{"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":"# 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\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 read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-05-23T17:15:18.105706Z","iopub.execute_input":"2021-05-23T17:15:18.106095Z","iopub.status.idle":"2021-05-23T17:15:18.154970Z","shell.execute_reply.started":"2021-05-23T17:15:18.106062Z","shell.execute_reply":"2021-05-23T17:15:18.154022Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import math, re, os\nimport tensorflow as tf\nfrom matplotlib import pyplot as plt\n%matplotlib inline\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":{"execution":{"iopub.status.busy":"2021-05-23T17:15:18.242414Z","iopub.execute_input":"2021-05-23T17:15:18.242777Z","iopub.status.idle":"2021-05-23T17:15:18.254064Z","shell.execute_reply.started":"2021-05-23T17:15:18.242746Z","shell.execute_reply":"2021-05-23T17:15:18.253312Z"},"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() \nprint(\"REPLICAS: \", strategy.num_replicas_in_sync)","metadata":{"execution":{"iopub.status.busy":"2021-05-23T17:15:18.427211Z","iopub.execute_input":"2021-05-23T17:15:18.427572Z","iopub.status.idle":"2021-05-23T17:15:18.435167Z","shell.execute_reply.started":"2021-05-23T17:15:18.427543Z","shell.execute_reply":"2021-05-23T17:15:18.434406Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"GCS_DS_PATH = KaggleDatasets().get_gcs_path() ","metadata":{"execution":{"iopub.status.busy":"2021-05-23T17:15:18.588408Z","iopub.execute_input":"2021-05-23T17:15:18.588737Z","iopub.status.idle":"2021-05-23T17:15:18.970072Z","shell.execute_reply.started":"2021-05-23T17:15:18.588710Z","shell.execute_reply":"2021-05-23T17:15:18.969217Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"IMAGE_SIZE = [512, 512]\nEPOCHS = 12\nBATCH_SIZE = 16 * strategy.num_replicas_in_sync\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') \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":{"execution":{"iopub.status.busy":"2021-05-23T17:15:18.971430Z","iopub.execute_input":"2021-05-23T17:15:18.971890Z","iopub.status.idle":"2021-05-23T17:15:19.381536Z","shell.execute_reply.started":"2021-05-23T17:15:18.971859Z","shell.execute_reply":"2021-05-23T17:15:19.380453Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.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: \n        numpy_labels = [None for _ in enumerate(numpy_images)]\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   \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\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: \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_xlabel('epoch')\n    ax.legend(['train', 'valid.'])","metadata":{"execution":{"iopub.status.busy":"2021-05-23T17:15:19.383163Z","iopub.execute_input":"2021-05-23T17:15:19.383508Z","iopub.status.idle":"2021-05-23T17:15:19.407951Z","shell.execute_reply.started":"2021-05-23T17:15:19.383467Z","shell.execute_reply":"2021-05-23T17:15:19.406869Z"},"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\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\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    }\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\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\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\ndef data_augment(image, label):    \n    image = tf.image.random_flip_left_right(image)   \n    return image, label   \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\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\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\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\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":{"execution":{"iopub.status.busy":"2021-05-23T17:15:19.409524Z","iopub.execute_input":"2021-05-23T17:15:19.409800Z","iopub.status.idle":"2021-05-23T17:15:19.430706Z","shell.execute_reply.started":"2021-05-23T17:15:19.409774Z","shell.execute_reply":"2021-05-23T17:15:19.429601Z"},"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())\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())\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":{"execution":{"iopub.status.busy":"2021-05-23T17:15:19.432023Z","iopub.execute_input":"2021-05-23T17:15:19.432439Z","iopub.status.idle":"2021-05-23T17:15:29.854094Z","shell.execute_reply.started":"2021-05-23T17:15:19.432397Z","shell.execute_reply":"2021-05-23T17:15:29.853071Z"},"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)","metadata":{"execution":{"iopub.status.busy":"2021-05-23T17:15:29.858984Z","iopub.execute_input":"2021-05-23T17:15:29.859353Z","iopub.status.idle":"2021-05-23T17:15:29.958853Z","shell.execute_reply.started":"2021-05-23T17:15:29.859314Z","shell.execute_reply":"2021-05-23T17:15:29.957997Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display_batch_of_images(next(train_batch))","metadata":{"execution":{"iopub.status.busy":"2021-05-23T17:15:29.960354Z","iopub.execute_input":"2021-05-23T17:15:29.960822Z","iopub.status.idle":"2021-05-23T17:15:37.581314Z","shell.execute_reply.started":"2021-05-23T17:15:29.960779Z","shell.execute_reply":"2021-05-23T17:15:37.580530Z"},"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)","metadata":{"execution":{"iopub.status.busy":"2021-05-23T17:15:37.583233Z","iopub.execute_input":"2021-05-23T17:15:37.583686Z","iopub.status.idle":"2021-05-23T17:15:37.650715Z","shell.execute_reply.started":"2021-05-23T17:15:37.583642Z","shell.execute_reply":"2021-05-23T17:15:37.649848Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display_batch_of_images(next(test_batch))","metadata":{"execution":{"iopub.status.busy":"2021-05-23T17:15:37.652390Z","iopub.execute_input":"2021-05-23T17:15:37.652839Z","iopub.status.idle":"2021-05-23T17:15:41.349816Z","shell.execute_reply.started":"2021-05-23T17:15:37.652797Z","shell.execute_reply":"2021-05-23T17:15:41.348660Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"label","metadata":{"execution":{"iopub.status.busy":"2021-05-23T17:15:41.351138Z","iopub.execute_input":"2021-05-23T17:15:41.351475Z","iopub.status.idle":"2021-05-23T17:15:41.357996Z","shell.execute_reply.started":"2021-05-23T17:15:41.351443Z","shell.execute_reply":"2021-05-23T17:15:41.356836Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from keras.models import Sequential\nfrom keras.layers import Dense,Activation,Flatten,Conv2D,MaxPool2D,Dropout","metadata":{"execution":{"iopub.status.busy":"2021-05-23T17:15:41.359407Z","iopub.execute_input":"2021-05-23T17:15:41.359753Z","iopub.status.idle":"2021-05-23T17:15:41.370646Z","shell.execute_reply.started":"2021-05-23T17:15:41.359724Z","shell.execute_reply":"2021-05-23T17:15:41.369595Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with strategy.scope():\n    model = Sequential()\n\n    model.add(Conv2D(32,(3,3),input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3)))\n    model.add(Activation('relu'))\n\n    model.add(Conv2D(64,(3,3)))\n    model.add(Activation('relu'))\n\n    model.add(MaxPool2D(pool_size=(2,2)))                 \n\n    model.add(Conv2D(16,(3,3)))\n    model.add(Activation('relu'))\n    \n    model.add(MaxPool2D(pool_size=(2,2))) \n    \n    model.add(Conv2D(64,(3,3)))\n    model.add(Activation('relu'))\n    \n    model.add(Conv2D(32,(3,3)))\n    model.add(Activation('relu'))\n    \n    model.add(MaxPool2D(pool_size=(2,2))) \n\n    model.add(Flatten())\n\n    model.add(Dropout(0.25))                               \n    \n    model.add(Dense(10))\n    model.add(Activation('relu'))\n\n    model.add(Dense(100))\n    model.add(Activation('relu'))\n    \n    model.add(Dense(100))\n    model.add(Activation('relu'))\n    \n    model.add(Dense(100))\n    model.add(Activation('relu'))\n    \n    model.add(Dense(100))\n    model.add(Activation('relu'))\n    \n    model.add(Dropout(0.25)) \n    \n    model.add(Dense(100))\n    model.add(Activation('relu'))\n    \n    model.add(Dense(100))\n    model.add(Activation('relu'))\n    \n    model.add(Dense(100))\n    model.add(Activation('relu'))\n    \n    model.add(Dropout(0.25)) \n    \n    model.add(Dense(100))\n    model.add(Activation('relu'))\n    \n    model.add(Dense(100))\n    model.add(Activation('relu'))\n    \n    model.add(Dense(100))\n    model.add(Activation('relu'))\n    \n    model.add(Dropout(0.25)) \n    \n    model.add(Dense(100))\n    model.add(Activation('relu'))\n    \n    model.add(Dense(100))\n    model.add(Activation('relu'))\n    \n    model.add(Dense(100))\n    model.add(Activation('relu'))\n    \n    model.add(Dropout(0.25)) \n    \n    model.add(Dense(256))\n    model.add(Activation('softmax'))\n    \n    model.summary()","metadata":{"execution":{"iopub.status.busy":"2021-05-23T17:15:41.372055Z","iopub.execute_input":"2021-05-23T17:15:41.372414Z","iopub.status.idle":"2021-05-23T17:15:41.679441Z","shell.execute_reply.started":"2021-05-23T17:15:41.372371Z","shell.execute_reply":"2021-05-23T17:15:41.678465Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.compile(optimizer='adam',\n             loss = 'sparse_categorical_crossentropy',\n                          metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2021-05-23T17:15:41.680922Z","iopub.execute_input":"2021-05-23T17:15:41.681351Z","iopub.status.idle":"2021-05-23T17:15:41.696472Z","shell.execute_reply.started":"2021-05-23T17:15:41.681306Z","shell.execute_reply":"2021-05-23T17:15:41.695561Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"execution":{"iopub.status.busy":"2021-05-23T17:15:41.697902Z","iopub.execute_input":"2021-05-23T17:15:41.698243Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_ds = get_test_dataset(ordered=True) \n\nprint('Computing predictions...')\ntest_images_ds = test_ds.map(lambda image, idnum: image)\nprobabilities = model.predict(test_images_ds)\npredictions = np.argmax(probabilities, axis=-1)\nprint(predictions)\n\nprint('Generating submission.csv file...')\ntest_ids_ds = test_ds.map(lambda image, idnum: idnum).unbatch()\ntest_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES))).numpy().astype('U') \nnp.savetxt('submission.csv', np.rec.fromarrays([test_ids, predictions]), fmt=['%s', '%d'], delimiter=',', header='id,label', comments='')","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}