{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"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 in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n# Input data files are available in the \"../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# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import math, re, os\nimport tensorflow as tf\nimport tensorflow.keras.backend as K\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix\nprint(\"Tensorflow version \" + tf.__version__)\nAUTO = tf.data.experimental.AUTOTUNE","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import logging, sys\n\nsys.stdout = open(\"/kaggle/working/logoutput.txt\", \"w\")\n\ndef setup_tf_logging():\n    tf.get_logger().handlers = []\n    tf.get_logger().addHandler(logging.StreamHandler(sys.stdout))\n\nsetup_tf_logging()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"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)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from kaggle_datasets import KaggleDatasets","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"GCS_DS_PATH = KaggleDatasets().get_gcs_path()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!gsutil ls $GCS_DS_PATH/","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"IMAGE_SIZE = [331, 331] # At this size, a GPU will run out of memory. Use the TPU.\n                        # For GPU training, please select 224 x 224 px image size.\nIMAGE_SIZE_1 = [512,512]\n#IMAGE_SIZE_2 = [331,331]\nEPOCHS = 40\nBATCH_SIZE = 16 * strategy.num_replicas_in_sync\n\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}\nGCS_PATH = GCS_PATH_SELECT[IMAGE_SIZE[0]]\nGCS_PATH_1 = GCS_PATH_SELECT[IMAGE_SIZE_1[0]]\n#GCS_PATH_2 = GCS_PATH_SELECT[IMAGE_SIZE_2[0]]\n\nTRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')\nTRAINING_FILENAMES2 = tf.io.gfile.glob(GCS_PATH_1 + '/train/*.tfrec')\n#TRAINING_FILENAMES3 = tf.io.gfile.glob(GCS_PATH_2 + '/train/*.tfrec')\nVALIDATION_FILENAMES = 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\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']                                                                                                                                               #","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"FULL_TRAINING=True\nif FULL_TRAINING:\n    TRAINING_FILENAMES+=VALIDATION_FILENAMES\nTRAINING_FILENAMES","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.set_printoptions(threshold=15, linewidth=80)\n# Y=[]\n# def count_flower_classes(databatch):\n#     images, labels = batch_to_numpy_images_and_labels(databatch)\n#     for label in labels:\n#         Y.append(CLASSES[label])\n#     return Y\n    \ndef batch_to_numpy_images_and_labels(data):\n    \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, 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 the case for test data)\n    \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    \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 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        \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\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.'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.utils import class_weight","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"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))\n\ndef 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","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\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\ndef decode_image2(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_1, 3]) # explicit size needed for TPU\n    return image\ndef decode_image3(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_2, 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    \n    return image, label # returns a dataset of (image, label) pairs\ndef read_labeled_tfrecord2(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_image2(example['image'])\n    label = tf.cast(example['class'], tf.int32)\n    \n    return image, label # returns a dataset of (image, label) pairs\n\ndef read_labeled_tfrecord3(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_image3(example['image'])\n    label = tf.cast(example['class'], tf.int32)\n    \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\n\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\ndef load_dataset2(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_tfrecord2 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\n\ndef load_dataset3(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_tfrecord3 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\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    \n   \n        \n    #image = tf.image.random_saturation(image, 0, 2)\n    #image = tf.image.resize_with_crop_or_pad(image, IMAGE_SIZE[0]+6, IMAGE_SIZE[0]+6) # Add 6 pixels of padding\n    #image = tf.image.random_crop(image, size=[IMAGE_SIZE[0], IMAGE_SIZE[0],3]) # Random crop back to 512x512\n    #image = tf.image.random_brightness(image, max_delta=0.5)\n   \n    return image, label  \n\ndef data_augment2(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    \n    print(f\"Resizing image with size {image.shape[1]} to {IMAGE_SIZE[0]}\")\n    image = tf.image.random_crop(image, size=[IMAGE_SIZE[0], IMAGE_SIZE[0],3])\n        \n    #image = tf.image.random_saturation(image, 0, 2)\n    #image = tf.image.resize_with_crop_or_pad(image, IMAGE_SIZE[0]+6, IMAGE_SIZE[0]+6) # Add 6 pixels of padding\n    #image = tf.image.random_crop(image, size=[IMAGE_SIZE[0], IMAGE_SIZE[0],3]) # Random crop back to 512x512\n    #image = tf.image.random_brightness(image, max_delta=0.5)\n   \n    return image, label   \n\ndef get_training_dataset():\n    dataset = load_dataset(TRAINING_FILENAMES, labeled=True)\n    dataset2 = load_dataset2(TRAINING_FILENAMES2, labeled=True)\n    #dataset3 = load_dataset3(TRAINING_FILENAMES3, labeled=True)\n    \n    #dataset = dataset.map(transform, num_parallel_calls=AUTO)\n    dataset = dataset.map(data_augment, num_parallel_calls=AUTO)\n    dataset2 = dataset2.map(data_augment2, num_parallel_calls=AUTO)\n    #dataset3 = dataset3.map(data_augment2, num_parallel_calls=AUTO)\n    dataset = dataset.concatenate(dataset2)\n    #dataset = dataset.concatenate(dataset3)\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\ndef get_class_weights():\n    dataset = load_dataset(TRAINING_FILENAMES, labeled=True)\n    dataset2 = load_dataset2(TRAINING_FILENAMES2, labeled=True)\n    #dataset3 = load_dataset3(TRAINING_FILENAMES3, labeled=True)\n    dataset = dataset.concatenate(dataset2)\n    #dataset = dataset.concatenate(dataset3)\n    flower_counts=[]\n    for images, labels in dataset:\n       flower_counts.append(labels) \n\n    print(np.array(flower_counts))\n    print(np.unique(flower_counts,return_counts=True))\n    \n    class_weights=class_weight.compute_class_weight('balanced',np.unique(flower_counts),np.array(flower_counts)  )\n    print(f'class_weights:{class_weights}')\n    return class_weights\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) # 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 = count_data_items(TRAINING_FILENAMES)+count_data_items(TRAINING_FILENAMES2)#+count_data_items(TRAINING_FILENAMES3)\nNUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)\nNUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)\nSTEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE\nprint('Dataset: {} training images, {} validation images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"get_training_dataset()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# print(\"Training data shapes:\")\n# for image, label in get_training_dataset().take(3):\n#     print(image.numpy().shape, label.numpy().shape)\n# print(\"Training data label examples:\", label.numpy())\n# print(\"Validation data shapes:\")\n# for image, label in get_validation_dataset().take(3):\n#     print(image.numpy().shape, label.numpy().shape)\n# print(\"Validation data label examples:\", label.numpy())\n# print(\"Test data shapes:\")\n# for image, idnum in get_test_dataset().take(3):\n#     print(image.numpy().shape, idnum.numpy().shape)\n# print(\"Test data IDs:\", idnum.numpy().astype('U')) # U=unicode string","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"training_dataset = get_training_dataset()\ntraining_dataset = training_dataset.unbatch().batch(20)\ntrain_batch = iter(training_dataset)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"display_batch_of_images(next(train_batch))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# test_dataset = get_test_dataset()\n# test_dataset = test_dataset.unbatch().batch(20)\n# test_batch = iter(test_dataset)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# display_batch_of_images(next(test_batch))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class_weights=get_class_weights()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install -q efficientnet","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from efficientnet.tfkeras import EfficientNetB7","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model 1: EfficientNetB7"},{"metadata":{"trusted":true},"cell_type":"code","source":"with strategy.scope():\n    #pretrained_model = tf.keras.applications.DenseNet201(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])\n    #pretrained_model = tf.keras.applications.Xception(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])\n    #pretrained_model = tf.keras.applications.VGG16(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])\n    pretrained_model_1  = EfficientNetB7(weights='noisy-student', include_top=False, input_shape=[*IMAGE_SIZE, 3])\n    pretrained_model_1.trainable = True # False = transfer learning, True = fine-tuning\n    \n    model = tf.keras.Sequential([\n        pretrained_model_1,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation='softmax')\n    ])\n        \nmodel.compile(\n    tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False),\n    loss = 'sparse_categorical_crossentropy',\n    metrics=['sparse_categorical_accuracy']\n)\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# print(\"Number of layers in the base model: \", len(pretrained_model_1.layers))\n# #pretrained_model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"LR_START = 0.0001\nLR_MAX = 0.00005 * strategy.num_replicas_in_sync\nLR_MIN = 0.00001\nLR_RAMPUP_EPOCHS = 4\nLR_SUSTAIN_EPOCHS = 6\nLR_EXP_DECAY = .8\n\ndef lrfn(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\n    \nlr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"if not FULL_TRAINING:\n    es_callback = tf.keras.callbacks.EarlyStopping(monitor=\"val_sparse_categorical_accuracy\",min_delta=0, patience=10, verbose=1, mode='auto', restore_best_weights=True)\nelse:\n    es_callback = tf.keras.callbacks.EarlyStopping(monitor=\"sparse_categorical_accuracy\",min_delta=0, patience=10, verbose=1, mode='auto', restore_best_weights=True)\n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"if not FULL_TRAINING:\n    history = model.fit(get_training_dataset(), steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS, validation_data=get_validation_dataset(),callbacks = [lr_callback, es_callback],class_weight=class_weights)\nelse:\n    history = model.fit(get_training_dataset(), steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS, callbacks = [lr_callback],class_weight=class_weights)\n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# !mkdir -p saved_model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n# model.save('saved_model/Flower_EFN.h5')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"display_training_curves(history.history['loss'], history.history['val_loss'], 'loss', 211)\ndisplay_training_curves(history.history['sparse_categorical_accuracy'], history.history['val_sparse_categorical_accuracy'], 'accuracy', 212)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model 2: Xception"},{"metadata":{"trusted":true},"cell_type":"code","source":"# with strategy.scope():\n#     #pretrained_model = tf.keras.applications.DenseNet201(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])\n#     pretrained_model_2 = tf.keras.applications.Xception(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])\n#     #pretrained_model = tf.keras.applications.VGG16(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])\n#     #pretrained_model  = EfficientNetB7(weights='noisy-student', include_top=False, input_shape=[*IMAGE_SIZE, 3])\n#     pretrained_model_2.trainable = True # False = transfer learning, True = fine-tuning\n    \n#     model_2 = tf.keras.Sequential([\n#         pretrained_model_2,\n#         tf.keras.layers.GlobalAveragePooling2D(),\n#         tf.keras.layers.Dense(len(CLASSES), activation='softmax')\n#     ])\n        \n# model_2.compile(\n#     tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False),\n#     loss = 'sparse_categorical_crossentropy',\n#     metrics=['sparse_categorical_accuracy']\n# )\n# model_2.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# if not FULL_TRAINING:\n#     history_2 = model_2.fit(get_training_dataset(), steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS, validation_data=get_validation_dataset(),callbacks = [lr_callback, es_callback])\n# else:\n#     history_2 = model_2.fit(get_training_dataset(), steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS, callbacks = [lr_callback])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# model_2.save('Flower_XCE.h5')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# print(\"Number of layers in the base model: \", len(pretrained_model_2.layers))\n#pretrained_model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# pretrained_model_2.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# pretrained_model_2.trainable = True\n# for layer in pretrained_model_2.layers[:-10]:\n#     layer.trainable = False\n# for layer in pretrained_model_2.layers:\n#     print(layer, layer.trainable)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# model_2.compile( tf.keras.optimizers.Adam(learning_rate=0.00001, beta_1=0.9, beta_2=0.999, amsgrad=False), loss = 'sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'] ) \n# model_2.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# fine_tune_epochs = 50\n# total_epochs = EPOCHS + fine_tune_epochs\n# history_2_fine = model_2.fit(get_training_dataset(), steps_per_epoch=STEPS_PER_EPOCH, epochs=total_epochs,initial_epoch = history_2.epoch[-1], validation_data=get_validation_dataset(),callbacks = [lr_callback, es_callback])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model 3: DenseNet201"},{"metadata":{"trusted":true},"cell_type":"code","source":"with strategy.scope():\n    pretrained_model_3 = tf.keras.applications.DenseNet201(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])\n    #pretrained_model_2 = tf.keras.applications.Xception(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])\n    #pretrained_model_4 = tf.keras.applications.VGG16(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])\n    #pretrained_model_1  = EfficientNetB7(weights='noisy-student', include_top=False, input_shape=[*IMAGE_SIZE, 3])\n    pretrained_model_3.trainable = True # False = transfer learning, True = fine-tuning\n    \n    model_3 = tf.keras.Sequential([\n        pretrained_model_3,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation='softmax')\n    ])\n        \nmodel_3.compile(\n    tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False),\n    loss = 'sparse_categorical_crossentropy',\n    metrics=['sparse_categorical_accuracy']\n)\nmodel_3.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"if not FULL_TRAINING:\n    history_3 = model_3.fit(get_training_dataset(), steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS, validation_data=get_validation_dataset(),callbacks = [lr_callback, es_callback], class_weight=class_weights)\nelse:\n    history_3 = model_3.fit(get_training_dataset(), steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS, callbacks = [lr_callback],class_weight=class_weights)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# model_3.save('Flower_Dense.h5')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Model 4: VGG 19"},{"metadata":{"trusted":true},"cell_type":"code","source":"# with strategy.scope():\n#     #pretrained_model = tf.keras.applications.DenseNet201(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])\n#     #pretrained_model = tf.keras.applications.Xception(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])\n#     pretrained_model_4 = tf.keras.applications.VGG16(weights='imagenet', include_top=False ,input_shape=[*IMAGE_SIZE, 3])\n#     #pretrained_model  = EfficientNetB7(weights='noisy-student', include_top=False, input_shape=[*IMAGE_SIZE, 3])\n#     pretrained_model.trainable = False # False = transfer learning, True = fine-tuning\n    \n#     model_4 = tf.keras.Sequential([\n#         pretrained_model_4,\n#         tf.keras.layers.GlobalAveragePooling2D(),\n#         tf.keras.layers.Dense(len(CLASSES), activation='softmax')\n#     ])\n        \n# model_4.compile(\n#     tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False),\n#     loss = 'sparse_categorical_crossentropy',\n#     metrics=['sparse_categorical_accuracy']\n# )\n# model_4.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# if not FULL_TRAINING:\n#     history_4 = model_4.fit(get_training_dataset(), steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS, validation_data=get_validation_dataset(),callbacks = [es_callback,lr_callback])\n# else:\n#     history_4 = model_4.fit(get_training_dataset(), steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS,callbacks = [lr_callback])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"if not FULL_TRAINING:\n    cmdataset = get_validation_dataset(ordered=True) # since we are splitting the dataset and iterating separately on images and labels, order matters.\n    images_ds = cmdataset.map(lambda image, label: image)\n    labels_ds = cmdataset.map(lambda image, label: label).unbatch()\n    cm_correct_labels = next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES))).numpy() # get everything as one batch\n    # cm_probabilities = (model_4.predict(images_ds)*0.1)+(model_3.predict(images_ds)*0.35)+(model_2.predict(images_ds)*0.25)+(model.predict(images_ds)*0.4)\n    pred_score=[]\n    dense_pred=model_3.predict(images_ds)\n    efn_pred=model.predict(images_ds)\n    for model_wt in np.linspace(0,1,100):\n        cm_probabilities= model_wt*dense_pred +(1-model_wt)*efn_pred\n        cm_predictions = np.argmax(cm_probabilities, axis=-1)\n        pred_score.append(f1_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro'))\n    #cm_probabilities = (model_3.predict(images_ds)*0.65)+(model.predict(images_ds)*0.35)\n    #cm_predictions = np.argmax(cm_probabilities, axis=-1)\n    plt.plot(pred_score)\n    best_model_wt=np.argmax(pred_score)/100\n    cm_probabilities= best_model_wt*dense_pred +(1-best_model_wt)*efn_pred\n    cm_predictions = np.argmax(cm_probabilities, axis=-1)\n    print(\"Correct   labels: \", cm_correct_labels.shape, cm_correct_labels)\n    print(\"Predicted labels: \", cm_predictions.shape, cm_predictions)\nelse:\n    best_model_wt=0.56","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(f'Best model weight {best_model_wt}')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"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')\ncmat = (cmat.T / cmat.sum(axis=1)).T # normalized\ndisplay_confusion_matrix(cmat, score, precision, recall)\nprint('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_ds = get_test_dataset(ordered=True) # since we are splitting the dataset and iterating separately on images and ids, order matters.\n\nprint('Computing predictions...')\ntest_images_ds = test_ds.map(lambda image, idnum: image)\n# probabilities =(model_4.predict(test_ds)*0.1)+(model_3.predict(test_ds)*0.3)+(model_2.predict(test_ds)*0.2)+(model.predict(test_ds)*0.4)\nprobabilities =(model_3.predict(test_ds)*best_model_wt)+(model.predict(test_ds)*best_model_wt)\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='')\n!head submission.csv","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# dataset = get_validation_dataset()\n# dataset = dataset.unbatch().batch(20)\n# batch = iter(dataset)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# # run this cell again for next set of images\n# images, labels = next(batch)\n# probabilities = model.predict(images)\n# predictions = np.argmax(probabilities, axis=-1)\n# display_batch_of_images((images, labels), predictions)","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}