_2 (Activation) (None, 1, 1, 24) 0 conv2d_6[0][0] \n__________________________________________________________________________________________________\nmultiply_2 (Multiply) (None, 112, 112, 24) 0 activation_2[0][0] \n swish_4[0][0] \n__________________________________________________________________________________________________\nconv2d_7 (Conv2D) (None, 112, 112, 24) 576 multiply_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_5 (BatchNor (None, 112, 112, 24) 96 conv2d_7[0][0] \n__________________________________________________________________________________________________\ndrop_connect_1 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_5[0][0] \n__________________________________________________________________________________________________\nadd_1 (Add) (None, 112, 112, 24) 0 drop_connect_1[0][0] \n batch_normalization_3[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_3 (DepthwiseCo (None, 112, 112, 24) 216 add_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_6 (BatchNor (None, 112, 112, 24) 96 depthwise_conv2d_3[0][0] \n__________________________________________________________________________________________________\nswish_6 (Swish) (None, 112, 112, 24) 0 batch_normalization_6[0][0] \n__________________________________________________________________________________________________\nlambda_3 (Lambda) (None, 1, 1, 24) 0 swish_6[0][0] \n__________________________________________________________________________________________________\nconv2d_8 (Conv2D) (None, 1, 1, 6) 150 lambda_3[0][0] \n__________________________________________________________________________________________________\nswish_7 (Swish) (None, 1, 1, 6) 0 conv2d_8[0][0] \n__________________________________________________________________________________________________\nconv2d_9 (Conv2D) (None, 1, 1, 24) 168 swish_7[0][0] \n__________________________________________________________________________________________________\nactivation_3 (Activation) (None, 1, 1, 24) 0 conv2d_9[0][0] \n__________________________________________________________________________________________________\nmultiply_3 (Multiply) (None, 112, 112, 24) 0 activation_3[0][0] \n swish_6[0][0] \n__________________________________________________________________________________________________\nconv2d_10 (Conv2D) (None, 112, 112, 24) 576 multiply_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_7 (BatchNor (None, 112, 112, 24) 96 conv2d_10[0][0] \n__________________________________________________________________________________________________\ndrop_connect_2 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_7[0][0] \n__________________________________________________________________________________________________\nadd_2 (Add) (None, 112, 112, 24) 0 drop_connect_2[0][0] \n add_1[0][0] \n__________________________________________________________________________________________________\nconv2d_11 (Conv2D) (None, 112, 112, 144 3456 add_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_8 (BatchNor (None, 112, 112, 144 576 conv2d_11[0][0] \n__________________________________________________________________________________________________\nswish_8 (Swish) (None, 112, 112, 144 0 batch_normalization_8[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_4 (DepthwiseCo (None, 56, 56, 144) 1296 swish_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_9 (BatchNor (None, 56, 56, 144) 576 depthwise_conv2d_4[0][0] \n__________________________________________________________________________________________________\nswish_9 (Swish) (None, 56, 56, 144) 0 batch_normalization_9[0][0] \n__________________________________________________________________________________________________\nlambda_4 (Lambda) (None, 1, 1, 144) 0 swish_9[0][0] \n__________________________________________________________________________________________________\nconv2d_12 (Conv2D) (None, 1, 1, 6) 870 lambda_4[0][0] \n__________________________________________________________________________________________________\nswish_10 (Swish) (None, 1, 1, 6) 0 conv2d_12[0][0] \n__________________________________________________________________________________________________\nconv2d_13 (Conv2D) (None, 1, 1, 144) 1008 swish_10[0][0] \n__________________________________________________________________________________________________\nactivation_4 (Activation) (None, 1, 1, 144) 0 conv2d_13[0][0] \n__________________________________________________________________________________________________\nmultiply_4 (Multiply) (None, 56, 56, 144) 0 activation_4[0][0] \n swish_9[0][0] \n__________________________________________________________________________________________________\nconv2d_14 (Conv2D) (None, 56, 56, 40) 5760 multiply_4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_10 (BatchNo (None, 56, 56, 40) 160 conv2d_14[0][0] \n__________________________________________________________________________________________________\nconv2d_15 (Conv2D) (None, 56, 56, 240) 9600 batch_normalization_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_11 (BatchNo (None, 56, 56, 240) 960 conv2d_15[0][0] \n__________________________________________________________________________________________________\nswish_11 (Swish) (None, 56, 56, 240) 0 batch_normalization_11[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_5 (DepthwiseCo (None, 56, 56, 240) 2160 swish_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_12 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_5[0][0] \n__________________________________________________________________________________________________\nswish_12 (Swish) (None, 56, 56, 240) 0 batch_normalization_12[0][0] \n__________________________________________________________________________________________________\nlambda_5 (Lambda) (None, 1, 1, 240) 0 swish_12[0][0] \n__________________________________________________________________________________________________\nconv2d_16 (Conv2D) (None, 1, 1, 10) 2410 lambda_5[0][0] \n__________________________________________________________________________________________________\nswish_13 (Swish) (None, 1, 1, 10) 0 conv2d_16[0][0] \n__________________________________________________________________________________________________\nconv2d_17 (Conv2D) (None, 1, 1, 240) 2640 swish_13[0][0] \n__________________________________________________________________________________________________\nactivation_5 (Activation) (None, 1, 1, 240) 0 conv2d_17[0][0] \n__________________________________________________________________________________________________\nmultiply_5 (Multiply) (None, 56, 56, 240) 0 activation_5[0][0] \n swish_12[0][0] \n__________________________________________________________________________________________________\nconv2d_18 (Conv2D) (None, 56, 56, 40) 9600 multiply_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_13 (BatchNo (None, 56, 56, 40) 160 conv2d_18[0][0] \n__________________________________________________________________________________________________\ndrop_connect_3 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_13[0][0] \n__________________________________________________________________________________________________\nadd_3 (Add) (None, 56, 56, 40) 0 drop_connect_3[0][0] \n batch_normalization_10[0][0] \n__________________________________________________________________________________________________\nconv2d_19 (Conv2D) (None, 56, 56, 240) 9600 add_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_14 (BatchNo (None, 56, 56, 240) 960 conv2d_19[0][0] \n__________________________________________________________________________________________________\nswish_14 (Swish) (None, 56, 56, 240) 0 batch_normalization_14[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_6 (DepthwiseCo (None, 56, 56, 240) 2160 swish_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_15 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_6[0][0] \n__________________________________________________________________________________________________\nswish_15 (Swish) (None, 56, 56, 240) 0 batch_normalization_15[0][0] \n__________________________________________________________________________________________________\nlambda_6 (Lambda) (None, 1, 1, 240) 0 swish_15[0][0] \n__________________________________________________________________________________________________\nconv2d_20 (Conv2D) (None, 1, 1, 10) 2410 lambda_6[0][0] \n__________________________________________________________________________________________________\nswish_16 (Swish) (None, 1, 1, 10) 0 conv2d_20[0][0] \n__________________________________________________________________________________________________\nconv2d_21 (Conv2D) (None, 1, 1, 240) 2640 swish_16[0][0] \n__________________________________________________________________________________________________\nactivation_6 (Activation) (None, 1, 1, 240) 0 conv2d_21[0][0] \n__________________________________________________________________________________________________\nmultiply_6 (Multiply) (None, 56, 56, 240) 0 activation_6[0][0] \n swish_15[0][0] \n__________________________________________________________________________________________________\nconv2d_22 (Conv2D) (None, 56, 56, 40) 9600 multiply_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_16 (BatchNo (None, 56, 56, 40) 160 conv2d_22[0][0] \n__________________________________________________________________________________________________\ndrop_connect_4 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_16[0][0] \n__________________________________________________________________________________________________\nadd_4 (Add) (None, 56, 56, 40) 0 drop_connect_4[0][0] \n add_3[0][0] \n__________________________________________________________________________________________________\nconv2d_23 (Conv2D) (None, 56, 56, 240) 9600 add_4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_17 (BatchNo (None, 56, 56, 240) 960 conv2d_23[0][0] \n__________________________________________________________________________________________________\nswish_17 (Swish) (None, 56, 56, 240) 0 batch_normalization_17[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_7 (DepthwiseCo (None, 56, 56, 240) 2160 swish_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_18 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_7[0][0] \n__________________________________________________________________________________________________\nswish_18 (Swish) (None, 56, 56, 240) 0 batch_normalization_18[0][0] \n__________________________________________________________________________________________________\nlambda_7 (Lambda) (None, 1, 1, 240) 0 swish_18[0][0] \n__________________________________________________________________________________________________\nconv2d_24 (Conv2D) (None, 1, 1, 10) 2410 lambda_7[0][0] \n__________________________________________________________________________________________________\nswish_19 (Swish) (None, 1, 1, 10) 0 conv2d_24[0][0] \n__________________________________________________________________________________________________\nconv2d_25 (Conv2D) (None, 1, 1, 240) 2640 swish_19[0][0] \n__________________________________________________________________________________________________\nactivation_7 (Activation) (None, 1, 1, 240) 0 conv2d_25[0][0] \n__________________________________________________________________________________________________\nmultiply_7 (Multiply) (None, 56, 56, 240) 0 activation_7[0][0] \n swish_18[0][0] \n__________________________________________________________________________________________________\nconv2d_26 (Conv2D) (None, 56, 56, 40) 9600 multiply_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_19 (BatchNo (None, 56, 56, 40) 160 conv2d_26[0][0] \n__________________________________________________________________________________________________\ndrop_connect_5 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_19[0][0] \n__________________________________________________________________________________________________\nadd_5 (Add) (None, 56, 56, 40) 0 drop_connect_5[0][0] \n add_4[0][0] \n__________________________________________________________________________________________________\nconv2d_27 (Conv2D) (None, 56, 56, 240) 9600 add_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_20 (BatchNo (None, 56, 56, 240) 960 conv2d_27[0][0] \n__________________________________________________________________________________________________\nswish_20 (Swish) (None, 56, 56, 240) 0 batch_normalization_20[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_8 (DepthwiseCo (None, 56, 56, 240) 2160 swish_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_21 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_8[0][0] \n__________________________________________________________________________________________________\nswish_21 (Swish) (None, 56, 56, 240) 0 batch_normalization_21[0][0] \n__________________________________________________________________________________________________\nlambda_8 (Lambda) (None, 1, 1, 240) 0 swish_21[0][0] \n__________________________________________________________________________________________________\nconv2d_28 (Conv2D) (None, 1, 1, 10) 2410 lambda_8[0][0] \n__________________________________________________________________________________________________\nswish_22 (Swish) (None, 1, 1, 10) 0 conv2d_28[0][0] \n__________________________________________________________________________________________________\nconv2d_29 (Conv2D) (None, 1, 1, 240) 2640 swish_22[0][0] \n__________________________________________________________________________________________________\nactivation_8 (Activation) (None, 1, 1, 240) 0 conv2d_29[0][0] \n__________________________________________________________________________________________________\nmultiply_8 (Multiply) (None, 56, 56, 240) 0 activation_8[0][0] \n swish_21[0][0] \n__________________________________________________________________________________________________\nconv2d_30 (Conv2D) (None, 56, 56, 40) 9600 multiply_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_22 (BatchNo (None, 56, 56, 40) 160 conv2d_30[0][0] \n__________________________________________________________________________________________________\ndrop_connect_6 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_22[0][0] \n__________________________________________________________________________________________________\nadd_6 (Add) (None, 56, 56, 40) 0 drop_connect_6[0][0] \n add_5[0][0] \n__________________________________________________________________________________________________\nconv2d_31 (Conv2D) (None, 56, 56, 240) 9600 add_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_23 (BatchNo (None, 56, 56, 240) 960 conv2d_31[0][0] \n__________________________________________________________________________________________________\nswish_23 (Swish) (None, 56, 56, 240) 0 batch_normalization_23[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_9 (DepthwiseCo (None, 28, 28, 240) 6000 swish_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_24 (BatchNo (None, 28, 28, 240) 960 depthwise_conv2d_9[0][0] \n__________________________________________________________________________________________________\nswish_24 (Swish) (None, 28, 28, 240) 0 batch_normalization_24[0][0] \n__________________________________________________________________________________________________\nlambda_9 (Lambda) (None, 1, 1, 240) 0 swish_24[0][0] \n__________________________________________________________________________________________________\nconv2d_32 (Conv2D) (None, 1, 1, 10) 2410 lambda_9[0][0] \n__________________________________________________________________________________________________\nswish_25 (Swish) (None, 1, 1, 10) 0 conv2d_32[0][0] \n__________________________________________________________________________________________________\nconv2d_33 (Conv2D) (None, 1, 1, 240) 2640 swish_25[0][0] \n__________________________________________________________________________________________________\nactivation_9 (Activation) (None, 1, 1, 240) 0 conv2d_33[0][0] \n__________________________________________________________________________________________________\nmultiply_9 (Multiply) (None, 28, 28, 240) 0 activation_9[0][0] \n swish_24[0][0] \n__________________________________________________________________________________________________\nconv2d_34 (Conv2D) (None, 28, 28, 64) 15360 multiply_9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_25 (BatchNo (None, 28, 28, 64) 256 conv2d_34[0][0] \n__________________________________________________________________________________________________\nconv2d_35 (Conv2D) (None, 28, 28, 384) 24576 batch_normalization_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_26 (BatchNo (None, 28, 28, 384) 1536 conv2d_35[0][0] \n__________________________________________________________________________________________________\nswish_26 (Swish) (None, 28, 28, 384) 0 batch_normalization_26[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_10 (DepthwiseC (None, 28, 28, 384) 9600 swish_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_27 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_10[0][0] \n__________________________________________________________________________________________________\nswish_27 (Swish) (None, 28, 28, 384) 0 batch_normalization_27[0][0] \n__________________________________________________________________________________________________\nlambda_10 (Lambda) (None, 1, 1, 384) 0 swish_27[0][0] \n__________________________________________________________________________________________________\nconv2d_36 (Conv2D) (None, 1, 1, 16) 6160 lambda_10[0][0] \n__________________________________________________________________________________________________\nswish_28 (Swish) (None, 1, 1, 16) 0 conv2d_36[0][0] \n__________________________________________________________________________________________________\nconv2d_37 (Conv2D) (None, 1, 1, 384) 6528 swish_28[0][0] \n__________________________________________________________________________________________________\nactivation_10 (Activation) (None, 1, 1, 384) 0 conv2d_37[0][0] \n__________________________________________________________________________________________________\nmultiply_10 (Multiply) (None, 28, 28, 384) 0 activation_10[0][0] \n swish_27[0][0] \n__________________________________________________________________________________________________\nconv2d_38 (Conv2D) (None, 28, 28, 64) 24576 multiply_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_28 (BatchNo (None, 28, 28, 64) 256 conv2d_38[0][0] \n__________________________________________________________________________________________________\ndrop_connect_7 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_28[0][0] \n__________________________________________________________________________________________________\nadd_7 (Add) (None, 28, 28, 64) 0 drop_connect_7[0][0] \n batch_normalization_25[0][0] \n__________________________________________________________________________________________________\nconv2d_39 (Conv2D) (None, 28, 28, 384) 24576 add_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_29 (BatchNo (None, 28, 28, 384) 1536 conv2d_39[0][0] \n__________________________________________________________________________________________________\nswish_29 (Swish) (None, 28, 28, 384) 0 batch_normalization_29[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_11 (DepthwiseC (None, 28, 28, 384) 9600 swish_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_30 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_11[0][0] \n__________________________________________________________________________________________________\nswish_30 (Swish) (None, 28, 28, 384) 0 batch_normalization_30[0][0] \n__________________________________________________________________________________________________\nlambda_11 (Lambda) (None, 1, 1, 384) 0 swish_30[0][0] \n__________________________________________________________________________________________________\nconv2d_40 (Conv2D) (None, 1, 1, 16) 6160 lambda_11[0][0] \n__________________________________________________________________________________________________\nswish_31 (Swish) (None, 1, 1, 16) 0 conv2d_40[0][0] \n__________________________________________________________________________________________________\nconv2d_41 (Conv2D) (None, 1, 1, 384) 6528 swish_31[0][0] \n__________________________________________________________________________________________________\nactivation_11 (Activation) (None, 1, 1, 384) 0 conv2d_41[0][0] \n__________________________________________________________________________________________________\nmultiply_11 (Multiply) (None, 28, 28, 384) 0 activation_11[0][0] \n swish_30[0][0] \n__________________________________________________________________________________________________\nconv2d_42 (Conv2D) (None, 28, 28, 64) 24576 multiply_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_31 (BatchNo (None, 28, 28, 64) 256 conv2d_42[0][0] \n__________________________________________________________________________________________________\ndrop_connect_8 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_31[0][0] \n__________________________________________________________________________________________________\nadd_8 (Add) (None, 28, 28, 64) 0 drop_connect_8[0][0] \n add_7[0][0] \n__________________________________________________________________________________________________\nconv2d_43 (Conv2D) (None, 28, 28, 384) 24576 add_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_32 (BatchNo (None, 28, 28, 384) 1536 conv2d_43[0][0] \n__________________________________________________________________________________________________\nswish_32 (Swish) (None, 28, 28, 384) 0 batch_normalization_32[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_12 (DepthwiseC (None, 28, 28, 384) 9600 swish_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_33 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_12[0][0] \n__________________________________________________________________________________________________\nswish_33 (Swish) (None, 28, 28, 384) 0 batch_normalization_33[0][0] \n__________________________________________________________________________________________________\nlambda_12 (Lambda) (None, 1, 1, 384) 0 swish_33[0][0] \n__________________________________________________________________________________________________\nconv2d_44 (Conv2D) (None, 1, 1, 16) 6160 lambda_12[0][0] \n__________________________________________________________________________________________________\nswish_34 (Swish) (None, 1, 1, 16) 0 conv2d_44[0][0] \n__________________________________________________________________________________________________\nconv2d_45 (Conv2D) (None, 1, 1, 384) 6528 swish_34[0][0] \n__________________________________________________________________________________________________\nactivation_12 (Activation) (None, 1, 1, 384) 0 conv2d_45[0][0] \n__________________________________________________________________________________________________\nmultiply_12 (Multiply) (None, 28, 28, 384) 0 activation_12[0][0] \n swish_33[0][0] \n__________________________________________________________________________________________________\nconv2d_46 (Conv2D) (None, 28, 28, 64) 24576 multiply_12[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_34 (BatchNo (None, 28, 28, 64) 256 conv2d_46[0][0] \n__________________________________________________________________________________________________\ndrop_connect_9 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_34[0][0] \n__________________________________________________________________________________________________\nadd_9 (Add) (None, 28, 28, 64) 0 drop_connect_9[0][0] \n add_8[0][0] \n__________________________________________________________________________________________________\nconv2d_47 (Conv2D) (None, 28, 28, 384) 24576 add_9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_35 (BatchNo (None, 28, 28, 384) 1536 conv2d_47[0][0] \n__________________________________________________________________________________________________\nswish_35 (Swish) (None, 28, 28, 384) 0 batch_normalization_35[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_13 (DepthwiseC (None, 28, 28, 384) 9600 swish_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_36 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_13[0][0] \n__________________________________________________________________________________________________\nswish_36 (Swish) (None, 28, 28, 384) 0 batch_normalization_36[0][0] \n__________________________________________________________________________________________________\nlambda_13 (Lambda) (None, 1, 1, 384) 0 swish_36[0][0] \n__________________________________________________________________________________________________\nconv2d_48 (Conv2D) (None, 1, 1, 16) 6160 lambda_13[0][0] \n__________________________________________________________________________________________________\nswish_37 (Swish) (None, 1, 1, 16) 0 conv2d_48[0][0] \n__________________________________________________________________________________________________\nconv2d_49 (Conv2D) (None, 1, 1, 384) 6528 swish_37[0][0] \n__________________________________________________________________________________________________\nactivation_13 (Activation) (None, 1, 1, 384) 0 conv2d_49[0][0] \n__________________________________________________________________________________________________\nmultiply_13 (Multiply) (None, 28, 28, 384) 0 activation_13[0][0] \n swish_36[0][0] \n__________________________________________________________________________________________________\nconv2d_50 (Conv2D) (None, 28, 28, 64) 24576 multiply_13[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_37 (BatchNo (None, 28, 28, 64) 256 conv2d_50[0][0] \n__________________________________________________________________________________________________\ndrop_connect_10 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_37[0][0] \n__________________________________________________________________________________________________\nadd_10 (Add) (None, 28, 28, 64) 0 drop_connect_10[0][0] \n add_9[0][0] \n__________________________________________________________________________________________________\nconv2d_51 (Conv2D) (None, 28, 28, 384) 24576 add_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_38 (BatchNo (None, 28, 28, 384) 1536 conv2d_51[0][0] \n__________________________________________________________________________________________________\nswish_38 (Swish) (None, 28, 28, 384) 0 batch_normalization_38[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_14 (DepthwiseC (None, 14, 14, 384) 3456 swish_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_39 (BatchNo (None, 14, 14, 384) 1536 depthwise_conv2d_14[0][0] \n__________________________________________________________________________________________________\nswish_39 (Swish) (None, 14, 14, 384) 0 batch_normalization_39[0][0] \n__________________________________________________________________________________________________\nlambda_14 (Lambda) (None, 1, 1, 384) 0 swish_39[0][0] \n__________________________________________________________________________________________________\nconv2d_52 (Conv2D) (None, 1, 1, 16) 6160 lambda_14[0][0] \n__________________________________________________________________________________________________\nswish_40 (Swish) (None, 1, 1, 16) 0 conv2d_52[0][0] \n__________________________________________________________________________________________________\nconv2d_53 (Conv2D) (None, 1, 1, 384) 6528 swish_40[0][0] \n__________________________________________________________________________________________________\nactivation_14 (Activation) (None, 1, 1, 384) 0 conv2d_53[0][0] \n__________________________________________________________________________________________________\nmultiply_14 (Multiply) (None, 14, 14, 384) 0 activation_14[0][0] \n swish_39[0][0] \n__________________________________________________________________________________________________\nconv2d_54 (Conv2D) (None, 14, 14, 128) 49152 multiply_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_40 (BatchNo (None, 14, 14, 128) 512 conv2d_54[0][0] \n__________________________________________________________________________________________________\nconv2d_55 (Conv2D) (None, 14, 14, 768) 98304 batch_normalization_40[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_41 (BatchNo (None, 14, 14, 768) 3072 conv2d_55[0][0] \n__________________________________________________________________________________________________\nswish_41 (Swish) (None, 14, 14, 768) 0 batch_normalization_41[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_15 (DepthwiseC (None, 14, 14, 768) 6912 swish_41[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_42 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_15[0][0] \n__________________________________________________________________________________________________\nswish_42 (Swish) (None, 14, 14, 768) 0 batch_normalization_42[0][0] \n__________________________________________________________________________________________________\nlambda_15 (Lambda) (None, 1, 1, 768) 0 swish_42[0][0] \n__________________________________________________________________________________________________\nconv2d_56 (Conv2D) (None, 1, 1, 32) 24608 lambda_15[0][0] \n__________________________________________________________________________________________________\nswish_43 (Swish) (None, 1, 1, 32) 0 conv2d_56[0][0] \n__________________________________________________________________________________________________\nconv2d_57 (Conv2D) (None, 1, 1, 768) 25344 swish_43[0][0] \n__________________________________________________________________________________________________\nactivation_15 (Activation) (None, 1, 1, 768) 0 conv2d_57[0][0] \n__________________________________________________________________________________________________\nmultiply_15 (Multiply) (None, 14, 14, 768) 0 activation_15[0][0] \n swish_42[0][0] \n__________________________________________________________________________________________________\nconv2d_58 (Conv2D) (None, 14, 14, 128) 98304 multiply_15[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_43 (BatchNo (None, 14, 14, 128) 512 conv2d_58[0][0] \n__________________________________________________________________________________________________\ndrop_connect_11 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_43[0][0] \n__________________________________________________________________________________________________\nadd_11 (Add) (None, 14, 14, 128) 0 drop_connect_11[0][0] \n batch_normalization_40[0][0] \n__________________________________________________________________________________________________\nconv2d_59 (Conv2D) (None, 14, 14, 768) 98304 add_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_44 (BatchNo (None, 14, 14, 768) 3072 conv2d_59[0][0] \n__________________________________________________________________________________________________\nswish_44 (Swish) (None, 14, 14, 768) 0 batch_normalization_44[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_16 (DepthwiseC (None, 14, 14, 768) 6912 swish_44[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_45 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_16[0][0] \n__________________________________________________________________________________________________\nswish_45 (Swish) (None, 14, 14, 768) 0 batch_normalization_45[0][0] \n__________________________________________________________________________________________________\nlambda_16 (Lambda) (None, 1, 1, 768) 0 swish_45[0][0] \n__________________________________________________________________________________________________\nconv2d_60 (Conv2D) (None, 1, 1, 32) 24608 lambda_16[0][0] \n__________________________________________________________________________________________________\nswish_46 (Swish) (None, 1, 1, 32) 0 conv2d_60[0][0] \n__________________________________________________________________________________________________\nconv2d_61 (Conv2D) (None, 1, 1, 768) 25344 swish_46[0][0] \n__________________________________________________________________________________________________\nactivation_16 (Activation) (None, 1, 1, 768) 0 conv2d_61[0][0] \n__________________________________________________________________________________________________\nmultiply_16 (Multiply) (None, 14, 14, 768) 0 activation_16[0][0] \n swish_45[0][0] \n__________________________________________________________________________________________________\nconv2d_62 (Conv2D) (None, 14, 14, 128) 98304 multiply_16[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_46 (BatchNo (None, 14, 14, 128) 512 conv2d_62[0][0] \n__________________________________________________________________________________________________\ndrop_connect_12 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_46[0][0] \n__________________________________________________________________________________________________\nadd_12 (Add) (None, 14, 14, 128) 0 drop_connect_12[0][0] \n add_11[0][0] \n__________________________________________________________________________________________________\nconv2d_63 (Conv2D) (None, 14, 14, 768) 98304 add_12[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_47 (BatchNo (None, 14, 14, 768) 3072 conv2d_63[0][0] \n__________________________________________________________________________________________________\nswish_47 (Swish) (None, 14, 14, 768) 0 batch_normalization_47[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_17 (DepthwiseC (None, 14, 14, 768) 6912 swish_47[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_48 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_17[0][0] \n__________________________________________________________________________________________________\nswish_48 (Swish) (None, 14, 14, 768) 0 batch_normalization_48[0][0] \n__________________________________________________________________________________________________\nlambda_17 (Lambda) (None, 1, 1, 768) 0 swish_48[0][0] \n__________________________________________________________________________________________________\nconv2d_64 (Conv2D) (None, 1, 1, 32) 24608 lambda_17[0][0] \n__________________________________________________________________________________________________\nswish_49 (Swish) (None, 1, 1, 32) 0 conv2d_64[0][0] \n__________________________________________________________________________________________________\nconv2d_65 (Conv2D) (None, 1, 1, 768) 25344 swish_49[0][0] \n__________________________________________________________________________________________________\nactivation_17 (Activation) (None, 1, 1, 768) 0 conv2d_65[0][0] \n__________________________________________________________________________________________________\nmultiply_17 (Multiply) (None, 14, 14, 768) 0 activation_17[0][0] \n swish_48[0][0] \n__________________________________________________________________________________________________\nconv2d_66 (Conv2D) (None, 14, 14, 128) 98304 multiply_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_49 (BatchNo (None, 14, 14, 128) 512 conv2d_66[0][0] \n__________________________________________________________________________________________________\ndrop_connect_13 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_49[0][0] \n__________________________________________________________________________________________________\nadd_13 (Add) (None, 14, 14, 128) 0 drop_connect_13[0][0] \n add_12[0][0] \n__________________________________________________________________________________________________\nconv2d_67 (Conv2D) (None, 14, 14, 768) 98304 add_13[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_50 (BatchNo (None, 14, 14, 768) 3072 conv2d_67[0][0] \n__________________________________________________________________________________________________\nswish_50 (Swish) (None, 14, 14, 768) 0 batch_normalization_50[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_18 (DepthwiseC (None, 14, 14, 768) 6912 swish_50[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_51 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_18[0][0] \n__________________________________________________________________________________________________\nswish_51 (Swish) (None, 14, 14, 768) 0 batch_normalization_51[0][0] \n__________________________________________________________________________________________________\nlambda_18 (Lambda) (None, 1, 1, 768) 0 swish_51[0][0] \n__________________________________________________________________________________________________\nconv2d_68 (Conv2D) (None, 1, 1, 32) 24608 lambda_18[0][0] \n__________________________________________________________________________________________________\nswish_52 (Swish) (None, 1, 1, 32) 0 conv2d_68[0][0] \n__________________________________________________________________________________________________\nconv2d_69 (Conv2D) (None, 1, 1, 768) 25344 swish_52[0][0] \n__________________________________________________________________________________________________\nactivation_18 (Activation) (None, 1, 1, 768) 0 conv2d_69[0][0] \n__________________________________________________________________________________________________\nmultiply_18 (Multiply) (None, 14, 14, 768) 0 activation_18[0][0] \n swish_51[0][0] \n__________________________________________________________________________________________________\nconv2d_70 (Conv2D) (None, 14, 14, 128) 98304 multiply_18[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_52 (BatchNo (None, 14, 14, 128) 512 conv2d_70[0][0] \n__________________________________________________________________________________________________\ndrop_connect_14 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_52[0][0] \n__________________________________________________________________________________________________\nadd_14 (Add) (None, 14, 14, 128) 0 drop_connect_14[0][0] \n add_13[0][0] \n__________________________________________________________________________________________________\nconv2d_71 (Conv2D) (None, 14, 14, 768) 98304 add_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_53 (BatchNo (None, 14, 14, 768) 3072 conv2d_71[0][0] \n__________________________________________________________________________________________________\nswish_53 (Swish) (None, 14, 14, 768) 0 batch_normalization_53[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_19 (DepthwiseC (None, 14, 14, 768) 6912 swish_53[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_54 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_19[0][0] \n__________________________________________________________________________________________________\nswish_54 (Swish) (None, 14, 14, 768) 0 batch_normalization_54[0][0] \n__________________________________________________________________________________________________\nlambda_19 (Lambda) (None, 1, 1, 768) 0 swish_54[0][0] \n__________________________________________________________________________________________________\nconv2d_72 (Conv2D) (None, 1, 1, 32) 24608 lambda_19[0][0] \n__________________________________________________________________________________________________\nswish_55 (Swish) (None, 1, 1, 32) 0 conv2d_72[0][0] \n__________________________________________________________________________________________________\nconv2d_73 (Conv2D) (None, 1, 1, 768) 25344 swish_55[0][0] \n__________________________________________________________________________________________________\nactivation_19 (Activation) (None, 1, 1, 768) 0 conv2d_73[0][0] \n__________________________________________________________________________________________________\nmultiply_19 (Multiply) (None, 14, 14, 768) 0 activation_19[0][0] \n swish_54[0][0] \n__________________________________________________________________________________________________\nconv2d_74 (Conv2D) (None, 14, 14, 128) 98304 multiply_19[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_55 (BatchNo (None, 14, 14, 128) 512 conv2d_74[0][0] \n__________________________________________________________________________________________________\ndrop_connect_15 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_55[0][0] \n__________________________________________________________________________________________________\nadd_15 (Add) (None, 14, 14, 128) 0 drop_connect_15[0][0] \n add_14[0][0] \n__________________________________________________________________________________________________\nconv2d_75 (Conv2D) (None, 14, 14, 768) 98304 add_15[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_56 (BatchNo (None, 14, 14, 768) 3072 conv2d_75[0][0] \n__________________________________________________________________________________________________\nswish_56 (Swish) (None, 14, 14, 768) 0 batch_normalization_56[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_20 (DepthwiseC (None, 14, 14, 768) 6912 swish_56[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_57 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_20[0][0] \n__________________________________________________________________________________________________\nswish_57 (Swish) (None, 14, 14, 768) 0 batch_normalization_57[0][0] \n__________________________________________________________________________________________________\nlambda_20 (Lambda) (None, 1, 1, 768) 0 swish_57[0][0] \n__________________________________________________________________________________________________\nconv2d_76 (Conv2D) (None, 1, 1, 32) 24608 lambda_20[0][0] \n__________________________________________________________________________________________________\nswish_58 (Swish) (None, 1, 1, 32) 0 conv2d_76[0][0] \n__________________________________________________________________________________________________\nconv2d_77 (Conv2D) (None, 1, 1, 768) 25344 swish_58[0][0] \n__________________________________________________________________________________________________\nactivation_20 (Activation) (None, 1, 1, 768) 0 conv2d_77[0][0] \n__________________________________________________________________________________________________\nmultiply_20 (Multiply) (None, 14, 14, 768) 0 activation_20[0][0] \n swish_57[0][0] \n__________________________________________________________________________________________________\nconv2d_78 (Conv2D) (None, 14, 14, 128) 98304 multiply_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_58 (BatchNo (None, 14, 14, 128) 512 conv2d_78[0][0] \n__________________________________________________________________________________________________\ndrop_connect_16 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_58[0][0] \n__________________________________________________________________________________________________\nadd_16 (Add) (None, 14, 14, 128) 0 drop_connect_16[0][0] \n add_15[0][0] \n__________________________________________________________________________________________________\nconv2d_79 (Conv2D) (None, 14, 14, 768) 98304 add_16[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_59 (BatchNo (None, 14, 14, 768) 3072 conv2d_79[0][0] \n__________________________________________________________________________________________________\nswish_59 (Swish) (None, 14, 14, 768) 0 batch_normalization_59[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_21 (DepthwiseC (None, 14, 14, 768) 19200 swish_59[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_60 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_21[0][0] \n__________________________________________________________________________________________________\nswish_60 (Swish) (None, 14, 14, 768) 0 batch_normalization_60[0][0] \n__________________________________________________________________________________________________\nlambda_21 (Lambda) (None, 1, 1, 768) 0 swish_60[0][0] \n__________________________________________________________________________________________________\nconv2d_80 (Conv2D) (None, 1, 1, 32) 24608 lambda_21[0][0] \n__________________________________________________________________________________________________\nswish_61 (Swish) (None, 1, 1, 32) 0 conv2d_80[0][0] \n__________________________________________________________________________________________________\nconv2d_81 (Conv2D) (None, 1, 1, 768) 25344 swish_61[0][0] \n__________________________________________________________________________________________________\nactivation_21 (Activation) (None, 1, 1, 768) 0 conv2d_81[0][0] \n__________________________________________________________________________________________________\nmultiply_21 (Multiply) (None, 14, 14, 768) 0 activation_21[0][0] \n swish_60[0][0] \n__________________________________________________________________________________________________\nconv2d_82 (Conv2D) (None, 14, 14, 176) 135168 multiply_21[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_61 (BatchNo (None, 14, 14, 176) 704 conv2d_82[0][0] \n__________________________________________________________________________________________________\nconv2d_83 (Conv2D) (None, 14, 14, 1056) 185856 batch_normalization_61[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_62 (BatchNo (None, 14, 14, 1056) 4224 conv2d_83[0][0] \n__________________________________________________________________________________________________\nswish_62 (Swish) (None, 14, 14, 1056) 0 batch_normalization_62[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_22 (DepthwiseC (None, 14, 14, 1056) 26400 swish_62[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_63 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_22[0][0] \n__________________________________________________________________________________________________\nswish_63 (Swish) (None, 14, 14, 1056) 0 batch_normalization_63[0][0] \n__________________________________________________________________________________________________\nlambda_22 (Lambda) (None, 1, 1, 1056) 0 swish_63[0][0] \n__________________________________________________________________________________________________\nconv2d_84 (Conv2D) (None, 1, 1, 44) 46508 lambda_22[0][0] \n__________________________________________________________________________________________________\nswish_64 (Swish) (None, 1, 1, 44) 0 conv2d_84[0][0] \n__________________________________________________________________________________________________\nconv2d_85 (Conv2D) (None, 1, 1, 1056) 47520 swish_64[0][0] \n__________________________________________________________________________________________________\nactivation_22 (Activation) (None, 1, 1, 1056) 0 conv2d_85[0][0] \n__________________________________________________________________________________________________\nmultiply_22 (Multiply) (None, 14, 14, 1056) 0 activation_22[0][0] \n swish_63[0][0] \n__________________________________________________________________________________________________\nconv2d_86 (Conv2D) (None, 14, 14, 176) 185856 multiply_22[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_64 (BatchNo (None, 14, 14, 176) 704 conv2d_86[0][0] \n__________________________________________________________________________________________________\ndrop_connect_17 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_64[0][0] \n__________________________________________________________________________________________________\nadd_17 (Add) (None, 14, 14, 176) 0 drop_connect_17[0][0] \n batch_normalization_61[0][0] \n__________________________________________________________________________________________________\nconv2d_87 (Conv2D) (None, 14, 14, 1056) 185856 add_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_65 (BatchNo (None, 14, 14, 1056) 4224 conv2d_87[0][0] \n__________________________________________________________________________________________________\nswish_65 (Swish) (None, 14, 14, 1056) 0 batch_normalization_65[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_23 (DepthwiseC (None, 14, 14, 1056) 26400 swish_65[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_66 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_23[0][0] \n__________________________________________________________________________________________________\nswish_66 (Swish) (None, 14, 14, 1056) 0 batch_normalization_66[0][0] \n__________________________________________________________________________________________________\nlambda_23 (Lambda) (None, 1, 1, 1056) 0 swish_66[0][0] \n__________________________________________________________________________________________________\nconv2d_88 (Conv2D) (None, 1, 1, 44) 46508 lambda_23[0][0] \n__________________________________________________________________________________________________\nswish_67 (Swish) (None, 1, 1, 44) 0 conv2d_88[0][0] \n__________________________________________________________________________________________________\nconv2d_89 (Conv2D) (None, 1, 1, 1056) 47520 swish_67[0][0] \n__________________________________________________________________________________________________\nactivation_23 (Activation) (None, 1, 1, 1056) 0 conv2d_89[0][0] \n__________________________________________________________________________________________________\nmultiply_23 (Multiply) (None, 14, 14, 1056) 0 activation_23[0][0] \n swish_66[0][0] \n__________________________________________________________________________________________________\nconv2d_90 (Conv2D) (None, 14, 14, 176) 185856 multiply_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_67 (BatchNo (None, 14, 14, 176) 704 conv2d_90[0][0] \n__________________________________________________________________________________________________\ndrop_connect_18 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_67[0][0] \n__________________________________________________________________________________________________\nadd_18 (Add) (None, 14, 14, 176) 0 drop_connect_18[0][0] \n add_17[0][0] \n__________________________________________________________________________________________________\nconv2d_91 (Conv2D) (None, 14, 14, 1056) 185856 add_18[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_68 (BatchNo (None, 14, 14, 1056) 4224 conv2d_91[0][0] \n__________________________________________________________________________________________________\nswish_68 (Swish) (None, 14, 14, 1056) 0 batch_normalization_68[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_24 (DepthwiseC (None, 14, 14, 1056) 26400 swish_68[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_69 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_24[0][0] \n__________________________________________________________________________________________________\nswish_69 (Swish) (None, 14, 14, 1056) 0 batch_normalization_69[0][0] \n__________________________________________________________________________________________________\nlambda_24 (Lambda) (None, 1, 1, 1056) 0 swish_69[0][0] \n__________________________________________________________________________________________________\nconv2d_92 (Conv2D) (None, 1, 1, 44) 46508 lambda_24[0][0] \n__________________________________________________________________________________________________\nswish_70 (Swish) (None, 1, 1, 44) 0 conv2d_92[0][0] \n__________________________________________________________________________________________________\nconv2d_93 (Conv2D) (None, 1, 1, 1056) 47520 swish_70[0][0] \n__________________________________________________________________________________________________\nactivation_24 (Activation) (None, 1, 1, 1056) 0 conv2d_93[0][0] \n__________________________________________________________________________________________________\nmultiply_24 (Multiply) (None, 14, 14, 1056) 0 activation_24[0][0] \n swish_69[0][0] \n__________________________________________________________________________________________________\nconv2d_94 (Conv2D) (None, 14, 14, 176) 185856 multiply_24[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_70 (BatchNo (None, 14, 14, 176) 704 conv2d_94[0][0] \n__________________________________________________________________________________________________\ndrop_connect_19 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_70[0][0] \n__________________________________________________________________________________________________\nadd_19 (Add) (None, 14, 14, 176) 0 drop_connect_19[0][0] \n add_18[0][0] \n__________________________________________________________________________________________________\nconv2d_95 (Conv2D) (None, 14, 14, 1056) 185856 add_19[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_71 (BatchNo (None, 14, 14, 1056) 4224 conv2d_95[0][0] \n__________________________________________________________________________________________________\nswish_71 (Swish) (None, 14, 14, 1056) 0 batch_normalization_71[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_25 (DepthwiseC (None, 14, 14, 1056) 26400 swish_71[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_72 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_25[0][0] \n__________________________________________________________________________________________________\nswish_72 (Swish) (None, 14, 14, 1056) 0 batch_normalization_72[0][0] \n__________________________________________________________________________________________________\nlambda_25 (Lambda) (None, 1, 1, 1056) 0 swish_72[0][0] \n__________________________________________________________________________________________________\nconv2d_96 (Conv2D) (None, 1, 1, 44) 46508 lambda_25[0][0] \n__________________________________________________________________________________________________\nswish_73 (Swish) (None, 1, 1, 44) 0 conv2d_96[0][0] \n__________________________________________________________________________________________________\nconv2d_97 (Conv2D) (None, 1, 1, 1056) 47520 swish_73[0][0] \n__________________________________________________________________________________________________\nactivation_25 (Activation) (None, 1, 1, 1056) 0 conv2d_97[0][0] \n__________________________________________________________________________________________________\nmultiply_25 (Multiply) (None, 14, 14, 1056) 0 activation_25[0][0] \n swish_72[0][0] \n__________________________________________________________________________________________________\nconv2d_98 (Conv2D) (None, 14, 14, 176) 185856 multiply_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_73 (BatchNo (None, 14, 14, 176) 704 conv2d_98[0][0] \n__________________________________________________________________________________________________\ndrop_connect_20 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_73[0][0] \n__________________________________________________________________________________________________\nadd_20 (Add) (None, 14, 14, 176) 0 drop_connect_20[0][0] \n add_19[0][0] \n__________________________________________________________________________________________________\nconv2d_99 (Conv2D) (None, 14, 14, 1056) 185856 add_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_74 (BatchNo (None, 14, 14, 1056) 4224 conv2d_99[0][0] \n__________________________________________________________________________________________________\nswish_74 (Swish) (None, 14, 14, 1056) 0 batch_normalization_74[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_26 (DepthwiseC (None, 14, 14, 1056) 26400 swish_74[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_75 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_26[0][0] \n__________________________________________________________________________________________________\nswish_75 (Swish) (None, 14, 14, 1056) 0 batch_normalization_75[0][0] \n__________________________________________________________________________________________________\nlambda_26 (Lambda) (None, 1, 1, 1056) 0 swish_75[0][0] \n__________________________________________________________________________________________________\nconv2d_100 (Conv2D) (None, 1, 1, 44) 46508 lambda_26[0][0] \n__________________________________________________________________________________________________\nswish_76 (Swish) (None, 1, 1, 44) 0 conv2d_100[0][0] \n__________________________________________________________________________________________________\nconv2d_101 (Conv2D) (None, 1, 1, 1056) 47520 swish_76[0][0] \n__________________________________________________________________________________________________\nactivation_26 (Activation) (None, 1, 1, 1056) 0 conv2d_101[0][0] \n__________________________________________________________________________________________________\nmultiply_26 (Multiply) (None, 14, 14, 1056) 0 activation_26[0][0] \n swish_75[0][0] \n__________________________________________________________________________________________________\nconv2d_102 (Conv2D) (None, 14, 14, 176) 185856 multiply_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_76 (BatchNo (None, 14, 14, 176) 704 conv2d_102[0][0] \n__________________________________________________________________________________________________\ndrop_connect_21 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_76[0][0] \n__________________________________________________________________________________________________\nadd_21 (Add) (None, 14, 14, 176) 0 drop_connect_21[0][0] \n add_20[0][0] \n__________________________________________________________________________________________________\nconv2d_103 (Conv2D) (None, 14, 14, 1056) 185856 add_21[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_77 (BatchNo (None, 14, 14, 1056) 4224 conv2d_103[0][0] \n__________________________________________________________________________________________________\nswish_77 (Swish) (None, 14, 14, 1056) 0 batch_normalization_77[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_27 (DepthwiseC (None, 14, 14, 1056) 26400 swish_77[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_78 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_27[0][0] \n__________________________________________________________________________________________________\nswish_78 (Swish) (None, 14, 14, 1056) 0 batch_normalization_78[0][0] \n__________________________________________________________________________________________________\nlambda_27 (Lambda) (None, 1, 1, 1056) 0 swish_78[0][0] \n__________________________________________________________________________________________________\nconv2d_104 (Conv2D) (None, 1, 1, 44) 46508 lambda_27[0][0] \n__________________________________________________________________________________________________\nswish_79 (Swish) (None, 1, 1, 44) 0 conv2d_104[0][0] \n__________________________________________________________________________________________________\nconv2d_105 (Conv2D) (None, 1, 1, 1056) 47520 swish_79[0][0] \n__________________________________________________________________________________________________\nactivation_27 (Activation) (None, 1, 1, 1056) 0 conv2d_105[0][0] \n__________________________________________________________________________________________________\nmultiply_27 (Multiply) (None, 14, 14, 1056) 0 activation_27[0][0] \n swish_78[0][0] \n__________________________________________________________________________________________________\nconv2d_106 (Conv2D) (None, 14, 14, 176) 185856 multiply_27[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_79 (BatchNo (None, 14, 14, 176) 704 conv2d_106[0][0] \n__________________________________________________________________________________________________\ndrop_connect_22 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_79[0][0] \n__________________________________________________________________________________________________\nadd_22 (Add) (None, 14, 14, 176) 0 drop_connect_22[0][0] \n add_21[0][0] \n__________________________________________________________________________________________________\nconv2d_107 (Conv2D) (None, 14, 14, 1056) 185856 add_22[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_80 (BatchNo (None, 14, 14, 1056) 4224 conv2d_107[0][0] \n__________________________________________________________________________________________________\nswish_80 (Swish) (None, 14, 14, 1056) 0 batch_normalization_80[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_28 (DepthwiseC (None, 7, 7, 1056) 26400 swish_80[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_81 (BatchNo (None, 7, 7, 1056) 4224 depthwise_conv2d_28[0][0] \n__________________________________________________________________________________________________\nswish_81 (Swish) (None, 7, 7, 1056) 0 batch_normalization_81[0][0] \n__________________________________________________________________________________________________\nlambda_28 (Lambda) (None, 1, 1, 1056) 0 swish_81[0][0] \n__________________________________________________________________________________________________\nconv2d_108 (Conv2D) (None, 1, 1, 44) 46508 lambda_28[0][0] \n__________________________________________________________________________________________________\nswish_82 (Swish) (None, 1, 1, 44) 0 conv2d_108[0][0] \n__________________________________________________________________________________________________\nconv2d_109 (Conv2D) (None, 1, 1, 1056) 47520 swish_82[0][0] \n__________________________________________________________________________________________________\nactivation_28 (Activation) (None, 1, 1, 1056) 0 conv2d_109[0][0] \n__________________________________________________________________________________________________\nmultiply_28 (Multiply) (None, 7, 7, 1056) 0 activation_28[0][0] \n swish_81[0][0] \n__________________________________________________________________________________________________\nconv2d_110 (Conv2D) (None, 7, 7, 304) 321024 multiply_28[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_82 (BatchNo (None, 7, 7, 304) 1216 conv2d_110[0][0] \n__________________________________________________________________________________________________\nconv2d_111 (Conv2D) (None, 7, 7, 1824) 554496 batch_normalization_82[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_83 (BatchNo (None, 7, 7, 1824) 7296 conv2d_111[0][0] \n__________________________________________________________________________________________________\nswish_83 (Swish) (None, 7, 7, 1824) 0 batch_normalization_83[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_29 (DepthwiseC (None, 7, 7, 1824) 45600 swish_83[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_84 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_29[0][0] \n__________________________________________________________________________________________________\nswish_84 (Swish) (None, 7, 7, 1824) 0 batch_normalization_84[0][0] \n__________________________________________________________________________________________________\nlambda_29 (Lambda) (None, 1, 1, 1824) 0 swish_84[0][0] \n__________________________________________________________________________________________________\nconv2d_112 (Conv2D) (None, 1, 1, 76) 138700 lambda_29[0][0] \n__________________________________________________________________________________________________\nswish_85 (Swish) (None, 1, 1, 76) 0 conv2d_112[0][0] \n__________________________________________________________________________________________________\nconv2d_113 (Conv2D) (None, 1, 1, 1824) 140448 swish_85[0][0] \n__________________________________________________________________________________________________\nactivation_29 (Activation) (None, 1, 1, 1824) 0 conv2d_113[0][0] \n__________________________________________________________________________________________________\nmultiply_29 (Multiply) (None, 7, 7, 1824) 0 activation_29[0][0] \n swish_84[0][0] \n__________________________________________________________________________________________________\nconv2d_114 (Conv2D) (None, 7, 7, 304) 554496 multiply_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_85 (BatchNo (None, 7, 7, 304) 1216 conv2d_114[0][0] \n__________________________________________________________________________________________________\ndrop_connect_23 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_85[0][0] \n__________________________________________________________________________________________________\nadd_23 (Add) (None, 7, 7, 304) 0 drop_connect_23[0][0] \n batch_normalization_82[0][0] \n__________________________________________________________________________________________________\nconv2d_115 (Conv2D) (None, 7, 7, 1824) 554496 add_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_86 (BatchNo (None, 7, 7, 1824) 7296 conv2d_115[0][0] \n__________________________________________________________________________________________________\nswish_86 (Swish) (None, 7, 7, 1824) 0 batch_normalization_86[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_30 (DepthwiseC (None, 7, 7, 1824) 45600 swish_86[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_87 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_30[0][0] \n__________________________________________________________________________________________________\nswish_87 (Swish) (None, 7, 7, 1824) 0 batch_normalization_87[0][0] \n__________________________________________________________________________________________________\nlambda_30 (Lambda) (None, 1, 1, 1824) 0 swish_87[0][0] \n__________________________________________________________________________________________________\nconv2d_116 (Conv2D) (None, 1, 1, 76) 138700 lambda_30[0][0] \n__________________________________________________________________________________________________\nswish_88 (Swish) (None, 1, 1, 76) 0 conv2d_116[0][0] \n__________________________________________________________________________________________________\nconv2d_117 (Conv2D) (None, 1, 1, 1824) 140448 swish_88[0][0] \n__________________________________________________________________________________________________\nactivation_30 (Activation) (None, 1, 1, 1824) 0 conv2d_117[0][0] \n__________________________________________________________________________________________________\nmultiply_30 (Multiply) (None, 7, 7, 1824) 0 activation_30[0][0] \n swish_87[0][0] \n__________________________________________________________________________________________________\nconv2d_118 (Conv2D) (None, 7, 7, 304) 554496 multiply_30[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_88 (BatchNo (None, 7, 7, 304) 1216 conv2d_118[0][0] \n__________________________________________________________________________________________________\ndrop_connect_24 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_88[0][0] \n__________________________________________________________________________________________________\nadd_24 (Add) (None, 7, 7, 304) 0 drop_connect_24[0][0] \n add_23[0][0] \n__________________________________________________________________________________________________\nconv2d_119 (Conv2D) (None, 7, 7, 1824) 554496 add_24[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_89 (BatchNo (None, 7, 7, 1824) 7296 conv2d_119[0][0] \n__________________________________________________________________________________________________\nswish_89 (Swish) (None, 7, 7, 1824) 0 batch_normalization_89[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_31 (DepthwiseC (None, 7, 7, 1824) 45600 swish_89[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_90 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_31[0][0] \n__________________________________________________________________________________________________\nswish_90 (Swish) (None, 7, 7, 1824) 0 batch_normalization_90[0][0] \n__________________________________________________________________________________________________\nlambda_31 (Lambda) (None, 1, 1, 1824) 0 swish_90[0][0] \n__________________________________________________________________________________________________\nconv2d_120 (Conv2D) (None, 1, 1, 76) 138700 lambda_31[0][0] \n__________________________________________________________________________________________________\nswish_91 (Swish) (None, 1, 1, 76) 0 conv2d_120[0][0] \n__________________________________________________________________________________________________\nconv2d_121 (Conv2D) (None, 1, 1, 1824) 140448 swish_91[0][0] \n__________________________________________________________________________________________________\nactivation_31 (Activation) (None, 1, 1, 1824) 0 conv2d_121[0][0] \n__________________________________________________________________________________________________\nmultiply_31 (Multiply) (None, 7, 7, 1824) 0 activation_31[0][0] \n swish_90[0][0] \n__________________________________________________________________________________________________\nconv2d_122 (Conv2D) (None, 7, 7, 304) 554496 multiply_31[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_91 (BatchNo (None, 7, 7, 304) 1216 conv2d_122[0][0] \n__________________________________________________________________________________________________\ndrop_connect_25 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_91[0][0] \n__________________________________________________________________________________________________\nadd_25 (Add) (None, 7, 7, 304) 0 drop_connect_25[0][0] \n add_24[0][0] \n__________________________________________________________________________________________________\nconv2d_123 (Conv2D) (None, 7, 7, 1824) 554496 add_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_92 (BatchNo (None, 7, 7, 1824) 7296 conv2d_123[0][0] \n__________________________________________________________________________________________________\nswish_92 (Swish) (None, 7, 7, 1824) 0 batch_normalization_92[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_32 (DepthwiseC (None, 7, 7, 1824) 45600 swish_92[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_93 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_32[0][0] \n__________________________________________________________________________________________________\nswish_93 (Swish) (None, 7, 7, 1824) 0 batch_normalization_93[0][0] \n__________________________________________________________________________________________________\nlambda_32 (Lambda) (None, 1, 1, 1824) 0 swish_93[0][0] \n__________________________________________________________________________________________________\nconv2d_124 (Conv2D) (None, 1, 1, 76) 138700 lambda_32[0][0] \n__________________________________________________________________________________________________\nswish_94 (Swish) (None, 1, 1, 76) 0 conv2d_124[0][0] \n__________________________________________________________________________________________________\nconv2d_125 (Conv2D) (None, 1, 1, 1824) 140448 swish_94[0][0] \n__________________________________________________________________________________________________\nactivation_32 (Activation) (None, 1, 1, 1824) 0 conv2d_125[0][0] \n__________________________________________________________________________________________________\nmultiply_32 (Multiply) (None, 7, 7, 1824) 0 activation_32[0][0] \n swish_93[0][0] \n__________________________________________________________________________________________________\nconv2d_126 (Conv2D) (None, 7, 7, 304) 554496 multiply_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_94 (BatchNo (None, 7, 7, 304) 1216 conv2d_126[0][0] \n__________________________________________________________________________________________________\ndrop_connect_26 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_94[0][0] \n__________________________________________________________________________________________________\nadd_26 (Add) (None, 7, 7, 304) 0 drop_connect_26[0][0] \n add_25[0][0] \n__________________________________________________________________________________________________\nconv2d_127 (Conv2D) (None, 7, 7, 1824) 554496 add_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_95 (BatchNo (None, 7, 7, 1824) 7296 conv2d_127[0][0] \n__________________________________________________________________________________________________\nswish_95 (Swish) (None, 7, 7, 1824) 0 batch_normalization_95[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_33 (DepthwiseC (None, 7, 7, 1824) 45600 swish_95[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_96 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_33[0][0] \n__________________________________________________________________________________________________\nswish_96 (Swish) (None, 7, 7, 1824) 0 batch_normalization_96[0][0] \n__________________________________________________________________________________________________\nlambda_33 (Lambda) (None, 1, 1, 1824) 0 swish_96[0][0] \n__________________________________________________________________________________________________\nconv2d_128 (Conv2D) (None, 1, 1, 76) 138700 lambda_33[0][0] \n__________________________________________________________________________________________________\nswish_97 (Swish) (None, 1, 1, 76) 0 conv2d_128[0][0] \n__________________________________________________________________________________________________\nconv2d_129 (Conv2D) (None, 1, 1, 1824) 140448 swish_97[0][0] \n__________________________________________________________________________________________________\nactivation_33 (Activation) (None, 1, 1, 1824) 0 conv2d_129[0][0] \n__________________________________________________________________________________________________\nmultiply_33 (Multiply) (None, 7, 7, 1824) 0 activation_33[0][0] \n swish_96[0][0] \n__________________________________________________________________________________________________\nconv2d_130 (Conv2D) (None, 7, 7, 304) 554496 multiply_33[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_97 (BatchNo (None, 7, 7, 304) 1216 conv2d_130[0][0] \n__________________________________________________________________________________________________\ndrop_connect_27 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_97[0][0] \n__________________________________________________________________________________________________\nadd_27 (Add) (None, 7, 7, 304) 0 drop_connect_27[0][0] \n add_26[0][0] \n__________________________________________________________________________________________________\nconv2d_131 (Conv2D) (None, 7, 7, 1824) 554496 add_27[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_98 (BatchNo (None, 7, 7, 1824) 7296 conv2d_131[0][0] \n__________________________________________________________________________________________________\nswish_98 (Swish) (None, 7, 7, 1824) 0 batch_normalization_98[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_34 (DepthwiseC (None, 7, 7, 1824) 45600 swish_98[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_99 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_34[0][0] \n__________________________________________________________________________________________________\nswish_99 (Swish) (None, 7, 7, 1824) 0 batch_normalization_99[0][0] \n__________________________________________________________________________________________________\nlambda_34 (Lambda) (None, 1, 1, 1824) 0 swish_99[0][0] \n__________________________________________________________________________________________________\nconv2d_132 (Conv2D) (None, 1, 1, 76) 138700 lambda_34[0][0] \n__________________________________________________________________________________________________\nswish_100 (Swish) (None, 1, 1, 76) 0 conv2d_132[0][0] \n__________________________________________________________________________________________________\nconv2d_133 (Conv2D) (None, 1, 1, 1824) 140448 swish_100[0][0] \n__________________________________________________________________________________________________\nactivation_34 (Activation) (None, 1, 1, 1824) 0 conv2d_133[0][0] \n__________________________________________________________________________________________________\nmultiply_34 (Multiply) (None, 7, 7, 1824) 0 activation_34[0][0] \n swish_99[0][0] \n__________________________________________________________________________________________________\nconv2d_134 (Conv2D) (None, 7, 7, 304) 554496 multiply_34[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_100 (BatchN (None, 7, 7, 304) 1216 conv2d_134[0][0] \n__________________________________________________________________________________________________\ndrop_connect_28 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_100[0][0] \n__________________________________________________________________________________________________\nadd_28 (Add) (None, 7, 7, 304) 0 drop_connect_28[0][0] \n add_27[0][0] \n__________________________________________________________________________________________________\nconv2d_135 (Conv2D) (None, 7, 7, 1824) 554496 add_28[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_101 (BatchN (None, 7, 7, 1824) 7296 conv2d_135[0][0] \n__________________________________________________________________________________________________\nswish_101 (Swish) (None, 7, 7, 1824) 0 batch_normalization_101[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_35 (DepthwiseC (None, 7, 7, 1824) 45600 swish_101[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_102 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_35[0][0] \n__________________________________________________________________________________________________\nswish_102 (Swish) (None, 7, 7, 1824) 0 batch_normalization_102[0][0] \n__________________________________________________________________________________________________\nlambda_35 (Lambda) (None, 1, 1, 1824) 0 swish_102[0][0] \n__________________________________________________________________________________________________\nconv2d_136 (Conv2D) (None, 1, 1, 76) 138700 lambda_35[0][0] \n__________________________________________________________________________________________________\nswish_103 (Swish) (None, 1, 1, 76) 0 conv2d_136[0][0] \n__________________________________________________________________________________________________\nconv2d_137 (Conv2D) (None, 1, 1, 1824) 140448 swish_103[0][0] \n__________________________________________________________________________________________________\nactivation_35 (Activation) (None, 1, 1, 1824) 0 conv2d_137[0][0] \n__________________________________________________________________________________________________\nmultiply_35 (Multiply) (None, 7, 7, 1824) 0 activation_35[0][0] \n swish_102[0][0] \n__________________________________________________________________________________________________\nconv2d_138 (Conv2D) (None, 7, 7, 304) 554496 multiply_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_103 (BatchN (None, 7, 7, 304) 1216 conv2d_138[0][0] \n__________________________________________________________________________________________________\ndrop_connect_29 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_103[0][0] \n__________________________________________________________________________________________________\nadd_29 (Add) (None, 7, 7, 304) 0 drop_connect_29[0][0] \n add_28[0][0] \n__________________________________________________________________________________________________\nconv2d_139 (Conv2D) (None, 7, 7, 1824) 554496 add_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_104 (BatchN (None, 7, 7, 1824) 7296 conv2d_139[0][0] \n__________________________________________________________________________________________________\nswish_104 (Swish) (None, 7, 7, 1824) 0 batch_normalization_104[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_36 (DepthwiseC (None, 7, 7, 1824) 45600 swish_104[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_105 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_36[0][0] \n__________________________________________________________________________________________________\nswish_105 (Swish) (None, 7, 7, 1824) 0 batch_normalization_105[0][0] \n__________________________________________________________________________________________________\nlambda_36 (Lambda) (None, 1, 1, 1824) 0 swish_105[0][0] \n__________________________________________________________________________________________________\nconv2d_140 (Conv2D) (None, 1, 1, 76) 138700 lambda_36[0][0] \n__________________________________________________________________________________________________\nswish_106 (Swish) (None, 1, 1, 76) 0 conv2d_140[0][0] \n__________________________________________________________________________________________________\nconv2d_141 (Conv2D) (None, 1, 1, 1824) 140448 swish_106[0][0] \n__________________________________________________________________________________________________\nactivation_36 (Activation) (None, 1, 1, 1824) 0 conv2d_141[0][0] \n__________________________________________________________________________________________________\nmultiply_36 (Multiply) (None, 7, 7, 1824) 0 activation_36[0][0] \n swish_105[0][0] \n__________________________________________________________________________________________________\nconv2d_142 (Conv2D) (None, 7, 7, 304) 554496 multiply_36[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_106 (BatchN (None, 7, 7, 304) 1216 conv2d_142[0][0] \n__________________________________________________________________________________________________\ndrop_connect_30 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_106[0][0] \n__________________________________________________________________________________________________\nadd_30 (Add) (None, 7, 7, 304) 0 drop_connect_30[0][0] \n add_29[0][0] \n__________________________________________________________________________________________________\nconv2d_143 (Conv2D) (None, 7, 7, 1824) 554496 add_30[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_107 (BatchN (None, 7, 7, 1824) 7296 conv2d_143[0][0] \n__________________________________________________________________________________________________\nswish_107 (Swish) (None, 7, 7, 1824) 0 batch_normalization_107[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_37 (DepthwiseC (None, 7, 7, 1824) 16416 swish_107[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_108 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_37[0][0] \n__________________________________________________________________________________________________\nswish_108 (Swish) (None, 7, 7, 1824) 0 batch_normalization_108[0][0] \n__________________________________________________________________________________________________\nlambda_37 (Lambda) (None, 1, 1, 1824) 0 swish_108[0][0] \n__________________________________________________________________________________________________\nconv2d_144 (Conv2D) (None, 1, 1, 76) 138700 lambda_37[0][0] \n__________________________________________________________________________________________________\nswish_109 (Swish) (None, 1, 1, 76) 0 conv2d_144[0][0] \n__________________________________________________________________________________________________\nconv2d_145 (Conv2D) (None, 1, 1, 1824) 140448 swish_109[0][0] \n__________________________________________________________________________________________________\nactivation_37 (Activation) (None, 1, 1, 1824) 0 conv2d_145[0][0] \n__________________________________________________________________________________________________\nmultiply_37 (Multiply) (None, 7, 7, 1824) 0 activation_37[0][0] \n swish_108[0][0] \n__________________________________________________________________________________________________\nconv2d_146 (Conv2D) (None, 7, 7, 512) 933888 multiply_37[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_109 (BatchN (None, 7, 7, 512) 2048 conv2d_146[0][0] \n__________________________________________________________________________________________________\nconv2d_147 (Conv2D) (None, 7, 7, 3072) 1572864 batch_normalization_109[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_110 (BatchN (None, 7, 7, 3072) 12288 conv2d_147[0][0] \n__________________________________________________________________________________________________\nswish_110 (Swish) (None, 7, 7, 3072) 0 batch_normalization_110[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_38 (DepthwiseC (None, 7, 7, 3072) 27648 swish_110[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_111 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_38[0][0] \n__________________________________________________________________________________________________\nswish_111 (Swish) (None, 7, 7, 3072) 0 batch_normalization_111[0][0] \n__________________________________________________________________________________________________\nlambda_38 (Lambda) (None, 1, 1, 3072) 0 swish_111[0][0] \n__________________________________________________________________________________________________\nconv2d_148 (Conv2D) (None, 1, 1, 128) 393344 lambda_38[0][0] \n__________________________________________________________________________________________________\nswish_112 (Swish) (None, 1, 1, 128) 0 conv2d_148[0][0] \n__________________________________________________________________________________________________\nconv2d_149 (Conv2D) (None, 1, 1, 3072) 396288 swish_112[0][0] \n__________________________________________________________________________________________________\nactivation_38 (Activation) (None, 1, 1, 3072) 0 conv2d_149[0][0] \n__________________________________________________________________________________________________\nmultiply_38 (Multiply) (None, 7, 7, 3072) 0 activation_38[0][0] \n swish_111[0][0] \n__________________________________________________________________________________________________\nconv2d_150 (Conv2D) (None, 7, 7, 512) 1572864 multiply_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_112 (BatchN (None, 7, 7, 512) 2048 conv2d_150[0][0] \n__________________________________________________________________________________________________\ndrop_connect_31 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_112[0][0] \n__________________________________________________________________________________________________\nadd_31 (Add) (None, 7, 7, 512) 0 drop_connect_31[0][0] \n batch_normalization_109[0][0] \n__________________________________________________________________________________________________\nconv2d_151 (Conv2D) (None, 7, 7, 3072) 1572864 add_31[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_113 (BatchN (None, 7, 7, 3072) 12288 conv2d_151[0][0] \n__________________________________________________________________________________________________\nswish_113 (Swish) (None, 7, 7, 3072) 0 batch_normalization_113[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_39 (DepthwiseC (None, 7, 7, 3072) 27648 swish_113[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_114 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_39[0][0] \n__________________________________________________________________________________________________\nswish_114 (Swish) (None, 7, 7, 3072) 0 batch_normalization_114[0][0] \n__________________________________________________________________________________________________\nlambda_39 (Lambda) (None, 1, 1, 3072) 0 swish_114[0][0] \n__________________________________________________________________________________________________\nconv2d_152 (Conv2D) (None, 1, 1, 128) 393344 lambda_39[0][0] \n__________________________________________________________________________________________________\nswish_115 (Swish) (None, 1, 1, 128) 0 conv2d_152[0][0] \n__________________________________________________________________________________________________\nconv2d_153 (Conv2D) (None, 1, 1, 3072) 396288 swish_115[0][0] \n__________________________________________________________________________________________________\nactivation_39 (Activation) (None, 1, 1, 3072) 0 conv2d_153[0][0] \n__________________________________________________________________________________________________\nmultiply_39 (Multiply) (None, 7, 7, 3072) 0 activation_39[0][0] \n swish_114[0][0] \n__________________________________________________________________________________________________\nconv2d_154 (Conv2D) (None, 7, 7, 512) 1572864 multiply_39[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_115 (BatchN (None, 7, 7, 512) 2048 conv2d_154[0][0] \n__________________________________________________________________________________________________\ndrop_connect_32 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_115[0][0] \n__________________________________________________________________________________________________\nadd_32 (Add) (None, 7, 7, 512) 0 drop_connect_32[0][0] \n add_31[0][0] \n__________________________________________________________________________________________________\nconv2d_155 (Conv2D) (None, 7, 7, 2048) 1048576 add_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_116 (BatchN (None, 7, 7, 2048) 8192 conv2d_155[0][0] \n__________________________________________________________________________________________________\nswish_116 (Swish) (None, 7, 7, 2048) 0 batch_normalization_116[0][0] \n__________________________________________________________________________________________________\nglobal_average_pooling2d_1 (Glo (None, 2048) 0 swish_116[0][0] \n__________________________________________________________________________________________________\nfinal_output (Dense) (None, 1) 2049 global_average_pooling2d_1[0][0] \n==================================================================================================\nTotal params: 28,515,569\nTrainable params: 2,049\nNon-trainable params: 28,513,520\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"STEP_SIZE_TRAIN = train_generator.n//train_generator.batch_size\nSTEP_SIZE_VALID = valid_generator.n//valid_generator.batch_size\n\nhistory_warmup = model.fit_generator(generator=train_generator,\n steps_per_epoch=STEP_SIZE_TRAIN,\n validation_data=valid_generator,\n validation_steps=STEP_SIZE_VALID,\n epochs=WARMUP_EPOCHS,\n callbacks=callback_list,\n verbose=2).history","metadata":{"execution":{"iopub.status.busy":"2024-05-04T02:37:30.162682Z","iopub.execute_input":"2024-05-04T02:37:30.162975Z","iopub.status.idle":"2024-05-04T02:41:06.734563Z","shell.execute_reply.started":"2024-05-04T02:37:30.162932Z","shell.execute_reply":"2024-05-04T02:41:06.733504Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Epoch 1/5\n - 52s - loss: 2.1398 - acc: 0.4190 - val_loss: 1.3616 - val_acc: 0.2315\nEpoch 2/5\n - 41s - loss: 1.1975 - acc: 0.4237 - val_loss: 1.9834 - val_acc: 0.3586\nEpoch 3/5\n - 41s - loss: 0.9753 - acc: 0.4566 - val_loss: 1.4663 - val_acc: 0.4271\nEpoch 4/5\n - 41s - loss: 0.8786 - acc: 0.4725 - val_loss: 1.3796 - val_acc: 0.3686\nEpoch 5/5\n - 41s - loss: 0.8580 - acc: 0.4541 - val_loss: 1.9358 - val_acc: 0.3843\n","output_type":"stream"}]},{"cell_type":"code","source":"for layer in model.layers:\n layer.trainable = True\n\nes = EarlyStopping(monitor='val_loss', mode='min', patience=ES_PATIENCE, restore_best_weights=True, verbose=1)\ncosine_lr_2nd = WarmUpCosineDecayScheduler(learning_rate_base=LEARNING_RATE,\n total_steps=TOTAL_STEPS_2nd,\n warmup_learning_rate=0.0,\n warmup_steps=WARMUP_STEPS_2nd,\n hold_base_rate_steps=(3 * STEP_SIZE))\n\ncallback_list = [es, cosine_lr_2nd]\noptimizer = optimizers.Adam(lr=LEARNING_RATE)\nmodel.compile(optimizer=optimizer, loss='mean_squared_error', metrics=metric_list)\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-05-04T02:44:59.119171Z","iopub.execute_input":"2024-05-04T02:44:59.119508Z","iopub.status.idle":"2024-05-04T02:44:59.365317Z","shell.execute_reply.started":"2024-05-04T02:44:59.11946Z","shell.execute_reply":"2024-05-04T02:44:59.36451Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_1 (InputLayer) (None, 224, 224, 3) 0 \n__________________________________________________________________________________________________\nconv2d_1 (Conv2D) (None, 112, 112, 48) 1296 input_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_1 (BatchNor (None, 112, 112, 48) 192 conv2d_1[0][0] \n__________________________________________________________________________________________________\nswish_1 (Swish) (None, 112, 112, 48) 0 batch_normalization_1[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_1 (DepthwiseCo (None, 112, 112, 48) 432 swish_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_2 (BatchNor (None, 112, 112, 48) 192 depthwise_conv2d_1[0][0] \n__________________________________________________________________________________________________\nswish_2 (Swish) (None, 112, 112, 48) 0 batch_normalization_2[0][0] \n__________________________________________________________________________________________________\nlambda_1 (Lambda) (None, 1, 1, 48) 0 swish_2[0][0] \n__________________________________________________________________________________________________\nconv2d_2 (Conv2D) (None, 1, 1, 12) 588 lambda_1[0][0] \n__________________________________________________________________________________________________\nswish_3 (Swish) (None, 1, 1, 12) 0 conv2d_2[0][0] \n__________________________________________________________________________________________________\nconv2d_3 (Conv2D) (None, 1, 1, 48) 624 swish_3[0][0] \n__________________________________________________________________________________________________\nactivation_1 (Activation) (None, 1, 1, 48) 0 conv2d_3[0][0] \n__________________________________________________________________________________________________\nmultiply_1 (Multiply) (None, 112, 112, 48) 0 activation_1[0][0] \n swish_2[0][0] \n__________________________________________________________________________________________________\nconv2d_4 (Conv2D) (None, 112, 112, 24) 1152 multiply_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_3 (BatchNor (None, 112, 112, 24) 96 conv2d_4[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_2 (DepthwiseCo (None, 112, 112, 24) 216 batch_normalization_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_4 (BatchNor (None, 112, 112, 24) 96 depthwise_conv2d_2[0][0] \n__________________________________________________________________________________________________\nswish_4 (Swish) (None, 112, 112, 24) 0 batch_normalization_4[0][0] \n__________________________________________________________________________________________________\nlambda_2 (Lambda) (None, 1, 1, 24) 0 swish_4[0][0] \n__________________________________________________________________________________________________\nconv2d_5 (Conv2D) (None, 1, 1, 6) 150 lambda_2[0][0] \n__________________________________________________________________________________________________\nswish_5 (Swish) (None, 1, 1, 6) 0 conv2d_5[0][0] \n__________________________________________________________________________________________________\nconv2d_6 (Conv2D) (None, 1, 1, 24) 168 swish_5[0][0] \n__________________________________________________________________________________________________\nactivation_2 (Activation) (None, 1, 1, 24) 0 conv2d_6[0][0] \n__________________________________________________________________________________________________\nmultiply_2 (Multiply) (None, 112, 112, 24) 0 activation_2[0][0] \n swish_4[0][0] \n__________________________________________________________________________________________________\nconv2d_7 (Conv2D) (None, 112, 112, 24) 576 multiply_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_5 (BatchNor (None, 112, 112, 24) 96 conv2d_7[0][0] \n__________________________________________________________________________________________________\ndrop_connect_1 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_5[0][0] \n__________________________________________________________________________________________________\nadd_1 (Add) (None, 112, 112, 24) 0 drop_connect_1[0][0] \n batch_normalization_3[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_3 (DepthwiseCo (None, 112, 112, 24) 216 add_1[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_6 (BatchNor (None, 112, 112, 24) 96 depthwise_conv2d_3[0][0] \n__________________________________________________________________________________________________\nswish_6 (Swish) (None, 112, 112, 24) 0 batch_normalization_6[0][0] \n__________________________________________________________________________________________________\nlambda_3 (Lambda) (None, 1, 1, 24) 0 swish_6[0][0] \n__________________________________________________________________________________________________\nconv2d_8 (Conv2D) (None, 1, 1, 6) 150 lambda_3[0][0] \n__________________________________________________________________________________________________\nswish_7 (Swish) (None, 1, 1, 6) 0 conv2d_8[0][0] \n__________________________________________________________________________________________________\nconv2d_9 (Conv2D) (None, 1, 1, 24) 168 swish_7[0][0] \n__________________________________________________________________________________________________\nactivation_3 (Activation) (None, 1, 1, 24) 0 conv2d_9[0][0] \n__________________________________________________________________________________________________\nmultiply_3 (Multiply) (None, 112, 112, 24) 0 activation_3[0][0] \n swish_6[0][0] \n__________________________________________________________________________________________________\nconv2d_10 (Conv2D) (None, 112, 112, 24) 576 multiply_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_7 (BatchNor (None, 112, 112, 24) 96 conv2d_10[0][0] \n__________________________________________________________________________________________________\ndrop_connect_2 (DropConnect) (None, 112, 112, 24) 0 batch_normalization_7[0][0] \n__________________________________________________________________________________________________\nadd_2 (Add) (None, 112, 112, 24) 0 drop_connect_2[0][0] \n add_1[0][0] \n__________________________________________________________________________________________________\nconv2d_11 (Conv2D) (None, 112, 112, 144 3456 add_2[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_8 (BatchNor (None, 112, 112, 144 576 conv2d_11[0][0] \n__________________________________________________________________________________________________\nswish_8 (Swish) (None, 112, 112, 144 0 batch_normalization_8[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_4 (DepthwiseCo (None, 56, 56, 144) 1296 swish_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_9 (BatchNor (None, 56, 56, 144) 576 depthwise_conv2d_4[0][0] \n__________________________________________________________________________________________________\nswish_9 (Swish) (None, 56, 56, 144) 0 batch_normalization_9[0][0] \n__________________________________________________________________________________________________\nlambda_4 (Lambda) (None, 1, 1, 144) 0 swish_9[0][0] \n__________________________________________________________________________________________________\nconv2d_12 (Conv2D) (None, 1, 1, 6) 870 lambda_4[0][0] \n__________________________________________________________________________________________________\nswish_10 (Swish) (None, 1, 1, 6) 0 conv2d_12[0][0] \n__________________________________________________________________________________________________\nconv2d_13 (Conv2D) (None, 1, 1, 144) 1008 swish_10[0][0] \n__________________________________________________________________________________________________\nactivation_4 (Activation) (None, 1, 1, 144) 0 conv2d_13[0][0] \n__________________________________________________________________________________________________\nmultiply_4 (Multiply) (None, 56, 56, 144) 0 activation_4[0][0] \n swish_9[0][0] \n__________________________________________________________________________________________________\nconv2d_14 (Conv2D) (None, 56, 56, 40) 5760 multiply_4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_10 (BatchNo (None, 56, 56, 40) 160 conv2d_14[0][0] \n__________________________________________________________________________________________________\nconv2d_15 (Conv2D) (None, 56, 56, 240) 9600 batch_normalization_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_11 (BatchNo (None, 56, 56, 240) 960 conv2d_15[0][0] \n__________________________________________________________________________________________________\nswish_11 (Swish) (None, 56, 56, 240) 0 batch_normalization_11[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_5 (DepthwiseCo (None, 56, 56, 240) 2160 swish_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_12 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_5[0][0] \n__________________________________________________________________________________________________\nswish_12 (Swish) (None, 56, 56, 240) 0 batch_normalization_12[0][0] \n__________________________________________________________________________________________________\nlambda_5 (Lambda) (None, 1, 1, 240) 0 swish_12[0][0] \n__________________________________________________________________________________________________\nconv2d_16 (Conv2D) (None, 1, 1, 10) 2410 lambda_5[0][0] \n__________________________________________________________________________________________________\nswish_13 (Swish) (None, 1, 1, 10) 0 conv2d_16[0][0] \n__________________________________________________________________________________________________\nconv2d_17 (Conv2D) (None, 1, 1, 240) 2640 swish_13[0][0] \n__________________________________________________________________________________________________\nactivation_5 (Activation) (None, 1, 1, 240) 0 conv2d_17[0][0] \n__________________________________________________________________________________________________\nmultiply_5 (Multiply) (None, 56, 56, 240) 0 activation_5[0][0] \n swish_12[0][0] \n__________________________________________________________________________________________________\nconv2d_18 (Conv2D) (None, 56, 56, 40) 9600 multiply_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_13 (BatchNo (None, 56, 56, 40) 160 conv2d_18[0][0] \n__________________________________________________________________________________________________\ndrop_connect_3 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_13[0][0] \n__________________________________________________________________________________________________\nadd_3 (Add) (None, 56, 56, 40) 0 drop_connect_3[0][0] \n batch_normalization_10[0][0] \n__________________________________________________________________________________________________\nconv2d_19 (Conv2D) (None, 56, 56, 240) 9600 add_3[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_14 (BatchNo (None, 56, 56, 240) 960 conv2d_19[0][0] \n__________________________________________________________________________________________________\nswish_14 (Swish) (None, 56, 56, 240) 0 batch_normalization_14[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_6 (DepthwiseCo (None, 56, 56, 240) 2160 swish_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_15 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_6[0][0] \n__________________________________________________________________________________________________\nswish_15 (Swish) (None, 56, 56, 240) 0 batch_normalization_15[0][0] \n__________________________________________________________________________________________________\nlambda_6 (Lambda) (None, 1, 1, 240) 0 swish_15[0][0] \n__________________________________________________________________________________________________\nconv2d_20 (Conv2D) (None, 1, 1, 10) 2410 lambda_6[0][0] \n__________________________________________________________________________________________________\nswish_16 (Swish) (None, 1, 1, 10) 0 conv2d_20[0][0] \n__________________________________________________________________________________________________\nconv2d_21 (Conv2D) (None, 1, 1, 240) 2640 swish_16[0][0] \n__________________________________________________________________________________________________\nactivation_6 (Activation) (None, 1, 1, 240) 0 conv2d_21[0][0] \n__________________________________________________________________________________________________\nmultiply_6 (Multiply) (None, 56, 56, 240) 0 activation_6[0][0] \n swish_15[0][0] \n__________________________________________________________________________________________________\nconv2d_22 (Conv2D) (None, 56, 56, 40) 9600 multiply_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_16 (BatchNo (None, 56, 56, 40) 160 conv2d_22[0][0] \n__________________________________________________________________________________________________\ndrop_connect_4 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_16[0][0] \n__________________________________________________________________________________________________\nadd_4 (Add) (None, 56, 56, 40) 0 drop_connect_4[0][0] \n add_3[0][0] \n__________________________________________________________________________________________________\nconv2d_23 (Conv2D) (None, 56, 56, 240) 9600 add_4[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_17 (BatchNo (None, 56, 56, 240) 960 conv2d_23[0][0] \n__________________________________________________________________________________________________\nswish_17 (Swish) (None, 56, 56, 240) 0 batch_normalization_17[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_7 (DepthwiseCo (None, 56, 56, 240) 2160 swish_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_18 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_7[0][0] \n__________________________________________________________________________________________________\nswish_18 (Swish) (None, 56, 56, 240) 0 batch_normalization_18[0][0] \n__________________________________________________________________________________________________\nlambda_7 (Lambda) (None, 1, 1, 240) 0 swish_18[0][0] \n__________________________________________________________________________________________________\nconv2d_24 (Conv2D) (None, 1, 1, 10) 2410 lambda_7[0][0] \n__________________________________________________________________________________________________\nswish_19 (Swish) (None, 1, 1, 10) 0 conv2d_24[0][0] \n__________________________________________________________________________________________________\nconv2d_25 (Conv2D) (None, 1, 1, 240) 2640 swish_19[0][0] \n__________________________________________________________________________________________________\nactivation_7 (Activation) (None, 1, 1, 240) 0 conv2d_25[0][0] \n__________________________________________________________________________________________________\nmultiply_7 (Multiply) (None, 56, 56, 240) 0 activation_7[0][0] \n swish_18[0][0] \n__________________________________________________________________________________________________\nconv2d_26 (Conv2D) (None, 56, 56, 40) 9600 multiply_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_19 (BatchNo (None, 56, 56, 40) 160 conv2d_26[0][0] \n__________________________________________________________________________________________________\ndrop_connect_5 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_19[0][0] \n__________________________________________________________________________________________________\nadd_5 (Add) (None, 56, 56, 40) 0 drop_connect_5[0][0] \n add_4[0][0] \n__________________________________________________________________________________________________\nconv2d_27 (Conv2D) (None, 56, 56, 240) 9600 add_5[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_20 (BatchNo (None, 56, 56, 240) 960 conv2d_27[0][0] \n__________________________________________________________________________________________________\nswish_20 (Swish) (None, 56, 56, 240) 0 batch_normalization_20[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_8 (DepthwiseCo (None, 56, 56, 240) 2160 swish_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_21 (BatchNo (None, 56, 56, 240) 960 depthwise_conv2d_8[0][0] \n__________________________________________________________________________________________________\nswish_21 (Swish) (None, 56, 56, 240) 0 batch_normalization_21[0][0] \n__________________________________________________________________________________________________\nlambda_8 (Lambda) (None, 1, 1, 240) 0 swish_21[0][0] \n__________________________________________________________________________________________________\nconv2d_28 (Conv2D) (None, 1, 1, 10) 2410 lambda_8[0][0] \n__________________________________________________________________________________________________\nswish_22 (Swish) (None, 1, 1, 10) 0 conv2d_28[0][0] \n__________________________________________________________________________________________________\nconv2d_29 (Conv2D) (None, 1, 1, 240) 2640 swish_22[0][0] \n__________________________________________________________________________________________________\nactivation_8 (Activation) (None, 1, 1, 240) 0 conv2d_29[0][0] \n__________________________________________________________________________________________________\nmultiply_8 (Multiply) (None, 56, 56, 240) 0 activation_8[0][0] \n swish_21[0][0] \n__________________________________________________________________________________________________\nconv2d_30 (Conv2D) (None, 56, 56, 40) 9600 multiply_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_22 (BatchNo (None, 56, 56, 40) 160 conv2d_30[0][0] \n__________________________________________________________________________________________________\ndrop_connect_6 (DropConnect) (None, 56, 56, 40) 0 batch_normalization_22[0][0] \n__________________________________________________________________________________________________\nadd_6 (Add) (None, 56, 56, 40) 0 drop_connect_6[0][0] \n add_5[0][0] \n__________________________________________________________________________________________________\nconv2d_31 (Conv2D) (None, 56, 56, 240) 9600 add_6[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_23 (BatchNo (None, 56, 56, 240) 960 conv2d_31[0][0] \n__________________________________________________________________________________________________\nswish_23 (Swish) (None, 56, 56, 240) 0 batch_normalization_23[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_9 (DepthwiseCo (None, 28, 28, 240) 6000 swish_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_24 (BatchNo (None, 28, 28, 240) 960 depthwise_conv2d_9[0][0] \n__________________________________________________________________________________________________\nswish_24 (Swish) (None, 28, 28, 240) 0 batch_normalization_24[0][0] \n__________________________________________________________________________________________________\nlambda_9 (Lambda) (None, 1, 1, 240) 0 swish_24[0][0] \n__________________________________________________________________________________________________\nconv2d_32 (Conv2D) (None, 1, 1, 10) 2410 lambda_9[0][0] \n__________________________________________________________________________________________________\nswish_25 (Swish) (None, 1, 1, 10) 0 conv2d_32[0][0] \n__________________________________________________________________________________________________\nconv2d_33 (Conv2D) (None, 1, 1, 240) 2640 swish_25[0][0] \n__________________________________________________________________________________________________\nactivation_9 (Activation) (None, 1, 1, 240) 0 conv2d_33[0][0] \n__________________________________________________________________________________________________\nmultiply_9 (Multiply) (None, 28, 28, 240) 0 activation_9[0][0] \n swish_24[0][0] \n__________________________________________________________________________________________________\nconv2d_34 (Conv2D) (None, 28, 28, 64) 15360 multiply_9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_25 (BatchNo (None, 28, 28, 64) 256 conv2d_34[0][0] \n__________________________________________________________________________________________________\nconv2d_35 (Conv2D) (None, 28, 28, 384) 24576 batch_normalization_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_26 (BatchNo (None, 28, 28, 384) 1536 conv2d_35[0][0] \n__________________________________________________________________________________________________\nswish_26 (Swish) (None, 28, 28, 384) 0 batch_normalization_26[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_10 (DepthwiseC (None, 28, 28, 384) 9600 swish_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_27 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_10[0][0] \n__________________________________________________________________________________________________\nswish_27 (Swish) (None, 28, 28, 384) 0 batch_normalization_27[0][0] \n__________________________________________________________________________________________________\nlambda_10 (Lambda) (None, 1, 1, 384) 0 swish_27[0][0] \n__________________________________________________________________________________________________\nconv2d_36 (Conv2D) (None, 1, 1, 16) 6160 lambda_10[0][0] \n__________________________________________________________________________________________________\nswish_28 (Swish) (None, 1, 1, 16) 0 conv2d_36[0][0] \n__________________________________________________________________________________________________\nconv2d_37 (Conv2D) (None, 1, 1, 384) 6528 swish_28[0][0] \n__________________________________________________________________________________________________\nactivation_10 (Activation) (None, 1, 1, 384) 0 conv2d_37[0][0] \n__________________________________________________________________________________________________\nmultiply_10 (Multiply) (None, 28, 28, 384) 0 activation_10[0][0] \n swish_27[0][0] \n__________________________________________________________________________________________________\nconv2d_38 (Conv2D) (None, 28, 28, 64) 24576 multiply_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_28 (BatchNo (None, 28, 28, 64) 256 conv2d_38[0][0] \n__________________________________________________________________________________________________\ndrop_connect_7 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_28[0][0] \n__________________________________________________________________________________________________\nadd_7 (Add) (None, 28, 28, 64) 0 drop_connect_7[0][0] \n batch_normalization_25[0][0] \n__________________________________________________________________________________________________\nconv2d_39 (Conv2D) (None, 28, 28, 384) 24576 add_7[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_29 (BatchNo (None, 28, 28, 384) 1536 conv2d_39[0][0] \n__________________________________________________________________________________________________\nswish_29 (Swish) (None, 28, 28, 384) 0 batch_normalization_29[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_11 (DepthwiseC (None, 28, 28, 384) 9600 swish_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_30 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_11[0][0] \n__________________________________________________________________________________________________\nswish_30 (Swish) (None, 28, 28, 384) 0 batch_normalization_30[0][0] \n__________________________________________________________________________________________________\nlambda_11 (Lambda) (None, 1, 1, 384) 0 swish_30[0][0] \n__________________________________________________________________________________________________\nconv2d_40 (Conv2D) (None, 1, 1, 16) 6160 lambda_11[0][0] \n__________________________________________________________________________________________________\nswish_31 (Swish) (None, 1, 1, 16) 0 conv2d_40[0][0] \n__________________________________________________________________________________________________\nconv2d_41 (Conv2D) (None, 1, 1, 384) 6528 swish_31[0][0] \n__________________________________________________________________________________________________\nactivation_11 (Activation) (None, 1, 1, 384) 0 conv2d_41[0][0] \n__________________________________________________________________________________________________\nmultiply_11 (Multiply) (None, 28, 28, 384) 0 activation_11[0][0] \n swish_30[0][0] \n__________________________________________________________________________________________________\nconv2d_42 (Conv2D) (None, 28, 28, 64) 24576 multiply_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_31 (BatchNo (None, 28, 28, 64) 256 conv2d_42[0][0] \n__________________________________________________________________________________________________\ndrop_connect_8 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_31[0][0] \n__________________________________________________________________________________________________\nadd_8 (Add) (None, 28, 28, 64) 0 drop_connect_8[0][0] \n add_7[0][0] \n__________________________________________________________________________________________________\nconv2d_43 (Conv2D) (None, 28, 28, 384) 24576 add_8[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_32 (BatchNo (None, 28, 28, 384) 1536 conv2d_43[0][0] \n__________________________________________________________________________________________________\nswish_32 (Swish) (None, 28, 28, 384) 0 batch_normalization_32[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_12 (DepthwiseC (None, 28, 28, 384) 9600 swish_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_33 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_12[0][0] \n__________________________________________________________________________________________________\nswish_33 (Swish) (None, 28, 28, 384) 0 batch_normalization_33[0][0] \n__________________________________________________________________________________________________\nlambda_12 (Lambda) (None, 1, 1, 384) 0 swish_33[0][0] \n__________________________________________________________________________________________________\nconv2d_44 (Conv2D) (None, 1, 1, 16) 6160 lambda_12[0][0] \n__________________________________________________________________________________________________\nswish_34 (Swish) (None, 1, 1, 16) 0 conv2d_44[0][0] \n__________________________________________________________________________________________________\nconv2d_45 (Conv2D) (None, 1, 1, 384) 6528 swish_34[0][0] \n__________________________________________________________________________________________________\nactivation_12 (Activation) (None, 1, 1, 384) 0 conv2d_45[0][0] \n__________________________________________________________________________________________________\nmultiply_12 (Multiply) (None, 28, 28, 384) 0 activation_12[0][0] \n swish_33[0][0] \n__________________________________________________________________________________________________\nconv2d_46 (Conv2D) (None, 28, 28, 64) 24576 multiply_12[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_34 (BatchNo (None, 28, 28, 64) 256 conv2d_46[0][0] \n__________________________________________________________________________________________________\ndrop_connect_9 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_34[0][0] \n__________________________________________________________________________________________________\nadd_9 (Add) (None, 28, 28, 64) 0 drop_connect_9[0][0] \n add_8[0][0] \n__________________________________________________________________________________________________\nconv2d_47 (Conv2D) (None, 28, 28, 384) 24576 add_9[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_35 (BatchNo (None, 28, 28, 384) 1536 conv2d_47[0][0] \n__________________________________________________________________________________________________\nswish_35 (Swish) (None, 28, 28, 384) 0 batch_normalization_35[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_13 (DepthwiseC (None, 28, 28, 384) 9600 swish_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_36 (BatchNo (None, 28, 28, 384) 1536 depthwise_conv2d_13[0][0] \n__________________________________________________________________________________________________\nswish_36 (Swish) (None, 28, 28, 384) 0 batch_normalization_36[0][0] \n__________________________________________________________________________________________________\nlambda_13 (Lambda) (None, 1, 1, 384) 0 swish_36[0][0] \n__________________________________________________________________________________________________\nconv2d_48 (Conv2D) (None, 1, 1, 16) 6160 lambda_13[0][0] \n__________________________________________________________________________________________________\nswish_37 (Swish) (None, 1, 1, 16) 0 conv2d_48[0][0] \n__________________________________________________________________________________________________\nconv2d_49 (Conv2D) (None, 1, 1, 384) 6528 swish_37[0][0] \n__________________________________________________________________________________________________\nactivation_13 (Activation) (None, 1, 1, 384) 0 conv2d_49[0][0] \n__________________________________________________________________________________________________\nmultiply_13 (Multiply) (None, 28, 28, 384) 0 activation_13[0][0] \n swish_36[0][0] \n__________________________________________________________________________________________________\nconv2d_50 (Conv2D) (None, 28, 28, 64) 24576 multiply_13[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_37 (BatchNo (None, 28, 28, 64) 256 conv2d_50[0][0] \n__________________________________________________________________________________________________\ndrop_connect_10 (DropConnect) (None, 28, 28, 64) 0 batch_normalization_37[0][0] \n__________________________________________________________________________________________________\nadd_10 (Add) (None, 28, 28, 64) 0 drop_connect_10[0][0] \n add_9[0][0] \n__________________________________________________________________________________________________\nconv2d_51 (Conv2D) (None, 28, 28, 384) 24576 add_10[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_38 (BatchNo (None, 28, 28, 384) 1536 conv2d_51[0][0] \n__________________________________________________________________________________________________\nswish_38 (Swish) (None, 28, 28, 384) 0 batch_normalization_38[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_14 (DepthwiseC (None, 14, 14, 384) 3456 swish_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_39 (BatchNo (None, 14, 14, 384) 1536 depthwise_conv2d_14[0][0] \n__________________________________________________________________________________________________\nswish_39 (Swish) (None, 14, 14, 384) 0 batch_normalization_39[0][0] \n__________________________________________________________________________________________________\nlambda_14 (Lambda) (None, 1, 1, 384) 0 swish_39[0][0] \n__________________________________________________________________________________________________\nconv2d_52 (Conv2D) (None, 1, 1, 16) 6160 lambda_14[0][0] \n__________________________________________________________________________________________________\nswish_40 (Swish) (None, 1, 1, 16) 0 conv2d_52[0][0] \n__________________________________________________________________________________________________\nconv2d_53 (Conv2D) (None, 1, 1, 384) 6528 swish_40[0][0] \n__________________________________________________________________________________________________\nactivation_14 (Activation) (None, 1, 1, 384) 0 conv2d_53[0][0] \n__________________________________________________________________________________________________\nmultiply_14 (Multiply) (None, 14, 14, 384) 0 activation_14[0][0] \n swish_39[0][0] \n__________________________________________________________________________________________________\nconv2d_54 (Conv2D) (None, 14, 14, 128) 49152 multiply_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_40 (BatchNo (None, 14, 14, 128) 512 conv2d_54[0][0] \n__________________________________________________________________________________________________\nconv2d_55 (Conv2D) (None, 14, 14, 768) 98304 batch_normalization_40[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_41 (BatchNo (None, 14, 14, 768) 3072 conv2d_55[0][0] \n__________________________________________________________________________________________________\nswish_41 (Swish) (None, 14, 14, 768) 0 batch_normalization_41[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_15 (DepthwiseC (None, 14, 14, 768) 6912 swish_41[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_42 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_15[0][0] \n__________________________________________________________________________________________________\nswish_42 (Swish) (None, 14, 14, 768) 0 batch_normalization_42[0][0] \n__________________________________________________________________________________________________\nlambda_15 (Lambda) (None, 1, 1, 768) 0 swish_42[0][0] \n__________________________________________________________________________________________________\nconv2d_56 (Conv2D) (None, 1, 1, 32) 24608 lambda_15[0][0] \n__________________________________________________________________________________________________\nswish_43 (Swish) (None, 1, 1, 32) 0 conv2d_56[0][0] \n__________________________________________________________________________________________________\nconv2d_57 (Conv2D) (None, 1, 1, 768) 25344 swish_43[0][0] \n__________________________________________________________________________________________________\nactivation_15 (Activation) (None, 1, 1, 768) 0 conv2d_57[0][0] \n__________________________________________________________________________________________________\nmultiply_15 (Multiply) (None, 14, 14, 768) 0 activation_15[0][0] \n swish_42[0][0] \n__________________________________________________________________________________________________\nconv2d_58 (Conv2D) (None, 14, 14, 128) 98304 multiply_15[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_43 (BatchNo (None, 14, 14, 128) 512 conv2d_58[0][0] \n__________________________________________________________________________________________________\ndrop_connect_11 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_43[0][0] \n__________________________________________________________________________________________________\nadd_11 (Add) (None, 14, 14, 128) 0 drop_connect_11[0][0] \n batch_normalization_40[0][0] \n__________________________________________________________________________________________________\nconv2d_59 (Conv2D) (None, 14, 14, 768) 98304 add_11[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_44 (BatchNo (None, 14, 14, 768) 3072 conv2d_59[0][0] \n__________________________________________________________________________________________________\nswish_44 (Swish) (None, 14, 14, 768) 0 batch_normalization_44[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_16 (DepthwiseC (None, 14, 14, 768) 6912 swish_44[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_45 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_16[0][0] \n__________________________________________________________________________________________________\nswish_45 (Swish) (None, 14, 14, 768) 0 batch_normalization_45[0][0] \n__________________________________________________________________________________________________\nlambda_16 (Lambda) (None, 1, 1, 768) 0 swish_45[0][0] \n__________________________________________________________________________________________________\nconv2d_60 (Conv2D) (None, 1, 1, 32) 24608 lambda_16[0][0] \n__________________________________________________________________________________________________\nswish_46 (Swish) (None, 1, 1, 32) 0 conv2d_60[0][0] \n__________________________________________________________________________________________________\nconv2d_61 (Conv2D) (None, 1, 1, 768) 25344 swish_46[0][0] \n__________________________________________________________________________________________________\nactivation_16 (Activation) (None, 1, 1, 768) 0 conv2d_61[0][0] \n__________________________________________________________________________________________________\nmultiply_16 (Multiply) (None, 14, 14, 768) 0 activation_16[0][0] \n swish_45[0][0] \n__________________________________________________________________________________________________\nconv2d_62 (Conv2D) (None, 14, 14, 128) 98304 multiply_16[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_46 (BatchNo (None, 14, 14, 128) 512 conv2d_62[0][0] \n__________________________________________________________________________________________________\ndrop_connect_12 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_46[0][0] \n__________________________________________________________________________________________________\nadd_12 (Add) (None, 14, 14, 128) 0 drop_connect_12[0][0] \n add_11[0][0] \n__________________________________________________________________________________________________\nconv2d_63 (Conv2D) (None, 14, 14, 768) 98304 add_12[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_47 (BatchNo (None, 14, 14, 768) 3072 conv2d_63[0][0] \n__________________________________________________________________________________________________\nswish_47 (Swish) (None, 14, 14, 768) 0 batch_normalization_47[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_17 (DepthwiseC (None, 14, 14, 768) 6912 swish_47[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_48 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_17[0][0] \n__________________________________________________________________________________________________\nswish_48 (Swish) (None, 14, 14, 768) 0 batch_normalization_48[0][0] \n__________________________________________________________________________________________________\nlambda_17 (Lambda) (None, 1, 1, 768) 0 swish_48[0][0] \n__________________________________________________________________________________________________\nconv2d_64 (Conv2D) (None, 1, 1, 32) 24608 lambda_17[0][0] \n__________________________________________________________________________________________________\nswish_49 (Swish) (None, 1, 1, 32) 0 conv2d_64[0][0] \n__________________________________________________________________________________________________\nconv2d_65 (Conv2D) (None, 1, 1, 768) 25344 swish_49[0][0] \n__________________________________________________________________________________________________\nactivation_17 (Activation) (None, 1, 1, 768) 0 conv2d_65[0][0] \n__________________________________________________________________________________________________\nmultiply_17 (Multiply) (None, 14, 14, 768) 0 activation_17[0][0] \n swish_48[0][0] \n__________________________________________________________________________________________________\nconv2d_66 (Conv2D) (None, 14, 14, 128) 98304 multiply_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_49 (BatchNo (None, 14, 14, 128) 512 conv2d_66[0][0] \n__________________________________________________________________________________________________\ndrop_connect_13 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_49[0][0] \n__________________________________________________________________________________________________\nadd_13 (Add) (None, 14, 14, 128) 0 drop_connect_13[0][0] \n add_12[0][0] \n__________________________________________________________________________________________________\nconv2d_67 (Conv2D) (None, 14, 14, 768) 98304 add_13[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_50 (BatchNo (None, 14, 14, 768) 3072 conv2d_67[0][0] \n__________________________________________________________________________________________________\nswish_50 (Swish) (None, 14, 14, 768) 0 batch_normalization_50[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_18 (DepthwiseC (None, 14, 14, 768) 6912 swish_50[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_51 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_18[0][0] \n__________________________________________________________________________________________________\nswish_51 (Swish) (None, 14, 14, 768) 0 batch_normalization_51[0][0] \n__________________________________________________________________________________________________\nlambda_18 (Lambda) (None, 1, 1, 768) 0 swish_51[0][0] \n__________________________________________________________________________________________________\nconv2d_68 (Conv2D) (None, 1, 1, 32) 24608 lambda_18[0][0] \n__________________________________________________________________________________________________\nswish_52 (Swish) (None, 1, 1, 32) 0 conv2d_68[0][0] \n__________________________________________________________________________________________________\nconv2d_69 (Conv2D) (None, 1, 1, 768) 25344 swish_52[0][0] \n__________________________________________________________________________________________________\nactivation_18 (Activation) (None, 1, 1, 768) 0 conv2d_69[0][0] \n__________________________________________________________________________________________________\nmultiply_18 (Multiply) (None, 14, 14, 768) 0 activation_18[0][0] \n swish_51[0][0] \n__________________________________________________________________________________________________\nconv2d_70 (Conv2D) (None, 14, 14, 128) 98304 multiply_18[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_52 (BatchNo (None, 14, 14, 128) 512 conv2d_70[0][0] \n__________________________________________________________________________________________________\ndrop_connect_14 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_52[0][0] \n__________________________________________________________________________________________________\nadd_14 (Add) (None, 14, 14, 128) 0 drop_connect_14[0][0] \n add_13[0][0] \n__________________________________________________________________________________________________\nconv2d_71 (Conv2D) (None, 14, 14, 768) 98304 add_14[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_53 (BatchNo (None, 14, 14, 768) 3072 conv2d_71[0][0] \n__________________________________________________________________________________________________\nswish_53 (Swish) (None, 14, 14, 768) 0 batch_normalization_53[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_19 (DepthwiseC (None, 14, 14, 768) 6912 swish_53[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_54 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_19[0][0] \n__________________________________________________________________________________________________\nswish_54 (Swish) (None, 14, 14, 768) 0 batch_normalization_54[0][0] \n__________________________________________________________________________________________________\nlambda_19 (Lambda) (None, 1, 1, 768) 0 swish_54[0][0] \n__________________________________________________________________________________________________\nconv2d_72 (Conv2D) (None, 1, 1, 32) 24608 lambda_19[0][0] \n__________________________________________________________________________________________________\nswish_55 (Swish) (None, 1, 1, 32) 0 conv2d_72[0][0] \n__________________________________________________________________________________________________\nconv2d_73 (Conv2D) (None, 1, 1, 768) 25344 swish_55[0][0] \n__________________________________________________________________________________________________\nactivation_19 (Activation) (None, 1, 1, 768) 0 conv2d_73[0][0] \n__________________________________________________________________________________________________\nmultiply_19 (Multiply) (None, 14, 14, 768) 0 activation_19[0][0] \n swish_54[0][0] \n__________________________________________________________________________________________________\nconv2d_74 (Conv2D) (None, 14, 14, 128) 98304 multiply_19[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_55 (BatchNo (None, 14, 14, 128) 512 conv2d_74[0][0] \n__________________________________________________________________________________________________\ndrop_connect_15 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_55[0][0] \n__________________________________________________________________________________________________\nadd_15 (Add) (None, 14, 14, 128) 0 drop_connect_15[0][0] \n add_14[0][0] \n__________________________________________________________________________________________________\nconv2d_75 (Conv2D) (None, 14, 14, 768) 98304 add_15[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_56 (BatchNo (None, 14, 14, 768) 3072 conv2d_75[0][0] \n__________________________________________________________________________________________________\nswish_56 (Swish) (None, 14, 14, 768) 0 batch_normalization_56[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_20 (DepthwiseC (None, 14, 14, 768) 6912 swish_56[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_57 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_20[0][0] \n__________________________________________________________________________________________________\nswish_57 (Swish) (None, 14, 14, 768) 0 batch_normalization_57[0][0] \n__________________________________________________________________________________________________\nlambda_20 (Lambda) (None, 1, 1, 768) 0 swish_57[0][0] \n__________________________________________________________________________________________________\nconv2d_76 (Conv2D) (None, 1, 1, 32) 24608 lambda_20[0][0] \n__________________________________________________________________________________________________\nswish_58 (Swish) (None, 1, 1, 32) 0 conv2d_76[0][0] \n__________________________________________________________________________________________________\nconv2d_77 (Conv2D) (None, 1, 1, 768) 25344 swish_58[0][0] \n__________________________________________________________________________________________________\nactivation_20 (Activation) (None, 1, 1, 768) 0 conv2d_77[0][0] \n__________________________________________________________________________________________________\nmultiply_20 (Multiply) (None, 14, 14, 768) 0 activation_20[0][0] \n swish_57[0][0] \n__________________________________________________________________________________________________\nconv2d_78 (Conv2D) (None, 14, 14, 128) 98304 multiply_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_58 (BatchNo (None, 14, 14, 128) 512 conv2d_78[0][0] \n__________________________________________________________________________________________________\ndrop_connect_16 (DropConnect) (None, 14, 14, 128) 0 batch_normalization_58[0][0] \n__________________________________________________________________________________________________\nadd_16 (Add) (None, 14, 14, 128) 0 drop_connect_16[0][0] \n add_15[0][0] \n__________________________________________________________________________________________________\nconv2d_79 (Conv2D) (None, 14, 14, 768) 98304 add_16[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_59 (BatchNo (None, 14, 14, 768) 3072 conv2d_79[0][0] \n__________________________________________________________________________________________________\nswish_59 (Swish) (None, 14, 14, 768) 0 batch_normalization_59[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_21 (DepthwiseC (None, 14, 14, 768) 19200 swish_59[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_60 (BatchNo (None, 14, 14, 768) 3072 depthwise_conv2d_21[0][0] \n__________________________________________________________________________________________________\nswish_60 (Swish) (None, 14, 14, 768) 0 batch_normalization_60[0][0] \n__________________________________________________________________________________________________\nlambda_21 (Lambda) (None, 1, 1, 768) 0 swish_60[0][0] \n__________________________________________________________________________________________________\nconv2d_80 (Conv2D) (None, 1, 1, 32) 24608 lambda_21[0][0] \n__________________________________________________________________________________________________\nswish_61 (Swish) (None, 1, 1, 32) 0 conv2d_80[0][0] \n__________________________________________________________________________________________________\nconv2d_81 (Conv2D) (None, 1, 1, 768) 25344 swish_61[0][0] \n__________________________________________________________________________________________________\nactivation_21 (Activation) (None, 1, 1, 768) 0 conv2d_81[0][0] \n__________________________________________________________________________________________________\nmultiply_21 (Multiply) (None, 14, 14, 768) 0 activation_21[0][0] \n swish_60[0][0] \n__________________________________________________________________________________________________\nconv2d_82 (Conv2D) (None, 14, 14, 176) 135168 multiply_21[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_61 (BatchNo (None, 14, 14, 176) 704 conv2d_82[0][0] \n__________________________________________________________________________________________________\nconv2d_83 (Conv2D) (None, 14, 14, 1056) 185856 batch_normalization_61[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_62 (BatchNo (None, 14, 14, 1056) 4224 conv2d_83[0][0] \n__________________________________________________________________________________________________\nswish_62 (Swish) (None, 14, 14, 1056) 0 batch_normalization_62[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_22 (DepthwiseC (None, 14, 14, 1056) 26400 swish_62[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_63 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_22[0][0] \n__________________________________________________________________________________________________\nswish_63 (Swish) (None, 14, 14, 1056) 0 batch_normalization_63[0][0] \n__________________________________________________________________________________________________\nlambda_22 (Lambda) (None, 1, 1, 1056) 0 swish_63[0][0] \n__________________________________________________________________________________________________\nconv2d_84 (Conv2D) (None, 1, 1, 44) 46508 lambda_22[0][0] \n__________________________________________________________________________________________________\nswish_64 (Swish) (None, 1, 1, 44) 0 conv2d_84[0][0] \n__________________________________________________________________________________________________\nconv2d_85 (Conv2D) (None, 1, 1, 1056) 47520 swish_64[0][0] \n__________________________________________________________________________________________________\nactivation_22 (Activation) (None, 1, 1, 1056) 0 conv2d_85[0][0] \n__________________________________________________________________________________________________\nmultiply_22 (Multiply) (None, 14, 14, 1056) 0 activation_22[0][0] \n swish_63[0][0] \n__________________________________________________________________________________________________\nconv2d_86 (Conv2D) (None, 14, 14, 176) 185856 multiply_22[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_64 (BatchNo (None, 14, 14, 176) 704 conv2d_86[0][0] \n__________________________________________________________________________________________________\ndrop_connect_17 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_64[0][0] \n__________________________________________________________________________________________________\nadd_17 (Add) (None, 14, 14, 176) 0 drop_connect_17[0][0] \n batch_normalization_61[0][0] \n__________________________________________________________________________________________________\nconv2d_87 (Conv2D) (None, 14, 14, 1056) 185856 add_17[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_65 (BatchNo (None, 14, 14, 1056) 4224 conv2d_87[0][0] \n__________________________________________________________________________________________________\nswish_65 (Swish) (None, 14, 14, 1056) 0 batch_normalization_65[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_23 (DepthwiseC (None, 14, 14, 1056) 26400 swish_65[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_66 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_23[0][0] \n__________________________________________________________________________________________________\nswish_66 (Swish) (None, 14, 14, 1056) 0 batch_normalization_66[0][0] \n__________________________________________________________________________________________________\nlambda_23 (Lambda) (None, 1, 1, 1056) 0 swish_66[0][0] \n__________________________________________________________________________________________________\nconv2d_88 (Conv2D) (None, 1, 1, 44) 46508 lambda_23[0][0] \n__________________________________________________________________________________________________\nswish_67 (Swish) (None, 1, 1, 44) 0 conv2d_88[0][0] \n__________________________________________________________________________________________________\nconv2d_89 (Conv2D) (None, 1, 1, 1056) 47520 swish_67[0][0] \n__________________________________________________________________________________________________\nactivation_23 (Activation) (None, 1, 1, 1056) 0 conv2d_89[0][0] \n__________________________________________________________________________________________________\nmultiply_23 (Multiply) (None, 14, 14, 1056) 0 activation_23[0][0] \n swish_66[0][0] \n__________________________________________________________________________________________________\nconv2d_90 (Conv2D) (None, 14, 14, 176) 185856 multiply_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_67 (BatchNo (None, 14, 14, 176) 704 conv2d_90[0][0] \n__________________________________________________________________________________________________\ndrop_connect_18 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_67[0][0] \n__________________________________________________________________________________________________\nadd_18 (Add) (None, 14, 14, 176) 0 drop_connect_18[0][0] \n add_17[0][0] \n__________________________________________________________________________________________________\nconv2d_91 (Conv2D) (None, 14, 14, 1056) 185856 add_18[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_68 (BatchNo (None, 14, 14, 1056) 4224 conv2d_91[0][0] \n__________________________________________________________________________________________________\nswish_68 (Swish) (None, 14, 14, 1056) 0 batch_normalization_68[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_24 (DepthwiseC (None, 14, 14, 1056) 26400 swish_68[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_69 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_24[0][0] \n__________________________________________________________________________________________________\nswish_69 (Swish) (None, 14, 14, 1056) 0 batch_normalization_69[0][0] \n__________________________________________________________________________________________________\nlambda_24 (Lambda) (None, 1, 1, 1056) 0 swish_69[0][0] \n__________________________________________________________________________________________________\nconv2d_92 (Conv2D) (None, 1, 1, 44) 46508 lambda_24[0][0] \n__________________________________________________________________________________________________\nswish_70 (Swish) (None, 1, 1, 44) 0 conv2d_92[0][0] \n__________________________________________________________________________________________________\nconv2d_93 (Conv2D) (None, 1, 1, 1056) 47520 swish_70[0][0] \n__________________________________________________________________________________________________\nactivation_24 (Activation) (None, 1, 1, 1056) 0 conv2d_93[0][0] \n__________________________________________________________________________________________________\nmultiply_24 (Multiply) (None, 14, 14, 1056) 0 activation_24[0][0] \n swish_69[0][0] \n__________________________________________________________________________________________________\nconv2d_94 (Conv2D) (None, 14, 14, 176) 185856 multiply_24[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_70 (BatchNo (None, 14, 14, 176) 704 conv2d_94[0][0] \n__________________________________________________________________________________________________\ndrop_connect_19 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_70[0][0] \n__________________________________________________________________________________________________\nadd_19 (Add) (None, 14, 14, 176) 0 drop_connect_19[0][0] \n add_18[0][0] \n__________________________________________________________________________________________________\nconv2d_95 (Conv2D) (None, 14, 14, 1056) 185856 add_19[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_71 (BatchNo (None, 14, 14, 1056) 4224 conv2d_95[0][0] \n__________________________________________________________________________________________________\nswish_71 (Swish) (None, 14, 14, 1056) 0 batch_normalization_71[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_25 (DepthwiseC (None, 14, 14, 1056) 26400 swish_71[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_72 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_25[0][0] \n__________________________________________________________________________________________________\nswish_72 (Swish) (None, 14, 14, 1056) 0 batch_normalization_72[0][0] \n__________________________________________________________________________________________________\nlambda_25 (Lambda) (None, 1, 1, 1056) 0 swish_72[0][0] \n__________________________________________________________________________________________________\nconv2d_96 (Conv2D) (None, 1, 1, 44) 46508 lambda_25[0][0] \n__________________________________________________________________________________________________\nswish_73 (Swish) (None, 1, 1, 44) 0 conv2d_96[0][0] \n__________________________________________________________________________________________________\nconv2d_97 (Conv2D) (None, 1, 1, 1056) 47520 swish_73[0][0] \n__________________________________________________________________________________________________\nactivation_25 (Activation) (None, 1, 1, 1056) 0 conv2d_97[0][0] \n__________________________________________________________________________________________________\nmultiply_25 (Multiply) (None, 14, 14, 1056) 0 activation_25[0][0] \n swish_72[0][0] \n__________________________________________________________________________________________________\nconv2d_98 (Conv2D) (None, 14, 14, 176) 185856 multiply_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_73 (BatchNo (None, 14, 14, 176) 704 conv2d_98[0][0] \n__________________________________________________________________________________________________\ndrop_connect_20 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_73[0][0] \n__________________________________________________________________________________________________\nadd_20 (Add) (None, 14, 14, 176) 0 drop_connect_20[0][0] \n add_19[0][0] \n__________________________________________________________________________________________________\nconv2d_99 (Conv2D) (None, 14, 14, 1056) 185856 add_20[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_74 (BatchNo (None, 14, 14, 1056) 4224 conv2d_99[0][0] \n__________________________________________________________________________________________________\nswish_74 (Swish) (None, 14, 14, 1056) 0 batch_normalization_74[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_26 (DepthwiseC (None, 14, 14, 1056) 26400 swish_74[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_75 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_26[0][0] \n__________________________________________________________________________________________________\nswish_75 (Swish) (None, 14, 14, 1056) 0 batch_normalization_75[0][0] \n__________________________________________________________________________________________________\nlambda_26 (Lambda) (None, 1, 1, 1056) 0 swish_75[0][0] \n__________________________________________________________________________________________________\nconv2d_100 (Conv2D) (None, 1, 1, 44) 46508 lambda_26[0][0] \n__________________________________________________________________________________________________\nswish_76 (Swish) (None, 1, 1, 44) 0 conv2d_100[0][0] \n__________________________________________________________________________________________________\nconv2d_101 (Conv2D) (None, 1, 1, 1056) 47520 swish_76[0][0] \n__________________________________________________________________________________________________\nactivation_26 (Activation) (None, 1, 1, 1056) 0 conv2d_101[0][0] \n__________________________________________________________________________________________________\nmultiply_26 (Multiply) (None, 14, 14, 1056) 0 activation_26[0][0] \n swish_75[0][0] \n__________________________________________________________________________________________________\nconv2d_102 (Conv2D) (None, 14, 14, 176) 185856 multiply_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_76 (BatchNo (None, 14, 14, 176) 704 conv2d_102[0][0] \n__________________________________________________________________________________________________\ndrop_connect_21 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_76[0][0] \n__________________________________________________________________________________________________\nadd_21 (Add) (None, 14, 14, 176) 0 drop_connect_21[0][0] \n add_20[0][0] \n__________________________________________________________________________________________________\nconv2d_103 (Conv2D) (None, 14, 14, 1056) 185856 add_21[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_77 (BatchNo (None, 14, 14, 1056) 4224 conv2d_103[0][0] \n__________________________________________________________________________________________________\nswish_77 (Swish) (None, 14, 14, 1056) 0 batch_normalization_77[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_27 (DepthwiseC (None, 14, 14, 1056) 26400 swish_77[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_78 (BatchNo (None, 14, 14, 1056) 4224 depthwise_conv2d_27[0][0] \n__________________________________________________________________________________________________\nswish_78 (Swish) (None, 14, 14, 1056) 0 batch_normalization_78[0][0] \n__________________________________________________________________________________________________\nlambda_27 (Lambda) (None, 1, 1, 1056) 0 swish_78[0][0] \n__________________________________________________________________________________________________\nconv2d_104 (Conv2D) (None, 1, 1, 44) 46508 lambda_27[0][0] \n__________________________________________________________________________________________________\nswish_79 (Swish) (None, 1, 1, 44) 0 conv2d_104[0][0] \n__________________________________________________________________________________________________\nconv2d_105 (Conv2D) (None, 1, 1, 1056) 47520 swish_79[0][0] \n__________________________________________________________________________________________________\nactivation_27 (Activation) (None, 1, 1, 1056) 0 conv2d_105[0][0] \n__________________________________________________________________________________________________\nmultiply_27 (Multiply) (None, 14, 14, 1056) 0 activation_27[0][0] \n swish_78[0][0] \n__________________________________________________________________________________________________\nconv2d_106 (Conv2D) (None, 14, 14, 176) 185856 multiply_27[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_79 (BatchNo (None, 14, 14, 176) 704 conv2d_106[0][0] \n__________________________________________________________________________________________________\ndrop_connect_22 (DropConnect) (None, 14, 14, 176) 0 batch_normalization_79[0][0] \n__________________________________________________________________________________________________\nadd_22 (Add) (None, 14, 14, 176) 0 drop_connect_22[0][0] \n add_21[0][0] \n__________________________________________________________________________________________________\nconv2d_107 (Conv2D) (None, 14, 14, 1056) 185856 add_22[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_80 (BatchNo (None, 14, 14, 1056) 4224 conv2d_107[0][0] \n__________________________________________________________________________________________________\nswish_80 (Swish) (None, 14, 14, 1056) 0 batch_normalization_80[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_28 (DepthwiseC (None, 7, 7, 1056) 26400 swish_80[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_81 (BatchNo (None, 7, 7, 1056) 4224 depthwise_conv2d_28[0][0] \n__________________________________________________________________________________________________\nswish_81 (Swish) (None, 7, 7, 1056) 0 batch_normalization_81[0][0] \n__________________________________________________________________________________________________\nlambda_28 (Lambda) (None, 1, 1, 1056) 0 swish_81[0][0] \n__________________________________________________________________________________________________\nconv2d_108 (Conv2D) (None, 1, 1, 44) 46508 lambda_28[0][0] \n__________________________________________________________________________________________________\nswish_82 (Swish) (None, 1, 1, 44) 0 conv2d_108[0][0] \n__________________________________________________________________________________________________\nconv2d_109 (Conv2D) (None, 1, 1, 1056) 47520 swish_82[0][0] \n__________________________________________________________________________________________________\nactivation_28 (Activation) (None, 1, 1, 1056) 0 conv2d_109[0][0] \n__________________________________________________________________________________________________\nmultiply_28 (Multiply) (None, 7, 7, 1056) 0 activation_28[0][0] \n swish_81[0][0] \n__________________________________________________________________________________________________\nconv2d_110 (Conv2D) (None, 7, 7, 304) 321024 multiply_28[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_82 (BatchNo (None, 7, 7, 304) 1216 conv2d_110[0][0] \n__________________________________________________________________________________________________\nconv2d_111 (Conv2D) (None, 7, 7, 1824) 554496 batch_normalization_82[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_83 (BatchNo (None, 7, 7, 1824) 7296 conv2d_111[0][0] \n__________________________________________________________________________________________________\nswish_83 (Swish) (None, 7, 7, 1824) 0 batch_normalization_83[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_29 (DepthwiseC (None, 7, 7, 1824) 45600 swish_83[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_84 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_29[0][0] \n__________________________________________________________________________________________________\nswish_84 (Swish) (None, 7, 7, 1824) 0 batch_normalization_84[0][0] \n__________________________________________________________________________________________________\nlambda_29 (Lambda) (None, 1, 1, 1824) 0 swish_84[0][0] \n__________________________________________________________________________________________________\nconv2d_112 (Conv2D) (None, 1, 1, 76) 138700 lambda_29[0][0] \n__________________________________________________________________________________________________\nswish_85 (Swish) (None, 1, 1, 76) 0 conv2d_112[0][0] \n__________________________________________________________________________________________________\nconv2d_113 (Conv2D) (None, 1, 1, 1824) 140448 swish_85[0][0] \n__________________________________________________________________________________________________\nactivation_29 (Activation) (None, 1, 1, 1824) 0 conv2d_113[0][0] \n__________________________________________________________________________________________________\nmultiply_29 (Multiply) (None, 7, 7, 1824) 0 activation_29[0][0] \n swish_84[0][0] \n__________________________________________________________________________________________________\nconv2d_114 (Conv2D) (None, 7, 7, 304) 554496 multiply_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_85 (BatchNo (None, 7, 7, 304) 1216 conv2d_114[0][0] \n__________________________________________________________________________________________________\ndrop_connect_23 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_85[0][0] \n__________________________________________________________________________________________________\nadd_23 (Add) (None, 7, 7, 304) 0 drop_connect_23[0][0] \n batch_normalization_82[0][0] \n__________________________________________________________________________________________________\nconv2d_115 (Conv2D) (None, 7, 7, 1824) 554496 add_23[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_86 (BatchNo (None, 7, 7, 1824) 7296 conv2d_115[0][0] \n__________________________________________________________________________________________________\nswish_86 (Swish) (None, 7, 7, 1824) 0 batch_normalization_86[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_30 (DepthwiseC (None, 7, 7, 1824) 45600 swish_86[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_87 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_30[0][0] \n__________________________________________________________________________________________________\nswish_87 (Swish) (None, 7, 7, 1824) 0 batch_normalization_87[0][0] \n__________________________________________________________________________________________________\nlambda_30 (Lambda) (None, 1, 1, 1824) 0 swish_87[0][0] \n__________________________________________________________________________________________________\nconv2d_116 (Conv2D) (None, 1, 1, 76) 138700 lambda_30[0][0] \n__________________________________________________________________________________________________\nswish_88 (Swish) (None, 1, 1, 76) 0 conv2d_116[0][0] \n__________________________________________________________________________________________________\nconv2d_117 (Conv2D) (None, 1, 1, 1824) 140448 swish_88[0][0] \n__________________________________________________________________________________________________\nactivation_30 (Activation) (None, 1, 1, 1824) 0 conv2d_117[0][0] \n__________________________________________________________________________________________________\nmultiply_30 (Multiply) (None, 7, 7, 1824) 0 activation_30[0][0] \n swish_87[0][0] \n__________________________________________________________________________________________________\nconv2d_118 (Conv2D) (None, 7, 7, 304) 554496 multiply_30[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_88 (BatchNo (None, 7, 7, 304) 1216 conv2d_118[0][0] \n__________________________________________________________________________________________________\ndrop_connect_24 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_88[0][0] \n__________________________________________________________________________________________________\nadd_24 (Add) (None, 7, 7, 304) 0 drop_connect_24[0][0] \n add_23[0][0] \n__________________________________________________________________________________________________\nconv2d_119 (Conv2D) (None, 7, 7, 1824) 554496 add_24[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_89 (BatchNo (None, 7, 7, 1824) 7296 conv2d_119[0][0] \n__________________________________________________________________________________________________\nswish_89 (Swish) (None, 7, 7, 1824) 0 batch_normalization_89[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_31 (DepthwiseC (None, 7, 7, 1824) 45600 swish_89[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_90 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_31[0][0] \n__________________________________________________________________________________________________\nswish_90 (Swish) (None, 7, 7, 1824) 0 batch_normalization_90[0][0] \n__________________________________________________________________________________________________\nlambda_31 (Lambda) (None, 1, 1, 1824) 0 swish_90[0][0] \n__________________________________________________________________________________________________\nconv2d_120 (Conv2D) (None, 1, 1, 76) 138700 lambda_31[0][0] \n__________________________________________________________________________________________________\nswish_91 (Swish) (None, 1, 1, 76) 0 conv2d_120[0][0] \n__________________________________________________________________________________________________\nconv2d_121 (Conv2D) (None, 1, 1, 1824) 140448 swish_91[0][0] \n__________________________________________________________________________________________________\nactivation_31 (Activation) (None, 1, 1, 1824) 0 conv2d_121[0][0] \n__________________________________________________________________________________________________\nmultiply_31 (Multiply) (None, 7, 7, 1824) 0 activation_31[0][0] \n swish_90[0][0] \n__________________________________________________________________________________________________\nconv2d_122 (Conv2D) (None, 7, 7, 304) 554496 multiply_31[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_91 (BatchNo (None, 7, 7, 304) 1216 conv2d_122[0][0] \n__________________________________________________________________________________________________\ndrop_connect_25 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_91[0][0] \n__________________________________________________________________________________________________\nadd_25 (Add) (None, 7, 7, 304) 0 drop_connect_25[0][0] \n add_24[0][0] \n__________________________________________________________________________________________________\nconv2d_123 (Conv2D) (None, 7, 7, 1824) 554496 add_25[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_92 (BatchNo (None, 7, 7, 1824) 7296 conv2d_123[0][0] \n__________________________________________________________________________________________________\nswish_92 (Swish) (None, 7, 7, 1824) 0 batch_normalization_92[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_32 (DepthwiseC (None, 7, 7, 1824) 45600 swish_92[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_93 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_32[0][0] \n__________________________________________________________________________________________________\nswish_93 (Swish) (None, 7, 7, 1824) 0 batch_normalization_93[0][0] \n__________________________________________________________________________________________________\nlambda_32 (Lambda) (None, 1, 1, 1824) 0 swish_93[0][0] \n__________________________________________________________________________________________________\nconv2d_124 (Conv2D) (None, 1, 1, 76) 138700 lambda_32[0][0] \n__________________________________________________________________________________________________\nswish_94 (Swish) (None, 1, 1, 76) 0 conv2d_124[0][0] \n__________________________________________________________________________________________________\nconv2d_125 (Conv2D) (None, 1, 1, 1824) 140448 swish_94[0][0] \n__________________________________________________________________________________________________\nactivation_32 (Activation) (None, 1, 1, 1824) 0 conv2d_125[0][0] \n__________________________________________________________________________________________________\nmultiply_32 (Multiply) (None, 7, 7, 1824) 0 activation_32[0][0] \n swish_93[0][0] \n__________________________________________________________________________________________________\nconv2d_126 (Conv2D) (None, 7, 7, 304) 554496 multiply_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_94 (BatchNo (None, 7, 7, 304) 1216 conv2d_126[0][0] \n__________________________________________________________________________________________________\ndrop_connect_26 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_94[0][0] \n__________________________________________________________________________________________________\nadd_26 (Add) (None, 7, 7, 304) 0 drop_connect_26[0][0] \n add_25[0][0] \n__________________________________________________________________________________________________\nconv2d_127 (Conv2D) (None, 7, 7, 1824) 554496 add_26[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_95 (BatchNo (None, 7, 7, 1824) 7296 conv2d_127[0][0] \n__________________________________________________________________________________________________\nswish_95 (Swish) (None, 7, 7, 1824) 0 batch_normalization_95[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_33 (DepthwiseC (None, 7, 7, 1824) 45600 swish_95[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_96 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_33[0][0] \n__________________________________________________________________________________________________\nswish_96 (Swish) (None, 7, 7, 1824) 0 batch_normalization_96[0][0] \n__________________________________________________________________________________________________\nlambda_33 (Lambda) (None, 1, 1, 1824) 0 swish_96[0][0] \n__________________________________________________________________________________________________\nconv2d_128 (Conv2D) (None, 1, 1, 76) 138700 lambda_33[0][0] \n__________________________________________________________________________________________________\nswish_97 (Swish) (None, 1, 1, 76) 0 conv2d_128[0][0] \n__________________________________________________________________________________________________\nconv2d_129 (Conv2D) (None, 1, 1, 1824) 140448 swish_97[0][0] \n__________________________________________________________________________________________________\nactivation_33 (Activation) (None, 1, 1, 1824) 0 conv2d_129[0][0] \n__________________________________________________________________________________________________\nmultiply_33 (Multiply) (None, 7, 7, 1824) 0 activation_33[0][0] \n swish_96[0][0] \n__________________________________________________________________________________________________\nconv2d_130 (Conv2D) (None, 7, 7, 304) 554496 multiply_33[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_97 (BatchNo (None, 7, 7, 304) 1216 conv2d_130[0][0] \n__________________________________________________________________________________________________\ndrop_connect_27 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_97[0][0] \n__________________________________________________________________________________________________\nadd_27 (Add) (None, 7, 7, 304) 0 drop_connect_27[0][0] \n add_26[0][0] \n__________________________________________________________________________________________________\nconv2d_131 (Conv2D) (None, 7, 7, 1824) 554496 add_27[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_98 (BatchNo (None, 7, 7, 1824) 7296 conv2d_131[0][0] \n__________________________________________________________________________________________________\nswish_98 (Swish) (None, 7, 7, 1824) 0 batch_normalization_98[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_34 (DepthwiseC (None, 7, 7, 1824) 45600 swish_98[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_99 (BatchNo (None, 7, 7, 1824) 7296 depthwise_conv2d_34[0][0] \n__________________________________________________________________________________________________\nswish_99 (Swish) (None, 7, 7, 1824) 0 batch_normalization_99[0][0] \n__________________________________________________________________________________________________\nlambda_34 (Lambda) (None, 1, 1, 1824) 0 swish_99[0][0] \n__________________________________________________________________________________________________\nconv2d_132 (Conv2D) (None, 1, 1, 76) 138700 lambda_34[0][0] \n__________________________________________________________________________________________________\nswish_100 (Swish) (None, 1, 1, 76) 0 conv2d_132[0][0] \n__________________________________________________________________________________________________\nconv2d_133 (Conv2D) (None, 1, 1, 1824) 140448 swish_100[0][0] \n__________________________________________________________________________________________________\nactivation_34 (Activation) (None, 1, 1, 1824) 0 conv2d_133[0][0] \n__________________________________________________________________________________________________\nmultiply_34 (Multiply) (None, 7, 7, 1824) 0 activation_34[0][0] \n swish_99[0][0] \n__________________________________________________________________________________________________\nconv2d_134 (Conv2D) (None, 7, 7, 304) 554496 multiply_34[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_100 (BatchN (None, 7, 7, 304) 1216 conv2d_134[0][0] \n__________________________________________________________________________________________________\ndrop_connect_28 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_100[0][0] \n__________________________________________________________________________________________________\nadd_28 (Add) (None, 7, 7, 304) 0 drop_connect_28[0][0] \n add_27[0][0] \n__________________________________________________________________________________________________\nconv2d_135 (Conv2D) (None, 7, 7, 1824) 554496 add_28[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_101 (BatchN (None, 7, 7, 1824) 7296 conv2d_135[0][0] \n__________________________________________________________________________________________________\nswish_101 (Swish) (None, 7, 7, 1824) 0 batch_normalization_101[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_35 (DepthwiseC (None, 7, 7, 1824) 45600 swish_101[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_102 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_35[0][0] \n__________________________________________________________________________________________________\nswish_102 (Swish) (None, 7, 7, 1824) 0 batch_normalization_102[0][0] \n__________________________________________________________________________________________________\nlambda_35 (Lambda) (None, 1, 1, 1824) 0 swish_102[0][0] \n__________________________________________________________________________________________________\nconv2d_136 (Conv2D) (None, 1, 1, 76) 138700 lambda_35[0][0] \n__________________________________________________________________________________________________\nswish_103 (Swish) (None, 1, 1, 76) 0 conv2d_136[0][0] \n__________________________________________________________________________________________________\nconv2d_137 (Conv2D) (None, 1, 1, 1824) 140448 swish_103[0][0] \n__________________________________________________________________________________________________\nactivation_35 (Activation) (None, 1, 1, 1824) 0 conv2d_137[0][0] \n__________________________________________________________________________________________________\nmultiply_35 (Multiply) (None, 7, 7, 1824) 0 activation_35[0][0] \n swish_102[0][0] \n__________________________________________________________________________________________________\nconv2d_138 (Conv2D) (None, 7, 7, 304) 554496 multiply_35[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_103 (BatchN (None, 7, 7, 304) 1216 conv2d_138[0][0] \n__________________________________________________________________________________________________\ndrop_connect_29 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_103[0][0] \n__________________________________________________________________________________________________\nadd_29 (Add) (None, 7, 7, 304) 0 drop_connect_29[0][0] \n add_28[0][0] \n__________________________________________________________________________________________________\nconv2d_139 (Conv2D) (None, 7, 7, 1824) 554496 add_29[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_104 (BatchN (None, 7, 7, 1824) 7296 conv2d_139[0][0] \n__________________________________________________________________________________________________\nswish_104 (Swish) (None, 7, 7, 1824) 0 batch_normalization_104[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_36 (DepthwiseC (None, 7, 7, 1824) 45600 swish_104[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_105 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_36[0][0] \n__________________________________________________________________________________________________\nswish_105 (Swish) (None, 7, 7, 1824) 0 batch_normalization_105[0][0] \n__________________________________________________________________________________________________\nlambda_36 (Lambda) (None, 1, 1, 1824) 0 swish_105[0][0] \n__________________________________________________________________________________________________\nconv2d_140 (Conv2D) (None, 1, 1, 76) 138700 lambda_36[0][0] \n__________________________________________________________________________________________________\nswish_106 (Swish) (None, 1, 1, 76) 0 conv2d_140[0][0] \n__________________________________________________________________________________________________\nconv2d_141 (Conv2D) (None, 1, 1, 1824) 140448 swish_106[0][0] \n__________________________________________________________________________________________________\nactivation_36 (Activation) (None, 1, 1, 1824) 0 conv2d_141[0][0] \n__________________________________________________________________________________________________\nmultiply_36 (Multiply) (None, 7, 7, 1824) 0 activation_36[0][0] \n swish_105[0][0] \n__________________________________________________________________________________________________\nconv2d_142 (Conv2D) (None, 7, 7, 304) 554496 multiply_36[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_106 (BatchN (None, 7, 7, 304) 1216 conv2d_142[0][0] \n__________________________________________________________________________________________________\ndrop_connect_30 (DropConnect) (None, 7, 7, 304) 0 batch_normalization_106[0][0] \n__________________________________________________________________________________________________\nadd_30 (Add) (None, 7, 7, 304) 0 drop_connect_30[0][0] \n add_29[0][0] \n__________________________________________________________________________________________________\nconv2d_143 (Conv2D) (None, 7, 7, 1824) 554496 add_30[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_107 (BatchN (None, 7, 7, 1824) 7296 conv2d_143[0][0] \n__________________________________________________________________________________________________\nswish_107 (Swish) (None, 7, 7, 1824) 0 batch_normalization_107[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_37 (DepthwiseC (None, 7, 7, 1824) 16416 swish_107[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_108 (BatchN (None, 7, 7, 1824) 7296 depthwise_conv2d_37[0][0] \n__________________________________________________________________________________________________\nswish_108 (Swish) (None, 7, 7, 1824) 0 batch_normalization_108[0][0] \n__________________________________________________________________________________________________\nlambda_37 (Lambda) (None, 1, 1, 1824) 0 swish_108[0][0] \n__________________________________________________________________________________________________\nconv2d_144 (Conv2D) (None, 1, 1, 76) 138700 lambda_37[0][0] \n__________________________________________________________________________________________________\nswish_109 (Swish) (None, 1, 1, 76) 0 conv2d_144[0][0] \n__________________________________________________________________________________________________\nconv2d_145 (Conv2D) (None, 1, 1, 1824) 140448 swish_109[0][0] \n__________________________________________________________________________________________________\nactivation_37 (Activation) (None, 1, 1, 1824) 0 conv2d_145[0][0] \n__________________________________________________________________________________________________\nmultiply_37 (Multiply) (None, 7, 7, 1824) 0 activation_37[0][0] \n swish_108[0][0] \n__________________________________________________________________________________________________\nconv2d_146 (Conv2D) (None, 7, 7, 512) 933888 multiply_37[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_109 (BatchN (None, 7, 7, 512) 2048 conv2d_146[0][0] \n__________________________________________________________________________________________________\nconv2d_147 (Conv2D) (None, 7, 7, 3072) 1572864 batch_normalization_109[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_110 (BatchN (None, 7, 7, 3072) 12288 conv2d_147[0][0] \n__________________________________________________________________________________________________\nswish_110 (Swish) (None, 7, 7, 3072) 0 batch_normalization_110[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_38 (DepthwiseC (None, 7, 7, 3072) 27648 swish_110[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_111 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_38[0][0] \n__________________________________________________________________________________________________\nswish_111 (Swish) (None, 7, 7, 3072) 0 batch_normalization_111[0][0] \n__________________________________________________________________________________________________\nlambda_38 (Lambda) (None, 1, 1, 3072) 0 swish_111[0][0] \n__________________________________________________________________________________________________\nconv2d_148 (Conv2D) (None, 1, 1, 128) 393344 lambda_38[0][0] \n__________________________________________________________________________________________________\nswish_112 (Swish) (None, 1, 1, 128) 0 conv2d_148[0][0] \n__________________________________________________________________________________________________\nconv2d_149 (Conv2D) (None, 1, 1, 3072) 396288 swish_112[0][0] \n__________________________________________________________________________________________________\nactivation_38 (Activation) (None, 1, 1, 3072) 0 conv2d_149[0][0] \n__________________________________________________________________________________________________\nmultiply_38 (Multiply) (None, 7, 7, 3072) 0 activation_38[0][0] \n swish_111[0][0] \n__________________________________________________________________________________________________\nconv2d_150 (Conv2D) (None, 7, 7, 512) 1572864 multiply_38[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_112 (BatchN (None, 7, 7, 512) 2048 conv2d_150[0][0] \n__________________________________________________________________________________________________\ndrop_connect_31 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_112[0][0] \n__________________________________________________________________________________________________\nadd_31 (Add) (None, 7, 7, 512) 0 drop_connect_31[0][0] \n batch_normalization_109[0][0] \n__________________________________________________________________________________________________\nconv2d_151 (Conv2D) (None, 7, 7, 3072) 1572864 add_31[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_113 (BatchN (None, 7, 7, 3072) 12288 conv2d_151[0][0] \n__________________________________________________________________________________________________\nswish_113 (Swish) (None, 7, 7, 3072) 0 batch_normalization_113[0][0] \n__________________________________________________________________________________________________\ndepthwise_conv2d_39 (DepthwiseC (None, 7, 7, 3072) 27648 swish_113[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_114 (BatchN (None, 7, 7, 3072) 12288 depthwise_conv2d_39[0][0] \n__________________________________________________________________________________________________\nswish_114 (Swish) (None, 7, 7, 3072) 0 batch_normalization_114[0][0] \n__________________________________________________________________________________________________\nlambda_39 (Lambda) (None, 1, 1, 3072) 0 swish_114[0][0] \n__________________________________________________________________________________________________\nconv2d_152 (Conv2D) (None, 1, 1, 128) 393344 lambda_39[0][0] \n__________________________________________________________________________________________________\nswish_115 (Swish) (None, 1, 1, 128) 0 conv2d_152[0][0] \n__________________________________________________________________________________________________\nconv2d_153 (Conv2D) (None, 1, 1, 3072) 396288 swish_115[0][0] \n__________________________________________________________________________________________________\nactivation_39 (Activation) (None, 1, 1, 3072) 0 conv2d_153[0][0] \n__________________________________________________________________________________________________\nmultiply_39 (Multiply) (None, 7, 7, 3072) 0 activation_39[0][0] \n swish_114[0][0] \n__________________________________________________________________________________________________\nconv2d_154 (Conv2D) (None, 7, 7, 512) 1572864 multiply_39[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_115 (BatchN (None, 7, 7, 512) 2048 conv2d_154[0][0] \n__________________________________________________________________________________________________\ndrop_connect_32 (DropConnect) (None, 7, 7, 512) 0 batch_normalization_115[0][0] \n__________________________________________________________________________________________________\nadd_32 (Add) (None, 7, 7, 512) 0 drop_connect_32[0][0] \n add_31[0][0] \n__________________________________________________________________________________________________\nconv2d_155 (Conv2D) (None, 7, 7, 2048) 1048576 add_32[0][0] \n__________________________________________________________________________________________________\nbatch_normalization_116 (BatchN (None, 7, 7, 2048) 8192 conv2d_155[0][0] \n__________________________________________________________________________________________________\nswish_116 (Swish) (None, 7, 7, 2048) 0 batch_normalization_116[0][0] \n__________________________________________________________________________________________________\nglobal_average_pooling2d_1 (Glo (None, 2048) 0 swish_116[0][0] \n__________________________________________________________________________________________________\nfinal_output (Dense) (None, 1) 2049 global_average_pooling2d_1[0][0] \n==================================================================================================\nTotal params: 28,515,569\nTrainable params: 28,342,833\nNon-trainable params: 172,736\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"history = model.fit_generator(generator=train_generator,\n steps_per_epoch=STEP_SIZE_TRAIN,\n validation_data=valid_generator,\n validation_steps=STEP_SIZE_VALID,\n epochs=EPOCHS,\n callbacks=callback_list,\n verbose=2).history","metadata":{"execution":{"iopub.status.busy":"2024-05-04T02:44:59.369094Z","iopub.execute_input":"2024-05-04T02:44:59.369395Z","iopub.status.idle":"2024-05-04T03:23:52.567907Z","shell.execute_reply.started":"2024-05-04T02:44:59.369343Z","shell.execute_reply":"2024-05-04T03:23:52.567032Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Epoch 1/20\n - 158s - loss: 0.8081 - acc: 0.4810 - val_loss: 0.5677 - val_acc: 0.5943\nEpoch 2/20\n - 119s - loss: 0.6848 - acc: 0.5244 - val_loss: 0.4879 - val_acc: 0.6229\nEpoch 3/20\n - 119s - loss: 0.5607 - acc: 0.5641 - val_loss: 0.4345 - val_acc: 0.6486\nEpoch 4/20\n - 119s - loss: 0.5148 - acc: 0.5981 - val_loss: 0.4255 - val_acc: 0.6300\nEpoch 5/20\n - 119s - loss: 0.4125 - acc: 0.6545 - val_loss: 0.3833 - val_acc: 0.7086\nEpoch 6/20\n - 119s - loss: 0.4219 - acc: 0.6737 - val_loss: 0.3045 - val_acc: 0.7557\nEpoch 7/20\n - 120s - loss: 0.3117 - acc: 0.7370 - val_loss: 0.3694 - val_acc: 0.7686\nEpoch 8/20\n - 119s - loss: 0.2876 - acc: 0.7550 - val_loss: 0.3138 - val_acc: 0.7457\nEpoch 9/20\n - 119s - loss: 0.2675 - acc: 0.7611 - val_loss: 0.2808 - val_acc: 0.7900\nEpoch 10/20\n - 119s - loss: 0.2628 - acc: 0.7675 - val_loss: 0.3105 - val_acc: 0.7900\nEpoch 11/20\n - 119s - loss: 0.2230 - acc: 0.7977 - val_loss: 0.3259 - val_acc: 0.7600\nEpoch 12/20\n - 119s - loss: 0.2041 - acc: 0.8107 - val_loss: 0.2895 - val_acc: 0.7814\nEpoch 13/20\n - 119s - loss: 0.1885 - acc: 0.8166 - val_loss: 0.3363 - val_acc: 0.7843\nEpoch 14/20\n - 119s - loss: 0.1677 - acc: 0.8283 - val_loss: 0.2463 - val_acc: 0.7971\nEpoch 15/20\n - 119s - loss: 0.1466 - acc: 0.8490 - val_loss: 0.2703 - val_acc: 0.8000\nEpoch 16/20\n - 119s - loss: 0.1347 - acc: 0.8582 - val_loss: 0.2942 - val_acc: 0.7871\nEpoch 17/20\n - 119s - loss: 0.1251 - acc: 0.8734 - val_loss: 0.2959 - val_acc: 0.7871\nEpoch 18/20\n - 119s - loss: 0.1088 - acc: 0.8785 - val_loss: 0.2922 - val_acc: 0.8000\nEpoch 19/20\n - 119s - loss: 0.1113 - acc: 0.8809 - val_loss: 0.2776 - val_acc: 0.7857\nRestoring model weights from the end of the best epoch\nEpoch 00019: early stopping\n","output_type":"stream"}]},{"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(2, 1, sharex='col', figsize=(20, 6))\n\nax1.plot(cosine_lr_1st.learning_rates)\nax1.set_title('Warm up learning rates')\n\nax2.plot(cosine_lr_2nd.learning_rates)\nax2.set_title('Fine-tune learning rates')\n\nplt.xlabel('Steps')\nplt.ylabel('Learning rate')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-04T03:23:52.569262Z","iopub.execute_input":"2024-05-04T03:23:52.569529Z","iopub.status.idle":"2024-05-04T03:23:53.06761Z","shell.execute_reply.started":"2024-05-04T03:23:52.569485Z","shell.execute_reply":"2024-05-04T03:23:53.066329Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(2, 1, sharex='col', figsize=(20, 14))\n\nax1.plot(history['loss'], label='Train loss')\nax1.plot(history['val_loss'], label='Validation loss')\nax1.legend(loc='best')\nax1.set_title('Loss')\n\nax2.plot(history['acc'], label='Train accuracy')\nax2.plot(history['val_acc'], label='Validation accuracy')\nax2.legend(loc='best')\nax2.set_title('Accuracy')\n\nplt.xlabel('Epochs')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-04T03:23:53.069661Z","iopub.execute_input":"2024-05-04T03:23:53.070088Z","iopub.status.idle":"2024-05-04T03:23:53.719645Z","shell.execute_reply.started":"2024-05-04T03:23:53.070011Z","shell.execute_reply":"2024-05-04T03:23:53.718949Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Create empty arays to keep the predictions and labels\ndf_preds = pd.DataFrame(columns=['label', 'pred', 'set'])\ntrain_generator.reset()\nvalid_generator.reset()\n\n# Add train predictions and labels\nfor i in range(STEP_SIZE_TRAIN + 1):\n im, lbl = next(train_generator)\n preds = model.predict(im, batch_size=train_generator.batch_size)\n for index in range(len(preds)):\n df_preds.loc[len(df_preds)] = [lbl[index], preds[index][0], 'train']\n\n# Add validation predictions and labels\nfor i in range(STEP_SIZE_VALID + 1):\n im, lbl = next(valid_generator)\n preds = model.predict(im, batch_size=valid_generator.batch_size)\n for index in range(len(preds)):\n df_preds.loc[len(df_preds)] = [lbl[index], preds[index][0], 'validation']\n\ndf_preds['label'] = df_preds['label'].astype('int')","metadata":{"execution":{"iopub.status.busy":"2024-05-04T03:23:53.72292Z","iopub.execute_input":"2024-05-04T03:23:53.723221Z","iopub.status.idle":"2024-05-04T03:25:22.394237Z","shell.execute_reply.started":"2024-05-04T03:23:53.723168Z","shell.execute_reply":"2024-05-04T03:25:22.393402Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"def classify(x):\n if x < 0.5:\n return 0\n elif x < 1.5:\n return 1\n elif x < 2.5:\n return 2\n elif x < 3.5:\n return 3\n return 4\n\n# Classify predictions\ndf_preds['predictions'] = df_preds['pred'].apply(lambda x: classify(x))\n\ntrain_preds = df_preds[df_preds['set'] == 'train']\nvalidation_preds = df_preds[df_preds['set'] == 'validation']","metadata":{"execution":{"iopub.status.busy":"2024-05-04T03:25:22.397293Z","iopub.execute_input":"2024-05-04T03:25:22.397564Z","iopub.status.idle":"2024-05-04T03:25:22.414877Z","shell.execute_reply.started":"2024-05-04T03:25:22.397519Z","shell.execute_reply":"2024-05-04T03:25:22.414238Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"labels = ['0 - No DR', '1 - Mild', '2 - Moderate', '3 - Severe', '4 - Proliferative DR']\ndef plot_confusion_matrix(train, validation, labels=labels):\n train_labels, train_preds = train\n validation_labels, validation_preds = validation\n fig, (ax1, ax2) = plt.subplots(1, 2, sharex='col', figsize=(24, 7))\n train_cnf_matrix = confusion_matrix(train_labels, train_preds)\n validation_cnf_matrix = confusion_matrix(validation_labels, validation_preds)\n\n train_cnf_matrix_norm = train_cnf_matrix.astype('float') / train_cnf_matrix.sum(axis=1)[:, np.newaxis]\n validation_cnf_matrix_norm = validation_cnf_matrix.astype('float') / validation_cnf_matrix.sum(axis=1)[:, np.newaxis]\n\n train_df_cm = pd.DataFrame(train_cnf_matrix_norm, index=labels, columns=labels)\n validation_df_cm = pd.DataFrame(validation_cnf_matrix_norm, index=labels, columns=labels)\n\n sns.heatmap(train_df_cm, annot=True, fmt='.2f', cmap=\"Blues\",ax=ax1).set_title('Train')\n sns.heatmap(validation_df_cm, annot=True, fmt='.2f', cmap=sns.cubehelix_palette(8),ax=ax2).set_title('Validation')\n plt.show()\n\nplot_confusion_matrix((train_preds['label'], train_preds['predictions']), (validation_preds['label'], validation_preds['predictions']))","metadata":{"execution":{"iopub.status.busy":"2024-05-04T03:25:22.416246Z","iopub.execute_input":"2024-05-04T03:25:22.416585Z","iopub.status.idle":"2024-05-04T03:25:23.395964Z","shell.execute_reply.started":"2024-05-04T03:25:22.416525Z","shell.execute_reply":"2024-05-04T03:25:23.394846Z"},"trusted":true},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"def evaluate_model(train, validation):\n train_labels, train_preds = train\n validation_labels, validation_preds = validation\n print(\"Train Cohen Kappa score: %.3f\" % cohen_kappa_score(train_preds, train_labels, weights='quadratic'))\n print(\"Validation Cohen Kappa score: %.3f\" % cohen_kappa_score(validation_preds, validation_labels, weights='quadratic'))\n print(\"Complete set Cohen Kappa score: %.3f\" % cohen_kappa_score(np.append(train_preds, validation_preds), np.append(train_labels, validation_labels), weights='quadratic'))\n \nevaluate_model((train_preds['label'], train_preds['predictions']), (validation_preds['label'], validation_preds['predictions']))","metadata":{"execution":{"iopub.status.busy":"2024-05-04T03:25:23.397814Z","iopub.execute_input":"2024-05-04T03:25:23.398436Z","iopub.status.idle":"2024-05-04T03:25:23.436187Z","shell.execute_reply.started":"2024-05-04T03:25:23.398368Z","shell.execute_reply":"2024-05-04T03:25:23.435491Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"Train Cohen Kappa score: 0.966\nValidation Cohen Kappa score: 0.898\nComplete set Cohen Kappa score: 0.952\n","output_type":"stream"}]},{"cell_type":"code","source":"def apply_tta(model, generator, steps=10):\n step_size = generator.n//generator.batch_size\n preds_tta = []\n for i in range(steps):\n generator.reset()\n preds = model.predict_generator(generator, steps=step_size)\n preds_tta.append(preds)\n\n return np.mean(preds_tta, axis=0)\n\npreds = apply_tta(model, test_generator)\npredictions = [classify(x) for x in preds]\n\nresults = pd.DataFrame({'id_code':test['id_code'], 'diagnosis':predictions})\nresults['id_code'] = results['id_code'].map(lambda x: str(x)[:-4])","metadata":{"execution":{"iopub.status.busy":"2024-05-04T03:25:23.437414Z","iopub.execute_input":"2024-05-04T03:25:23.437628Z","iopub.status.idle":"2024-05-04T03:34:08.37717Z","shell.execute_reply.started":"2024-05-04T03:25:23.43759Z","shell.execute_reply":"2024-05-04T03:34:08.376355Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"# Cleaning created directories\nif os.path.exists(train_dest_path):\n shutil.rmtree(train_dest_path)\nif os.path.exists(validation_dest_path):\n shutil.rmtree(validation_dest_path)\nif os.path.exists(test_dest_path):\n shutil.rmtree(test_dest_path)","metadata":{"execution":{"iopub.status.busy":"2024-05-04T03:34:08.378347Z","iopub.execute_input":"2024-05-04T03:34:08.378592Z","iopub.status.idle":"2024-05-04T03:34:08.645425Z","shell.execute_reply.started":"2024-05-04T03:34:08.37855Z","shell.execute_reply":"2024-05-04T03:34:08.644452Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"fig = plt.subplots(sharex='col', figsize=(24, 8.7))\nsns.countplot(x=\"diagnosis\", data=results).set_title('Test')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-04T03:34:08.646835Z","iopub.execute_input":"2024-05-04T03:34:08.647736Z","iopub.status.idle":"2024-05-04T03:34:09.031335Z","shell.execute_reply.started":"2024-05-04T03:34:08.647664Z","shell.execute_reply":"2024-05-04T03:34:09.030634Z"},"trusted":true},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"results.to_csv('submission_5.csv', index=False)\ndisplay(results.head())","metadata":{"execution":{"iopub.status.busy":"2024-05-04T03:34:09.032465Z","iopub.execute_input":"2024-05-04T03:34:09.032686Z","iopub.status.idle":"2024-05-04T03:34:09.176994Z","shell.execute_reply.started":"2024-05-04T03:34:09.032648Z","shell.execute_reply":"2024-05-04T03:34:09.176312Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":" id_code diagnosis\n0 0005cfc8afb6 2\n1 003f0afdcd15 3\n2 006efc72b638 2\n3 00836aaacf06 2\n4 009245722fa4 2","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosis
00005cfc8afb62
1003f0afdcd153
2006efc72b6382
300836aaacf062
4009245722fa42
\n
"},"metadata":{}}]},{"cell_type":"code","source":"# model.save_weights('../working/effNetB5_bs32_img224_fold5.h5')","metadata":{"execution":{"iopub.status.busy":"2024-05-04T03:34:09.178273Z","iopub.execute_input":"2024-05-04T03:34:09.17858Z","iopub.status.idle":"2024-05-04T03:34:09.182152Z","shell.execute_reply.started":"2024-05-04T03:34:09.17853Z","shell.execute_reply":"2024-05-04T03:34:09.181158Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"markdown","source":"# 5_FOLD","metadata":{}},{"cell_type":"code","source":"import os\nimport sys\nimport cv2\nimport shutil\nimport random\nimport warnings\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom tensorflow import set_random_seed\nfrom sklearn.metrics import confusion_matrix, cohen_kappa_score\nfrom keras.models import Model\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.layers import Dense, GlobalAveragePooling2D, Input\n\ndef seed_everything(seed=0):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n set_random_seed(0)\n\nseed = 0\nseed_everything(seed)\n%matplotlib inline\nsns.set(style=\"whitegrid\")\nwarnings.filterwarnings(\"ignore\")\nsys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))\nfrom efficientnet import *","metadata":{"execution":{"iopub.status.busy":"2024-06-03T13:40:04.083648Z","iopub.execute_input":"2024-06-03T13:40:04.083966Z","iopub.status.idle":"2024-06-03T13:40:04.111409Z","shell.execute_reply.started":"2024-06-03T13:40:04.083895Z","shell.execute_reply":"2024-06-03T13:40:04.110538Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"hold_out_set = pd.read_csv('/kaggle/input/main-data/hold-out.csv')\nX_train = hold_out_set[hold_out_set['set'] == 'train']\nX_val = hold_out_set[hold_out_set['set'] == 'validation']\ntest = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')\nprint('Number of train samples: ', X_train.shape[0])\nprint('Number of validation samples: ', X_val.shape[0])\nprint('Number of test samples: ', test.shape[0])\n\n# Preprocecss data\nX_train[\"id_code\"] = X_train[\"id_code\"].apply(lambda x: x + \".png\")\nX_val[\"id_code\"] = X_val[\"id_code\"].apply(lambda x: x + \".png\")\ntest[\"id_code\"] = test[\"id_code\"].apply(lambda x: x + \".png\")\ndisplay(X_train.head())","metadata":{"execution":{"iopub.status.busy":"2024-06-03T13:40:04.113806Z","iopub.execute_input":"2024-06-03T13:40:04.114137Z","iopub.status.idle":"2024-06-03T13:40:04.349462Z","shell.execute_reply.started":"2024-06-03T13:40:04.114081Z","shell.execute_reply":"2024-06-03T13:40:04.348577Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Number of train samples: 2929\nNumber of validation samples: 733\nNumber of test samples: 1928\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" id_code diagnosis height width set\n0 55eac26bd383.png 1 1736.0 2416.0 train\n1 44e0d56e9d42.png 2 2136.0 3216.0 train\n2 aa4407aab872.png 0 1050.0 1050.0 train\n3 cffc50047828.png 0 614.0 819.0 train\n4 b0f0fa677d5f.png 0 1050.0 1050.0 train","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosisheightwidthset
055eac26bd383.png11736.02416.0train
144e0d56e9d42.png22136.03216.0train
2aa4407aab872.png01050.01050.0train
3cffc50047828.png0614.0819.0train
4b0f0fa677d5f.png01050.01050.0train
\n
"},"metadata":{}}]},{"cell_type":"code","source":"# Model parameters\nHEIGHT = 224\nWIDTH = 224\nCHANNELS = 3\n\nweights_paths = ['/kaggle/input/main-data/effNetB5_bs32_img224_fold1.h5', '/kaggle/input/main-data/effNetB5_bs32_img224_fold4.h5', \n '/kaggle/input/main-data/effNetB5_bs32_img224_fold2.h5', '/kaggle/input/main-data/effNetB5_bs32_img224_fold5.h5', \n '/kaggle/input/main-data/effNetB5_bs32_img224_fold3.h5']\nn_folds = len(weights_paths)","metadata":{"execution":{"iopub.status.busy":"2024-06-03T13:40:04.351171Z","iopub.execute_input":"2024-06-03T13:40:04.351517Z","iopub.status.idle":"2024-06-03T13:40:04.35642Z","shell.execute_reply.started":"2024-06-03T13:40:04.351456Z","shell.execute_reply":"2024-06-03T13:40:04.355632Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"labels = ['0 - No DR', '1 - Mild', '2 - Moderate', '3 - Severe', '4 - Proliferative DR']\ndef plot_confusion_matrix(train, validation, labels=labels):\n train_labels, train_preds = train\n validation_labels, validation_preds = validation\n fig, (ax1, ax2) = plt.subplots(1, 2, sharex='col', figsize=(24, 7))\n train_cnf_matrix = confusion_matrix(train_labels, train_preds)\n validation_cnf_matrix = confusion_matrix(validation_labels, validation_preds)\n\n train_cnf_matrix_norm = train_cnf_matrix.astype('float') / train_cnf_matrix.sum(axis=1)[:, np.newaxis]\n validation_cnf_matrix_norm = validation_cnf_matrix.astype('float') / validation_cnf_matrix.sum(axis=1)[:, np.newaxis]\n\n train_df_cm = pd.DataFrame(train_cnf_matrix_norm, index=labels, columns=labels)\n validation_df_cm = pd.DataFrame(validation_cnf_matrix_norm, index=labels, columns=labels)\n\n sns.heatmap(train_df_cm, annot=True, fmt='.2f', cmap=\"Blues\",ax=ax1).set_title('Train')\n sns.heatmap(validation_df_cm, annot=True, fmt='.2f', cmap=sns.cubehelix_palette(8),ax=ax2).set_title('Validation')\n plt.show()\n \ndef evaluate_model(train, validation):\n train_labels, train_preds = train\n validation_labels, validation_preds = validation\n print(\"Train Cohen Kappa score: %.3f\" % cohen_kappa_score(train_preds, train_labels, weights='quadratic'))\n print(\"Validation Cohen Kappa score: %.3f\" % cohen_kappa_score(validation_preds, validation_labels, weights='quadratic'))\n print(\"Complete set Cohen Kappa score: %.3f\" % cohen_kappa_score(np.append(train_preds, validation_preds), np.append(train_labels, validation_labels), weights='quadratic'))\n\ndef classify(x):\n if x < 0.5:\n return 0\n elif x < 1.5:\n return 1\n elif x < 2.5:\n return 2\n elif x < 3.5:\n return 3\n return 4\n\ndef ensemble_preds(model_list, generator):\n preds_ensemble = []\n for model in model_list:\n generator.reset()\n preds = model.predict_generator(generator, steps=generator.n)\n preds_ensemble.append(preds)\n\n return np.mean(preds_ensemble, axis=0)\n\ndef apply_tta(model, generator, steps=10):\n step_size = generator.n//generator.batch_size\n preds_tta = []\n for i in range(steps):\n generator.reset()\n preds = model.predict_generator(generator, steps=step_size)\n preds_tta.append(preds)\n\n return np.mean(preds_tta, axis=0)\n\ndef test_ensemble_preds(model_list, generator):\n preds_ensemble = []\n for model in model_list:\n preds = apply_tta(model, generator)\n preds_ensemble.append(preds)\n\n return np.mean(preds_ensemble, axis=0)","metadata":{"execution":{"iopub.status.busy":"2024-06-03T13:40:04.357871Z","iopub.execute_input":"2024-06-03T13:40:04.358166Z","iopub.status.idle":"2024-06-03T13:40:04.381863Z","shell.execute_reply.started":"2024-06-03T13:40:04.358102Z","shell.execute_reply":"2024-06-03T13:40:04.381129Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"train_base_path = '../input/aptos2019-blindness-detection/train_images/'\ntest_base_path = '../input/aptos2019-blindness-detection/test_images/'\ntrain_dest_path = 'base_dir/train_images/'\nvalidation_dest_path = 'base_dir/validation_images/'\ntest_dest_path = 'base_dir/test_images/'\n\n# Making sure directories don't exist\nif os.path.exists(train_dest_path):\n shutil.rmtree(train_dest_path)\nif os.path.exists(validation_dest_path):\n shutil.rmtree(validation_dest_path)\nif os.path.exists(test_dest_path):\n shutil.rmtree(test_dest_path)\n \n# Creating train, validation and test directories\nos.makedirs(train_dest_path)\nos.makedirs(validation_dest_path)\nos.makedirs(test_dest_path)\n\ndef crop_image(img, tol=7):\n if img.ndim ==2:\n mask = img>tol\n return img[np.ix_(mask.any(1),mask.any(0))]\n elif img.ndim==3:\n gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n mask = gray_img>tol\n check_shape = img[:,:,0][np.ix_(mask.any(1),mask.any(0))].shape[0]\n if (check_shape == 0): # image is too dark so that we crop out everything,\n return img # return original image\n else:\n img1=img[:,:,0][np.ix_(mask.any(1),mask.any(0))]\n img2=img[:,:,1][np.ix_(mask.any(1),mask.any(0))]\n img3=img[:,:,2][np.ix_(mask.any(1),mask.any(0))]\n img = np.stack([img1,img2,img3],axis=-1)\n \n return img\n\ndef circle_crop(img):\n img = crop_image(img)\n\n height, width, depth = img.shape\n largest_side = np.max((height, width))\n img = cv2.resize(img, (largest_side, largest_side))\n\n height, width, depth = img.shape\n\n x = width//2\n y = height//2\n r = np.amin((x, y))\n\n circle_img = np.zeros((height, width), np.uint8)\n cv2.circle(circle_img, (x, y), int(r), 1, thickness=-1)\n img = cv2.bitwise_and(img, img, mask=circle_img)\n img = crop_image(img)\n\n return img\n \ndef preprocess_image(base_path, save_path, image_id, HEIGHT, WIDTH, sigmaX=10):\n image = cv2.imread(base_path + image_id)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image = circle_crop(image)\n image = cv2.resize(image, (HEIGHT, WIDTH))\n image = cv2.addWeighted(image, 4, cv2.GaussianBlur(image, (0,0), sigmaX), -4 , 128)\n cv2.imwrite(save_path + image_id, image)\n \n# Pre-procecss train set\nfor i, image_id in enumerate(X_train['id_code']):\n preprocess_image(train_base_path, train_dest_path, image_id, HEIGHT, WIDTH)\n \n# Pre-procecss validation set\nfor i, image_id in enumerate(X_val['id_code']):\n preprocess_image(train_base_path, validation_dest_path, image_id, HEIGHT, WIDTH)\n \n# Pre-procecss test set\nfor i, image_id in enumerate(test['id_code']):\n preprocess_image(test_base_path, test_dest_path, image_id, HEIGHT, WIDTH)","metadata":{"execution":{"iopub.status.busy":"2024-06-03T13:40:04.385176Z","iopub.execute_input":"2024-06-03T13:40:04.385461Z","iopub.status.idle":"2024-06-03T14:02:56.859615Z","shell.execute_reply.started":"2024-06-03T13:40:04.385396Z","shell.execute_reply":"2024-06-03T14:02:56.858873Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"datagen=ImageDataGenerator(rescale=1./255, \n rotation_range=360,\n horizontal_flip=True,\n vertical_flip=True)\n\ntrain_generator=datagen.flow_from_dataframe(\n dataframe=X_train,\n directory=train_dest_path,\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n class_mode=\"raw\",\n batch_size=1,\n shuffle=False,\n target_size=(HEIGHT, WIDTH),\n seed=seed)\n\nvalid_generator=datagen.flow_from_dataframe(\n dataframe=X_val,\n directory=validation_dest_path,\n x_col=\"id_code\",\n y_col=\"diagnosis\",\n class_mode=\"raw\",\n batch_size=1,\n shuffle=False,\n target_size=(HEIGHT, WIDTH),\n seed=seed)\n\ntest_generator=datagen.flow_from_dataframe( \n dataframe=test,\n directory=test_dest_path,\n x_col=\"id_code\",\n batch_size=1,\n class_mode=None,\n shuffle=False,\n target_size=(HEIGHT, WIDTH),\n seed=seed)","metadata":{"execution":{"iopub.status.busy":"2024-06-03T14:02:56.861128Z","iopub.execute_input":"2024-06-03T14:02:56.861358Z","iopub.status.idle":"2024-06-03T14:02:56.94505Z","shell.execute_reply.started":"2024-06-03T14:02:56.86132Z","shell.execute_reply":"2024-06-03T14:02:56.944326Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Found 2929 validated image filenames.\nFound 733 validated image filenames.\nFound 1928 validated image filenames.\n","output_type":"stream"}]},{"cell_type":"code","source":"def create_model(input_shape, weights_path):\n input_tensor = Input(shape=input_shape)\n base_model = EfficientNetB5(weights=None, \n include_top=False,\n input_tensor=input_tensor)\n\n x = GlobalAveragePooling2D()(base_model.output)\n final_output = Dense(1, activation='linear', name='final_output')(x)\n model = Model(input_tensor, final_output)\n model.load_weights(weights_path)\n \n return model","metadata":{"execution":{"iopub.status.busy":"2024-06-03T14:02:56.94638Z","iopub.execute_input":"2024-06-03T14:02:56.946666Z","iopub.status.idle":"2024-06-03T14:02:56.953262Z","shell.execute_reply.started":"2024-06-03T14:02:56.946597Z","shell.execute_reply":"2024-06-03T14:02:56.952277Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"model_list = []\n\nfor weights_path in weights_paths:\n model_list.append(create_model(input_shape=(HEIGHT, WIDTH, CHANNELS), weights_path=weights_path))","metadata":{"execution":{"iopub.status.busy":"2024-06-03T14:02:56.954555Z","iopub.execute_input":"2024-06-03T14:02:56.95484Z","iopub.status.idle":"2024-06-03T14:05:32.578325Z","shell.execute_reply.started":"2024-06-03T14:02:56.954779Z","shell.execute_reply":"2024-06-03T14:05:32.57723Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"# Train predictions\npreds_ensemble = ensemble_preds(model_list, train_generator)\npreds_ensemble = [classify(x) for x in preds_ensemble]\ntrain_preds = pd.DataFrame({'label':train_generator.labels, 'pred':preds_ensemble})\n\n# Validation predictions\npreds_ensemble = ensemble_preds(model_list, valid_generator)\npreds_ensemble = [classify(x) for x in preds_ensemble]\nvalidation_preds = pd.DataFrame({'label':valid_generator.labels, 'pred':preds_ensemble})","metadata":{"execution":{"iopub.status.busy":"2024-06-03T14:05:32.579783Z","iopub.execute_input":"2024-06-03T14:05:32.580063Z","iopub.status.idle":"2024-06-03T14:15:19.495006Z","shell.execute_reply.started":"2024-06-03T14:05:32.58002Z","shell.execute_reply":"2024-06-03T14:15:19.494288Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"plot_confusion_matrix((train_preds['label'], train_preds['pred']), (validation_preds['label'], validation_preds['pred']))","metadata":{"execution":{"iopub.status.busy":"2024-06-03T14:15:19.496491Z","iopub.execute_input":"2024-06-03T14:15:19.496838Z","iopub.status.idle":"2024-06-03T14:15:20.518372Z","shell.execute_reply.started":"2024-06-03T14:15:19.496777Z","shell.execute_reply":"2024-06-03T14:15:20.51746Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"evaluate_model((train_preds['label'], train_preds['pred']), (validation_preds['label'], validation_preds['pred']))","metadata":{"execution":{"iopub.status.busy":"2024-06-03T14:15:20.519727Z","iopub.execute_input":"2024-06-03T14:15:20.520042Z","iopub.status.idle":"2024-06-03T14:15:20.544094Z","shell.execute_reply.started":"2024-06-03T14:15:20.519982Z","shell.execute_reply":"2024-06-03T14:15:20.543111Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Train Cohen Kappa score: 0.961\nValidation Cohen Kappa score: 0.963\nComplete set Cohen Kappa score: 0.961\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\nlabels = ['0 - No DR', '1 - Mild', '2 - Moderate', '3 - Severe', '4 - Proliferative DR']\nreport = classification_report(train_preds['label'],train_preds['pred'], target_names=labels)\nprint(report)\naccuracy = accuracy_score(train_preds['label'],train_preds['pred'])\n\nprint(f\"Accuracy: {accuracy}\")","metadata":{"execution":{"iopub.status.busy":"2024-06-03T16:03:58.262346Z","iopub.execute_input":"2024-06-03T16:03:58.262645Z","iopub.status.idle":"2024-06-03T16:03:58.279606Z","shell.execute_reply.started":"2024-06-03T16:03:58.262597Z","shell.execute_reply":"2024-06-03T16:03:58.278681Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":" precision recall f1-score support\n\n 0 - No DR 0.99 0.99 0.99 1452\n 1 - Mild 0.87 0.65 0.75 299\n 2 - Moderate 0.84 0.88 0.86 791\n 3 - Severe 0.43 0.74 0.54 162\n4 - Proliferative DR 0.91 0.61 0.73 225\n\n accuracy 0.88 2929\n macro avg 0.81 0.78 0.77 2929\n weighted avg 0.90 0.88 0.89 2929\n\nAccuracy: 0.8835780129737112\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\nlabels = ['0 - No DR', '1 - Mild', '2 - Moderate', '3 - Severe', '4 - Proliferative DR']\nreport = classification_report(validation_preds['label'], validation_preds['pred'], target_names=labels)\nprint(report)\naccuracy = accuracy_score(validation_preds['label'], validation_preds['pred']) #data_train['diagnosis'].astype('int'), validation_preds)\n\nprint(f\"Accuracy: {accuracy}\")","metadata":{"execution":{"iopub.status.busy":"2024-06-03T16:03:45.117304Z","iopub.execute_input":"2024-06-03T16:03:45.11761Z","iopub.status.idle":"2024-06-03T16:03:45.131407Z","shell.execute_reply.started":"2024-06-03T16:03:45.117567Z","shell.execute_reply":"2024-06-03T16:03:45.130465Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":" precision recall f1-score support\n\n 0 - No DR 0.99 0.98 0.99 353\n 1 - Mild 0.81 0.77 0.79 71\n 2 - Moderate 0.90 0.88 0.89 208\n 3 - Severe 0.35 0.74 0.47 31\n4 - Proliferative DR 0.95 0.59 0.73 70\n\n accuracy 0.89 733\n macro avg 0.80 0.79 0.77 733\n weighted avg 0.91 0.89 0.89 733\n\nAccuracy: 0.8867667121418826\n","output_type":"stream"}]},{"cell_type":"code","source":"preds = test_ensemble_preds(model_list, test_generator)\npredictions = [classify(x) for x in preds]\n\nresults = pd.DataFrame({'id_code':test['id_code'], 'diagnosis':predictions})\nresults['id_code'] = results['id_code'].map(lambda x: str(x)[:-4])","metadata":{"execution":{"iopub.status.busy":"2024-06-03T14:15:20.545696Z","iopub.execute_input":"2024-06-03T14:15:20.546172Z","iopub.status.idle":"2024-06-03T15:03:14.623628Z","shell.execute_reply.started":"2024-06-03T14:15:20.545971Z","shell.execute_reply":"2024-06-03T15:03:14.622975Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"# Cleaning created directories\nif os.path.exists(train_dest_path):\n shutil.rmtree(train_dest_path)\nif os.path.exists(validation_dest_path):\n shutil.rmtree(validation_dest_path)\nif os.path.exists(test_dest_path):\n shutil.rmtree(test_dest_path)","metadata":{"execution":{"iopub.status.busy":"2024-06-03T15:03:14.625041Z","iopub.execute_input":"2024-06-03T15:03:14.625353Z","iopub.status.idle":"2024-06-03T15:03:14.893539Z","shell.execute_reply.started":"2024-06-03T15:03:14.625297Z","shell.execute_reply":"2024-06-03T15:03:14.892899Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"fig = plt.subplots(sharex='col', figsize=(24, 8.7))\nsns.countplot(x=\"diagnosis\", data=results).set_title('Test')\nsns.despine()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-03T15:03:14.895067Z","iopub.execute_input":"2024-06-03T15:03:14.895374Z","iopub.status.idle":"2024-06-03T15:03:15.136127Z","shell.execute_reply.started":"2024-06-03T15:03:14.895318Z","shell.execute_reply":"2024-06-03T15:03:15.134848Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"
","image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":"results.to_csv('submission_final.csv', index=False)\ndisplay(results.head())","metadata":{"execution":{"iopub.status.busy":"2024-06-03T15:03:15.137693Z","iopub.execute_input":"2024-06-03T15:03:15.138264Z","iopub.status.idle":"2024-06-03T15:03:15.358846Z","shell.execute_reply.started":"2024-06-03T15:03:15.138021Z","shell.execute_reply":"2024-06-03T15:03:15.358015Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":" id_code diagnosis\n0 0005cfc8afb6 2\n1 003f0afdcd15 3\n2 006efc72b638 2\n3 00836aaacf06 2\n4 009245722fa4 2","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
id_codediagnosis
00005cfc8afb62
1003f0afdcd153
2006efc72b6382
300836aaacf062
4009245722fa42
\n
"},"metadata":{}}]}]}