{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install -q efficientnet","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import math, re, gc\nimport numpy as np # linear algebra\nimport pickle\nfrom datetime import datetime, timedelta\nimport tensorflow as tf\nimport efficientnet.tfkeras as efficientnet\nfrom matplotlib import pyplot as plt\nfrom kaggle_datasets import KaggleDatasets\nfrom sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix\nprint('TensorFlow version', tf.__version__)\nAUTO = tf.data.experimental.AUTOTUNE","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"try:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\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()\n\nprint('Replicas:', strategy.num_replicas_in_sync)\n\n!ls -l /kaggle/input/tf-flower-photo-tfrec/*/tfrecords-jpeg-224x224/*.tfrec\n!ls -l /kaggle/input/tf-flower-photo-tfrec/imagenet/tfrecords-jpeg-224x224/*.tfrec\n!ls -l /kaggle/input/tf-flower-photo-tfrec/inaturalist/tfrecords-jpeg-224x224/*.tfrec\n!ls -l /kaggle/input/tf-flower-photo-tfrec/openimage/tfrecords-jpeg-224x224/*.tfrec\n!ls -l /kaggle/input/tf-flower-photo-tfrec/oxford_102/tfrecords-jpeg-224x224/*.tfrec\n!ls -l /kaggle/input/tf-flower-photo-tfrec/tf_flowers/tfrecords-jpeg-224x224/*.tfrec\n#!ls -l /kaggle/input/flower-classification-with-tpus/tfrecords-jpeg-224x224/train/*.tfrec\n\n#GCS_DS_PATH = KaggleDatasets().get_gcs_path('flower-classification-with-tpus')\n\nGCS_DS_PATH = KaggleDatasets().get_gcs_path('tpu-getting-started')\nMORE_IMAGES_GCS_DS_PATH = KaggleDatasets().get_gcs_path('tf-flower-photo-tfrec')\n#OXFORD_GCS_DS_PATH = KaggleDatasets().get_gcs_path('oxford-flowers-tfrecords')   \n\nprint(GCS_DS_PATH, '\\n', MORE_IMAGES_GCS_DS_PATH)\n#!ls -l /kaggle/input/tf-flower-photo-tfrec/*/tfrecords-jpeg-224x224/*.tfrec\n#!ls -l /kaggle/input/tf-flower-photo-tfrec/imagenet/tfrecords-jpeg-224x224/*.tfrec\n#!ls -l /kaggle/input/tf-flower-photo-tfrec/inaturalist/tfrecords-jpeg-224x224/*.tfrec\n#!ls -l /kaggle/input/tf-flower-photo-tfrec/openimage/tfrecords-jpeg-224x224/*.tfrec\n#!ls -l /kaggle/input/tf-flower-photo-tfrec/oxford_102/tfrecords-jpeg-224x224/*.tfrec\n#!ls -l /kaggle/input/tf-flower-photo-tfrec/tf_flowers/tfrecords-jpeg-224x224/*.tfrec","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# TPU or GPU detection","metadata":{}},{"cell_type":"markdown","source":"# Competition data access\nTPUs read data directly from Google Cloud Storage (GCS). This Kaggle utility will copy the dataset to a GCS bucket co-located with the TPU. If you have multiple datasets attached to the notebook, you can pass the name of a specific dataset to the get_gcs_path function. The name of the dataset is the name of the directory it is mounted in. Use `!ls /kaggle/input/` to list attached datasets.","metadata":{}},{"cell_type":"markdown","source":"# Configuration","metadata":{}},{"cell_type":"code","source":"start_time = datetime.now()\nprint('Time now is', start_time)\nend_training_by_tdelta = timedelta(seconds=8400)\nthis_run_file_prefix = start_time.strftime('%Y%m%d_%H%M_')\nprint(this_run_file_prefix)\n\nIMAGE_SIZE = [512, 512] #[224, 224] \n\nEPOCHS = 10\nBATCH_SIZE = 16 * strategy.num_replicas_in_sync   ####16*8=128\n\n\n###############################\nGCS_PATH_SELECT = {\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')   \nVALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')\nTEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec')\n#################################\n\n\n#############################\nMOREIMAGES_PATH_SELECT = {\n    192: '/tfrecords-jpeg-192x192',\n    224: '/tfrecords-jpeg-224x224',\n    331: '/tfrecords-jpeg-331x331',\n    512: '/tfrecords-jpeg-512x512'\n}\nMOREIMAGES_PATH = MOREIMAGES_PATH_SELECT[IMAGE_SIZE[0]]\n\nIMAGENET_FILES = tf.io.gfile.glob(MORE_IMAGES_GCS_DS_PATH + '/imagenet' + MOREIMAGES_PATH + '/*.tfrec')\nINATURELIST_FILES = tf.io.gfile.glob(MORE_IMAGES_GCS_DS_PATH + '/inaturalist' + MOREIMAGES_PATH + '/*.tfrec')\nOPENIMAGE_FILES = tf.io.gfile.glob(MORE_IMAGES_GCS_DS_PATH + '/openimage' + MOREIMAGES_PATH + '/*.tfrec')\nOXFORD_FILES = tf.io.gfile.glob(MORE_IMAGES_GCS_DS_PATH + '/oxford_102' + MOREIMAGES_PATH + '/*.tfrec')\nTENSORFLOW_FILES = tf.io.gfile.glob(MORE_IMAGES_GCS_DS_PATH + '/tf_flowers' + MOREIMAGES_PATH + '/*.tfrec')\n\nADDITIONAL_TRAINING_FILENAMES = IMAGENET_FILES + INATURELIST_FILES + OPENIMAGE_FILES + OXFORD_FILES + TENSORFLOW_FILES  \n######################################\n\n\n#############################\n#OXFORD_PATH_SELECT = {\n #   192: OXFORD_GCS_DS_PATH + '/tfrecords-png-192x192',\n  #  224: OXFORD_GCS_DS_PATH + '/tfrecords-png-224x224',\n   # 331: OXFORD_GCS_DS_PATH + '/tfrecords-png-331x331',\n    #512: OXFORD_GCS_DS_PATH + '/tfrecords-png-512x512'\n#}\n#OXFORD_PATH = OXFORD_PATH_SELECT[IMAGE_SIZE[0]]\n#TRAINING_FILENAMES_1 = tf.io.gfile.glob(OXFORD_PATH + '/*.tfrec')\n##############################\n\n\n\n\n#print(VALIDATION_FILENAMES)\nprint('----')\nTRAINING_FILENAMES = TRAINING_FILENAMES + ADDITIONAL_TRAINING_FILENAMES + VALIDATION_FILENAMES  \n#TRAINING_FILENAMES = TRAINING_FILENAMES + ADDITIONAL_TRAINING_FILENAMES + TRAINING_FILENAMES_1 \n\n#print(TRAINING_FILENAMES)\n\n# TRAINING_FILENAMES = TRAINING_FILENAMES + VALIDATION_FILENAMES\n# VALIDATION_FILENAMES = TRAINING_FILENAMES\n\nCLASSES = ['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","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"LR_START = 0.00001\nLR_MAX = 0.00005 * strategy.num_replicas_in_sync\nLR_MIN = LR_START\nLR_RAMPUP_EPOCHS = 5 #5\nLR_SUSTAIN_EPOCHS = 0 # 0\nLR_EXP_DECAY = 0.80\n\ndef lrfn(epoch):\n    if epoch < LR_RAMPUP_EPOCHS:  \n        lr = LR_START + (epoch * (LR_MAX - LR_START) / LR_RAMPUP_EPOCHS)   #   np.random.random_sample() * LR_START\n    elif epoch < (LR_RAMPUP_EPOCHS + LR_SUSTAIN_EPOCHS):  ####5-7lun\n        lr = LR_MAX\n    else:    \n        lr = LR_MIN + (LR_MAX - LR_MIN) * LR_EXP_DECAY ** (epoch - LR_RAMPUP_EPOCHS - LR_SUSTAIN_EPOCHS)\n#    print('For epoch', epoch, 'setting lr to', lr)\n    return lr\n\nlr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose = True)\n\nrng = [i for i in range(30)]\ny = [lrfn(x) for x in rng]\nplt.plot(rng, y)\nprint(y)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# numpy and matplotlib defaults\nnp.set_printoptions(threshold=15, linewidth=80)\n\ndef batch_to_numpy_images_and_labels(data):\n    images, labels = data\n    numpy_images = images.numpy()\n    numpy_labels = labels.numpy()\n    if numpy_labels.dtype == object: # binary string in this case, these are image ID strings\n        numpy_labels = [None for _ in enumerate(numpy_images)]\n    # If no labels, only image IDs, return None for labels (this is the case for test data)\n    return numpy_images, numpy_labels\n\ndef title_from_label_and_target(label, correct_label):\n    if correct_label is None:\n        return CLASSES[label], True\n    correct = (label == correct_label)\n    return \"{} [{}{}{}]\".format(CLASSES[label], 'OK' if correct else 'NO', u\"\\u2192\" if not correct else '',\n                                CLASSES[correct_label] if not correct else ''), correct\n\ndef display_one_flower(image, title, subplot, red=False, titlesize=16):\n    plt.subplot(*subplot)\n    plt.axis('off')\n    plt.imshow(image)\n    if len(title) > 0:\n        plt.title(title, fontsize=int(titlesize) if not red else int(titlesize/1.2), color='red' if red else 'black', fontdict={'verticalalignment':'center'}, pad=int(titlesize/1.5))\n    return (subplot[0], subplot[1], subplot[2]+1)\n    \ndef display_batch_of_images(databatch, predictions=None):\n    \"\"\"This will work with:\n    display_batch_of_images(images)\n    display_batch_of_images(images, predictions)\n    display_batch_of_images((images, labels))\n    display_batch_of_images((images, labels), predictions)\n    \"\"\"\n    # data\n    images, labels = batch_to_numpy_images_and_labels(databatch)\n    if labels is None:\n        labels = [None for _ in enumerate(images)]\n        \n    # auto-squaring: this will drop data that does not fit into square or square-ish rectangle\n    rows = int(math.sqrt(len(images)))\n    cols = len(images)//rows\n        \n    # size and spacing\n    FIGSIZE = 13.0\n    SPACING = 0.1\n    subplot=(rows,cols,1)\n    if rows < cols:\n        plt.figure(figsize=(FIGSIZE,FIGSIZE/cols*rows))\n    else:\n        plt.figure(figsize=(FIGSIZE/rows*cols,FIGSIZE))\n    \n    # display\n    for i, (image, label) in enumerate(zip(images[:rows*cols], labels[:rows*cols])):\n        title = '' if label is None else CLASSES[label]\n        correct = True\n        if predictions is not None:\n            title, correct = title_from_label_and_target(predictions[i], label)\n        dynamic_titlesize = FIGSIZE*SPACING/max(rows,cols)*40+3 # magic formula tested to work from 1x1 to 10x10 images\n        subplot = display_one_flower(image, title, subplot, not correct, titlesize=dynamic_titlesize)\n    \n    #layout\n    plt.tight_layout()\n    if label is None and predictions is None:\n        plt.subplots_adjust(wspace=0, hspace=0)\n    else:\n        plt.subplots_adjust(wspace=SPACING, hspace=SPACING)\n    plt.show()\n\ndef 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.'])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def decode_image(image_data):\n    image = tf.image.decode_jpeg(image_data, channels = 3)\n    image = tf.cast(image, tf.float32) / 255.0\n    image = tf.reshape(image, [*IMAGE_SIZE, 3])\n    return image\n#\n\ndef read_labeled_tfrecord(example):\n    LABELED_TFREC_FORMAT = {\n        'image': tf.io.FixedLenFeature([], tf.string),\n        'class': tf.io.FixedLenFeature([], tf.int64),\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    return image, label\n#\n\ndef read_unlabeled_tfrecord(example):\n    UNLABELED_TFREC_FORMAT = {\n        'image': tf.io.FixedLenFeature([], tf.string),\n        'id': tf.io.FixedLenFeature([], tf.string),\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\n#\n\ndef load_dataset(filenames, labeled = True, ordered = False):\n    ignore_order = tf.data.Options()\n    if not ordered:\n        ignore_order.experimental_deterministic = False\n    dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads = AUTO)\n    dataset = dataset.with_options(ignore_order)\n    dataset = dataset.map(read_labeled_tfrecord if labeled else read_unlabeled_tfrecord, num_parallel_calls = AUTO)\n    return dataset\n#\n\ndef data_augment(image, label):\n    image = tf.image.random_flip_left_right(image)\n    image = tf.image.random_saturation(image, 0, 2)\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()\n    dataset = dataset.shuffle(2048)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO)\n    return dataset\n#\n\ndef get_validation_dataset(ordered = False):\n    dataset = load_dataset(VALIDATION_FILENAMES, labeled = True, ordered=ordered)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.cache()\n    dataset = dataset.prefetch(AUTO)\n    return dataset\n#\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)\n    return dataset\n#\n\ndef count_data_items(filenames):\n    n = [int(re.compile(r\"-([0-9]*)\\.\").search(filename).group(1)) for filename in filenames]\n    return np.sum(n)\n#\n\nNUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)\nNUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)\nNUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)\nSTEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE\nprint('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Training data shapes')\nfor image, label in get_training_dataset().take(3):\n    print(image.numpy().shape, label.numpy().shape)\nprint('Training data label examples:', label.numpy())\n#\n\nprint('Validation data shapes')\nfor image, label in get_validation_dataset().take(3):\n    print(image.numpy().shape, label.numpy().shape)\nprint('Validation data label examples:', label.numpy())\n#\n\nprint('Test data shapes')\nfor image, idnum in get_test_dataset().take(3):\n    print(image.numpy().shape, idnum.numpy().shape)\nprint('Test data IDs:', idnum.numpy().astype('U'))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"training_dataset = get_training_dataset()\ntraining_dataset = training_dataset.unbatch().batch(20)\ntrain_batch = iter(training_dataset)\n#\n#display_batch_of_images(next(train_batch))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_dataset = get_test_dataset()\ntest_dataset = test_dataset.unbatch().batch(20)\ntest_batch = iter(test_dataset)\n#\n#display_batch_of_images(next(test_batch))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# peer at test data\ntest_dataset = get_test_dataset()\ntest_dataset = test_dataset.unbatch().batch(20)\ntest_batch = iter(test_dataset)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=20, restore_best_weights = True)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Datasets","metadata":{}},{"cell_type":"markdown","source":"# Dataset visualizations","metadata":{}},{"cell_type":"code","source":"def create_VGG16_model():\n    pretrained_model = tf.keras.applications.VGG16(weights = 'imagenet', include_top = False, input_shape = [*IMAGE_SIZE, 3])\n    pretrained_model.trainable = True # False\n\n    model = tf.keras.Sequential([\n        pretrained_model,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation = 'softmax')\n    ])\n\n    model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['sparse_categorical_accuracy'])\n    return model","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def create_Xception_model():\n    pretrained_model = tf.keras.applications.Xception(include_top = False, input_shape = [*IMAGE_SIZE, 3])\n    pretrained_model.trainable = True\n\n    model = tf.keras.Sequential([\n        pretrained_model,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation = 'softmax')\n    ])\n\n    model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['sparse_categorical_accuracy'])\n    return model","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def create_DenseNet_model():\n    pretrained_model = tf.keras.applications.DenseNet201(weights = 'imagenet', include_top = False, input_shape = [*IMAGE_SIZE, 3])\n    pretrained_model.trainable = True\n\n    model = tf.keras.Sequential([\n        pretrained_model,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation = 'softmax')\n    ])\n\n    model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['sparse_categorical_accuracy'])\n    return model","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def create_EfficientNet_model():\n    pretrained_model = efficientnet.EfficientNetB7(weights = 'noisy-student', include_top = False, input_shape = [*IMAGE_SIZE, 3])\n    pretrained_model.trainable = True\n\n    model = tf.keras.Sequential([\n        pretrained_model,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation = 'softmax')\n    ])\n\n    model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['sparse_categorical_accuracy'])\n    return model","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def create_InceptionV3_model():\n    pretrained_model = tf.keras.applications.InceptionV3(weights = 'imagenet', include_top = False, input_shape = [*IMAGE_SIZE, 3])\n    pretrained_model.trainable = True\n\n    model = tf.keras.Sequential([\n        pretrained_model,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation = 'softmax')\n    ])\n\n    model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['sparse_categorical_accuracy'])\n    return model","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def create_ResNet152_model():\n    pretrained_model = tf.keras.applications.ResNet152V2(weights = 'imagenet', include_top = False, input_shape = [*IMAGE_SIZE, 3])\n    pretrained_model.trainable = True\n\n    model = tf.keras.Sequential([\n        pretrained_model,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation = 'softmax')\n    ])\n\n    model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['sparse_categorical_accuracy'])\n    return model","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def create_MobileNetV2_model():\n    pretrained_model = tf.keras.applications.MobileNetV2(weights = 'imagenet', include_top = False, input_shape = [*IMAGE_SIZE, 3])\n    pretrained_model.trainable = True\n\n    model = tf.keras.Sequential([\n        pretrained_model,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation = 'softmax')\n    ])\n\n    model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['sparse_categorical_accuracy'])\n    return model","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def create_InceptionResNetV2_model():\n    pretrained_model = tf.keras.applications.InceptionResNetV2(weights = 'imagenet', include_top = False, input_shape = [*IMAGE_SIZE, 3])\n    pretrained_model.trainable = True\n\n    model = tf.keras.Sequential([\n        pretrained_model,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation = 'softmax')\n    ])\n\n    model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['sparse_categorical_accuracy'])\n    return model","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"no_of_models = 2   \nmodels = [0] * no_of_models\nstart_model = 0\nend_model = 2     ####1 2\nmodel_indx_0 = start_model\nmodel_indx_1 = start_model + 1\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"val_probabilities = [0] * no_of_models\ntest_probabilities = [0] * no_of_models\nall_probabilities = [0] * no_of_models","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with strategy.scope():\n    for j in range(no_of_models):\n#        models[j] = create_VGG16_model()\n#        models[j] = create_Xception_model()\n#        models[j] = create_DenseNet_model()\n        models[j] = create_EfficientNet_model()\n#        models[j] = create_InceptionV3_model()\n#        models[j] = create_ResNet152_model()\n#        models[j] = create_MobileNetV2_model()\n#        models[j] = create_InceptionResNetV2_model()\n#\nmodels[0].summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def write_history(j):\n    history_dict = [0] * no_of_models\n    for i in range(j + 1):\n        if (historys[i] != 0):\n            history_dict[i] = historys[i].history\n#\n    filename = './' + this_run_file_prefix + 'model_history_' + str(j) + '.pkl'\n    pklfile = open(filename, 'ab')\n    pickle.dump(history_dict, pklfile)\n    pklfile.close()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"EPOCHS = 20  # 50 # 35  # 20 \nhistorys = [0] * no_of_models\n#lr_exp_decay_values = [0.5,0.6,0.5,0.7] # [0.6,0.7,0.8,0.9,0.6,0.7,0.8,0.9,0.6,0.7,0.8,0.9,0.6,0.7,0.8,0.9,0.5,0.5,0.5,0.5]\n#lr_max_values = [0.00005,0.00003,0.00004,0.00003] # [0.00003,0.00003,0.00003,0.00003,0.00004,0.00004,0.00004,0.00004,0.00005,0.00005,0.00005,0.00005,0.00006,0.00006,0.00006,0.00006,0.00003,0.00004,0.00005,0.00006]\nfinished_models = 0\n\nfor j in range(start_model, end_model):\n    start_training = datetime.now()\n    print(start_training)\n    time_from_start_program_tdelta = start_training - start_time\n    if time_from_start_program_tdelta > end_training_by_tdelta:\n        print(j, 'time limit for doing training over, get out')\n        break\n#    with strategy.scope():\n#        models[j] = create_DenseNet_model()\n#    if j == 0:\n#        models[0].summary()\n#        print('----------------------------------------------------')\n#    LR_EXP_DECAY = lr_exp_decay_values[j]\n#    LR_MAX = lr_max_values[j] * strategy.num_replicas_in_sync\n    print('LR_EXP_DECAY:', LR_EXP_DECAY, '. LR_MAX:', LR_MAX)\n    historys[j] = models[j].fit(get_training_dataset(), steps_per_epoch = STEPS_PER_EPOCH, epochs = EPOCHS, validation_data = get_validation_dataset(), callbacks = [lr_callback, early_stop])\n    write_history(j)\n    filename = this_run_file_prefix + 'models_' + str(j) + '.h5'\n    models[j].save(filename)\n#    model_to_delete = models[j]\n#    models[j] = 0\n#    del model_to_delete\n    gc.collect()\n    finished_models = j + 1   ###1,2\n\nprint(datetime.now())\n#","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cmdataset = get_validation_dataset(ordered = True)\nimages_ds = cmdataset.map(lambda image, label: image)\nlabels_ds = cmdataset.map(lambda image, label: label).unbatch()\ncm_correct_labels = next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES))).numpy()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_ds = get_test_dataset(ordered = True)\n\n#print('Computing predictions...')\ntest_images_ds = test_ds.map(lambda image, idnum: image)\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset = get_validation_dataset()\ndataset = dataset.unbatch().batch(20)\nbatch = iter(dataset)\n\nimages, labels = next(batch)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(datetime.now())\n#\nfor j in range(start_model, end_model):\n    val_probabilities[j] = models[j].predict(images_ds)\n    test_probabilities[j] = models[j].predict(test_images_ds)\n    all_probabilities[j] = models[j].predict(images)\n\nprint(datetime.now())\n#","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for j in range(start_model, finished_models):   \n    display_training_curves(historys[j].history['loss'], historys[j].history['val_loss'], 'loss', 211)\n    display_training_curves(historys[j].history['sparse_categorical_accuracy'], historys[j].history['val_sparse_categorical_accuracy'], 'accuracy', 212)\n#\nfor j in range(start_model, finished_models):  \n    print('model number:', j, ', Train Accuracy:', max(historys[j].history['sparse_categorical_accuracy']), ', Validation Accuracy:', max(historys[j].history['val_sparse_categorical_accuracy']))\nfor j in range(start_model, finished_models):   \n    print('model number:', j, ', Train Loss:', min(historys[j].history['loss']), ', Validation Loss:', min(historys[j].history['val_loss']))","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cm_probabilities = np.zeros((val_probabilities[0].shape)) \nfor j in range(no_of_models):  ##j=0,1\n    cm_probabilities = cm_probabilities + val_probabilities[j] \n\ncm_predictions = np.argmax(cm_probabilities, axis = -1)\nprint('Correct labels: ', cm_correct_labels.shape, cm_correct_labels)\nprint('Predicted labels: ', cm_predictions.shape, cm_predictions)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def getFitPrecisionRecall(correct_labels, predictions):  \n    score = f1_score(correct_labels, predictions, labels = range(len(CLASSES)), average = 'macro')\n    precision = precision_score(correct_labels, predictions, labels = range(len(CLASSES)), average = 'macro')\n    recall = recall_score(correct_labels, predictions, labels = range(len(CLASSES)), average = 'macro')\n    return score, precision, recall","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cmat = confusion_matrix(cm_correct_labels, cm_predictions, labels = range(len(CLASSES)))\nscore, precision, recall = getFitPrecisionRecall(cm_correct_labels, cm_predictions)\ncmat = (cmat.T / cmat.sum(axis = -1)).T\ndisplay_confusion_matrix(cmat, score, precision, recall)\nprint('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))  ","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def create_submission_file(filename, probabilities):\n    predictions = np.argmax(probabilities, axis = -1)\n    print('Generating submission file...', filename)\n    test_ids_ds = test_ds.map(lambda image, idnum: idnum).unbatch()\n    test_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES))).numpy().astype('U')\n\n    np.savetxt(filename, np.rec.fromarrays([test_ids, predictions]), fmt = ['%s', '%d'], delimiter = ',', header = 'id,label', comments = '')\n#","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"probabilities = np.zeros((test_probabilities[0].shape)) # = test_probabilities[0] + test_probabilities[1] \nfor j in range(no_of_models):\n    probabilities = probabilities + test_probabilities[j]\n\nfilename = this_run_file_prefix + 'submission.csv'   \ncreate_submission_file(filename, probabilities)\ncreate_submission_file('submission.csv', probabilities)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def combine_two(correct_labels, probability_0, probability_1):\n    print('Start. ', datetime.now())\n    alphas0_to_try = np.linspace(0, 1, 101)\n    best_score = -1\n    best_alpha0 = -1\n    best_alpha1 = -1\n    best_precision = -1\n    best_recall = -1\n    best_val_predictions = None\n\n    for alpha0 in alphas0_to_try:\n        alpha1 = 1.0 - alpha0\n        probabilities = alpha0 * probability_0 + alpha1 * probability_1 #\n        predictions = np.argmax(probabilities, axis = -1)\n\n        score, precision, recall = getFitPrecisionRecall(correct_labels, predictions)\n        if score > best_score:\n            best_alpha0 = alpha0\n            best_alpha1 = alpha1\n            best_score = score\n            best_precision = precision\n            best_recall = recall\n            best_val_predictions = predictions\n    #\n    return best_alpha0, best_alpha1, best_val_predictions, best_score, best_precision, best_recall","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def combine_three(correct_labels, probability_0, probability_1, probability_2):\n    print('Start. ', datetime.now())\n    alphas0_to_try = np.linspace(0, 1, 101)\n    alphas1_to_try = np.linspace(0, 1, 101)\n    best_score = -1\n    best_alpha0 = -1\n    best_alpha1 = -1\n    best_alpha2 = -1\n    best_precision = -1\n    best_recall = -1\n    best_val_predictions = None\n\n    for alpha0 in alphas0_to_try:\n        for alpha1 in alphas1_to_try:\n            if (alpha0 + alpha1) > 1.0:\n                break\n\n            alpha2 = 1.0 - alpha0 - alpha1\n            probabilities = alpha0 * probability_0 + alpha1 * probability_1 + alpha2 * probability_2\n            predictions = np.argmax(probabilities, axis = -1)\n\n            score, precision, recall = getFitPrecisionRecall(correct_labels, predictions)\n            if score > best_score:\n                best_alpha0 = alpha0\n                best_alpha1 = alpha1\n                best_alpha2 = alpha2\n                best_score = score\n                best_precision = precision\n                best_recall = recall\n                best_val_predictions = predictions\n    #\n    return best_alpha0, best_alpha1, best_alpha2, best_val_predictions, best_score, best_precision, best_recall","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_best_combination(no_models, cm_correct_labels, val_probabilities, test_probabilities):\n    best_fit_score = -10000.0\n    best_predictions = 0\n    choose_filename = ''\n\n    curr_predictions = np.argmax(val_probabilities[0], axis = -1)\n    score, precision, recall = getFitPrecisionRecall(cm_correct_labels, curr_predictions)\n    print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))\n    filename = this_run_file_prefix + 'submission_0.csv'\n    if best_fit_score < score:\n        best_fit_score = score\n        best_predictions = curr_predictions\n        choose_filename = filename\n        create_submission_file('./submission.csv', test_probabilities[0])\n    create_submission_file(filename, test_probabilities[0])\n\n    if no_models > 1:\n        curr_predictions = np.argmax(val_probabilities[1], axis = -1)\n        score, precision, recall = getFitPrecisionRecall(cm_correct_labels, curr_predictions)\n        print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))\n        filename = this_run_file_prefix + 'submission_1.csv'\n        if best_fit_score < score:\n            best_fit_score = score\n            best_predictions = curr_predictions\n            choose_filename = filename\n            create_submission_file('./submission.csv', test_probabilities[1])\n        create_submission_file(filename, test_probabilities[1])\n\n    if no_models > 2:\n        curr_predictions = np.argmax(val_probabilities[2], axis = -1)\n        score, precision, recall = getFitPrecisionRecall(cm_correct_labels, curr_predictions)\n        print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))\n        filename = this_run_file_prefix + 'submission_2.csv'\n        if best_fit_score < score:\n            best_fit_score = score\n            best_predictions = curr_predictions\n            choose_filename = filename\n            create_submission_file('./submission.csv', test_probabilities[2])\n        create_submission_file(filename, test_probabilities[2])\n\n    if no_models > 1:\n        best_alpha0, best_alpha1, best_val_predictions, best_score, best_precision, best_recall = combine_two(cm_correct_labels, val_probabilities[0], val_probabilities[1])\n        print('For indx', [0, 1], 'best_alpha0:', best_alpha0, 'best_alpha1:', best_alpha1, '. ', datetime.now())\n        print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(best_score, best_precision, best_recall))\n        combined_probabilities = best_alpha0 * test_probabilities[0] + best_alpha1 * test_probabilities[1]\n        filename = this_run_file_prefix + 'submission_01.csv'\n        if best_fit_score < best_score:\n            best_fit_score = best_score\n            best_predictions = best_val_predictions\n            choose_filename = filename\n            create_submission_file('./submission.csv', combined_probabilities)\n        create_submission_file(filename, combined_probabilities)\n\n    if no_models > 2:\n        best_alpha0, best_alpha1, best_val_predictions, best_score, best_precision, best_recall = combine_two(cm_correct_labels, val_probabilities[0], val_probabilities[2])\n        print('For indx', [0, 2], 'best_alpha0:', best_alpha0, 'best_alpha1:', best_alpha1, '. ', datetime.now())\n        print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(best_score, best_precision, best_recall))\n        combined_probabilities = best_alpha0 * test_probabilities[0] + best_alpha1 * test_probabilities[2]\n        filename = this_run_file_prefix + 'submission_02.csv'\n        if best_fit_score < best_score:\n            best_fit_score = best_score\n            best_predictions = best_val_predictions\n            choose_filename = filename\n            create_submission_file('./submission.csv', combined_probabilities)\n        create_submission_file(filename, combined_probabilities)\n\n        best_alpha0, best_alpha1, best_val_predictions, best_score, best_precision, best_recall = combine_two(cm_correct_labels, val_probabilities[1], val_probabilities[2])\n        print('For indx', [1, 2], 'best_alpha0:', best_alpha0, 'best_alpha1:', best_alpha1, '. ', datetime.now())\n        print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(best_score, best_precision, best_recall))\n        combined_probabilities = best_alpha0 * test_probabilities[1] + best_alpha1 * test_probabilities[2]\n        filename = this_run_file_prefix + 'submission_12.csv'\n        if best_fit_score < best_score:\n            best_fit_score = best_score\n            best_predictions = best_val_predictions\n            choose_filename = filename\n            create_submission_file('./submission.csv', combined_probabilities)\n        create_submission_file(filename, combined_probabilities)\n\n        best_alpha0, best_alpha1, best_alpha2, best_val_predictions, best_score, best_precision, best_recall = combine_three(cm_correct_labels, val_probabilities[0], val_probabilities[1], val_probabilities[2])\n        print('For indx', [0, 1, 2], 'best_alpha0:', best_alpha0, 'best_alpha1:', best_alpha1, 'best_alpha2:', best_alpha2, '. ', datetime.now())\n        print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(best_score, best_precision, best_recall))\n        combined_probabilities = best_alpha0 * test_probabilities[0] + best_alpha1 * test_probabilities[1] + best_alpha2 * test_probabilities[2]\n        filename = this_run_file_prefix + 'submission_012.csv'\n        if best_fit_score < best_score:\n            best_fit_score = best_score\n            best_predictions = best_val_predictions\n            choose_filename = filename\n            create_submission_file('./submission.csv', combined_probabilities)\n        create_submission_file(filename, combined_probabilities)\n#\n    cmat = confusion_matrix(cm_correct_labels, best_predictions, labels = range(len(CLASSES)))\n    cmat = (cmat.T / cmat.sum(axis = -1)).T\n    display_confusion_matrix(cmat, best_fit_score, precision, recall)\n#\n    print('Best score from all combination was', best_fit_score, '. For submission file used is', choose_filename)\n    return best_predictions\n#","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"best_predictions = cm_predictions\nif no_of_models > 1:\n#    bp = get_best_combination(no_of_models, cm_correct_labels_results[0], val_probabilities, test_probabilities)\n    bp = get_best_combination(no_of_models, cm_correct_labels, val_probabilities, test_probabilities)\n    best_predictions = bp\n#","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"probabilities = np.zeros((all_probabilities[0].shape)) # = all_probabilities[0] + all_probabilities[1] + all_probabilities[2]\nfor j in range(no_of_models):\n    probabilities = probabilities + all_probabilities[j]\n\npredictions = np.argmax(probabilities, axis =-1)\ndisplay_batch_of_images((images, labels), predictions)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#val_probs = [cm_correct_labels, val_probabilities[0], val_probabilities[1], val_probabilities[2], test_probabilities[0], test_probabilities[1], test_probabilities[2]]\nval_probs = [cm_correct_labels, cm_predictions, val_probabilities[0], val_probabilities[1], test_probabilities[0], test_probabilities[1]]\n#val_probs = [cm_correct_labels, cm_predictions, val_probabilities[0], test_probabilities[model_indx_0]]\nfilename = this_run_file_prefix + 'tests_vals_01.pkl'\npklfile = open(filename, 'ab')\npickle.dump(val_probs, pklfile)\npklfile.close()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#images_ds_unbatched = images_ds.unbatch()\n#cm_images_ds_numpy = next(iter(images_ds_unbatched.batch(NUM_VALIDATION_IMAGES))).numpy()\nuse_correct_labels = cm_correct_labels\nuse_val_predictions = best_predictions","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('type of labels_ds is {}'.format(type(labels_ds)))\nprint('type of use_val_predictions is {}. shape of use_val_predictions is {}'.format(type(use_val_predictions), use_val_predictions.shape))\n#print('type of use_correct_labels is {}, cm_images_ds_numpy is {}'.format(type(use_correct_labels), type(cm_images_ds_numpy)))\n#print('shape of use_correct_labels is {}, cm_images_ds_numpy is {}'.format(use_correct_labels.shape, cm_images_ds_numpy.shape))","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"correct_labels_cnt = 0\nincorrect_labels_cnt = 0\ncorrect_labels = []\nincorrect_labels = []\nvals_actual_true = {}\nvals_tp = {}\nvals_fn = {}\nvals_fp = {}\nfor i in range(len(CLASSES)):\n    vals_actual_true[i] = 0\n    vals_tp[i] = 0\n    vals_fn[i] = 0\n    vals_fp[i] = 0\n\nfor i in range(len(use_correct_labels)):\n    correct_label = use_correct_labels[i]\n    predict_label = use_val_predictions[i]\n    vals_actual_true[correct_label] = vals_actual_true[correct_label] + 1\n    if use_val_predictions[i] != use_correct_labels[i]:\n        incorrect_labels_cnt = incorrect_labels_cnt + 1  \n        incorrect_labels.append(i)\n        vals_fn[correct_label] = vals_fn[correct_label] + 1\n        vals_fp[predict_label] = vals_fp[predict_label] + 1\n    else:\n        correct_labels_cnt = correct_labels_cnt + 1        \n        correct_labels.append(i)\n        vals_tp[correct_label] = vals_tp[correct_label] + 1\n#        print(i)\nprint('Number of correct_labels is {}, incorrect_labels is {}'.format(correct_labels_cnt, incorrect_labels_cnt))\n#print('Correct labels', correct_labels)\nprint('Incorrect labels', incorrect_labels)   \n#","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def display_my_batch_of_images(databatch, rows = 0, cols = 0, predictions=None):\n    \"\"\"This will work with:\n    display_batch_of_images(images)\n    display_batch_of_images(images, predictions)\n    display_batch_of_images((images, labels))\n    display_batch_of_images((images, labels), predictions)\n    \"\"\"\n    # data\n    images, labels = databatch\n    if labels is None:\n        labels = [None for _ in enumerate(images)]\n\n    # auto-squaring: this will drop data that does not fit into square or square-ish rectangle\n    if rows == 0 or cols == 0:\n        rows = int(math.sqrt(len(images)))\n        cols = (len(images) + rows - 1)//rows\n    print('Total number of images is {}, rows is {}, cols is {}'.format(len(images), rows, cols))\n\n    # size and spacing\n    FIGSIZE = 20.0\n    SPACING = 0.1\n    subplot=(rows,cols,1)\n    if rows < cols:\n        plt.figure(figsize=(FIGSIZE,FIGSIZE/cols*rows))\n    else:\n        plt.figure(figsize=(FIGSIZE/rows*cols,FIGSIZE))\n\n    # display\n    for i, (image, label) in enumerate(zip(images[:rows*cols], labels[:rows*cols])):\n        title = '' if label is None else CLASSES[label]\n        correct = True\n        if predictions is not None:\n            title, correct = title_from_label_and_target(predictions[i], label)\n        dynamic_titlesize = FIGSIZE*SPACING/max(rows,cols)*40+3 # magic formula tested to work from 1x1 to 10x10 images\n        subplot = display_one_flower(image, title, subplot, not correct, titlesize=dynamic_titlesize)\n\n    #layout\n    plt.tight_layout()\n    if label is None and predictions is None:\n        plt.subplots_adjust(wspace=0, hspace=0)\n    else:\n        plt.subplots_adjust(wspace=SPACING, hspace=SPACING)\n    plt.show()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"disp_labels = []\ndisp_predictions = []\nfor i in range(54):\n    if i >= incorrect_labels_cnt:\n        break\n    id = incorrect_labels[i]\n    disp_labels.append(use_correct_labels[id])\n    disp_predictions.append(use_val_predictions[id])\n#    disp_images.append(cm_images_ds_numpy[id])\n#\nprint(disp_labels)\nprint(disp_predictions)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#print('type of disp_images is {}, disp_labels is {}'.format(type(disp_images), type(disp_labels)))\n#print('length of disp_images is {}, disp_labels is {}'.format(len(disp_images), len(disp_labels)))\n#print('type of disp_images[0] is {}, disp_labels[0] is {}'.format(type(disp_images[0]), type(disp_labels[0])))","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#display_my_batch_of_images((disp_images, disp_labels), rows = 9, cols = 6, predictions = disp_predictions)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"val_ids = list(range(len(use_correct_labels)))\nfilename = this_run_file_prefix + 'validation_results.csv'\nnp.savetxt(filename, np.rec.fromarrays([val_ids, use_correct_labels, use_val_predictions]), fmt = ['%d', '%d', '%d'], delimiter = ',', header = 'id,correct_label,predicted_label', comments = '')\n#\ncls_ids = list(range(len(CLASSES)))\n#print(len(cls_ids), len(vals_actual_true), len(vals_tp), len(vals_fn), len(vals_fp))\nfilename = this_run_file_prefix + 'validation_statistics.csv'\nnp.savetxt(filename, np.rec.fromarrays([cls_ids, list(vals_actual_true.values()), list(vals_tp.values()), list(vals_fn.values()), list(vals_fp.values())]), fmt = ['%d', '%d', '%d', '%d', '%d'], delimiter = ',', header = 'cls_id,actual_true,true_positive,false_negative,false_positive', comments = '')","metadata":{},"execution_count":null,"outputs":[]}]}