{"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":"# Data Augmentation using GPU/TPU for Maximum Speed!\nThis notebook shows how perform rotation, shear, zoom, and shift data augmentation for the GPU/TPU with `TensorFlow.data.Dataset`. Data augmentation is a technique to increase model accuracy and using GPU/TPU achieves this goal quicker. \n\n![cpu.jpg](attachment:cpu.jpg)\n\nGPUs and TPUs can consume 200 or more images sized 512x512x3 in one second (while training DenseNet201)! That's incredible. If we perform data augmentation beforehand, we need to make sure we are preparing at least 200 images per second. Otherwise we will slow down our GPU/TPU training.\n\nThis is the advantage of `tensorflow.data.Dataset`. After writing augmentation in TensorFlow language, your program will optimize these operations for GPU/TPU. Similarily, you can use libraries like Nvidia DALI [here][1] for GPU image preprocess and/or Nvidia RAPIDS [here][2] for GPU tabular preprocess.\n\n[1]: https://docs.nvidia.com/deeplearning/sdk/dali-developer-guide/docs/index.html\n[2]: https://developer.nvidia.com/rapids","metadata":{},"attachments":{"cpu.jpg":{"image/jpeg":"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"}}},{"cell_type":"code","source":"import random, re, math\nimport numpy as np, pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix\nimport tensorflow as tf, tensorflow.keras.backend as K\nfrom kaggle_datasets import KaggleDatasets\nprint('Tensorflow version ' + tf.__version__)\nfrom sklearn.model_selection import KFold","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-output":true,"execution":{"iopub.status.busy":"2021-11-19T17:13:55.528266Z","iopub.execute_input":"2021-11-19T17:13:55.528592Z","iopub.status.idle":"2021-11-19T17:13:55.537239Z","shell.execute_reply.started":"2021-11-19T17:13:55.52854Z","shell.execute_reply":"2021-11-19T17:13:55.536133Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Configurations","metadata":{}},{"cell_type":"code","source":"# Detect hardware, return appropriate distribution strategy\ntry:\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":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"AUTO = tf.data.experimental.AUTOTUNE\n\n# Configuration\nIMAGE_SIZE = [224, 224]\nEPOCHS = 5\nFOLDS = 3\nSEED = 777\nBATCH_SIZE = 16 * strategy.num_replicas_in_sync","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Mixed Precision and/or XLA\nThe following booleans can enable mixed precision and/or XLA on GPU/TPU. By default TPU already uses some mixed precision but we can add more. These allow the GPU/TPU memory to handle larger batch sizes and can speed up the training process. The Nvidia V100 GPU has special Tensor Cores which get utilized when mixed precision is enabled. Unfortunately Kaggle's Nvidia P100 GPU does not have Tensor Cores to receive speed up.","metadata":{}},{"cell_type":"code","source":"MIXED_PRECISION = False\nXLA_ACCELERATE = False\n\nif MIXED_PRECISION:\n    from tensorflow.keras.mixed_precision import experimental as mixed_precision\n    if tpu: policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')\n    else: policy = tf.keras.mixed_precision.experimental.Policy('mixed_float16')\n    mixed_precision.set_policy(policy)\n    print('Mixed precision enabled')\n\nif XLA_ACCELERATE:\n    tf.config.optimizer.set_jit(True)\n    print('Accelerated Linear Algebra enabled')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Data Directories","metadata":{}},{"cell_type":"code","source":"# Data access\nGCS_DS_PATH = KaggleDatasets().get_gcs_path('tpu-getting-started')\n\nGCS_PATH_SELECT = { # available image sizes\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}\n\nGCS_PATH = GCS_PATH_SELECT[IMAGE_SIZE[0]]\n\nTRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec') + tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')\nTEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec') # predictions on this dataset should be submitted for the competition","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Classes","metadata":{}},{"cell_type":"code","source":"CLASSES = ['pink primrose',    'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea',     'wild geranium',     'tiger lily',           'moon orchid',              'bird of paradise', 'monkshood',        'globe thistle',         # 00 - 09\n           'snapdragon',       \"colt's foot\",               'king protea',      'spear thistle', 'yellow iris',       'globe-flower',         'purple coneflower',        'peruvian lily',    'balloon flower',   'giant white arum lily', # 10 - 19\n           'fire lily',        'pincushion flower',         'fritillary',       'red ginger',    'grape hyacinth',    'corn poppy',           'prince of wales feathers', 'stemless gentian', 'artichoke',        'sweet william',         # 20 - 29\n           'carnation',        'garden phlox',              'love in the mist', 'cosmos',        'alpine sea holly',  'ruby-lipped cattleya', 'cape flower',              'great masterwort', 'siam tulip',       'lenten rose',           # 30 - 39\n           'barberton daisy',  'daffodil',                  'sword lily',       'poinsettia',    'bolero deep blue',  'wallflower',           'marigold',                 'buttercup',        'daisy',            'common dandelion',      # 40 - 49\n           'petunia',          'wild pansy',                'primula',          'sunflower',     'lilac hibiscus',    'bishop of llandaff',   'gaura',                    'geranium',         'orange dahlia',    'pink-yellow dahlia',    # 50 - 59\n           'cautleya spicata', 'japanese anemone',          'black-eyed susan', 'silverbush',    'californian poppy', 'osteospermum',         'spring crocus',            'iris',             'windflower',       'tree poppy',            # 60 - 69\n           'gazania',          'azalea',                    'water lily',       'rose',          'thorn apple',       'morning glory',        'passion flower',           'lotus',            'toad lily',        'anthurium',             # 70 - 79\n           'frangipani',       'clematis',                  'hibiscus',         'columbine',     'desert-rose',       'tree mallow',          'magnolia',                 'cyclamen ',        'watercress',       'canna lily',            # 80 - 89\n           'hippeastrum ',     'bee balm',                  'pink quill',       'foxglove',      'bougainvillea',     'camellia',             'mallow',                   'mexican petunia',  'bromelia',         'blanket flower',        # 90 - 99\n           'trumpet creeper',  'blackberry lily',           'common tulip',     'wild rose']                                                                                                                                               # 100 - 102","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Custom LR scheduler\nFrom starter [kernel][1]\n\n[1]: https://www.kaggle.com/mgornergoogle/getting-started-with-100-flowers-on-tpu","metadata":{}},{"cell_type":"code","source":"# Learning rate schedule for TPU, GPU and CPU.\n# Using an LR ramp up because fine-tuning a pre-trained model.\n# Starting with a high LR would break the pre-trained weights.\n\nLR_START = 0.00001\nLR_MAX = 0.00005 * strategy.num_replicas_in_sync\nLR_MIN = 0.00001\nLR_RAMPUP_EPOCHS = 5\nLR_SUSTAIN_EPOCHS = 0\nLR_EXP_DECAY = .8\n\ndef lrfn(epoch):\n    if epoch < LR_RAMPUP_EPOCHS:\n        lr = (LR_MAX - LR_START) / LR_RAMPUP_EPOCHS * epoch + 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\n    \nlr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose = True)\n\nrng = [i for i in range(25 if EPOCHS<25 else EPOCHS)]\ny = [lrfn(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":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Dataset Functions\nFrom starter [kernel][1]\n\n[1]: https://www.kaggle.com/mgornergoogle/getting-started-with-100-flowers-on-tpu","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 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    # Diregarding 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) # use 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) # returns a dataset of (image, label) pairs if labeled = True or (image, id) pair if labeld = False\n    return dataset\n\ndef data_augment(image, label):\n    # data augmentation. Thanks to the dataset.prefetch(AUTO) statement in the next function (below),\n    # this happens essentially for free on TPU. Data pipeline code is executed on the \"CPU\" part\n    # of the TPU while the TPU itself is computing gradients.\n    image = tf.image.random_flip_left_right(image)\n    return image, label   \n\ndef get_training_dataset(dataset,do_aug=True):\n    dataset = dataset.map(data_augment, num_parallel_calls=AUTO)\n    if do_aug: dataset = dataset.map(transform, 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(dataset):\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.cache()\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\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) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef count_data_items(filenames):\n    # the number of data items is written in the name of the .tfrec 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 = int( count_data_items(TRAINING_FILENAMES) * (FOLDS-1.)/FOLDS )\nNUM_VALIDATION_IMAGES = int( count_data_items(TRAINING_FILENAMES) * (1./FOLDS) )\nNUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)\nSTEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE\n\nprint('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Data Augmentation\nThe following code does random rotations, shear, zoom, and shift using the GPU/TPU. When an image gets moved away from an edge revealing blank space, the blank space is filled by stretching the colors on the original edge. Change the variables in function `transform()` below to control the desired amount of augmentation. Here's a diagram illustrating the mathematics.\n\n![rotate.JPG](attachment:rotate.JPG)","metadata":{},"attachments":{"rotate.JPG":{"image/jpeg":"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"}}},{"cell_type":"code","source":"def get_mat(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):\n    # returns 3x3 transformmatrix which transforms indicies\n        \n    # CONVERT DEGREES TO RADIANS\n    rotation = math.pi * rotation / 180.\n    shear = math.pi * shear / 180.\n    \n    # ROTATION MATRIX\n    c1 = tf.math.cos(rotation)\n    s1 = tf.math.sin(rotation)\n    one = tf.constant([1],dtype='float32')\n    zero = tf.constant([0],dtype='float32')\n    rotation_matrix = tf.reshape( tf.concat([c1,s1,zero, -s1,c1,zero, zero,zero,one],axis=0),[3,3] )\n        \n    # SHEAR MATRIX\n    c2 = tf.math.cos(shear)\n    s2 = tf.math.sin(shear)\n    shear_matrix = tf.reshape( tf.concat([one,s2,zero, zero,c2,zero, zero,zero,one],axis=0),[3,3] )    \n    \n    # ZOOM MATRIX\n    zoom_matrix = tf.reshape( tf.concat([one/height_zoom,zero,zero, zero,one/width_zoom,zero, zero,zero,one],axis=0),[3,3] )\n    \n    # SHIFT MATRIX\n    shift_matrix = tf.reshape( tf.concat([one,zero,height_shift, zero,one,width_shift, zero,zero,one],axis=0),[3,3] )\n    \n    return K.dot(K.dot(rotation_matrix, shear_matrix), K.dot(zoom_matrix, shift_matrix))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def transform(image,label):\n    # input image - is one image of size [dim,dim,3] not a batch of [b,dim,dim,3]\n    # output - image randomly rotated, sheared, zoomed, and shifted\n    DIM = IMAGE_SIZE[0]\n    XDIM = DIM%2 #fix for size 331\n    \n    rot = 15. * tf.random.normal([1],dtype='float32')\n    shr = 5. * tf.random.normal([1],dtype='float32') \n    h_zoom = 1.0 + tf.random.normal([1],dtype='float32')/10.\n    w_zoom = 1.0 + tf.random.normal([1],dtype='float32')/10.\n    h_shift = 16. * tf.random.normal([1],dtype='float32') \n    w_shift = 16. * tf.random.normal([1],dtype='float32') \n  \n    # GET TRANSFORMATION MATRIX\n    m = get_mat(rot,shr,h_zoom,w_zoom,h_shift,w_shift) \n\n    # LIST DESTINATION PIXEL INDICES\n    x = tf.repeat( tf.range(DIM//2,-DIM//2,-1), DIM )\n    y = tf.tile( tf.range(-DIM//2,DIM//2),[DIM] )\n    z = tf.ones([DIM*DIM],dtype='int32')\n    idx = tf.stack( [x,y,z] )\n    \n    # ROTATE DESTINATION PIXELS ONTO ORIGIN PIXELS\n    idx2 = K.dot(m,tf.cast(idx,dtype='float32'))\n    idx2 = K.cast(idx2,dtype='int32')\n    idx2 = K.clip(idx2,-DIM//2+XDIM+1,DIM//2)\n    \n    # FIND ORIGIN PIXEL VALUES           \n    idx3 = tf.stack( [DIM//2-idx2[0,], DIM//2-1+idx2[1,]] )\n    d = tf.gather_nd(image,tf.transpose(idx3))\n        \n    return tf.reshape(d,[DIM,DIM,3]),label","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Display Example Augmentation\nBelow are examples of 3 training images where each is randomly augmented 12 different times.","metadata":{}},{"cell_type":"code","source":"row = 3; col = 4;\nall_elements = get_training_dataset(load_dataset(TRAINING_FILENAMES),do_aug=False).unbatch()\none_element = tf.data.Dataset.from_tensors( next(iter(all_elements)) )\naugmented_element = one_element.repeat().map(transform).batch(row*col)\n\nfor (img,label) in augmented_element:\n    plt.figure(figsize=(15,int(15*row/col)))\n    for j in range(row*col):\n        plt.subplot(row,col,j+1)\n        plt.axis('off')\n        plt.imshow(img[j,])\n    plt.show()\n    break","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"row = 3; col = 4;\nall_elements = get_training_dataset(load_dataset(TRAINING_FILENAMES),do_aug=False).unbatch()\none_element = tf.data.Dataset.from_tensors( next(iter(all_elements)) )\naugmented_element = one_element.repeat().map(transform).batch(row*col)\n\nfor (img,label) in augmented_element:\n    plt.figure(figsize=(15,int(15*row/col)))\n    for j in range(row*col):\n        plt.subplot(row,col,j+1)\n        plt.axis('off')\n        plt.imshow(img[j,])\n    plt.show()\n    break","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"row = 3; col = 4;\nall_elements = get_training_dataset(load_dataset(TRAINING_FILENAMES),do_aug=False).unbatch()\none_element = tf.data.Dataset.from_tensors( next(iter(all_elements)) )\naugmented_element = one_element.repeat().map(transform).batch(row*col)\n\nfor (img,label) in augmented_element:\n    plt.figure(figsize=(15,int(15*row/col)))\n    for j in range(row*col):\n        plt.subplot(row,col,j+1)\n        plt.axis('off')\n        plt.imshow(img[j,])\n    plt.show()\n    break","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Build, Train, Infer Model\nThis is the 5-Fold workflow copied from Ragnar's notebook [here][1]. Now we add data augmentation to the training images on the fly! Notice how his notebook completes epochs in 70 seconds using TPU. This notebook also completes epochs in 70 seconds (when we turn on TPU) and we are augmentating every image! Augmenting a single image requires 5,000,000 calculations (a batch requires 600,000,000 calculations!) We see that our augmentation is occuring as fast as the GPU/TPU training! We are augmenting 200+ images per second. In other words we are performing 1,000,000,000 calculations per second in addition to normal training computation! Wow!\n\n[1]: https://www.kaggle.com/ragnar123/5-kfold-densenet201","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.applications import DenseNet201\n\ndef get_model():\n    with strategy.scope():\n        rnet = DenseNet201(\n            input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3),\n            weights='imagenet',\n            include_top=False\n        )\n        # trainable rnet\n        rnet.trainable = True\n        model = tf.keras.Sequential([\n            rnet,\n            tf.keras.layers.GlobalAveragePooling2D(),\n            tf.keras.layers.Dense(len(CLASSES), activation='softmax',dtype='float32')\n        ])\n    model.compile(\n        optimizer='adam',\n        loss = 'sparse_categorical_crossentropy',\n        metrics=['sparse_categorical_accuracy']\n    )\n    return model\n\ndef train_cross_validate(folds = 5):\n    histories = []\n    models = []\n    early_stopping = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', patience = 3)\n    kfold = KFold(folds, shuffle = True, random_state = SEED)\n    for f, (trn_ind, val_ind) in enumerate(kfold.split(TRAINING_FILENAMES)):\n        print(); print('#'*25)\n        print('### FOLD',f+1)\n        print('#'*25)\n        train_dataset = load_dataset(list(pd.DataFrame({'TRAINING_FILENAMES': TRAINING_FILENAMES}).loc[trn_ind]['TRAINING_FILENAMES']), labeled = True)\n        val_dataset = load_dataset(list(pd.DataFrame({'TRAINING_FILENAMES': TRAINING_FILENAMES}).loc[val_ind]['TRAINING_FILENAMES']), labeled = True, ordered = True)\n        model = get_model()\n        history = model.fit(\n            get_training_dataset(train_dataset), \n            steps_per_epoch = STEPS_PER_EPOCH,\n            epochs = EPOCHS,\n            callbacks = [lr_callback],#, early_stopping],\n            validation_data = get_validation_dataset(val_dataset),\n            verbose=2\n        )\n        models.append(model)\n        histories.append(history)\n    return histories, models\n\ndef train_and_predict(folds = 5):\n    test_ds = get_test_dataset(ordered=True) # since we are splitting the dataset and iterating separately on images and ids, order matters.\n    test_images_ds = test_ds.map(lambda image, idnum: image)\n    print('Start training %i folds'%folds)\n    histories, models = train_cross_validate(folds = folds)\n    print('Computing predictions...')\n    # get the mean probability of the folds models\n    probabilities = np.average([models[i].predict(test_images_ds) for i in range(folds)], axis = 0)\n    predictions = np.argmax(probabilities, axis=-1)\n    print('Generating submission.csv file...')\n    test_ids_ds = test_ds.map(lambda image, idnum: idnum).unbatch()\n    test_ids = next(iter(test_ids_ds.batch(NUM_TEST_IMAGES))).numpy().astype('U') # all in one batch\n    np.savetxt('submission.csv', np.rec.fromarrays([test_ids, predictions]), fmt=['%s', '%d'], delimiter=',', header='id,label', comments='')\n    return histories, models\n    \n# run train and predict\nhistories, models = train_and_predict(folds = FOLDS)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Confusion Matrix and Validation Score\nTry forking and modifying this notebook to maximize validation score below. Tune the data augmentation and/or train for more epochs to increase accuracy. Good luck! (Code below is from starter [kernel][1]).\n\n[1]: https://www.kaggle.com/mgornergoogle/getting-started-with-100-flowers-on-tpu","metadata":{}},{"cell_type":"code","source":"def 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()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nall_labels = []; all_prob = []; all_pred = []\nkfold = KFold(FOLDS, shuffle = True, random_state = SEED)\nfor j, (trn_ind, val_ind) in enumerate( kfold.split(TRAINING_FILENAMES) ):\n    print('Inferring fold',j+1,'validation images...')\n    VAL_FILES = list(pd.DataFrame({'TRAINING_FILENAMES': TRAINING_FILENAMES}).loc[val_ind]['TRAINING_FILENAMES'])\n    NUM_VALIDATION_IMAGES = count_data_items(VAL_FILES)\n    cmdataset = get_validation_dataset(load_dataset(VAL_FILES, labeled = True, ordered = True))\n    images_ds = cmdataset.map(lambda image, label: image)\n    labels_ds = cmdataset.map(lambda image, label: label).unbatch()\n    all_labels.append( next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES))).numpy() ) # get everything as one batch\n    prob = models[j].predict(images_ds)\n    all_prob.append( prob )\n    all_pred.append( np.argmax(prob, axis=-1) )\ncm_correct_labels = np.concatenate(all_labels)\ncm_probabilities = np.concatenate(all_prob)\ncm_predictions = np.concatenate(all_pred)","metadata":{"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Correct   labels: \", cm_correct_labels.shape, cm_correct_labels)\nprint(\"Predicted labels: \", cm_predictions.shape, cm_predictions); print()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cmat = confusion_matrix(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)))\nscore = f1_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro')\nprecision = precision_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro')\nrecall = recall_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro')\ndisplay_confusion_matrix(cmat, score, precision, recall)\nprint('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall)); print()","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}