{"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":"2023-01-21T16:14:53.589551Z","iopub.execute_input":"2023-01-21T16:14:53.590121Z","iopub.status.idle":"2023-01-21T16:14:53.818175Z","shell.execute_reply.started":"2023-01-21T16:14:53.590021Z","shell.execute_reply":"2023-01-21T16:14:53.817172Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import math, re, os\nimport numpy as np\nimport tensorflow as tf\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nprint(\"Tensorflow version \" + tf.__version__)","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:16:05.190603Z","iopub.execute_input":"2023-01-21T16:16:05.190929Z","iopub.status.idle":"2023-01-21T16:16:05.199867Z","shell.execute_reply.started":"2023-01-21T16:16:05.190897Z","shell.execute_reply":"2023-01-21T16:16:05.198835Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Distribution Strategy**","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":"2023-01-21T16:16:18.890705Z","iopub.execute_input":"2023-01-21T16:16:18.890983Z","iopub.status.idle":"2023-01-21T16:16:23.836515Z","shell.execute_reply.started":"2023-01-21T16:16:18.890945Z","shell.execute_reply":"2023-01-21T16:16:23.835162Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Loading Dataset**","metadata":{}},{"cell_type":"code","source":"from kaggle_datasets import KaggleDatasets\nGCS_DS_PATH = KaggleDatasets().get_gcs_path()\nprint(GCS_DS_PATH)","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:20:25.489262Z","iopub.execute_input":"2023-01-21T16:20:25.489575Z","iopub.status.idle":"2023-01-21T16:20:25.878647Z","shell.execute_reply.started":"2023-01-21T16:20:25.489548Z","shell.execute_reply":"2023-01-21T16:20:25.877760Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"IMAGE_SIZE = [512, 512]\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')\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']                                                            ","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:20:31.641759Z","iopub.execute_input":"2023-01-21T16:20:31.642032Z","iopub.status.idle":"2023-01-21T16:20:31.898258Z","shell.execute_reply.started":"2023-01-21T16:20:31.642005Z","shell.execute_reply":"2023-01-21T16:20:31.897346Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"AUTO = tf.data.experimental.AUTOTUNE","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:21:43.769968Z","iopub.execute_input":"2023-01-21T16:21:43.770685Z","iopub.status.idle":"2023-01-21T16:21:43.775178Z","shell.execute_reply.started":"2023-01-21T16:21:43.770640Z","shell.execute_reply":"2023-01-21T16:21:43.774311Z"},"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 # 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    \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":"2023-01-21T16:22:57.679677Z","iopub.execute_input":"2023-01-21T16:22:57.679966Z","iopub.status.idle":"2023-01-21T16:22:57.690826Z","shell.execute_reply.started":"2023-01-21T16:22:57.679935Z","shell.execute_reply":"2023-01-21T16:22:57.689722Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def data_augment_train(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.resize(image, [512, 512])\n    image = tf.image.random_flip_left_right(image)\n    return image, label \n\ndef data_augment_test_val(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.resize(image, [512, 512])\n    return image, label   \n\ndef get_training_dataset():\n    dataset = load_dataset(TRAINING_FILENAMES, labeled=True)\n    dataset = dataset.map(data_augment_train, 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.map(data_augment_test_val, num_parallel_calls=AUTO)\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.map(data_augment_test_val, num_parallel_calls=AUTO)\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('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:23:28.381317Z","iopub.execute_input":"2023-01-21T16:23:28.381620Z","iopub.status.idle":"2023-01-21T16:23:28.398282Z","shell.execute_reply.started":"2023-01-21T16:23:28.381589Z","shell.execute_reply":"2023-01-21T16:23:28.397279Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"BATCH_SIZE = 16 * strategy.num_replicas_in_sync\n\nds_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":"2023-01-21T16:23:45.570172Z","iopub.execute_input":"2023-01-21T16:23:45.570454Z","iopub.status.idle":"2023-01-21T16:23:45.969966Z","shell.execute_reply.started":"2023-01-21T16:23:45.570426Z","shell.execute_reply":"2023-01-21T16:23:45.969024Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.set_printoptions(threshold=15, linewidth=80)\n\nprint(\"Test data shapes:\")\nfor image, idnum in ds_train.take(3):\n    print(image.numpy().shape, idnum.numpy().shape)\nprint(\"Test data IDs:\", idnum.numpy().astype('U'))","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:24:04.029387Z","iopub.execute_input":"2023-01-21T16:24:04.029701Z","iopub.status.idle":"2023-01-21T16:24:10.656407Z","shell.execute_reply.started":"2023-01-21T16:24:04.029668Z","shell.execute_reply":"2023-01-21T16:24:10.655133Z"},"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'))","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:24:35.694325Z","iopub.execute_input":"2023-01-21T16:24:35.696507Z","iopub.status.idle":"2023-01-21T16:24:40.470630Z","shell.execute_reply.started":"2023-01-21T16:24:35.696429Z","shell.execute_reply":"2023-01-21T16:24:40.469587Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Explore DATA**","metadata":{}},{"cell_type":"code","source":"from matplotlib import pyplot as plt\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,\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.'])\n","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:25:41.147679Z","iopub.execute_input":"2023-01-21T16:25:41.148319Z","iopub.status.idle":"2023-01-21T16:25:41.169357Z","shell.execute_reply.started":"2023-01-21T16:25:41.148279Z","shell.execute_reply":"2023-01-21T16:25:41.168314Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ds_iter = iter(ds_train.unbatch().batch(20))","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:52:40.218096Z","iopub.execute_input":"2023-01-21T16:52:40.218393Z","iopub.status.idle":"2023-01-21T16:52:40.236845Z","shell.execute_reply.started":"2023-01-21T16:52:40.218364Z","shell.execute_reply":"2023-01-21T16:52:40.236031Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"one_batch = next(ds_iter)\ndisplay_batch_of_images(one_batch)\n","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:52:44.972128Z","iopub.execute_input":"2023-01-21T16:52:44.972743Z","iopub.status.idle":"2023-01-21T16:52:49.362755Z","shell.execute_reply.started":"2023-01-21T16:52:44.972706Z","shell.execute_reply":"2023-01-21T16:52:49.361377Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**MODELLING**\n\nUSING RESNET50","metadata":{}},{"cell_type":"code","source":"EPOCHS = 50\nfrom tensorflow.keras.models import Model\n\n\nwith strategy.scope():\n    pretrained_model = tf.keras.applications.resnet50.ResNet50(\n        weights='imagenet',\n        include_top=False ,\n        input_shape=[*IMAGE_SIZE, 3],\n        pooling='avg',\n    )\n    pretrained_model.trainable = True\n    \n    x = pretrained_model.output\n    predictions = tf.keras.layers.Dense(len(CLASSES), activation='softmax')(x)\n    model = tf.keras.models.Model(inputs=pretrained_model.input, outputs=predictions)","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:26:49.472887Z","iopub.execute_input":"2023-01-21T16:26:49.473217Z","iopub.status.idle":"2023-01-21T16:27:01.341438Z","shell.execute_reply.started":"2023-01-21T16:26:49.473168Z","shell.execute_reply":"2023-01-21T16:27:01.340553Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.compile(\n    optimizer='nadam',\n    loss = 'sparse_categorical_crossentropy',\n    metrics=['sparse_categorical_accuracy'],\n)\n\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:27:11.248695Z","iopub.execute_input":"2023-01-21T16:27:11.248988Z","iopub.status.idle":"2023-01-21T16:27:11.382653Z","shell.execute_reply.started":"2023-01-21T16:27:11.248959Z","shell.execute_reply":"2023-01-21T16:27:11.381647Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.input","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:27:45.142486Z","iopub.execute_input":"2023-01-21T16:27:45.142766Z","iopub.status.idle":"2023-01-21T16:27:45.150349Z","shell.execute_reply.started":"2023-01-21T16:27:45.142738Z","shell.execute_reply":"2023-01-21T16:27:45.149217Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**TRAINING**","metadata":{}},{"cell_type":"code","source":"# Learning Rate Schedule for Fine Tuning #\ndef exponential_lr(epoch,\n                   start_lr = 0.00001, min_lr = 0.00001, max_lr = 0.00005,\n                   rampup_epochs = 5, sustain_epochs = 0,\n                   exp_decay = 0.8):\n\n    def lr(epoch, start_lr, min_lr, max_lr, rampup_epochs, sustain_epochs, exp_decay):\n        # linear increase from start to rampup_epochs\n        if epoch < rampup_epochs:\n            lr = ((max_lr - start_lr) /\n                  rampup_epochs * epoch + start_lr)\n        # constant max_lr during sustain_epochs\n        elif epoch < rampup_epochs + sustain_epochs:\n            lr = max_lr\n        # exponential decay towards min_lr\n        else:\n            lr = ((max_lr - min_lr) *\n                  exp_decay**(epoch - rampup_epochs - sustain_epochs) +\n                  min_lr)\n        return lr\n    return lr(epoch,\n              start_lr,\n              min_lr,\n              max_lr,\n              rampup_epochs,\n              sustain_epochs,\n              exp_decay)\n\nlr_callback = tf.keras.callbacks.LearningRateScheduler(exponential_lr, verbose=True)\n\nrng = [i for i in range(EPOCHS)]\ny = [exponential_lr(x) for x in rng]\nplt.plot(rng, y)\nprint(\"Learning rate schedule: {:.3g} to {:.3g} to {:.3g}\".format(y[0], max(y), y[-1]))","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:28:19.208163Z","iopub.execute_input":"2023-01-21T16:28:19.208476Z","iopub.status.idle":"2023-01-21T16:28:19.477794Z","shell.execute_reply.started":"2023-01-21T16:28:19.208445Z","shell.execute_reply":"2023-01-21T16:28:19.476757Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**FIT THE MODEL**","metadata":{}},{"cell_type":"code","source":"# Define training epochs\nEPOCHS = 30\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=[lr_callback],\n)","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:28:44.408417Z","iopub.execute_input":"2023-01-21T16:28:44.408728Z","iopub.status.idle":"2023-01-21T16:47:20.200151Z","shell.execute_reply.started":"2023-01-21T16:28:44.408696Z","shell.execute_reply":"2023-01-21T16:47:20.198858Z"},"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":"2023-01-21T16:48:28.902453Z","iopub.execute_input":"2023-01-21T16:48:28.902752Z","iopub.status.idle":"2023-01-21T16:48:29.557169Z","shell.execute_reply.started":"2023-01-21T16:48:28.902723Z","shell.execute_reply":"2023-01-21T16:48:29.556084Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**EVALUATE THE MODEL**","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix\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":{"execution":{"iopub.status.busy":"2023-01-21T16:49:19.648062Z","iopub.execute_input":"2023-01-21T16:49:19.649055Z","iopub.status.idle":"2023-01-21T16:49:20.422176Z","shell.execute_reply.started":"2023-01-21T16:49:19.649012Z","shell.execute_reply":"2023-01-21T16:49:20.420905Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**CONFUSUION MATRIX**","metadata":{}},{"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()\n\ncm_correct_labels = next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES))).numpy()\ncm_probabilities = model.predict(images_ds)\ncm_predictions = np.argmax(cm_probabilities, axis=-1)\n\nlabels = range(len(CLASSES))\ncmat = confusion_matrix(\n    cm_correct_labels,\n    cm_predictions,\n    labels=labels,\n)\ncmat = (cmat.T / cmat.sum(axis=1)).T # normalize","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:49:39.552878Z","iopub.execute_input":"2023-01-21T16:49:39.553421Z","iopub.status.idle":"2023-01-21T16:49:52.091609Z","shell.execute_reply.started":"2023-01-21T16:49:39.553387Z","shell.execute_reply":"2023-01-21T16:49:52.090802Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"score = f1_score(\n    cm_correct_labels,\n    cm_predictions,\n    labels=labels,\n    average='macro',\n)\nprecision = precision_score(\n    cm_correct_labels,\n    cm_predictions,\n    labels=labels,\n    average='macro',\n)\nrecall = recall_score(\n    cm_correct_labels,\n    cm_predictions,\n    labels=labels,\n    average='macro',\n)\ndisplay_confusion_matrix(cmat, score, precision, recall)","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:49:59.092309Z","iopub.execute_input":"2023-01-21T16:49:59.092837Z","iopub.status.idle":"2023-01-21T16:50:04.348799Z","shell.execute_reply.started":"2023-01-21T16:49:59.092777Z","shell.execute_reply":"2023-01-21T16:50:04.348170Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**VISUAL VALIDATION**","metadata":{}},{"cell_type":"code","source":"dataset = get_validation_dataset()\ndataset = dataset.unbatch().batch(20)\nbatch = iter(dataset)","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:50:19.212969Z","iopub.execute_input":"2023-01-21T16:50:19.213311Z","iopub.status.idle":"2023-01-21T16:50:19.266822Z","shell.execute_reply.started":"2023-01-21T16:50:19.213275Z","shell.execute_reply":"2023-01-21T16:50:19.265821Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"images, labels = next(batch)\nprobabilities = model.predict(images)\npredictions = np.argmax(probabilities, axis=-1)\ndisplay_batch_of_images((images, labels), predictions)","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:50:31.962860Z","iopub.execute_input":"2023-01-21T16:50:31.963134Z","iopub.status.idle":"2023-01-21T16:50:46.128721Z","shell.execute_reply.started":"2023-01-21T16:50:31.963106Z","shell.execute_reply":"2023-01-21T16:50:46.127660Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**TEST PREDICTIONS**","metadata":{}},{"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)","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:51:09.532120Z","iopub.execute_input":"2023-01-21T16:51:09.532429Z","iopub.status.idle":"2023-01-21T16:51:21.804093Z","shell.execute_reply.started":"2023-01-21T16:51:09.532399Z","shell.execute_reply":"2023-01-21T16:51:21.803009Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**SUBMISSION**","metadata":{}},{"cell_type":"code","source":"test_ids_ds = test_ds.map(lambda image, idnum: idnum).unbatch()\ntest_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES))).numpy().astype('U')","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:51:21.806251Z","iopub.execute_input":"2023-01-21T16:51:21.806591Z","iopub.status.idle":"2023-01-21T16:51:26.564610Z","shell.execute_reply.started":"2023-01-21T16:51:21.806542Z","shell.execute_reply":"2023-01-21T16:51:26.563696Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = {\n    'id':test_ids,\n    'label':predictions\n}\ndf = pd.DataFrame(df)\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:51:36.962866Z","iopub.execute_input":"2023-01-21T16:51:36.963164Z","iopub.status.idle":"2023-01-21T16:51:36.995392Z","shell.execute_reply.started":"2023-01-21T16:51:36.963135Z","shell.execute_reply":"2023-01-21T16:51:36.994316Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.to_csv('submission.csv',index=False)","metadata":{"execution":{"iopub.status.busy":"2023-01-21T16:51:49.602686Z","iopub.execute_input":"2023-01-21T16:51:49.602960Z","iopub.status.idle":"2023-01-21T16:51:49.621845Z","shell.execute_reply.started":"2023-01-21T16:51:49.602932Z","shell.execute_reply":"2023-01-21T16:51:49.620740Z"},"trusted":true},"execution_count":null,"outputs":[]}]}