{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":1,"outputs":[{"output_type":"stream","text":"['keras-pretrain-model-weights', 'imet-2019-fgvc6']\n","name":"stdout"}]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"import os, sys\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport skimage.io\nfrom skimage.transform import resize\nfrom imgaug import augmenters as iaa\nfrom tqdm import tqdm\nimport PIL\nfrom PIL import Image\nimport cv2\nfrom sklearn.utils import class_weight, shuffle\nfrom keras.losses import binary_crossentropy\nimport keras.backend as K\nimport tensorflow as tf\nfrom sklearn.metrics import f1_score\nfrom keras.utils import Sequence\nWORKERS = 2\nCHANNEL = 3\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nSIZE = 224\n\nculture_epochs = 4\ntag_epochs = 22\nbatch_size = 32\nSPLIT_RATIO = 0.1\nPOST_PROCESS = False\nLR = 1e-4\nLR_FACTOR = 0.45\nLR_WARM = 1e-3\ngamma = 2.0 # focal loss\ncheckpoint_file = '../working/resnet50_focal.h5'\ncheckpoint_file2 = '../working/resnet50_focal2.h5'","execution_count":2,"outputs":[{"output_type":"stream","text":"Using TensorFlow backend.\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"!ls ../input","execution_count":3,"outputs":[{"output_type":"stream","text":"imet-2019-fgvc6 keras-pretrain-model-weights\r\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# dataset splitting\ntrain_df = pd.read_csv(\"../input/imet-2019-fgvc6/train.csv\")\ntrain_df[\"attribute_ids\"]=train_df[\"attribute_ids\"].apply(lambda x:list(map(int, x.split(\" \"))))\ntrain_df[\"culture_ids\"] = train_df[\"attribute_ids\"].apply(lambda ids: [attribute_id for attribute_id in ids if attribute_id<398])\ntrain_df[\"tag_ids\"] = train_df[\"attribute_ids\"].apply(lambda ids: [attribute_id for attribute_id in ids if attribute_id>=398])\nculture_train_df = train_df[[\"id\", \"culture_ids\"]]\ntag_train_df = train_df[[\"id\", \"tag_ids\"]]\n\nculture_train_df = culture_train_df[culture_train_df.astype(str)['culture_ids'] != '[]']\ntag_train_df = tag_train_df[tag_train_df.astype(str)['tag_ids'] != '[]']","execution_count":4,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Load culture dataset info\npath_to_train = '../input/imet-2019-fgvc6/train/'\n\nculture_train_dataset_info = []\nfor name, labels in zip(culture_train_df['id'], culture_train_df['culture_ids']):\n culture_train_dataset_info.append({\n 'path':os.path.join(path_to_train, name),\n 'labels':np.array([int(label) for label in labels])})\nculture_train_dataset_info = np.array(culture_train_dataset_info)","execution_count":5,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"tag_train_dataset_info = []\nfor name, labels in zip(tag_train_df['id'], tag_train_df['tag_ids']):\n tag_train_dataset_info.append({\n 'path':os.path.join(path_to_train, name),\n 'labels':np.array([int(label) for label in labels])})\ntag_train_dataset_info = np.array(tag_train_dataset_info)","execution_count":6,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":true},"cell_type":"code","source":"# https://www.kaggle.com/lopuhin/imet-2019-submission/output\nimport argparse\nfrom collections import defaultdict, Counter\nimport random\n\nimport pandas as pd\n\nDATA_ROOT = '../input/imet-2019-fgvc6/'\n\n\ndef make_folds(n_folds: int) -> pd.DataFrame:\n df = pd.read_csv(DATA_ROOT+ 'train.csv')\n cls_counts = Counter(cls for classes in df['attribute_ids'].str.split()\n for cls in classes)\n fold_cls_counts = defaultdict(int)\n folds = [-1] * len(df)\n for item in tqdm(df.sample(frac=1, random_state=42).itertuples(),\n total=len(df)):\n cls = min(item.attribute_ids.split(), key=lambda cls: cls_counts[cls])\n fold_counts = [(f, fold_cls_counts[f, cls]) for f in range(n_folds)]\n min_count = min([count for _, count in fold_counts])\n random.seed(item.Index)\n fold = random.choice([f for f, count in fold_counts\n if count == min_count])\n folds[item.Index] = fold\n for cls in item.attribute_ids.split():\n fold_cls_counts[fold, cls] += 1\n df['fold'] = folds\n return df","execution_count":7,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"from tensorflow.keras.callbacks import Callback\nimport matplotlib.pyplot as plt\nimport tensorflow.keras.backend as K\n\nclass LRFinder(Callback):\n \n '''\n A simple callback for finding the optimal learning rate range for your model + dataset. \n \n # Usage\n ```python\n lr_finder = LRFinder(min_lr=1e-5, \n max_lr=1e-2, \n steps_per_epoch=np.ceil(epoch_size/batch_size), \n epochs=3)\n model.fit(X_train, Y_train, callbacks=[lr_finder])\n \n lr_finder.plot_loss()\n ```\n \n # Arguments\n min_lr: The lower bound of the learning rate range for the experiment.\n max_lr: The upper bound of the learning rate range for the experiment.\n steps_per_epoch: Number of mini-batches in the dataset. Calculated as `np.ceil(epoch_size/batch_size)`. \n epochs: Number of epochs to run experiment. Usually between 2 and 4 epochs is sufficient. \n \n # References\n Blog post: jeremyjordan.me/nn-learning-rate\n Original paper: https://arxiv.org/abs/1506.01186\n '''\n \n def __init__(self, min_lr=1e-5, max_lr=1e-2, steps_per_epoch=None, epochs=None):\n super().__init__()\n \n self.min_lr = min_lr\n self.max_lr = max_lr\n self.total_iterations = steps_per_epoch * epochs\n self.iteration = 0\n self.history = {}\n \n def clr(self):\n '''Calculate the learning rate.'''\n x = self.iteration / self.total_iterations \n return self.min_lr + (self.max_lr-self.min_lr) * x\n \n def on_train_begin(self, logs=None):\n '''Initialize the learning rate to the minimum value at the start of training.'''\n logs = logs or {}\n K.set_value(self.model.optimizer.lr, self.min_lr)\n \n def on_batch_end(self, epoch, logs=None):\n '''Record previous batch statistics and update the learning rate.'''\n logs = logs or {}\n self.iteration += 1\n\n self.history.setdefault('lr', []).append(K.get_value(self.model.optimizer.lr))\n self.history.setdefault('iterations', []).append(self.iteration)\n\n for k, v in logs.items():\n self.history.setdefault(k, []).append(v)\n \n K.set_value(self.model.optimizer.lr, self.clr())\n \n def plot_lr(self):\n '''Helper function to quickly inspect the learning rate schedule.'''\n plt.plot(self.history['iterations'], self.history['lr'])\n plt.yscale('log')\n plt.xlabel('Iteration')\n plt.ylabel('Learning rate')\n plt.show()\n \n def plot_loss(self):\n '''Helper function to quickly observe the learning rate experiment results.'''\n plt.plot(self.history['lr'], self.history['loss'])\n plt.xscale('log')\n plt.xlabel('Learning rate')\n plt.ylabel('Loss')\n plt.show()","execution_count":8,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"\nepsilon = K.epsilon()\ndef focal_loss(y_true, y_pred):\n pt = y_pred * y_true + (1-y_pred) * (1-y_true)\n pt = K.clip(pt, epsilon, 1-epsilon)\n CE = -K.log(pt)\n FL = K.pow(1-pt, gamma) * CE\n loss = K.sum(FL, axis=1)\n return loss","execution_count":9,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"beta_f2=2\n\n# if gamma == 0.0:\n# F2_THRESHOLD = 0.1\n# elif gamma == 1.0:\n# F2_THRESHOLD = 0.2\n# else:\n# F2_THRESHOLD = 0.3\n\n# print(F2_THRESHOLD)\n \ndef f2(y_true, y_pred):\n #y_pred = K.round(y_pred)\n# y_pred = K.cast(K.greater(K.clip(y_pred, 0, 1), F2_THRESHOLD), K.floatx())\n tp = K.sum(K.cast(y_true*y_pred, 'float'), axis=1)\n tn = K.sum(K.cast((1-y_true)*(1-y_pred), 'float'), axis=1)\n fp = K.sum(K.cast((1-y_true)*y_pred, 'float'), axis=1)\n fn = K.sum(K.cast(y_true*(1-y_pred), 'float'), axis=1)\n\n p = tp / (tp + fp + K.epsilon())\n r = tp / (tp + fn + K.epsilon())\n\n f2 = (1+beta_f2**2)*p*r / (p*beta_f2**2 + r + K.epsilon())\n f2 = tf.where(tf.is_nan(f2), tf.zeros_like(f2), f2)\n return K.mean(f2)","execution_count":10,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.applications.resnet50 import preprocess_input\n\nclass data_generator(Sequence):\n \n def create_train(dataset_info, batch_size, shape, n_classes, augument=True):\n assert shape[2] == 3\n decrement = 0\n if n_classes == 705:\n decrement = 398\n while True:\n dataset_info = shuffle(dataset_info)\n for start in range(0, len(dataset_info), batch_size):\n end = min(start + batch_size, len(dataset_info))\n batch_images = []\n X_train_batch = dataset_info[start:end]\n batch_labels = np.zeros((len(X_train_batch), n_classes))\n for i in range(len(X_train_batch)):\n image = data_generator.load_image(\n X_train_batch[i]['path'], shape) \n if augument:\n image = data_generator.augment(image)\n batch_images.append(preprocess_input(image))\n batch_labels[i][X_train_batch[i]['labels']-decrement] = 1\n \n yield np.array(batch_images, np.float32), batch_labels\n\n def create_valid(dataset_info, batch_size, shape, n_classes, augument=False):\n assert shape[2] == 3\n decrement = 0\n if n_classes == 705:\n decrement = 398\n while True:\n # dataset_info = shuffle(dataset_info)\n for start in range(0, len(dataset_info), batch_size):\n end = min(start + batch_size, len(dataset_info))\n batch_images = []\n X_train_batch = dataset_info[start:end]\n batch_labels = np.zeros((len(X_train_batch), n_classes))\n for i in range(len(X_train_batch)):\n image = data_generator.load_image(\n X_train_batch[i]['path'], shape) \n if augument:\n image = data_generator.augment(image)\n batch_images.append(preprocess_input(image))\n batch_labels[i][X_train_batch[i]['labels']-decrement] = 1\n yield np.array(batch_images, np.float32), batch_labels\n\n\n def load_image(path, shape):\n image = cv2.imread(path+'.png')\n image = cv2.resize(image, (SIZE, SIZE))\n return image\n\n def augment(image):\n augment_img = iaa.Sequential([\n iaa.SomeOf((0,4),[\n# iaa.OneOf([\n# iaa.Affine(rotate=0),\n# iaa.Affine(rotate=90),\n# iaa.Affine(rotate=180),\n# iaa.Affine(rotate=270),\n iaa.Crop(percent=(0, 0.1)),\n iaa.ContrastNormalization((0.8, 1.2)),\n iaa.Multiply((0.9, 1.1), per_channel=0.2),\n iaa.Fliplr(0.5),\n iaa.GaussianBlur(sigma=(0, 0.6)),\n iaa.Affine(\n scale={\"x\": (0.9, 1.1), \"y\": (0.9, 1.1)},\n translate_percent={\"x\": (-0.1, 0.1), \"y\": (-0.1, 0.1)},\n rotate=(-180, 180),\n )\n ])], random_order=True)\n\n image_aug = augment_img.augment_image(image)\n return image_aug","execution_count":11,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential, load_model\nfrom keras.layers import (Activation, Dropout, Flatten, Dense, GlobalMaxPooling2D,GlobalAveragePooling2D,\n BatchNormalization, Input, Conv2D, Concatenate)\nfrom keras.applications.resnet50 import ResNet50\nfrom keras.callbacks import ModelCheckpoint\nfrom keras import metrics\nfrom keras.optimizers import Adam \nfrom keras import backend as K\nimport keras\nfrom keras.models import Model","execution_count":12,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"# reference link: https://gist.github.com/drscotthawley/d1818aabce8d1bf082a6fb37137473ae\nfrom keras.callbacks import Callback\n\ndef get_1cycle_schedule(lr_max=1e-3, n_data_points=8000, epochs=200, batch_size=40, verbose=0): \n \"\"\"\n Creates a look-up table of learning rates for 1cycle schedule with cosine annealing\n See @sgugger's & @jeremyhoward's code in fastai library: https://github.com/fastai/fastai/blob/master/fastai/train.py\n Wrote this to use with my Keras and (non-fastai-)PyTorch codes.\n Note that in Keras, the LearningRateScheduler callback (https://keras.io/callbacks/#learningratescheduler) only operates once per epoch, not per batch\n So see below for Keras callback\n\n Keyword arguments:\n lr_max chosen by user after lr_finder\n n_data_points data points per epoch (e.g. size of training set)\n epochs number of epochs\n batch_size batch size\n Output: \n lrs look-up table of LR's, with length equal to total # of iterations\n Then you can use this in your PyTorch code by counting iteration number and setting\n optimizer.param_groups[0]['lr'] = lrs[iter_count]\n \"\"\"\n if verbose > 0:\n print(\"Setting up 1Cycle LR schedule...\")\n pct_start, div_factor = 0.3, 25. # @sgugger's parameters in fastai code\n lr_start = lr_max/div_factor\n lr_end = lr_start/1e4\n n_iter = (n_data_points * epochs // batch_size) + 1 # number of iterations\n a1 = int(n_iter * pct_start)\n a2 = n_iter - a1\n\n # make look-up table\n lrs_first = np.linspace(lr_start, lr_max, a1) # linear growth\n lrs_second = (lr_max-lr_end)*(1+np.cos(np.linspace(0,np.pi,a2)))/2 + lr_end # cosine annealing\n lrs = np.concatenate((lrs_first, lrs_second))\n return lrs\n\n\nclass OneCycleScheduler(Callback):\n \"\"\"My modification of Keras' Learning rate scheduler to do 1Cycle learning\n which increments per BATCH, not per epoch\n Keyword arguments\n **kwargs: keyword arguments to pass to get_1cycle_schedule()\n Also, verbose: int. 0: quiet, 1: update messages.\n\n Sample usage (from my train.py):\n lrsched = OneCycleScheduler(lr_max=1e-4, n_data_points=X_train.shape[0],\n epochs=epochs, batch_size=batch_size, verbose=1)\n \"\"\"\n def __init__(self, **kwargs):\n super(OneCycleScheduler, self).__init__()\n self.verbose = kwargs.get('verbose', 0)\n self.lrs = get_1cycle_schedule(**kwargs)\n self.iteration = 0\n\n def on_batch_begin(self, batch, logs=None):\n lr = self.lrs[self.iteration]\n K.set_value(self.model.optimizer.lr, lr) # here's where the assignment takes place\n if self.verbose > 0:\n print('\\nIteration %06d: OneCycleScheduler setting learning '\n 'rate to %s.' % (self.iteration, lr))\n self.iteration += 1\n\n def on_epoch_end(self, epoch, logs=None): # this is unchanged from Keras LearningRateScheduler\n logs = logs or {}\n logs['lr'] = K.get_value(self.model.optimizer.lr)\n self.iteration = 0\n\n","execution_count":13,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras_applications import imagenet_utils as utils\n\ndef ResNet(stack_fn,\n preact,\n use_bias,\n model_name='resnet',\n include_top=True,\n weights='imagenet',\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n **kwargs):\n \"\"\"Instantiates the ResNet, ResNetV2, and ResNeXt architecture.\n Optionally loads weights pre-trained on ImageNet.\n Note that the data format convention used by the model is\n the one specified in your Keras config at `~/.keras/keras.json`.\n # Arguments\n stack_fn: a function that returns output tensor for the\n stacked residual blocks.\n preact: whether to use pre-activation or not\n (True for ResNetV2, False for ResNet and ResNeXt).\n use_bias: whether to use biases for convolutional layers or not\n (True for ResNet and ResNetV2, False for ResNeXt).\n model_name: string, model name.\n include_top: whether to include the fully-connected\n layer at the top of the network.\n weights: one of `None` (random initialization),\n 'imagenet' (pre-training on ImageNet),\n or the path to the weights file to be loaded.\n input_tensor: optional Keras tensor\n (i.e. output of `layers.Input()`)\n to use as image input for the model.\n input_shape: optional shape tuple, only to be specified\n if `include_top` is False (otherwise the input shape\n has to be `(224, 224, 3)` (with `channels_last` data format)\n or `(3, 224, 224)` (with `channels_first` data format).\n It should have exactly 3 inputs channels.\n pooling: optional pooling mode for feature extraction\n when `include_top` is `False`.\n - `None` means that the output of the model will be\n the 4D tensor output of the\n last convolutional layer.\n - `avg` means that global average pooling\n will be applied to the output of the\n last convolutional layer, and thus\n the output of the model will be a 2D tensor.\n - `max` means that global max pooling will\n be applied.\n classes: optional number of classes to classify images\n into, only to be specified if `include_top` is True, and\n if no `weights` argument is specified.\n # Returns\n A Keras model instance.\n # Raises\n ValueError: in case of invalid argument for `weights`,\n or invalid input shape.\n \"\"\"\n global backend, layers, models, keras_utils\n # backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)\n backend, layers, models, keras_utils = keras.backend, keras.layers, keras.models, keras.utils\n\n if not (weights in {'imagenet', None} or os.path.exists(weights)):\n raise ValueError('The `weights` argument should be either '\n '`None` (random initialization), `imagenet` '\n '(pre-training on ImageNet), '\n 'or the path to the weights file to be loaded.')\n\n if weights == 'imagenet' and include_top and classes != 1000:\n raise ValueError('If using `weights` as `\"imagenet\"` with `include_top`'\n ' as true, `classes` should be 1000')\n\n # Determine proper input shape\n input_shape = utils._obtain_input_shape(input_shape,\n default_size=224,\n min_size=32,\n data_format=backend.image_data_format(),\n require_flatten=include_top,\n weights=weights)\n\n if input_tensor is None:\n img_input = layers.Input(shape=input_shape)\n else:\n if not backend.is_keras_tensor(input_tensor):\n img_input = layers.Input(tensor=input_tensor, shape=input_shape)\n else:\n img_input = input_tensor\n\n bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1\n\n x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)), name='conv1_pad')(img_input)\n x = layers.Conv2D(64, 7, strides=2, use_bias=use_bias, name='conv1_conv')(x)\n\n if preact is False:\n x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,\n name='conv1_bn')(x)\n x = layers.Activation('relu', name='conv1_relu')(x)\n\n x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name='pool1_pad')(x)\n x = layers.MaxPooling2D(3, strides=2, name='pool1_pool')(x)\n\n x = stack_fn(x)\n\n if preact is True:\n x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,\n name='post_bn')(x)\n x = layers.Activation('relu', name='post_relu')(x)\n\n if include_top:\n x = layers.GlobalAveragePooling2D(name='avg_pool')(x)\n x = layers.Dense(classes, activation='softmax', name='probs')(x)\n else:\n if pooling == 'avg':\n x = layers.GlobalAveragePooling2D(name='avg_pool')(x)\n elif pooling == 'max':\n x = layers.GlobalMaxPooling2D(name='max_pool')(x)\n\n # Ensure that the model takes into account\n # any potential predecessors of `input_tensor`.\n if input_tensor is not None:\n inputs = keras_utils.get_source_inputs(input_tensor)\n else:\n inputs = img_input\n\n # Create model.\n model = models.Model(inputs, x, name=model_name)\n\n # Load weights.\n if (weights == 'imagenet') and (model_name in WEIGHTS_HASHES):\n if include_top:\n file_name = model_name + '_weights_tf_dim_ordering_tf_kernels.h5'\n file_hash = WEIGHTS_HASHES[model_name][0]\n else:\n file_name = model_name + '_weights_tf_dim_ordering_tf_kernels_notop.h5'\n file_hash = WEIGHTS_HASHES[model_name][1]\n weights_path = keras_utils.get_file(file_name,\n BASE_WEIGHTS_PATH + file_name,\n cache_subdir='models',\n file_hash=file_hash)\n model.load_weights(weights_path)\n elif weights is not None:\n model.load_weights(weights)\n\n return model","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\ndef block2(x, filters, kernel_size=3, stride=1,\n conv_shortcut=False, name=None):\n \"\"\"A residual block.\n # Arguments\n x: input tensor.\n filters: integer, filters of the bottleneck layer.\n kernel_size: default 3, kernel size of the bottleneck layer.\n stride: default 1, stride of the first layer.\n conv_shortcut: default False, use convolution shortcut if True,\n otherwise identity shortcut.\n name: string, block label.\n # Returns\n Output tensor for the residual block.\n \"\"\"\n bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1\n\n preact = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,\n name=name + '_preact_bn')(x)\n preact = layers.Activation('relu', name=name + '_preact_relu')(preact)\n\n if conv_shortcut is True:\n shortcut = layers.Conv2D(4 * filters, 1, strides=stride,\n name=name + '_0_conv')(preact)\n else:\n shortcut = layers.MaxPooling2D(1, strides=stride)(x) if stride > 1 else x\n\n x = layers.Conv2D(filters, 1, strides=1, use_bias=False,\n name=name + '_1_conv')(preact)\n x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,\n name=name + '_1_bn')(x)\n x = layers.Activation('relu', name=name + '_1_relu')(x)\n\n x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + '_2_pad')(x)\n x = layers.Conv2D(filters, kernel_size, strides=stride,\n use_bias=False, name=name + '_2_conv')(x)\n x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,\n name=name + '_2_bn')(x)\n x = layers.Activation('relu', name=name + '_2_relu')(x)\n\n x = layers.Conv2D(4 * filters, 1, name=name + '_3_conv')(x)\n x = layers.Add(name=name + '_out')([shortcut, x])\n return x\n\n\ndef stack2(x, filters, blocks, stride1=2, name=None):\n \"\"\"A set of stacked residual blocks.\n # Arguments\n x: input tensor.\n filters: integer, filters of the bottleneck layer in a block.\n blocks: integer, blocks in the stacked blocks.\n stride1: default 2, stride of the first layer in the first block.\n name: string, stack label.\n # Returns\n Output tensor for the stacked blocks.\n \"\"\"\n x = block2(x, filters, conv_shortcut=True, name=name + '_block1')\n for i in range(2, blocks):\n x = block2(x, filters, name=name + '_block' + str(i))\n x = block2(x, filters, stride=stride1, name=name + '_block' + str(blocks))\n return x\n\ndef ResNet50V2(include_top=True,\n weights='imagenet',\n input_tensor=None,\n input_shape=None,\n pooling=None,\n classes=1000,\n **kwargs):\n def stack_fn(x):\n x = stack2(x, 64, 3, name='conv2')\n x = stack2(x, 128, 4, name='conv3')\n x = stack2(x, 256, 6, name='conv4')\n x = stack2(x, 512, 3, stride1=1, name='conv5')\n return x\n return ResNet(stack_fn, True, True, 'resnet50v2',\n include_top, weights,\n input_tensor, input_shape,\n pooling, classes,\n **kwargs)","execution_count":15,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.applications.resnet50 import ResNet50\n\n\n# pretrained model is of 3 channels\ndef create_model_resnet50(n_out, final_activation):\n\n base_model =ResNet50V2(weights=None, include_top=False)\n base_model.load_weights('../input/keras-pretrain-model-weights/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5')\n \n x0 = base_model.output\n x1 = GlobalAveragePooling2D()(x0)\n x2 = GlobalMaxPooling2D()(x0)\n x = Concatenate()([x1,x2])\n \n x = BatchNormalization()(x)\n x = Dropout(0.5)(x)\n x = Dense(2048, activation='relu')(x)\n x = BatchNormalization()(x)\n x = Dropout(0.5)(x)\n\n predictions = Dense(n_out, activation=final_activation)(x)\n\n # this is the model we will train\n model = Model(inputs=base_model.input, outputs=predictions)\n return model","execution_count":16,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# create callbacks list\nfrom keras.callbacks import (ModelCheckpoint, LearningRateScheduler,\n EarlyStopping, ReduceLROnPlateau,CSVLogger)\n\ncheckpoint = ModelCheckpoint(checkpoint_file, monitor='val_loss', verbose=1, \n save_best_only=True, mode='min', save_weights_only = False)\n\ncheckpoint2 = ModelCheckpoint(checkpoint_file2, monitor='val_loss', verbose=1, \n save_best_only=True, mode='min', save_weights_only = False)\n\nreduceLROnPlat = ReduceLROnPlateau(monitor='val_loss', factor=LR_FACTOR, patience=2, \n verbose=1, mode='auto', epsilon=0.0001)\n\nreduceLROnPlat2 = ReduceLROnPlateau(monitor='val_loss', factor=LR_FACTOR, patience=2, \n verbose=1, mode='auto', epsilon=0.0001)\n\ncsv_logger = CSVLogger(filename='../working/training_log.csv',\n separator=',',\n append=True)\n\ncsv_logger2 = CSVLogger(filename='../working/training_log2.csv',\n separator=',',\n append=True)\n\ncallbacks_list = [checkpoint, csv_logger, reduceLROnPlat]\ncallbacks_list2 = [checkpoint2, csv_logger2, reduceLROnPlat2]","execution_count":17,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#CULTURE data set preparation\n\nfrom sklearn.model_selection import train_test_split\n\nN_CULTURE_CLASSES = 398\n\n# split data into train, valid\nindexes = np.arange(culture_train_dataset_info.shape[0])\nculture_train_indexes, culture_valid_indexes = train_test_split(indexes, test_size=SPLIT_RATIO, random_state=8)\n\n# create train and valid datagens\nculture_train_generator = data_generator.create_train(\n culture_train_dataset_info[culture_train_indexes], batch_size, (SIZE,SIZE,3), N_CULTURE_CLASSES, augument=True)\nculture_train_generator_warmup = data_generator.create_train(\n culture_train_dataset_info[culture_train_indexes], batch_size, (SIZE,SIZE,3), N_CULTURE_CLASSES, augument=False)\nculture_validation_generator = data_generator.create_valid(\n culture_train_dataset_info[culture_valid_indexes], batch_size, (SIZE,SIZE,3), N_CULTURE_CLASSES, augument=False)","execution_count":18,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"##TAG data set preparation\n\nN_TAG_CLASSES = 705\n\n# split data into train, valid\nindexes = np.arange(tag_train_dataset_info.shape[0])\ntag_train_indexes, tag_valid_indexes = train_test_split(indexes, test_size=SPLIT_RATIO, random_state=8)\n\n# create train and valid datagens\ntag_train_generator = data_generator.create_train(\n tag_train_dataset_info[tag_train_indexes], batch_size, (SIZE,SIZE,3), N_TAG_CLASSES, augument=True)\ntag_train_generator_warmup = data_generator.create_train(\n tag_train_dataset_info[tag_train_indexes], batch_size, (SIZE,SIZE,3), N_TAG_CLASSES, augument=False)\ntag_validation_generator = data_generator.create_valid(\n tag_train_dataset_info[tag_valid_indexes], batch_size, (SIZE,SIZE,3), N_TAG_CLASSES, augument=False)\n","execution_count":19,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"CULTURE\")\nprint(np.ceil(float(len(culture_train_indexes)) / float(batch_size)))\nprint(np.ceil(float(len(culture_valid_indexes)) / float(batch_size)))\nprint(len(culture_train_indexes), batch_size)\n\nprint(\"TAG\")\nprint(np.ceil(float(len(tag_train_indexes)) / float(batch_size)))\nprint(np.ceil(float(len(tag_valid_indexes)) / float(batch_size)))\nprint(len(tag_train_indexes), batch_size)","execution_count":20,"outputs":[{"output_type":"stream","text":"CULTURE\n2739.0\n305.0\n87628 32\nTAG\n3067.0\n341.0\n98115 32\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"culture_model = create_model_resnet50(\n n_out=N_CULTURE_CLASSES, final_activation='softmax')\n\nfor layer in culture_model.layers:\n layer.trainable = False\n\nfor i in range(-6,0):\n culture_model.layers[i].trainable = True\n\nculture_model.compile(\n loss='binary_crossentropy',\n optimizer=Adam(LR_WARM),metrics=['acc',f2])","execution_count":21,"outputs":[{"output_type":"stream","text":"WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nColocations handled automatically by placer.\nWARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\nInstructions for updating:\nPlease use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"tag_model = create_model_resnet50(\n n_out=N_TAG_CLASSES, final_activation='sigmoid')\n\nfor layer in tag_model.layers:\n layer.trainable = False\n\nfor i in range(-6,0):\n tag_model.layers[i].trainable = True\n\ntag_model.compile(\n loss='binary_crossentropy',\n #loss=focal_loss,\n optimizer=Adam(LR_WARM),metrics=['acc',f2])","execution_count":22,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":false},"cell_type":"code","source":"culture_model.fit_generator(\n culture_train_generator_warmup,\n steps_per_epoch=np.ceil(float(len(culture_train_indexes)) / float(128)),\n epochs=1,\n max_queue_size=16, workers=WORKERS, use_multiprocessing=True,\n verbose=1)\n\nprint(K.eval(culture_model.optimizer.lr))","execution_count":23,"outputs":[{"output_type":"stream","text":"WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nUse tf.cast instead.\nEpoch 1/1\n685/685 [==============================] - 164s 240ms/step - loss: 0.0154 - acc: 0.9969 - f2: 0.1812\n0.001\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#CULTURE FITTING\nfor layer in culture_model.layers:\n layer.trainable = True\n\nculture_model.compile(\n loss='binary_crossentropy',\n #loss=focal_loss,\n optimizer=Adam(lr=LR),\n metrics=['acc',f2])\n\nculture_hist = culture_model.fit_generator(\n culture_train_generator,\n steps_per_epoch=np.ceil(float(len(culture_train_indexes)) / float(batch_size)),\n validation_data=culture_validation_generator,\n validation_steps=np.ceil(float(len(culture_valid_indexes)) / float(batch_size)),\n epochs=culture_epochs,\n verbose=1,\n max_queue_size=16, workers=WORKERS, use_multiprocessing=True,\n callbacks=callbacks_list)","execution_count":24,"outputs":[{"output_type":"stream","text":"WARNING:tensorflow:From /opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\nInstructions for updating:\nDeprecated in favor of operator or tf.math.divide.\nEpoch 1/4\n 316/2739 [==>...........................] - ETA: 15:59 - loss: 0.0106 - acc: 0.9973 - f2: 0.2525","name":"stdout"},{"output_type":"stream","text":"Process ForkPoolWorker-3:\nProcess ForkPoolWorker-6:\nProcess ForkPoolWorker-4:\nTraceback (most recent call last):\nProcess ForkPoolWorker-5:\nTraceback (most recent call last):\nTraceback (most recent call last):\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n self.run()\nTraceback (most recent call last):\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n self.run()\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n self._target(*self._args, **self._kwargs)\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n self._target(*self._args, **self._kwargs)\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n self.run()\n File \"/opt/conda/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n task = get()\n File \"/opt/conda/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n result = (True, func(*args, **kwds))\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n self._target(*self._args, **self._kwargs)\n File \"/opt/conda/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 626, in next_sample\n return six.next(_SHARED_SEQUENCES[uid])\n File \"/opt/conda/lib/python3.6/multiprocessing/queues.py\", line 335, in get\n res = self._reader.recv_bytes()\n File \"\", line 19, in create_train\n X_train_batch[i]['path'], shape)\n File \"/opt/conda/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n result = (True, func(*args, **kwds))\n File \"/opt/conda/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n buf = self._recv_bytes(maxlength)\n File \"\", line 50, in load_image\n image = cv2.imread(path+'.png')\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n self.run()\nKeyboardInterrupt\n File \"/opt/conda/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 626, in next_sample\n return six.next(_SHARED_SEQUENCES[uid])\n File \"/opt/conda/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n buf = self._recv(4)\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n self._target(*self._args, **self._kwargs)\n File \"/opt/conda/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n task = get()\n File \"\", line 19, in create_train\n X_train_batch[i]['path'], shape)\n File \"/opt/conda/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n chunk = read(handle, remaining)\nKeyboardInterrupt\n File \"\", line 50, in load_image\n image = cv2.imread(path+'.png')\nKeyboardInterrupt\n","name":"stderr"},{"output_type":"stream","text":"Epoch 1/4\n","name":"stdout"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mWORKERS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m callbacks=callbacks_list)\n\u001b[0m","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m 90\u001b[0m 'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 92\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 1416\u001b[0m \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1417\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m 1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1420\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0msteps_done\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__len__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 683\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 684\u001b[0m \u001b[0;32mwhile\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 637\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 550\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 552\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 296\u001b[0m \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]},{"output_type":"stream","text":"Process ForkPoolWorker-8:\nProcess ForkPoolWorker-7:\nTraceback (most recent call last):\nTraceback (most recent call last):\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n self.run()\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n self.run()\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n self._target(*self._args, **self._kwargs)\n File \"/opt/conda/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n task = get()\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n self._target(*self._args, **self._kwargs)\n File \"/opt/conda/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n task = get()\n File \"/opt/conda/lib/python3.6/multiprocessing/queues.py\", line 335, in get\n res = self._reader.recv_bytes()\n File \"/opt/conda/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n with self._rlock:\n File \"/opt/conda/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n buf = self._recv_bytes(maxlength)\n File \"/opt/conda/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n buf = self._recv(4)\n File \"/opt/conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n return self._semlock.__enter__()\n File \"/opt/conda/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n chunk = read(handle, remaining)\nKeyboardInterrupt\nKeyboardInterrupt\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"tag_model.fit_generator(\n tag_train_generator_warmup,\n steps_per_epoch=np.ceil(float(len(tag_train_indexes)) / float(128)),\n epochs=2,\n max_queue_size=16, workers=WORKERS, use_multiprocessing=True,\n verbose=1)\n\nprint(K.eval(tag_model.optimizer.lr))","execution_count":25,"outputs":[{"output_type":"stream","text":"Epoch 1/2\n 49/767 [>.............................] - ETA: 3:40 - loss: 0.8404 - acc: 0.5576 - f2: 0.0168","name":"stdout"},{"output_type":"stream","text":"Process ForkPoolWorker-10:\nProcess ForkPoolWorker-9:\nTraceback (most recent call last):\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n self.run()\nTraceback (most recent call last):\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n self._target(*self._args, **self._kwargs)\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n self.run()\n File \"/opt/conda/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n result = (True, func(*args, **kwds))\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n self._target(*self._args, **self._kwargs)\n File \"/opt/conda/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 626, in next_sample\n return six.next(_SHARED_SEQUENCES[uid])\n File \"/opt/conda/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n result = (True, func(*args, **kwds))\n File \"\", line 19, in create_train\n X_train_batch[i]['path'], shape)\n File \"/opt/conda/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 626, in next_sample\n return six.next(_SHARED_SEQUENCES[uid])\n File \"\", line 50, in load_image\n image = cv2.imread(path+'.png')\n File \"\", line 19, in create_train\n X_train_batch[i]['path'], shape)\n File \"\", line 50, in load_image\n image = cv2.imread(path+'.png')\nKeyboardInterrupt\nKeyboardInterrupt\n","name":"stderr"},{"output_type":"stream","text":"Epoch 1/2\n","name":"stdout"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mWORKERS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m verbose=1)\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtag_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m 90\u001b[0m 'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 92\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 1416\u001b[0m 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model.train_on_batch(x, y,\n\u001b[1;32m 216\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m class_weight=class_weight)\n\u001b[0m\u001b[1;32m 218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m 1215\u001b[0m \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1216\u001b[0m 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tag_model.fit_generator(\n tag_train_generator,\n steps_per_epoch=np.ceil(float(len(tag_train_indexes)) / float(batch_size)),\n validation_data=tag_validation_generator,\n validation_steps=np.ceil(float(len(tag_valid_indexes)) / float(batch_size)),\n epochs=tag_epochs,\n verbose=1,\n max_queue_size=16, workers=WORKERS, use_multiprocessing=True,\n callbacks=callbacks_list2)","execution_count":26,"outputs":[{"output_type":"stream","text":"Epoch 1/22\n 29/3067 [..............................] - ETA: 50:00 - loss: 0.5974 - acc: 0.6936 - f2: 0.0203","name":"stdout"},{"output_type":"stream","text":"Process ForkPoolWorker-15:\nProcess ForkPoolWorker-16:\nProcess ForkPoolWorker-18:\nTraceback (most recent call last):\nTraceback (most recent call last):\nProcess ForkPoolWorker-17:\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n self.run()\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n self._target(*self._args, **self._kwargs)\n File \"/opt/conda/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n result = (True, func(*args, **kwds))\n File \"/opt/conda/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 626, in next_sample\n return six.next(_SHARED_SEQUENCES[uid])\n File \"\", line 19, in create_train\n X_train_batch[i]['path'], shape)\nTraceback (most recent call last):\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n self.run()\n File \"\", line 50, in load_image\n image = cv2.imread(path+'.png')\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n self._target(*self._args, **self._kwargs)\nKeyboardInterrupt\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n self.run()\n File \"/opt/conda/lib/python3.6/multiprocessing/pool.py\", line 119, in worker\n result = (True, func(*args, **kwds))\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n self._target(*self._args, **self._kwargs)\nTraceback (most recent call last):\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 258, in _bootstrap\n self.run()\n File \"/opt/conda/lib/python3.6/site-packages/keras/utils/data_utils.py\", line 626, in next_sample\n return six.next(_SHARED_SEQUENCES[uid])\n File \"/opt/conda/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n task = get()\n File \"/opt/conda/lib/python3.6/multiprocessing/queues.py\", line 334, in get\n with self._rlock:\n File \"/opt/conda/lib/python3.6/multiprocessing/process.py\", line 93, in run\n self._target(*self._args, **self._kwargs)\n File \"\", line 21, in create_train\n image = data_generator.augment(image)\n File \"/opt/conda/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n return self._semlock.__enter__()\n File \"/opt/conda/lib/python3.6/multiprocessing/pool.py\", line 108, in worker\n task = get()\n File \"\", line 74, in augment\n image_aug = augment_img.augment_image(image)\n File \"/opt/conda/lib/python3.6/site-packages/imgaug/augmenters/meta.py\", line 407, in augment_image\n return self.augment_images([image], hooks=hooks)[0]\n File \"/opt/conda/lib/python3.6/multiprocessing/queues.py\", line 335, in get\n res = self._reader.recv_bytes()\n File \"/opt/conda/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n buf = self._recv_bytes(maxlength)\n File \"/opt/conda/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n buf = self._recv(4)\n File \"/opt/conda/lib/python3.6/site-packages/imgaug/augmenters/meta.py\", line 540, in augment_images\n hooks=hooks\nKeyboardInterrupt\n File \"/opt/conda/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n chunk = read(handle, remaining)\n File \"/opt/conda/lib/python3.6/site-packages/imgaug/augmenters/meta.py\", line 1956, in _augment_images\n hooks=hooks\n File \"/opt/conda/lib/python3.6/site-packages/imgaug/augmenters/meta.py\", line 452, in augment_images\n hooks=hooks\nKeyboardInterrupt\n File \"/opt/conda/lib/python3.6/site-packages/imgaug/augmenters/meta.py\", line 2239, in _augment_images\n hooks=hooks\n File \"/opt/conda/lib/python3.6/site-packages/imgaug/augmenters/meta.py\", line 452, in augment_images\n hooks=hooks\n File \"/opt/conda/lib/python3.6/site-packages/imgaug/augmenters/size.py\", line 794, in _augment_images\n image_cr_pa = ia.imresize_single_image(image_cr_pa, (height, width))\n File \"/opt/conda/lib/python3.6/site-packages/imgaug/imgaug.py\", line 1185, in imresize_single_image\n rs = imresize_many_images(image[np.newaxis, :, :, :], sizes, interpolation=interpolation)\n File \"/opt/conda/lib/python3.6/site-packages/imgaug/imgaug.py\", line 1131, in imresize_many_images\n result_img = cv2.resize(image, (width, height), interpolation=ip)\nKeyboardInterrupt\n","name":"stderr"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mWORKERS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m callbacks=callbacks_list2)\n\u001b[0m","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 89\u001b[0m warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m 90\u001b[0m 'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 92\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mreturn\u001b[0m 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defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mculture_hist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mculture_hist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"loss\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"culture train loss\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m 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ax2 = plt.subplots(1, 2, figsize=(15,5))\nax2[0].set_title('loss')\nax2[0].plot(tag_hist.epoch, tag_hist.history[\"loss\"], label=\"tag train loss\")\nax2[0].plot(tag_hist.epoch, tag_hist.history[\"val_loss\"], label=\"tag validation loss\")\nax2[1].set_title('f2')\nax2[1].plot(tag_hist.epoch, tag_hist.history[\"f2\"], label=\"tag train F2\")\nax2[1].plot(tag_hist.epoch, tag_hist.history[\"val_f2\"], label=\"tag validation F2\")\nax2[0].legend()\nax2[1].legend()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(os.listdir('../working/'))\nculture_model.load_weights(checkpoint_file)\ntag_model.load_weights(checkpoint_file2)","execution_count":28,"outputs":[{"output_type":"stream","text":"['training_log.csv', 'training_log2.csv', '.ipynb_checkpoints', '__notebook_source__.ipynb']\n","name":"stdout"},{"output_type":"error","ename":"OSError","evalue":"Unable to open file (unable to open file: name = '../working/resnet50_focal.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../working/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mculture_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mtag_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint_file2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/engine/network.py\u001b[0m in \u001b[0;36mload_weights\u001b[0;34m(self, filepath, by_name, skip_mismatch, reshape)\u001b[0m\n\u001b[1;32m 1155\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mh5py\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1156\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'`load_weights` requires h5py.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1157\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m 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**kwds)\u001b[0m\n\u001b[1;32m 392\u001b[0m fid = make_fid(name, mode, userblock_size,\n\u001b[1;32m 393\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmake_fcpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 394\u001b[0;31m swmr=swmr)\n\u001b[0m\u001b[1;32m 395\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 396\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mswmr_support\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.6/site-packages/h5py/_hl/files.py\u001b[0m in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m 168\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mswmr\u001b[0m \u001b[0;32mand\u001b[0m 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512\nfullCultureValGen = data_generator.create_valid(\n culture_train_dataset_info[culture_valid_indexes], BATCH, (SIZE,SIZE,3), N_CULTURE_CLASSES)\n\nn_val = round(culture_train_dataset_info.shape[0]*0.15)//BATCH\nprint(n_val)\n\nlastFullCultureValPred = np.empty((0, N_CULTURE_CLASSES))\nlastFullCultureValLabels = np.empty((0, N_CULTURE_CLASSES))\nfor i in tqdm(range(n_val+1)): \n im, lbl = next(fullCultureValGen)\n scores = culture_model.predict(im)\n lastFullCultureValPred = np.append(lastFullCultureValPred, scores, axis=0)\n lastFullCultureValLabels = np.append(lastFullCultureValLabels, lbl, axis=0)\nprint(lastFullCultureValPred.shape, lastFullCultureValLabels.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"'''Search for the best threshold regarding the TAG validation set'''\n\nBATCH = 512\nfullTagValGen = data_generator.create_valid(\n tag_train_dataset_info[tag_valid_indexes], BATCH, (SIZE,SIZE,3), N_TAG_CLASSES)\n\nn_val = round(tag_train_dataset_info.shape[0]*0.15)//BATCH\nprint(n_val)\n\nlastFullTagValPred = np.empty((0, N_TAG_CLASSES))\nlastFullTagValLabels = np.empty((0, N_TAG_CLASSES))\nfor i in tqdm(range(n_val+1)): \n im, lbl = next(fullTagValGen)\n scores = tag_model.predict(im)\n lastFullTagValPred = np.append(lastFullTagValPred, scores, axis=0)\n lastFullTagValLabels = np.append(lastFullTagValLabels, lbl, axis=0)\nprint(lastFullTagValPred.shape, lastFullTagValLabels.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def my_f2(y_true, y_pred):\n assert y_true.shape[0] == y_pred.shape[0]\n\n tp = np.sum((y_true == 1) & (y_pred == 1), axis=1)\n tn = np.sum((y_true == 0) & (y_pred == 0), axis=1)\n fp = np.sum((y_true == 0) & (y_pred == 1), axis=1)\n fn = np.sum((y_true == 1) & (y_pred == 0), axis=1)\n\n p = tp / (tp + fp + K.epsilon())\n r = tp / (tp + fn + K.epsilon())\n\n f2 = (1+beta_f2**2)*p*r / (p*beta_f2**2 + r + 1e-15)\n\n return np.mean(f2)\n\ndef find_best_fixed_threshold(preds, targs, do_plot=True):\n score = []\n thrs = np.arange(0, 0.5, 0.01)\n for thr in tqdm(thrs):\n score.append(my_f2(targs, (preds > thr).astype(int) ))\n score = np.array(score)\n pm = score.argmax()\n best_thr, best_score = thrs[pm], score[pm].item()\n print(f'thr={best_thr:.3f}', f'F2={best_score:.3f}')\n if do_plot:\n plt.plot(thrs, score)\n plt.vlines(x=best_thr, ymin=score.min(), ymax=score.max())\n plt.text(best_thr+0.03, best_score-0.01, f'$F_{2}=${best_score:.3f}', fontsize=14);\n plt.show()\n return best_thr, best_score","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"best_culture_thr, best_culture_score = find_best_fixed_threshold(lastFullCultureValPred, lastFullCultureValLabels, do_plot=True)\nbest_tag_thr, best_tag_score = find_best_fixed_threshold(lastFullTagValPred, lastFullTagValLabels, do_plot=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submit = pd.read_csv('../input/imet-2019-fgvc6/sample_submission.csv')\npredicted = []\n\nfor i, name in tqdm(enumerate(submit['id'])):\n path = os.path.join('../input/imet-2019-fgvc6/test/', name)\n image = data_generator.load_image(path, (SIZE,SIZE,3))\n label_predict = []\n \n # X-TOP method, started with C:2 and T:5, should down to C:1 and T:4\n #TODO: change this to Threshold method (0.130) and get as many labels as the model estimates surpassing the threshold\n culture_score_predict = culture_model.predict(preprocess_input(image[np.newaxis]))\n culture_indexes = np.arange(N_CULTURE_CLASSES)[culture_score_predict[0]>=best_culture_thr]\n \n tag_score_predict = tag_model.predict(preprocess_input(image[np.newaxis]))\n tag_indexes = np.arange(N_CULTURE_CLASSES,N_CULTURE_CLASSES+N_TAG_CLASSES)[tag_score_predict[0]>=best_tag_thr]\n \n label_predict = np.append(culture_indexes,tag_indexes)\n \n str_predict_label = ' '.join(str(l) for l in label_predict)\n predicted.append(str_predict_label)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submit['attribute_ids'] = predicted\nif POST_PROCESS:\n submit.to_csv('submission_not_process.csv', index=False)\nelse:\n submit.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}