{"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":"markdown","source":"# Flower Classification with TPU","metadata":{}},{"cell_type":"code","source":"import math\nimport re \nimport os\nimport tensorflow as tf\nimport numpy as np","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:45:27.257029Z","iopub.execute_input":"2023-07-06T03:45:27.257327Z","iopub.status.idle":"2023-07-06T03:45:56.973664Z","shell.execute_reply.started":"2023-07-06T03:45:27.257300Z","shell.execute_reply":"2023-07-06T03:45:56.972634Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('tf ver:',tf.__version__)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:45:56.975329Z","iopub.execute_input":"2023-07-06T03:45:56.975820Z","iopub.status.idle":"2023-07-06T03:45:56.980496Z","shell.execute_reply.started":"2023-07-06T03:45:56.975792Z","shell.execute_reply":"2023-07-06T03:45:56.979583Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Detect TPU","metadata":{}},{"cell_type":"code","source":"# Detect TPU, return appropriate distribution strategy\ntry:\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)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:45:56.981534Z","iopub.execute_input":"2023-07-06T03:45:56.981808Z","iopub.status.idle":"2023-07-06T03:46:05.663385Z","shell.execute_reply.started":"2023-07-06T03:45:56.981783Z","shell.execute_reply":"2023-07-06T03:46:05.662428Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Load Dataset","metadata":{}},{"cell_type":"code","source":"from kaggle_datasets import KaggleDatasets","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:46:05.664543Z","iopub.execute_input":"2023-07-06T03:46:05.664842Z","iopub.status.idle":"2023-07-06T03:46:05.669933Z","shell.execute_reply.started":"2023-07-06T03:46:05.664813Z","shell.execute_reply":"2023-07-06T03:46:05.668953Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"GCS_DS_PATH = KaggleDatasets().get_gcs_path('tpu-getting-started')\nprint(GCS_DS_PATH) # what do gcs paths look like?","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:46:05.672304Z","iopub.execute_input":"2023-07-06T03:46:05.672569Z","iopub.status.idle":"2023-07-06T03:46:05.682578Z","shell.execute_reply.started":"2023-07-06T03:46:05.672546Z","shell.execute_reply":"2023-07-06T03:46:05.681680Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"IMAGE_SIZE = [512, 512]\nGCS_PATH = GCS_DS_PATH + '/tfrecords-jpeg-512x512'\nBATCH_SIZE = 16 * strategy.num_replicas_in_sync\nAUTO = tf.data.experimental.AUTOTUNE\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\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\n\n\ndef decode_image(image_data):\n    image = tf.image.decode_jpeg(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    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","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:46:05.683653Z","iopub.execute_input":"2023-07-06T03:46:05.683973Z","iopub.status.idle":"2023-07-06T03:46:05.722797Z","shell.execute_reply.started":"2023-07-06T03:46:05.683948Z","shell.execute_reply":"2023-07-06T03:46:05.721851Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data Pipelines","metadata":{}},{"cell_type":"code","source":"def data_augment(image, label):\n    # Thanks to the dataset.prefetch(AUTO)\n    # statement in the next function (below), this happens essentially\n    # for free on TPU. Data pipeline code is executed on the \"CPU\"\n    # part of the TPU while the TPU itself is computing gradients.\n    image = tf.image.random_flip_left_right(image)\n    \n    # Random saturation\n    image = tf.image.random_saturation(image, lower=0.8, upper=2)\n    \n    \n    # Random brightness\n#     image = tf.image.random_brightness(image, 0.2)\n    #image = tf.image.random_saturation(image, 0, 2)\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() # the training dataset must repeat for several epochs\n    dataset = dataset.shuffle(2048)\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_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    # the number of data items is written in the name of the .tfrec\n    # 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)\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)\nprint('{} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:46:05.723948Z","iopub.execute_input":"2023-07-06T03:46:05.724333Z","iopub.status.idle":"2023-07-06T03:46:05.738875Z","shell.execute_reply.started":"2023-07-06T03:46:05.724299Z","shell.execute_reply":"2023-07-06T03:46:05.737884Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ds_train = get_training_dataset()\nds_valid = get_validation_dataset()\nds_test = get_test_dataset()\n\nprint(\"Training:\", ds_train)\nprint (\"Validation:\", ds_valid)\nprint(\"Test:\", ds_test)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:46:05.740163Z","iopub.execute_input":"2023-07-06T03:46:05.740468Z","iopub.status.idle":"2023-07-06T03:46:06.146107Z","shell.execute_reply.started":"2023-07-06T03:46:05.740441Z","shell.execute_reply":"2023-07-06T03:46:06.145058Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Training data shapes:\")\nfor image, label in ds_train.take(3):\n    print(image.numpy().shape, label.numpy().shape)\nprint(\"Training data label examples:\", label.numpy())","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:46:06.147331Z","iopub.execute_input":"2023-07-06T03:46:06.147650Z","iopub.status.idle":"2023-07-06T03:46:09.215340Z","shell.execute_reply.started":"2023-07-06T03:46:06.147615Z","shell.execute_reply":"2023-07-06T03:46:09.213725Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Test data shapes:\")\nfor image, idnum in ds_test.take(3):\n    print(image.numpy().shape, idnum.numpy().shape)\nprint(\"Test data IDs:\", idnum.numpy().astype('U')) # U=unicode string","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:46:09.216737Z","iopub.execute_input":"2023-07-06T03:46:09.217116Z","iopub.status.idle":"2023-07-06T03:46:10.245654Z","shell.execute_reply.started":"2023-07-06T03:46:09.217083Z","shell.execute_reply":"2023-07-06T03:46:10.244462Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Explore Data","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:46:10.246965Z","iopub.execute_input":"2023-07-06T03:46:10.247390Z","iopub.status.idle":"2023-07-06T03:46:10.676866Z","shell.execute_reply.started":"2023-07-06T03:46:10.247360Z","shell.execute_reply":"2023-07-06T03:46:10.675713Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def 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,\n                                     # 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\n    # 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\n    # 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\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":{"execution":{"iopub.status.busy":"2023-07-06T03:46:10.677991Z","iopub.execute_input":"2023-07-06T03:46:10.678353Z","iopub.status.idle":"2023-07-06T03:46:10.701171Z","shell.execute_reply.started":"2023-07-06T03:46:10.678329Z","shell.execute_reply":"2023-07-06T03:46:10.700156Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ds_iter = iter(ds_train.unbatch().batch(15))","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:46:10.702298Z","iopub.execute_input":"2023-07-06T03:46:10.702683Z","iopub.status.idle":"2023-07-06T03:46:10.741944Z","shell.execute_reply.started":"2023-07-06T03:46:10.702657Z","shell.execute_reply":"2023-07-06T03:46:10.741037Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"one_batch = next(ds_iter)\ndisplay_batch_of_images(one_batch)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:46:10.745280Z","iopub.execute_input":"2023-07-06T03:46:10.745538Z","iopub.status.idle":"2023-07-06T03:46:15.272798Z","shell.execute_reply.started":"2023-07-06T03:46:10.745515Z","shell.execute_reply":"2023-07-06T03:46:15.271695Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Define Model\nUse pre-trained InceptionV3 Model with weight from ImageNet","metadata":{}},{"cell_type":"code","source":"with strategy.scope():\n    pretrained_model = tf.keras.applications.InceptionV3(\n        weights='imagenet',\n        include_top=False ,\n        input_shape=[*IMAGE_SIZE, 3]\n    )\n#     pretrained_model = tf.keras.applications.InceptionResNetV2(\n#             weights='imagenet',\n#             include_top=False ,\n#             input_shape=[*IMAGE_SIZE, 3]\n#         )\n    pretrained_model.trainable = False\n    \n    model = tf.keras.Sequential([\n        # base\n        pretrained_model,\n        \n        # head\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dropout(0.1),\n        tf.keras.layers.Dense(len(CLASSES), activation='softmax')\n    ])\n\nmodel.compile(\n    optimizer='adam',\n    loss = 'sparse_categorical_crossentropy',\n    metrics=['sparse_categorical_accuracy'],\n)\n\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:46:15.273978Z","iopub.execute_input":"2023-07-06T03:46:15.274266Z","iopub.status.idle":"2023-07-06T03:46:49.239733Z","shell.execute_reply.started":"2023-07-06T03:46:15.274241Z","shell.execute_reply":"2023-07-06T03:46:49.238675Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Define early stopping condition\nfrom keras.callbacks import EarlyStopping\nes = EarlyStopping(monitor='val_loss', mode='min', verbose=1)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:46:49.240818Z","iopub.execute_input":"2023-07-06T03:46:49.241115Z","iopub.status.idle":"2023-07-06T03:46:49.245653Z","shell.execute_reply.started":"2023-07-06T03:46:49.241090Z","shell.execute_reply":"2023-07-06T03:46:49.244765Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Model Fit","metadata":{}},{"cell_type":"code","source":"# Define training epochs\nEPOCHS = 20\nSTEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE\n\nhistory = model.fit(\n    ds_train,\n    validation_data=ds_valid,\n    epochs=EPOCHS,\n    steps_per_epoch=STEPS_PER_EPOCH,\n    callbacks=[es],\n)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T03:46:49.246728Z","iopub.execute_input":"2023-07-06T03:46:49.247020Z","iopub.status.idle":"2023-07-06T04:02:31.759982Z","shell.execute_reply.started":"2023-07-06T03:46:49.246995Z","shell.execute_reply":"2023-07-06T04:02:31.758616Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display_training_curves(\n    history.history['loss'],\n    history.history['val_loss'],\n    'loss',\n    211,\n)\ndisplay_training_curves(\n    history.history['sparse_categorical_accuracy'],\n    history.history['val_sparse_categorical_accuracy'],\n    'accuracy',\n    212,\n)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T04:02:31.762208Z","iopub.execute_input":"2023-07-06T04:02:31.762526Z","iopub.status.idle":"2023-07-06T04:02:32.453796Z","shell.execute_reply.started":"2023-07-06T04:02:31.762497Z","shell.execute_reply":"2023-07-06T04:02:32.452704Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Submission","metadata":{}},{"cell_type":"code","source":"print('Computing predictions...')\ntest_ds = get_test_dataset(ordered=True)\ntest_images = test_ds.map(lambda image, idnum: image)\nprobabilities = model.predict(test_images)\npredictions = np.argmax(probabilities, axis=-1)\nprint(predictions)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T04:10:42.999093Z","iopub.execute_input":"2023-07-06T04:10:42.999543Z","iopub.status.idle":"2023-07-06T04:11:46.859033Z","shell.execute_reply.started":"2023-07-06T04:10:42.999509Z","shell.execute_reply":"2023-07-06T04:11:46.857650Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Generating submission.csv file...')\n# Get image ids from test set and convert to unicode\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')\n\n# Write the submission file\nnp.savetxt(\n    'submission.csv',\n    np.rec.fromarrays([test_ids, predictions]),\n    fmt=['%s', '%d'],\n    delimiter=',',\n    header='id,label',\n    comments='',\n)","metadata":{"execution":{"iopub.status.busy":"2023-07-06T04:11:46.860795Z","iopub.execute_input":"2023-07-06T04:11:46.861167Z","iopub.status.idle":"2023-07-06T04:11:51.323719Z","shell.execute_reply.started":"2023-07-06T04:11:46.861135Z","shell.execute_reply":"2023-07-06T04:11:51.322364Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}