{"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":"import tensorflow as tf\nimport pandas as pd\nfrom kaggle_datasets import KaggleDatasets\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras import applications\nimport re\nimport tensorflow_hub as hub","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-11-18T06:56:20.433395Z","iopub.execute_input":"2021-11-18T06:56:20.433939Z","iopub.status.idle":"2021-11-18T06:56:26.074695Z","shell.execute_reply.started":"2021-11-18T06:56:20.433833Z","shell.execute_reply":"2021-11-18T06:56:26.073744Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This solution involves using EfficientNetV2XL with random weights (ty to AMARTYA (MARTY) MUKHERJEE)","metadata":{}},{"cell_type":"markdown","source":"# Step 1: Turn on TPU","metadata":{}},{"cell_type":"code","source":"try:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()  # TPU detection. No parameters necessary if TPU_NAME environment variable is set. On Kaggle this is always the case.\n    print('Running on TPU ', tpu.master())\nexcept ValueError:\n    tpu = None\n\nif tpu:\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    strategy = tf.distribute.experimental.TPUStrategy(tpu)\nelse:\n    strategy = tf.distribute.get_strategy() # default distribution strategy in Tensorflow. Works on CPU and single GPU.\n\nprint(\"REPLICAS: \", strategy.num_replicas_in_sync)","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:56:26.076317Z","iopub.execute_input":"2021-11-18T06:56:26.076659Z","iopub.status.idle":"2021-11-18T06:56:31.805347Z","shell.execute_reply.started":"2021-11-18T06:56:26.076615Z","shell.execute_reply":"2021-11-18T06:56:31.804733Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Step 2: Get Data, images resolution 512x512","metadata":{}},{"cell_type":"code","source":"IMAGE_SIZE = [512, 512] # at this size, a GPU will run out of memory. Use the TPU\nEPOCHS = 150\nBATCH_SIZE = 16 *strategy.num_replicas_in_sync\n\nNUM_TRAINING_IMAGES = 12753\nNUM_TEST_IMAGES = 7382\nSTEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE\nGCS_DS_PATH = KaggleDatasets().get_gcs_path() # you can list the bucket with \"!gsutil ls $GCS_DS_PATH\"\nprint(GCS_DS_PATH)","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:56:31.806454Z","iopub.execute_input":"2021-11-18T06:56:31.807Z","iopub.status.idle":"2021-11-18T06:56:32.219893Z","shell.execute_reply.started":"2021-11-18T06:56:31.806969Z","shell.execute_reply":"2021-11-18T06:56:32.21925Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"GCS_PATH_2 = GCS_DS_PATH + '/tfrecords-jpeg-512x512'\nAUTO = tf.data.experimental.AUTOTUNE\n\nTRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH_2 + '/train/*.tfrec')\nVALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH_2 + '/val/*.tfrec')\nTEST_FILENAMES = tf.io.gfile.glob(GCS_PATH_2 + '/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\nprint(len(CLASSES))","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:56:35.702769Z","iopub.execute_input":"2021-11-18T06:56:35.70306Z","iopub.status.idle":"2021-11-18T06:56:35.937291Z","shell.execute_reply.started":"2021-11-18T06:56:35.703027Z","shell.execute_reply":"2021-11-18T06:56:35.936271Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Step 3: Helper functions","metadata":{}},{"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  # 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 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\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) # 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)\n    # returns a dataset of (image, label) pairs if labeled=True or (image, id) pairs if labeled=False\n    return dataset\n\ndef get_training_dataset():\n    dataset = load_dataset(tf.io.gfile.glob(GCS_DS_PATH + '/tfrecords-jpeg-512x512/train/*.tfrec'), 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)\n    return dataset\n\ndef get_validation_dataset(ordered=False):\n    dataset = load_dataset(tf.io.gfile.glob(GCS_DS_PATH + '/tfrecords-jpeg-512x512/val/*.tfrec'), 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","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:56:37.864106Z","iopub.execute_input":"2021-11-18T06:56:37.864745Z","iopub.status.idle":"2021-11-18T06:56:37.88292Z","shell.execute_reply.started":"2021-11-18T06:56:37.864691Z","shell.execute_reply":"2021-11-18T06:56:37.881899Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Step 4: Analyze our data","metadata":{}},{"cell_type":"code","source":"def 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)\n\nprint('Dataset: {} training images, {} validation images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES))","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:56:40.875334Z","iopub.execute_input":"2021-11-18T06:56:40.875654Z","iopub.status.idle":"2021-11-18T06:56:40.883966Z","shell.execute_reply.started":"2021-11-18T06:56:40.875622Z","shell.execute_reply":"2021-11-18T06:56:40.883382Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def sample_images(images, row_count, column_count):\n    fig, axs = plt.subplots(row_count, column_count, figsize=(10,10))\n    for i in range(row_count):\n        for j in range(column_count):\n            axs[i,j].imshow(images[i * column_count + j])\n            axs[i,j].axis('off')\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:56:42.628455Z","iopub.execute_input":"2021-11-18T06:56:42.629066Z","iopub.status.idle":"2021-11-18T06:56:42.636241Z","shell.execute_reply.started":"2021-11-18T06:56:42.629Z","shell.execute_reply":"2021-11-18T06:56:42.635481Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def find_mean_img(full_mat, title, size = (512, 512, 3)):\n    # calculate the average\n    mean_img = np.mean(full_mat, axis = 0)\n    # reshape it back to a matrix\n    mean_img = mean_img.reshape(size)\n    plt.imshow(mean_img, vmin=0, vmax=255)\n    plt.title(f'Average {title}')\n    plt.axis('off')\n    plt.show()\n    return mean_img","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:56:43.857829Z","iopub.execute_input":"2021-11-18T06:56:43.858131Z","iopub.status.idle":"2021-11-18T06:56:43.864044Z","shell.execute_reply.started":"2021-11-18T06:56:43.8581Z","shell.execute_reply":"2021-11-18T06:56:43.863113Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Step 5: Prepare data to datasets","metadata":{}},{"cell_type":"code","source":"training_dataset = get_training_dataset()\nvalidation_dataset = get_validation_dataset()","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:56:45.787654Z","iopub.execute_input":"2021-11-18T06:56:45.788305Z","iopub.status.idle":"2021-11-18T06:56:46.172566Z","shell.execute_reply.started":"2021-11-18T06:56:45.78827Z","shell.execute_reply":"2021-11-18T06:56:46.171464Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for item in training_dataset:\n    images = item[0].numpy()\n    labels = item[1].numpy()\n    break\nimages.shape, labels.shape","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:56:47.231901Z","iopub.execute_input":"2021-11-18T06:56:47.232597Z","iopub.status.idle":"2021-11-18T06:56:59.149081Z","shell.execute_reply.started":"2021-11-18T06:56:47.232556Z","shell.execute_reply":"2021-11-18T06:56:59.148303Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.unique(labels, return_counts = True)","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:56:59.150374Z","iopub.execute_input":"2021-11-18T06:56:59.15063Z","iopub.status.idle":"2021-11-18T06:56:59.158211Z","shell.execute_reply.started":"2021-11-18T06:56:59.150603Z","shell.execute_reply":"2021-11-18T06:56:59.157434Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_images(images, 4, 4)","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:56:59.159644Z","iopub.execute_input":"2021-11-18T06:56:59.159955Z","iopub.status.idle":"2021-11-18T06:57:00.919956Z","shell.execute_reply.started":"2021-11-18T06:56:59.159914Z","shell.execute_reply":"2021-11-18T06:57:00.919329Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(labels) \n\nfull_mat_F1 = images [labels == 4, :,:,:]\nfull_mat_F2 = images [labels == 67,:,:,:]\nfull_mat_F3 = images [labels == 73,:,:,:]","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:57:00.921599Z","iopub.execute_input":"2021-11-18T06:57:00.921976Z","iopub.status.idle":"2021-11-18T06:57:00.954778Z","shell.execute_reply.started":"2021-11-18T06:57:00.921947Z","shell.execute_reply":"2021-11-18T06:57:00.954151Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"F1_mean = find_mean_img(full_mat_F1, 'F1')\nF2_mean = find_mean_img(full_mat_F2, 'F2')\nF3_mean = find_mean_img(full_mat_F3, 'F3')","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:57:00.956027Z","iopub.execute_input":"2021-11-18T06:57:00.956267Z","iopub.status.idle":"2021-11-18T06:57:01.595029Z","shell.execute_reply.started":"2021-11-18T06:57:00.95624Z","shell.execute_reply":"2021-11-18T06:57:01.594321Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Step 6: Define the model(and add some callbacks)","metadata":{}},{"cell_type":"code","source":"early_stop = tf.keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True)","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:57:01.596238Z","iopub.execute_input":"2021-11-18T06:57:01.596459Z","iopub.status.idle":"2021-11-18T06:57:01.600865Z","shell.execute_reply.started":"2021-11-18T06:57:01.596433Z","shell.execute_reply":"2021-11-18T06:57:01.599913Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"checkpoint_path = \"modelEfficientV2-XL.h5\"\ncheckpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path, save_best_only=True)","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:57:01.602234Z","iopub.execute_input":"2021-11-18T06:57:01.602475Z","iopub.status.idle":"2021-11-18T06:57:01.611021Z","shell.execute_reply.started":"2021-11-18T06:57:01.602443Z","shell.execute_reply":"2021-11-18T06:57:01.610297Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"LR_START = 0.00005\nLR_MAX =   0.00005 * strategy.num_replicas_in_sync \nLR_MIN =   0.0000025\nLR_RAMPUP_EPOCHS = 3\nLR_SUSTAIN_EPOCHS = 6\nLR_EXP_DECAY = .8\ndef scheduler_callback(epoch):\n    if epoch < LR_RAMPUP_EPOCHS:\n        lr =  np.random.random_sample() * LR_START\n    elif epoch < LR_RAMPUP_EPOCHS + LR_SUSTAIN_EPOCHS:\n        lr = LR_MAX\n    else:\n        lr = (LR_MAX - LR_MIN) * LR_EXP_DECAY**(epoch - LR_RAMPUP_EPOCHS - LR_SUSTAIN_EPOCHS) + LR_MIN\n    return lr\nscheduler = tf.keras.callbacks.LearningRateScheduler(scheduler_callback, verbose=True)","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:57:01.612874Z","iopub.execute_input":"2021-11-18T06:57:01.613501Z","iopub.status.idle":"2021-11-18T06:57:01.622248Z","shell.execute_reply.started":"2021-11-18T06:57:01.61346Z","shell.execute_reply":"2021-11-18T06:57:01.621456Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"callbacks = [early_stop, scheduler, checkpoint]","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:57:01.737201Z","iopub.execute_input":"2021-11-18T06:57:01.737469Z","iopub.status.idle":"2021-11-18T06:57:01.742018Z","shell.execute_reply.started":"2021-11-18T06:57:01.737442Z","shell.execute_reply":"2021-11-18T06:57:01.741159Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"optimizer = tf.keras.optimizers.Adam(learning_rate=1e-7, \n                                                 )","metadata":{"execution":{"iopub.status.busy":"2021-11-18T06:57:03.555155Z","iopub.execute_input":"2021-11-18T06:57:03.555825Z","iopub.status.idle":"2021-11-18T06:57:03.560475Z","shell.execute_reply.started":"2021-11-18T06:57:03.555782Z","shell.execute_reply":"2021-11-18T06:57:03.559547Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"You can use some of that models and look on results, or make net yourself. In this notebook i use EfficienNetB7\n\nLook at available ready to use models in keras\n\n![image.png](attachment:ebfb06be-cb88-42a0-92ec-84a2eab5433d.png)\n\n![image.png](attachment:210338e1-ef42-40d7-86af-ceca7d523aee.png)","metadata":{},"attachments":{"ebfb06be-cb88-42a0-92ec-84a2eab5433d.png":{"image/png":"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"},"210338e1-ef42-40d7-86af-ceca7d523aee.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"You can read about EfficientNet from that paper: https://arxiv.org/pdf/1905.11946.pdf\nAlso you need to know what resolution need to input in model for best results, just look at table in keras\n\n![image.png](attachment:b7d387df-e50b-4e9b-a262-99ee35059adc.png)\n\nI assume that for best results, you should use an image generator that will create new data for the training sample by linear operations on the image (pivot, tilt, etc.). And also use an ensemble of networks, such as EfficientNetB7 and DenseNet201","metadata":{},"attachments":{"b7d387df-e50b-4e9b-a262-99ee35059adc.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# Step 7: Train the model","metadata":{}},{"cell_type":"code","source":"with strategy.scope():  \n    input_shape = [*IMAGE_SIZE, 3]   \n    hub_url = 'gs://cloud-tpu-checkpoints/efficientnet/v2/hub/efficientnetv2-xl-21k/classification'\n    model = tf.keras.Sequential([\n        tf.keras.layers.InputLayer(input_shape=input_shape),\n        hub.KerasLayer(hub_url, trainable=True), \n        tf.keras.layers.Dropout(0.2), \n        tf.keras.layers.Dense(104, activation='softmax',name=\"pred\")\n    ])\n    model.compile(\n        optimizer=optimizer,\n        loss = 'sparse_categorical_crossentropy',\n        metrics=['sparse_categorical_accuracy']\n    )\n    history = model.fit(training_dataset, \n                        steps_per_epoch=STEPS_PER_EPOCH, \n                        epochs=EPOCHS, \n                        validation_data=validation_dataset, \n                        callbacks=callbacks\n                       )\n    pd.DataFrame(history.history).plot()\n    plt.show()","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2021-11-18T06:58:34.933721Z","iopub.execute_input":"2021-11-18T06:58:34.934023Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Step 8: Look at predictions","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.'])\ncmdataset = 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\nscore = 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":"2021-11-16T17:20:42.775707Z","iopub.execute_input":"2021-11-16T17:20:42.775992Z","iopub.status.idle":"2021-11-16T17:21:26.726498Z","shell.execute_reply.started":"2021-11-16T17:20:42.775958Z","shell.execute_reply":"2021-11-16T17:21:26.725622Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset = get_validation_dataset()\ndataset = dataset.unbatch().batch(20)\nbatch = iter(dataset)","metadata":{"execution":{"iopub.status.busy":"2021-11-16T17:21:39.242588Z","iopub.execute_input":"2021-11-16T17:21:39.242903Z","iopub.status.idle":"2021-11-16T17:21:39.357453Z","shell.execute_reply.started":"2021-11-16T17:21:39.242871Z","shell.execute_reply":"2021-11-16T17:21:39.356545Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from matplotlib import pyplot as plt\nimport math\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.'])","metadata":{"execution":{"iopub.status.busy":"2021-11-16T17:21:41.932815Z","iopub.execute_input":"2021-11-16T17:21:41.933112Z","iopub.status.idle":"2021-11-16T17:21:41.954045Z","shell.execute_reply.started":"2021-11-16T17:21:41.93308Z","shell.execute_reply":"2021-11-16T17:21:41.953313Z"},"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":"2021-11-16T17:21:43.289545Z","iopub.execute_input":"2021-11-16T17:21:43.290037Z","iopub.status.idle":"2021-11-16T17:21:56.325624Z","shell.execute_reply.started":"2021-11-16T17:21:43.290005Z","shell.execute_reply":"2021-11-16T17:21:56.324513Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Step 9: Make submisson ","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)\ntest_ds = get_test_dataset(ordered=True) # since we are splitting the dataset and iterating separately on images and ids, order matters.\nmodel.load_weights(checkpoint_path)\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') # all in one batch\nnp.savetxt('submission.csv', np.rec.fromarrays([test_ids, predictions]), fmt=['%s', '%d'], delimiter=',', header='id,label', comments='')","metadata":{"execution":{"iopub.status.busy":"2021-11-16T17:22:10.04894Z","iopub.execute_input":"2021-11-16T17:22:10.049251Z","iopub.status.idle":"2021-11-16T17:24:08.105077Z","shell.execute_reply.started":"2021-11-16T17:22:10.049218Z","shell.execute_reply":"2021-11-16T17:24:08.104206Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}