{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install -U --pre efficientnet","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"cell_type":"code","source":"import math, re, os\nimport tensorflow as tf\nimport pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom kaggle_datasets import KaggleDatasets\nimport efficientnet.tfkeras as efn\nfrom sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix\nfrom tensorflow.keras.applications import DenseNet201\nprint(\"Tensorflow version \" + tf.__version__)\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Configurations"},{"metadata":{"trusted":false},"cell_type":"code","source":"'''\nAUTO = tf.data.experimental.AUTOTUNE\n\n# Create strategy from tpu\ntpu = tf.distribute.cluster_resolver.TPUClusterResolver()\ntf.config.experimental_connect_to_cluster(tpu)\ntf.tpu.experimental.initialize_tpu_system(tpu)\nstrategy = tf.distribute.experimental.TPUStrategy(tpu)\n\n# Data access\nGCS_DS_PATH = KaggleDatasets().get_gcs_path()\n\n# Configuration\nIMAGE_SIZE = [512, 512]\nEPOCHS1 =25\nEPOCHS2 =18\nBATCH_SIZE = 2 * 16 * strategy.num_replicas_in_sync\n'''","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"AUTO = tf.data.experimental.AUTOTUNE\n\n# Detect hardware, return appropriate distribution strategy\ntry:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()  # TPU detection. No parameters necessary if TPU_NAME environment variable is set. On Kaggle this is always the case.\n    print('Running on TPU ', tpu.master())\nexcept ValueError:\n    tpu = None\n\nif tpu:\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    strategy = tf.distribute.experimental.TPUStrategy(tpu)\nelse:\n    strategy = tf.distribute.get_strategy() # default distribution strategy in Tensorflow. Works on CPU and single GPU.\n\nprint(\"REPLICAS: \", strategy.num_replicas_in_sync)\n\n\nGCS_DS_PATH = 'gs://kds-a44744780cb28b908f65e7fa3b4a473ef199bb9ffe8048dbbf29ffeb'#'gs://flowers-public' \n#GCS_DS_PATH = KaggleDatasets().get_gcs_path() # \n\n\n# Configuration\nIMAGE_SIZE = [512, 512]\nEPOCHS1=40\nEPOCHS2=40\nBATCH_SIZE = 1 * 16 * strategy.num_replicas_in_sync","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Custom LR schedule"},{"metadata":{"trusted":true},"cell_type":"code","source":"LR_START = 0.0001\nLR_MAX = 0.00005 * strategy.num_replicas_in_sync\nLR_MIN = 0.00001\nLR_RAMPUP_EPOCHS = 5\nLR_SUSTAIN_EPOCHS = 5\nLR_EXP_DECAY = .8\n\ndef lrfn(epoch):\n    if epoch < LR_RAMPUP_EPOCHS:\n        lr = np.random.random_sample() * LR_START\n    elif epoch < LR_RAMPUP_EPOCHS + LR_SUSTAIN_EPOCHS:\n        lr = LR_MAX\n    else:\n        lr = (LR_MAX - LR_MIN) * LR_EXP_DECAY**(epoch - LR_RAMPUP_EPOCHS - LR_SUSTAIN_EPOCHS) + LR_MIN\n    return lr\n    \nlr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def count_data_items(filenames):\n    # the number of data items is written in the name of the .tfrec files, i.e. flowers00-230.tfrec = 230 data items\n    n = [int(re.compile(r\"-([0-9]*)\\.\").search(filename).group(1)) for filename in filenames]\n    return np.sum(n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"GCS_PATH_SELECT = { # available image sizes\n    192: GCS_DS_PATH + '/tfrecords-jpeg-192x192',\n    224: GCS_DS_PATH + '/tfrecords-jpeg-224x224',\n    331: GCS_DS_PATH + '/tfrecords-jpeg-331x331',\n    512: GCS_DS_PATH + '/tfrecords-jpeg-512x512'\n}\nGCS_PATH = GCS_PATH_SELECT[IMAGE_SIZE[0]]\n\nTRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')\n\nVALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')\n\nTEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec') # predictions on this dataset should be submitted for the competition\n\n# watch out for overfitting!\nSKIP_VALIDATION = False\nif SKIP_VALIDATION:\n    TRAINING_FILENAMES = TRAINING_FILENAMES + VALIDATION_FILENAMES","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"VALIDATION_FILENAMES_1 = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')[:int(len(VALIDATION_FILENAMES)/2)]\nVALIDATION_FILENAMES_2 = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')[int(len(VALIDATION_FILENAMES)/2):]\n\nTRAINING_FILENAMES_1 = TRAINING_FILENAMES + VALIDATION_FILENAMES_1\nTRAINING_FILENAMES_2 = TRAINING_FILENAMES + VALIDATION_FILENAMES_2\n\nNUM_TRAINING_IMAGES_1 = count_data_items(TRAINING_FILENAMES_1)\nNUM_TRAINING_IMAGES_2 = count_data_items(TRAINING_FILENAMES_2)\n\nNUM_VALIDATION_IMAGES_1 = (1 - SKIP_VALIDATION) * count_data_items(VALIDATION_FILENAMES_1)\nNUM_VALIDATION_IMAGES_2 = (1 - SKIP_VALIDATION) * count_data_items(VALIDATION_FILENAMES_2)\n\nNUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)\n\nSTEPS_PER_EPOCH_1 = NUM_TRAINING_IMAGES_1 // BATCH_SIZE\nSTEPS_PER_EPOCH_2 = NUM_TRAINING_IMAGES_2 // BATCH_SIZE\n\nprint('Dataset: {} training_1 images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES_1, NUM_VALIDATION_IMAGES_1, NUM_TEST_IMAGES))\nprint('Dataset: {} training_2 images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES_2, NUM_VALIDATION_IMAGES_2, NUM_TEST_IMAGES))","execution_count":null,"outputs":[]},{"metadata":{"_kg_hide-input":true,"trusted":false},"cell_type":"code","source":"CLASSES = ['pink primrose',    'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea',     'wild geranium',     'tiger lily',           'moon orchid',              'bird of paradise', 'monkshood',        'globe thistle',         # 00 - 09\n           'snapdragon',       \"colt's foot\",               'king protea',      'spear thistle', 'yellow iris',       'globe-flower',         'purple coneflower',        'peruvian lily',    'balloon flower',   'giant white arum lily', # 10 - 19\n           'fire lily',        'pincushion flower',         'fritillary',       'red ginger',    'grape hyacinth',    'corn poppy',           'prince of wales feathers', 'stemless gentian', 'artichoke',        'sweet william',         # 20 - 29\n           'carnation',        'garden phlox',              'love in the mist', 'cosmos',        'alpine sea holly',  'ruby-lipped cattleya', 'cape flower',              'great masterwort', 'siam tulip',       'lenten rose',           # 30 - 39\n           'barberton daisy',  'daffodil',                  'sword lily',       'poinsettia',    'bolero deep blue',  'wallflower',           'marigold',                 'buttercup',        'daisy',            'common dandelion',      # 40 - 49\n           'petunia',          'wild pansy',                'primula',          'sunflower',     'lilac hibiscus',    'bishop of llandaff',   'gaura',                    'geranium',         'orange dahlia',    'pink-yellow dahlia',    # 50 - 59\n           'cautleya spicata', 'japanese anemone',          'black-eyed susan', 'silverbush',    'californian poppy', 'osteospermum',         'spring crocus',            'iris',             'windflower',       'tree poppy',            # 60 - 69\n           'gazania',          'azalea',                    'water lily',       'rose',          'thorn apple',       'morning glory',        'passion flower',           'lotus',            'toad lily',        'anthurium',             # 70 - 79\n           'frangipani',       'clematis',                  'hibiscus',         'columbine',     'desert-rose',       'tree mallow',          'magnolia',                 'cyclamen ',        'watercress',       'canna lily',            # 80 - 89\n           'hippeastrum ',     'bee balm',                  'pink quill',       'foxglove',      'bougainvillea',     'camellia',             'mallow',                   'mexican petunia',  'bromelia',         'blanket flower',        # 90 - 99\n           'trumpet creeper',  'blackberry lily',           'common tulip',     'wild rose']                                                                                                                                               # 100 - 102","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Helper Functions\n## Visualization"},{"metadata":{"_kg_hide-input":true,"trusted":false},"cell_type":"code","source":"def display_confusion_matrix(cmat, score, precision, recall):\n    plt.figure(figsize=(15,15))\n    ax = plt.gca()\n    ax.matshow(cmat, cmap='Reds')\n    ax.set_xticks(range(len(CLASSES)))\n    ax.set_xticklabels(CLASSES, fontdict={'fontsize': 7})\n    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"left\", rotation_mode=\"anchor\")\n    ax.set_yticks(range(len(CLASSES)))\n    ax.set_yticklabels(CLASSES, fontdict={'fontsize': 7})\n    plt.setp(ax.get_yticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n    titlestring = \"\"\n    if score is not None:\n        titlestring += 'f1 = {:.3f} '.format(score)\n    if precision is not None:\n        titlestring += '\\nprecision = {:.3f} '.format(precision)\n    if recall is not None:\n        titlestring += '\\nrecall = {:.3f} '.format(recall)\n    if len(titlestring) > 0:\n        ax.text(101, 1, titlestring, fontdict={'fontsize': 18, 'horizontalalignment':'right', 'verticalalignment':'top', 'color':'#804040'})\n    plt.show()\n    \ndef display_training_curves(training, validation, title, subplot):\n    if subplot%10==1: # set up the subplots on the first call\n        plt.subplots(figsize=(10,10), facecolor='#F0F0F0')\n        plt.tight_layout()\n    ax = plt.subplot(subplot)\n    ax.set_facecolor('#F8F8F8')\n    ax.plot(training)\n    ax.plot(validation)\n    ax.set_title('model '+ title)\n    ax.set_ylabel(title)\n    #ax.set_ylim(0.28,1.05)\n    ax.set_xlabel('epoch')\n    ax.legend(['train', 'valid.'])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Datasets Functions"},{"metadata":{"trusted":false},"cell_type":"code","source":"img_size=512\ndef random_blockout(img, sl=0.1, sh=0.2, rl=0.4):\n\n    h, w, c = img_size, img_size, 3\n    origin_area = tf.cast(h*w, tf.float32)\n\n    e_size_l = tf.cast(tf.round(tf.sqrt(origin_area * sl * rl)), tf.int32)\n    e_size_h = tf.cast(tf.round(tf.sqrt(origin_area * sh / rl)), tf.int32)\n\n    e_height_h = tf.minimum(e_size_h, h)\n    e_width_h = tf.minimum(e_size_h, w)\n\n    erase_height = tf.random.uniform(shape=[], minval=e_size_l, maxval=e_height_h, dtype=tf.int32)\n    erase_width = tf.random.uniform(shape=[], minval=e_size_l, maxval=e_width_h, dtype=tf.int32)\n\n    erase_area = tf.zeros(shape=[erase_height, erase_width, c])\n    erase_area = tf.cast(erase_area, tf.uint8)\n\n    pad_h = h - erase_height\n    pad_top = tf.random.uniform(shape=[], minval=0, maxval=pad_h, dtype=tf.int32)\n    pad_bottom = pad_h - pad_top\n\n    pad_w = w - erase_width\n    pad_left = tf.random.uniform(shape=[], minval=0, maxval=pad_w, dtype=tf.int32)\n    pad_right = pad_w - pad_left\n\n    erase_mask = tf.pad([erase_area], [[0,0],[pad_top, pad_bottom], [pad_left, pad_right], [0,0]], constant_values=1)\n    erase_mask = tf.squeeze(erase_mask, axis=0)\n    erased_img = tf.multiply(tf.cast(img,tf.float32), tf.cast(erase_mask, tf.float32))\n\n    return tf.cast(erased_img, img.dtype)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def decode_image(image_data):\n    image = tf.image.decode_jpeg(image_data, channels=3)\n    #image = tf.io.decode_image(image_data, channels=3)\n    image = tf.cast(image, tf.float32) / 255.0  # convert image to floats in [0, 1] range\n    image = tf.reshape(image, [*IMAGE_SIZE, 3]) # explicit size needed for TPU\n    return image\n\ndef read_labeled_tfrecord(example):\n    LABELED_TFREC_FORMAT = {\n        \"image\": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring\n        \"class\": tf.io.FixedLenFeature([], tf.int64),  # shape [] means single element\n    }\n    example = tf.io.parse_single_example(example, LABELED_TFREC_FORMAT)\n    image = decode_image(example['image'])\n    label = tf.cast(example['class'], tf.int32)\n    #label = tf.one_hot(label, len(CLASSES))\n    return image, label # returns a dataset of (image, label) pairs\n\ndef read_unlabeled_tfrecord(example):\n    UNLABELED_TFREC_FORMAT = {\n        \"image\": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring\n        \"id\": tf.io.FixedLenFeature([], tf.string),  # shape [] means single element\n        # class is missing, this competitions's challenge is to predict flower classes for the test dataset\n    }\n    example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT)\n    image = decode_image(example['image'])\n    idnum = example['id']\n    return image, idnum # returns a dataset of image(s)\n\ndef load_dataset(filenames, labeled=True, ordered=False):\n    # Read from TFRecords. For optimal performance, reading from multiple files at once and\n    # disregarding data order. Order does not matter since we will be shuffling the data anyway.\n\n    ignore_order = tf.data.Options()\n    if not ordered:\n        ignore_order.experimental_deterministic = False # disable order, increase speed\n\n    dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO) # automatically interleaves reads from multiple files\n    dataset = dataset.with_options(ignore_order) # uses data as soon as it streams in, rather than in its original order\n    dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls=AUTO)\n    # returns a dataset of (image, label) pairs if labeled=True or (image, id) pairs if labeled=False\n    return dataset\n\ndef data_augment(image, label):\n    # data augmentation. Thanks to the dataset.prefetch(AUTO) statement in the next function (below),\n    # this happens essentially for free on TPU. Data pipeline code is executed on the \"CPU\" part\n    # of the TPU while the TPU itself is computing gradients.\n    image = tf.image.random_flip_left_right(image)\n    #image = tf.image.random_saturation(image, 0, 2)\n    image= random_blockout(image)\n    return image, label   \n\n\ndef get_training_dataset():\n    dataset = load_dataset(TRAINING_FILENAMES, labeled=True)\n    #dataset = dataset.map(data_augment, num_parallel_calls=AUTO)    \n    dataset = dataset.repeat() # the training dataset must repeat for several epochs\n    dataset = dataset.shuffle(3456)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef get_training_dataset1():\n    dataset = load_dataset(TRAINING_FILENAMES_1, labeled=True)\n    dataset = dataset.map(data_augment, num_parallel_calls=AUTO)    \n    dataset = dataset.repeat() # the training dataset must repeat for several epochs\n    dataset = dataset.shuffle(3456)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef get_training_dataset2():\n    dataset = load_dataset(TRAINING_FILENAMES_2, labeled=True)\n    dataset = dataset.map(data_augment, num_parallel_calls=AUTO)    \n    dataset = dataset.repeat() # the training dataset must repeat for several epochs\n    dataset = dataset.shuffle(3456)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef get_validation_dataset1(ordered=False):\n    dataset = load_dataset(VALIDATION_FILENAMES_1, labeled=True, ordered=ordered)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.cache()\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef get_validation_dataset2(ordered=False):\n    dataset = load_dataset(VALIDATION_FILENAMES_2, labeled=True, ordered=ordered)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.cache()\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef get_validation_dataset3(ordered=False):\n    dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=ordered)\n    dataset = dataset.map(data_augment, num_parallel_calls=AUTO)  \n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.cache()\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef get_test_dataset(ordered=False):\n    dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef data_augment2(image, label):\n    # data augmentation. Thanks to the dataset.prefetch(AUTO) statement in the next function (below),\n    # this happens essentially for free on TPU. Data pipeline code is executed on the \"CPU\" part\n    # of the TPU while the TPU itself is computing gradients.\n    image = tf.image.flip_left_right(image)\n    #image = tf.image.random_saturation(image, 0, 2)\n    #image= random_blockout(image)\n    return image, label   \n\ndef get_test_dataset2(ordered=False):\n    dataset = load_dataset(TEST_FILENAMES, labeled=False, ordered=ordered)\n    dataset = dataset.map(data_augment2, num_parallel_calls=AUTO)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\n\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Training Model"},{"metadata":{},"cell_type":"markdown","source":"## Load Model into TPU"},{"metadata":{"trusted":false},"cell_type":"code","source":"# Need this line so Google will recite some incantations\n# for Turing to magically load the model onto the TPU\nwith strategy.scope():\n    enet = efn.EfficientNetB7(\n        input_shape=(512, 512, 3),\n        weights='noisy-student',\n        include_top=False\n    )\n\n    model = tf.keras.Sequential([\n        enet,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation='softmax')\n    ])\n        \nmodel.compile(\n    optimizer=tf.keras.optimizers.Adam(lr=0.0001),\n    loss = 'sparse_categorical_crossentropy',\n    metrics=[]\n)\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Training"},{"metadata":{"trusted":true},"cell_type":"code","source":"#scheduler = tf.keras.callbacks.ReduceLROnPlateau(patience=3, verbose=1)\n\n#patience=5 \n#lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', mode='min', factor=(5/10), patience=patience, min_lr=0.00001, verbose= 1)    \n#es = tf.keras.callbacks.EarlyStopping(monitor='val_f1', mode='max', verbose=1, patience=patience) #restore_best_weights=True,\n#mc = tf.keras.callbacks.ModelCheckpoint('EfficientNetB7.h5', verbose=1, monitor='val_f1_score', mode='max', \n#                                        save_best_only=True, save_weights_only=True) \n\n#es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=patience) #restore_best_weights=True,\n#mc = tf.keras.callbacks.ModelCheckpoint('EfficientNetB7_Fold0_25.h5', verbose=1, monitor='val_loss', mode='min', \n#                                        save_best_only=True, save_weights_only=True) \n","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"mc = tf.keras.callbacks.ModelCheckpoint('EfficientNetB7_validation2_40.h5', verbose=1, monitor='val_loss', mode='min', \n                                        save_best_only=True, save_weights_only=True) \n\nhistory = model.fit(\n    get_training_dataset1(), \n    steps_per_epoch=STEPS_PER_EPOCH_1,\n    epochs=EPOCHS1,\n    callbacks=[lr_callback,mc],\n    validation_data=None if SKIP_VALIDATION else get_validation_dataset2()\n)\n\n#model.save_weights('EfficientNetB7_25.h5')\n#model.load_weights('../input/104-flower-classification-tpu/EfficientNetB7_25.h5')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with strategy.scope():\n    enet = efn.EfficientNetB7(\n        input_shape=(512, 512, 3),\n        weights='noisy-student',\n        include_top=False\n    )\n\n    model2 = tf.keras.Sequential([\n        enet,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation='softmax')\n    ])\n        \nmodel2.compile(\n    optimizer=tf.keras.optimizers.Adam(lr=0.0001),\n    loss = 'sparse_categorical_crossentropy',\n    metrics=[]\n)\nmodel2.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mc = tf.keras.callbacks.ModelCheckpoint('EfficientNetB7_validation1_40.h5', verbose=1, monitor='val_loss', mode='min', \n                                        save_best_only=True, save_weights_only=True) \n\nhistory2 = model2.fit(\n    get_training_dataset2(), \n    steps_per_epoch=STEPS_PER_EPOCH_2,\n    epochs=EPOCHS2,\n    callbacks=[lr_callback,mc],\n    validation_data=None if SKIP_VALIDATION else get_validation_dataset1()\n)\n\n#model.save_weights('EfficientNetB7_25.h5')\n#model.load_weights('../input/104-flower-classification-tpu/EfficientNetB7_25.h5')","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.6"}},"nbformat":4,"nbformat_minor":4}