{"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":"## Introduction\n\nWelcome to Petals to Metal: Flower Competition, here are you are challenged to build a deep learning model for classifying flowers\n\n![images.jpeg](attachment:images.jpeg)\n\n","metadata":{},"attachments":{"images.jpeg":{"image/jpeg":"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"}}},{"cell_type":"markdown","source":"### Table of contents\n\n1. [What are Tensor Processing Units?](#tpu)\n2. [Peeking into TPU Hardware](#peek) \n3. [Exploring flowers](#eda) \n4. [What's EfficentNet](#efficentnet)\n5. [Densenet](#densenet)\n5. [Final model submission](#submit)","metadata":{"trusted":true}},{"cell_type":"markdown","source":"# What are TPU's? <a class=\"tpu\" id=\"prepare\"></a>\n\n\n\nTPU's are holy grail of computers for any Machine Learning Practitioners! A tensor processing unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google specifically for neural network machine learning. \nTPUs are hardware accelerators specialized in deep learning tasks. In this code lab, you will see how to use them with Keras and Tensorflow 2. Cloud TPUs are available in a base configuration with 8 cores and also in larger configurations called \"TPU pods\" of up to 2048 cores. The extra hardware can be used to accelerate training by increasing the training batch size.\n\n\n## Why TPUs?\n\nModern GPUs are organized around programmable \"cores\", a very flexible architecture that allows them to handle a variety of tasks such as 3D rendering, deep learning, physical simulations, etc.. TPUs on the other hand pair a classic vector processor with a dedicated matrix multiply unit and excel at any task where large matrix multiplications dominate, such as neural networks.\n","metadata":{}},{"cell_type":"markdown","source":"<html>\n<body>\n\n<p><font size=\"4\" color=\"Blue\"> The following video from Kaggle explains the main components of TPU like systolic arrays and bfloat16 number formats, and how these two components of TPUs help reduce deep learning model training times </font></p>\n</body>\n</html>","metadata":{}},{"cell_type":"code","source":"from IPython.display import YouTubeVideo\nYouTubeVideo(\"JC84GCU7zqA\")","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:30:36.332501Z","iopub.execute_input":"2022-07-01T01:30:36.332942Z","iopub.status.idle":"2022-07-01T01:30:36.463684Z","shell.execute_reply.started":"2022-07-01T01:30:36.332837Z","shell.execute_reply":"2022-07-01T01:30:36.462492Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Peeking into TPU Hardware [source](https://codelabs.developers.google.com/codelabs/keras-flowers-tpu/#2)<a class=\"anchor\" id=\"peek\"></a>\n\n\n\n## MXU and VPU\n\n\nA TPU v3 core is made of a Matrix Multiply Unit (MXU) which runs matrix multiplications and a Vector Processing Unit (VPU) for all other tasks such as activations, softmax, etc. The VPU handles float32 and int32 computations. The MXU on the other hand operates in a mixed precision 16-32 bit floating point format. The give image below shows a TPUv3 which operates at 420 TeraFlops and 128 GB HBM.\n\n![Screenshot_2020-02-26%20Keras%20and%20modern%20convnets,%20on%20TPUs.png](attachment:Screenshot_2020-02-26%20Keras%20and%20modern%20convnets,%20on%20TPUs.png)\n\n## Mixed precision floating point and bfloat16\n\nThe MXU computes matrix multiplications using bfloat16 inputs and float32 outputs. Intermediate accumulations are performed in float32 precision.\n\nNeural network training is typically resistant to the noise introduced by a reduced floating point precision. There are cases where noise even helps the optimizer converge. 16-bit floating point precision has traditionally been used to accelerate computations but float16 and float32 formats have very different ranges. Reducing the precision from float32 to float16 usually results in over and underflows. Solutions exist but additional work is typically required to make float16 work.\n\nThat is why Google introduced the bfloat16 format in TPUs. bfloat16 is a truncated float32 with exactly the same exponent bits and range as float32. This, added to the fact that TPUs compute matrix multiplications in mixed precision with bfloat16 inputs but float32 outputs, means that, typically, no code changes are necessary to benefit from the performance gains of reduced precision.\n\n\n> The use of bfloat16/float32 mixed precision is the default on TPUs. No code changes are necessary in your Tensorflow code to enable it\n\n## Systolic arrays\n\nCPUs are made to run pretty much any calculation. Therefore, CPU store values in registers and a program sends a set of instructions to the Arithmetic Logic Unit to read a given register, perform an operation and register the output into the right register. This comes at some cost in terms of power and chip area.\n\nFor an MXU, matrix multiplication reuses both inputs many times, \n\n\n\n## Under the hood: XLA\n\nTensorflow programs define computation graphs. The TPU does not directly run Python code, it runs the computation graph defined by your Tensorflow program. Under the hood, a compiler called XLA (accelerated Linear Algebra compiler) transforms the Tensorflow graph of computation nodes into TPU machine code. This compiler also performs many advanced optimizations on your code and your memory layout. The compilation happens automatically as work is sent to the TPU. You do not have to include XLA in your build chain explicitly.\n\n\n## Using TPUs in Keras\n\nTPUs are supported through the Keras API as of Tensorflow 2.1. Keras support works on TPUs and TPU pods.\n\nDon't worry TPU is also supported in Pytorch, check out @abhishek, 4X Kaggle grandmaster's video on [training BERT's in TPU](https://www.youtube.com/watch?v=s-3zts7FTDA)\n\n\n[Do check out System Architecture of TPU](https://cloud.google.com/tpu/docs/system-architecture) gives more detials of TPU configurations and various versions of TPU 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"}}},{"cell_type":"markdown","source":"<html>\n<body>\n\n<p><font size=\"4\" color=\"Blue\"> This video explains in detail about main differences between TPUv2 and TPUv3 </font></p>\n</body>\n</html>\n\n","metadata":{}},{"cell_type":"code","source":"from IPython.display import YouTubeVideo\nYouTubeVideo(\"kBjYK3K3P6M\")","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:30:36.466334Z","iopub.execute_input":"2022-07-01T01:30:36.466713Z","iopub.status.idle":"2022-07-01T01:30:36.530090Z","shell.execute_reply.started":"2022-07-01T01:30:36.466670Z","shell.execute_reply":"2022-07-01T01:30:36.529268Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Importing library","metadata":{}},{"cell_type":"code","source":"!pip install  efficientnet\n\nimport efficientnet.tfkeras as efn\nimport re\nimport math\nimport numpy as np\nimport seaborn as sns\n\nfrom kaggle_datasets import KaggleDatasets\nfrom matplotlib import pyplot as plt\nfrom sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.callbacks import LearningRateScheduler\nfrom tensorflow.keras.metrics import TruePositives, FalsePositives, FalseNegatives\nfrom tensorflow.keras.layers.experimental.preprocessing import CenterCrop\nprint(\"Tensorflow version \" + tf.__version__)","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:30:36.531658Z","iopub.execute_input":"2022-07-01T01:30:36.532534Z","iopub.status.idle":"2022-07-01T01:30:55.883976Z","shell.execute_reply.started":"2022-07-01T01:30:36.532488Z","shell.execute_reply":"2022-07-01T01:30:55.882783Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Dataset Processing","metadata":{}},{"cell_type":"code","source":"# This is basically -1\nAUTO = tf.data.experimental.AUTOTUNE\nAUTO\n","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:30:55.886880Z","iopub.execute_input":"2022-07-01T01:30:55.887541Z","iopub.status.idle":"2022-07-01T01:30:55.894263Z","shell.execute_reply.started":"2022-07-01T01:30:55.887493Z","shell.execute_reply":"2022-07-01T01:30:55.893188Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Cluster Resolver for Google Cloud TPUs.\ntpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n\n# Connects to the given cluster.\ntf.config.experimental_connect_to_cluster(tpu)\n\n# Initialize the TPU devices.\ntf.tpu.experimental.initialize_tpu_system(tpu)\n\n# TPU distribution strategy implementation.\nstrategy = tf.distribute.experimental.TPUStrategy(tpu)","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:30:55.895471Z","iopub.execute_input":"2022-07-01T01:30:55.896344Z","iopub.status.idle":"2022-07-01T01:31:02.378477Z","shell.execute_reply.started":"2022-07-01T01:30:55.896296Z","shell.execute_reply":"2022-07-01T01:31:02.377530Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Configurations\nIMAGE_SIZE = [512, 512]\nEPOCHS = 30\nBATCH_SIZE = 32 * strategy.num_replicas_in_sync\nLEARNING_RATE = 1e-4\nTTA_NUM =11","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:31:02.379702Z","iopub.execute_input":"2022-07-01T01:31:02.379928Z","iopub.status.idle":"2022-07-01T01:31:02.385668Z","shell.execute_reply.started":"2022-07-01T01:31:02.379901Z","shell.execute_reply":"2022-07-01T01:31:02.384702Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Batch size used: \", BATCH_SIZE)","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:31:02.386910Z","iopub.execute_input":"2022-07-01T01:31:02.387146Z","iopub.status.idle":"2022-07-01T01:31:02.398130Z","shell.execute_reply.started":"2022-07-01T01:31:02.387119Z","shell.execute_reply":"2022-07-01T01:31:02.397247Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# As TPUs require access to the GCS path\nGCS_DS_PATH = KaggleDatasets().get_gcs_path('tpu-getting-started')\nMORE_IMAGES_GCS_DS_PATH = KaggleDatasets().get_gcs_path('tf-flower-photo-tfrec')","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:31:02.399270Z","iopub.execute_input":"2022-07-01T01:31:02.399613Z","iopub.status.idle":"2022-07-01T01:31:03.342952Z","shell.execute_reply.started":"2022-07-01T01:31:02.399567Z","shell.execute_reply":"2022-07-01T01:31:03.342115Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"GCS_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]]\n\nTRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec')\nVALIDATION_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')\nTEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec') # predictions on this dataset should be submitted for the competition\n\nMOREIMAGES_PATH_SELECT = {\n    192: '/tfrecords-jpeg-192x192',\n    224: '/tfrecords-jpeg-224x224',\n    331: '/tfrecords-jpeg-331x331',\n    512: '/tfrecords-jpeg-512x512'\n}\nMOREIMAGES_PATH = MOREIMAGES_PATH_SELECT[IMAGE_SIZE[0]]\n\nIMAGENET_FILES = tf.io.gfile.glob(MORE_IMAGES_GCS_DS_PATH + '/imagenet' + MOREIMAGES_PATH + '/*.tfrec')\nINATURELIST_FILES = tf.io.gfile.glob(MORE_IMAGES_GCS_DS_PATH + '/inaturalist' + MOREIMAGES_PATH + '/*.tfrec')\nOPENIMAGE_FILES = tf.io.gfile.glob(MORE_IMAGES_GCS_DS_PATH + '/openimage' + MOREIMAGES_PATH + '/*.tfrec')","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:31:03.344529Z","iopub.execute_input":"2022-07-01T01:31:03.344855Z","iopub.status.idle":"2022-07-01T01:31:03.822767Z","shell.execute_reply.started":"2022-07-01T01:31:03.344813Z","shell.execute_reply":"2022-07-01T01:31:03.821255Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"SKIP_VALIDATION = True\n\nif SKIP_VALIDATION:\n    TRAINING_FILENAMES = TRAINING_FILENAMES + VALIDATION_FILENAMES + IMAGENET_FILES + INATURELIST_FILES + OPENIMAGE_FILES\n\n","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:31:03.826848Z","iopub.execute_input":"2022-07-01T01:31:03.827219Z","iopub.status.idle":"2022-07-01T01:31:03.833079Z","shell.execute_reply.started":"2022-07-01T01:31:03.827182Z","shell.execute_reply":"2022-07-01T01:31:03.831823Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Unhide the code to see the classes in Dataset","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']               \nprint(f\"No of Flower classes in dataset: {len(CLASSES)}\")","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-01T01:31:03.834109Z","iopub.execute_input":"2022-07-01T01:31:03.834334Z","iopub.status.idle":"2022-07-01T01:31:03.846500Z","shell.execute_reply.started":"2022-07-01T01:31:03.834307Z","shell.execute_reply":"2022-07-01T01:31:03.845807Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Helper functions","metadata":{}},{"cell_type":"markdown","source":"### Model functions","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 = .7\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(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]))\n\n","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-01T01:31:03.847727Z","iopub.execute_input":"2022-07-01T01:31:03.848848Z","iopub.status.idle":"2022-07-01T01:31:04.306195Z","shell.execute_reply.started":"2022-07-01T01:31:03.848809Z","shell.execute_reply":"2022-07-01T01:31:04.305153Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Visualisation Helper Fuctions","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()\n    \ndef display_training_curves(training, validation, title, subplot):\n    with plt.xkcd():\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":{"_kg_hide-output":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-01T01:31:04.307866Z","iopub.execute_input":"2022-07-01T01:31:04.308205Z","iopub.status.idle":"2022-07-01T01:31:04.322850Z","shell.execute_reply.started":"2022-07-01T01:31:04.308157Z","shell.execute_reply":"2022-07-01T01:31:04.321828Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def 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 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# Visualize model predictions\ndef dataset_to_numpy_util(dataset, N):\n    dataset = dataset.unbatch().batch(N)\n    for images, labels in dataset:\n        numpy_images = images.numpy()\n        numpy_labels = labels.numpy()\n        break;  \n    return numpy_images, numpy_labels\n\ndef title_from_label_and_target(label, correct_label):\n    label = np.argmax(label, axis=-1)\n    correct = (label == correct_label)\n    return \"{} [{}{}{}]\".format(CLASSES[label], str(correct), ', shoud be ' if not correct else '',\n                                CLASSES[correct_label] if not correct else ''), correct\n\ndef display_one_flower_eval(image, title, subplot, red=False):\n    plt.subplot(subplot)\n    plt.axis('off')\n    plt.imshow(image)\n    plt.title(title, fontsize=14, color='red' if red else 'black')\n    return subplot+1\n\ndef display_9_images_with_predictions(images, predictions, labels):\n    subplot=331\n    plt.figure(figsize=(13,13))\n    for i, image in enumerate(images):\n        title, correct = title_from_label_and_target(predictions[i], labels[i])\n        subplot = display_one_flower_eval(image, title, subplot, not correct)\n        if i >= 8:\n            break;\n              \n    plt.tight_layout()\n    plt.subplots_adjust(wspace=0.1, hspace=0.1)\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:31:04.324698Z","iopub.execute_input":"2022-07-01T01:31:04.324996Z","iopub.status.idle":"2022-07-01T01:31:04.349154Z","shell.execute_reply.started":"2022-07-01T01:31:04.324931Z","shell.execute_reply":"2022-07-01T01:31:04.348236Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Dataset 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 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\n\n\ndef data_augment(image, label, seed=2020):\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, seed=seed)\n#     image = tf.image.random_flip_up_down(image, seed=seed)\n#     image = tf.image.random_brightness(image, 0.1, seed=seed)\n    \n#     image = tf.image.random_jpeg_quality(image, 85, 100, seed=seed)\n#     image = tf.image.resize(image, [530, 530])\n#     image = tf.image.random_crop(image, [512, 512], seed=seed)\n    #image = tf.image.random_saturation(image, 0, 2)\n    return image, label   \n\ndef get_training_dataset():\n    dataset = load_dataset(TRAINING_FILENAMES, labeled=True)\n    dataset = dataset.map(data_augment, num_parallel_calls=AUTO)\n    dataset = dataset.repeat() # the training dataset must repeat for several epochs\n    dataset = dataset.shuffle(2048)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef get_validation_dataset(ordered=False):\n    dataset = load_dataset(VALIDATION_FILENAMES, labeled=True, ordered=ordered)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.cache()\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef get_train_valid_datasets():\n    dataset = load_dataset(TRAINING_FILENAMES + VALIDATION_FILENAMES, labeled=True)\n    dataset = dataset.map(data_augment, num_parallel_calls=AUTO)\n    dataset = dataset.repeat() # the training dataset must repeat for several epochs\n    dataset = dataset.shuffle(2048)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)\n    return dataset\n\ndef get_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)","metadata":{"_kg_hide-output":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-01T01:31:04.350722Z","iopub.execute_input":"2022-07-01T01:31:04.351661Z","iopub.status.idle":"2022-07-01T01:31:04.373658Z","shell.execute_reply.started":"2022-07-01T01:31:04.351606Z","shell.execute_reply":"2022-07-01T01:31:04.372479Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"models = []\nhistories = []\n","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:31:04.375321Z","iopub.execute_input":"2022-07-01T01:31:04.375763Z","iopub.status.idle":"2022-07-01T01:31:04.389033Z","shell.execute_reply.started":"2022-07-01T01:31:04.375716Z","shell.execute_reply":"2022-07-01T01:31:04.388301Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Exploring Flower(EDA)<a class=\"anchor\" id=\"eda\"></a>","metadata":{}},{"cell_type":"markdown","source":"Heavily borrowed from [dimitreoliveira EDA kernel](https://www.kaggle.com/dimitreoliveira/flower-classification-with-tpus-eda-and-baseline/notebook)","metadata":{}},{"cell_type":"code","source":"# No of images in dataset\nNUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)\nNUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)\nNUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)\nSTEPS_PER_EPOCH = (NUM_TRAINING_IMAGES + NUM_VALIDATION_IMAGES) // BATCH_SIZE\nprint('Dataset: {} training images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES+NUM_VALIDATION_IMAGES, NUM_TEST_IMAGES))","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:31:04.390119Z","iopub.execute_input":"2022-07-01T01:31:04.390631Z","iopub.status.idle":"2022-07-01T01:31:04.404252Z","shell.execute_reply.started":"2022-07-01T01:31:04.390588Z","shell.execute_reply":"2022-07-01T01:31:04.403628Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def 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\n\ndef get_training_dataset_preview(ordered=True):\n    dataset = load_dataset(TRAINING_FILENAMES, labeled=True, ordered=ordered)\n    dataset = dataset.batch(BATCH_SIZE)\n    dataset = dataset.cache()\n    dataset = dataset.prefetch(AUTO)\n    return dataset\n\n# Visualization utility functions\nnp.set_printoptions(threshold=15, linewidth=80)\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, 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    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\n\n# Visualize model predictions\ndef dataset_to_numpy_util(dataset, N):\n    dataset = dataset.unbatch().batch(N)\n    for images, labels in dataset:\n        numpy_images = images.numpy()\n        numpy_labels = labels.numpy()\n        break;  \n    return numpy_images, numpy_labels","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:31:04.405773Z","iopub.execute_input":"2022-07-01T01:31:04.406504Z","iopub.status.idle":"2022-07-01T01:31:04.421817Z","shell.execute_reply.started":"2022-07-01T01:31:04.406452Z","shell.execute_reply":"2022-07-01T01:31:04.420981Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_dataset = get_training_dataset_preview(ordered=True)\ny_train = next(iter(train_dataset.unbatch().map(lambda image, label: label).batch(NUM_TRAINING_IMAGES))).numpy()\nprint('Number of training images %d' % NUM_TRAINING_IMAGES)","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:31:04.422782Z","iopub.execute_input":"2022-07-01T01:31:04.423218Z","iopub.status.idle":"2022-07-01T01:31:54.480928Z","shell.execute_reply.started":"2022-07-01T01:31:04.423189Z","shell.execute_reply":"2022-07-01T01:31:54.480006Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display_batch_of_images(next(iter(train_dataset.unbatch().batch(20))))","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:31:54.482121Z","iopub.execute_input":"2022-07-01T01:31:54.482772Z","iopub.status.idle":"2022-07-01T01:31:57.372304Z","shell.execute_reply.started":"2022-07-01T01:31:54.482734Z","shell.execute_reply":"2022-07-01T01:31:57.369699Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# # Label distribution\n# train_stack = np.asarray([[label, (y_train == index).sum()] for index, label in enumerate(CLASSES)])\n\n# fig, (ax1) = plt.subplots(1, 1, figsize=(24, 32))\n\n# ax1 = sns.barplot(x=train_stack[...,1], y=train_stack[...,0], order=CLASSES,ax=ax1)\n# ax1.set_title('Training labels', fontsize=30)\n# ax1.tick_params(labelsize=16)\n","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:34:47.569293Z","iopub.execute_input":"2022-07-01T01:34:47.569887Z","iopub.status.idle":"2022-07-01T01:34:47.574439Z","shell.execute_reply.started":"2022-07-01T01:34:47.569853Z","shell.execute_reply":"2022-07-01T01:34:47.573623Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# peer at test data\ntest_dataset = get_test_dataset()\ntest_dataset = test_dataset.unbatch().batch(20)\ntest_batch = iter(test_dataset)","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:34:48.197985Z","iopub.execute_input":"2022-07-01T01:34:48.198627Z","iopub.status.idle":"2022-07-01T01:34:48.304576Z","shell.execute_reply.started":"2022-07-01T01:34:48.198591Z","shell.execute_reply":"2022-07-01T01:34:48.303648Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# run this cell again for next set of images\ndisplay_batch_of_images(next(test_batch))","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:34:57.629415Z","iopub.execute_input":"2022-07-01T01:34:57.629748Z","iopub.status.idle":"2022-07-01T01:35:00.431738Z","shell.execute_reply.started":"2022-07-01T01:34:57.629716Z","shell.execute_reply":"2022-07-01T01:35:00.430949Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Augmentations","metadata":{}},{"cell_type":"markdown","source":"The 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.","metadata":{}},{"cell_type":"code","source":"# def get_mat(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):\n#     rotation = math.pi * rotation / 180.\n#     shear = math.pi * shear/ 180.\n    \n#     c1 = tf.math.cos(rotation)\n#     c2 = tf.math.sin(rotation)\n#     one = tf.constant([1], dtype='float32')\n#     zero = tf.constant([0], dtype='float32')\n#     rotation_mat = tf.reshape(tf.concat([c1, s1, zero, -s1, c1, zero, \\\n#                                          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_mat = tf.reshape(tf.concat([one, s2, zero, zero, c2, \\\n#                                          zero, zero, zero, one], axis=0), [3,3])\n    \n#     zoom_mat = tf.reshape(tf.concat([one/height_zoom, zero, zero, zero, \\\n#                                     oneb/width_zoom, zero, zero, zero, one], axis=0), [3,3])\n    \n#     shift_mat = tf.reshape(tf.concat([one_zero, height_shift, zero, one, width_shift, zero, \\\n#                                       zero, one], axis=0), [3,3])\n    \n#     return K.dot(K.dot(rotation_mat, shear_mat), K.dot(zoom_mat, shift_mat))","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:35:00.433059Z","iopub.execute_input":"2022-07-01T01:35:00.433628Z","iopub.status.idle":"2022-07-01T01:35:00.437660Z","shell.execute_reply.started":"2022-07-01T01:35:00.433596Z","shell.execute_reply":"2022-07-01T01:35:00.436851Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# def transform(image, label):\n#     DIM = 512\n#     XIM = DIM%2\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') / 1.0\n#     w_zoom = 1.0 + tf.random.normal([1], dtype='float32')/1.0\n    \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":{"execution":{"iopub.status.busy":"2022-07-01T01:35:00.438808Z","iopub.execute_input":"2022-07-01T01:35:00.439121Z","iopub.status.idle":"2022-07-01T01:35:00.454206Z","shell.execute_reply.started":"2022-07-01T01:35:00.439095Z","shell.execute_reply":"2022-07-01T01:35:00.453454Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# row = 3; col = 4;\n# all_elements = get_training_dataset(load_dataset(TRAINING_FILENAMES),do_aug=False).unbatch()\n# one_element = tf.data.Dataset.from_tensors( next(iter(all_elements)) )\n# augmented_element = one_element.repeat().map(transform).batch(row*col)\n\n# for (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\n","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:35:00.455786Z","iopub.execute_input":"2022-07-01T01:35:00.456164Z","iopub.status.idle":"2022-07-01T01:35:00.469177Z","shell.execute_reply.started":"2022-07-01T01:35:00.456129Z","shell.execute_reply":"2022-07-01T01:35:00.468318Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# What's EfficientNet? <a class=\"anchor\" id=\"efficentnet\"></a>\n\nMost of people in this competition has used Efficient Net for this competiton. I was curious about what's Efficient and why everyone is getting good scores with \nthis new Image architecture.\n\n<html>\n<body>\n\n<p><font size=\"4\" color=\"Blue\"> Finally I stumbled in this ICML 2019 paper, Efficient Net</font></p>\n</body>\n</html>\nIn the paper “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”, we propose a novel model scaling method that uses a simple yet highly effective compound coefficient to scale up CNNs in a more structured manner. Unlike conventional approaches that arbitrarily scale network dimensions, such as width, depth and resolution, our method uniformly scales each dimension with a fixed set of scaling coefficients. Powered by this novel scaling method and recent progress on AutoML, we have developed a family of models, called EfficientNets, which superpass state-of-the-art accuracy with up to 10x better efficiency (smaller and faster).\n\n![Screenshot_2020-02-26%20EfficientNet%20Rethinking%20Model%20Scaling%20for%20Convolutional%20Neural%20Networks%20-%20tan19a%20pdf.png](attachment:Screenshot_2020-02-26%20EfficientNet%20Rethinking%20Model%20Scaling%20for%20Convolutional%20Neural%20Networks%20-%20tan19a%20pdf.png)","metadata":{},"attachments":{"Screenshot_2020-02-26%20EfficientNet%20Rethinking%20Model%20Scaling%20for%20Convolutional%20Neural%20Networks%20-%20tan19a%20pdf.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"As the image show's,  EfficientNet tops the current state of the art both in accuracy and in computational efficiency. How did they do this?\n\n## Lesson 1\n\nThey learned that CNN’s must be scaled up in multiple dimensions. Scaling CNN’s only in one direction (eg depth only) will result in rapidly deteriorating gains relative to the computational increase needed.Most CNN’s are typically scaled up by adding more layers or deeper . e.g. ResNet18, ResNet34, ResNet152, etc. The numbers represent the total number of blocks (layers) and in general, the more layers the more ‘power’ the CNN has. Going wider is another often used scaling method, and tends to capture finer details and can be easier to train. However, it’s benefits quickly saturate as well.\n\nThere are three scaling dimensions of a CNN: depth, width, and resolution. Depth simply means how deep the networks is which is equivalent to the number of layers in it. Width simply means how wide the network is. One measure of width, for example, is the number of channels in a Conv layer whereas Resolution is simply the image resolution that is being passed to a CNN. The figure below(from the paper itself) will give you a clear idea of what scaling means across different dimensions. We will discuss these in detail as well.\n\n![Screenshot_2020-02-27%20EfficientNet%20Rethinking%20Model%20Scaling%20for%20Convolutional%20Neural%20Networks.png](attachment:Screenshot_2020-02-27%20EfficientNet%20Rethinking%20Model%20Scaling%20for%20Convolutional%20Neural%20Networks.png)\n\n## Lesson 2: Compound Scaling\n\nIn order to scale up efficiently, all dimensions of depth, width and resolution have to be scaled together, and there is an optimal balance for each dimension relative to the others. Intuition says that as the resolution of the images is increased, depth and width of the network should be increased as well. As the depth is increased, larger receptive fields can capture similar features that include more pixels in an image. Also, as the width is increased, more fine-grained features will be captured. To validate this intuition, the authors ran a number of experiments with different scaling values for each dimension. \n\nThese results lead to our second observation: It is critical to balance all dimensions of a network (width, depth, and resolution) during CNNs scaling for getting improved accuracy and efficiency.\n\nThe authors discovered that there is a synergy in scaling multiple dimensions together, and after an extensive grid search derived the theoretically optimal formula of “compound scaling” using the following co-efficients: Depth = 1.20, Width = 1.10, Resolution = 1.15\n\nThe authors proposed a simple yet very effective scaling technique which uses a compound coefficient ɸ to uniformly scale network width, depth, and resolution in a principled way\n\n![Screenshot_2020-02-27%20EfficientNet%20Rethinking%20Model%20Scaling%20for%20Convolutional%20Neural%20Networks%281%29.png](attachment:Screenshot_2020-02-27%20EfficientNet%20Rethinking%20Model%20Scaling%20for%20Convolutional%20Neural%20Networks%281%29.png)\n\nɸ is a user-specified coefficient that controls how many resources are available whereas α, β, and γ specify how to assign these resources to network depth, width, and resolution respectively.\n\nIn a CNN, Conv layers are the most compute expensive part of the network. Also, FLOPS of a regular convolution op is almost proportional to d, w², r², i.e. doubling the depth will double the FLOPS while doubling width or resolution increases FLOPS almost by four times. Hence, in order to make sure that the total FLOPS don’t exceed 2^ϕ, the constraint applied is that (α * β² * γ²) ≈ 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"},"Screenshot_2020-02-27%20EfficientNet%20Rethinking%20Model%20Scaling%20for%20Convolutional%20Neural%20Networks%281%29.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"## EfficientNet Architecture\n\nThe effectiveness of model scaling also relies heavily on the baseline network. So, to further improve performance, we have also developed a new baseline network by performing a neural architecture search using the AutoML MNAS framework, which optimizes both accuracy and efficiency (FLOPS). The resulting architecture uses mobile inverted bottleneck convolution (MBConv), similar to MobileNetV2 and MnasNet, but is slightly larger due to an increased FLOP budget. We then scale up the baseline network to obtain a family of models, called EfficientNets. \n\nThe MBConv block is nothing fancy but an Inverted Residual Block (used in MobileNetV2) with a Squeeze and Excite block injected sometimes.\n\nNow we have the base network, we can search for optimal values for our scaling parameters. If you revisit the equation, you will quickly realize that we have a total of four parameters to search for: α, β, γ, and ϕ. In order to make the search space smaller and making the search operation less costly, the search for these parameters can be completed in two steps.\n\n- Fix ϕ =1, assuming that twice more resources are available, and do a small grid search for α, β, and γ. For baseline network B0, it turned out the optimal values are α =1.2, β = 1.1, and γ = 1.15 such that α * β² * γ² ≈ 2\n- Now fix α, β, and γ as constants (with values found in above step) and experiment with different values of ϕ. The different values of ϕ produce EfficientNets B1-B7.\n\n\n\nThe effectiveness of model scaling also relies heavily on the baseline network. So, to further improve performance, we have also developed a new baseline network by performing a neural architecture search using the AutoML MNAS framework, which optimizes both accuracy and efficiency (FLOPS). The resulting architecture uses mobile inverted bottleneck convolution (MBConv), similar to MobileNetV2 and MnasNet, but is slightly larger due to an increased FLOP budget. We then scale up the baseline network to obtain a family of models, called EfficientNets. 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"}}},{"cell_type":"code","source":"# Need this line so Google will recite some incantations\n# for Turing to magically load the model onto the TPU\nwith strategy.scope():\n    enet = efn.EfficientNetB3(\n        input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3),\n        weights='imagenet',\n        include_top=False\n    )\n    \n    enet.trainable = True\n    model1 = tf.keras.Sequential([\n        enet,\n        tf.keras.layers.GlobalAveragePooling2D(name=\"Layer1\"),\n        tf.keras.layers.Dropout(0.7),\n        tf.keras.layers.Dense(len(CLASSES), activation='softmax')\n    ])\n        \n# METRICS = ['TruePositives','FalsePositives', 'FalseNegatives']\nmodel1.compile(\n    optimizer=tf.keras.optimizers.Adam(lr=0.0001),\n    loss = 'sparse_categorical_crossentropy',\n    metrics = \"sparse_categorical_accuracy\"\n)\n\nmodel1.summary()\n\nmodels.append(model1)","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:35:00.470179Z","iopub.execute_input":"2022-07-01T01:35:00.470797Z","iopub.status.idle":"2022-07-01T01:35:21.867055Z","shell.execute_reply.started":"2022-07-01T01:35:00.470759Z","shell.execute_reply":"2022-07-01T01:35:21.866041Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# schedule = StepDecay(initAlpha=1e-4, factor=0.25, dropEvery=15)\n\n# callbacks = [LearningRateScheduler(schedule)]","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:35:21.868734Z","iopub.execute_input":"2022-07-01T01:35:21.868974Z","iopub.status.idle":"2022-07-01T01:35:21.873934Z","shell.execute_reply.started":"2022-07-01T01:35:21.868942Z","shell.execute_reply":"2022-07-01T01:35:21.872616Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Visualising the Model architecture\ntf.keras.utils.plot_model(\n    model1, to_file='model.png', show_shapes=True, show_layer_names=True,\n)\n","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:35:21.874974Z","iopub.execute_input":"2022-07-01T01:35:21.875202Z","iopub.status.idle":"2022-07-01T01:35:23.323168Z","shell.execute_reply.started":"2022-07-01T01:35:21.875168Z","shell.execute_reply":"2022-07-01T01:35:23.322361Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nCheckpoint=tf.keras.callbacks.ModelCheckpoint(f\"Enet_model.h5\", monitor='val_accuracy', verbose=1, save_best_only=True,\n       save_weights_only=True,mode='max')\n\ntrain_history1 = model1.fit(\n    get_training_dataset(), \n    steps_per_epoch=STEPS_PER_EPOCH,\n    epochs=EPOCHS,\n    callbacks=[lr_callback, Checkpoint, keras.callbacks.EarlyStopping(\n        monitor=\"val_loss\",\n        min_delta=1e-2,\n        patience=2,\n        verbose=1,\n    )],\n)\n\nhistories.append(train_history1)\n\n","metadata":{"execution":{"iopub.status.busy":"2022-07-01T01:35:23.324848Z","iopub.execute_input":"2022-07-01T01:35:23.325202Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Training accuracy and loss curves","metadata":{}},{"cell_type":"code","source":"def plot_training(H):\n\t# construct a plot that plots and saves the training history\n\twith plt.xkcd():\n\t\tplt.figure()\n\t\tplt.plot(H.history[\"loss\"], label=\"train_loss\")\n\t\tplt.plot(H.history[\"sparse_categorical_accuracy\"], label=\"train_accuracy\")\n\t\tplt.title(\"Training Loss and Accuracy\")\n\t\tplt.xlabel(\"Epoch #\")\n\t\tplt.ylabel(\"Loss/Accuracy\")\n\t\tplt.legend(loc=\"lower left\")\n\t\tplt.show()","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_training(train_history1)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### TPU Utilisation\n\n![Screenshot_2020-03-03%20Introduction%20kernel%20what's%20EfficientNet%20Kaggle.png](attachment:Screenshot_2020-03-03%20Introduction%20kernel%20what's%20EfficientNet%20Kaggle.png)","metadata":{},"attachments":{"Screenshot_2020-03-03%20Introduction%20kernel%20what's%20EfficientNet%20Kaggle.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# DensenetNet model <a class=\"anchor\" id=\"densenet\"></a>","metadata":{}},{"cell_type":"code","source":"with strategy.scope():\n    densenet = tf.keras.applications.DenseNet201(input_shape=[*IMAGE_SIZE, 3], weights='imagenet', include_top=False)\n    densenet.trainable = True\n    \n    model2 = tf.keras.Sequential([\n        densenet,\n        tf.keras.layers.GlobalAveragePooling2D(),\n        tf.keras.layers.Dense(len(CLASSES), activation='softmax')\n    ])\n        \nmodel2.compile(\n    optimizer=tf.keras.optimizers.Adam(),\n    loss = 'sparse_categorical_crossentropy',\n    metrics=['sparse_categorical_accuracy']\n)\nmodel2.summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Visualising the Model architecture\ntf.keras.utils.plot_model(\n    model1, to_file='model.png', show_shapes=True, show_layer_names=True,\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nCheckpoint=tf.keras.callbacks.ModelCheckpoint(f\"Dnet_model.h5\", monitor='val_accuracy', verbose=1, save_best_only=True,\n       save_weights_only=True,mode='max')\ntrain_history2 = model2.fit(get_training_dataset(), \n                    steps_per_epoch=STEPS_PER_EPOCH,\n                    epochs=EPOCHS, \n                    callbacks = [lr_callback, Checkpoint, keras.callbacks.EarlyStopping(\n        # Stop training when `val_loss` is no longer improving\n        monitor=\"val_loss\",\n        # \"no longer improving\" being defined as \"no better than 1e-2 less\"\n        min_delta=1e-2,\n        # \"no longer improving\" being further defined as \"for at least 2 epochs\"\n        patience=2,\n        verbose=1,\n    )])\n\nhistories.append(train_history2)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_training(train_history2)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Submitting Model Predicions <a class=\"tpu\" id=\"submit\"></a>\n\nEnsembling tricks and TTA borrowed from [notebook](https://www.kaggle.com/atamazian/flower-classification-ensemble-effnet-densenet).","metadata":{}},{"cell_type":"markdown","source":"## Find best alpha","metadata":{}},{"cell_type":"code","source":"if not SKIP_VALIDATION:\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    m1 = model1.predict(images_ds)\n    m2 = model2.predict(images_ds)\n    scores = []\n    for alpha in np.linspace(0,1,100):\n        cm_probabilities = alpha*m1+(1-alpha)*m2\n        cm_predictions = np.argmax(cm_probabilities, axis=-1)\n        scores.append(f1_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro'))\n\n    best_alpha = np.argmax(scores)/100\nelse:\n    best_alpha = 0.51  # change to value calculated with SKIP_VALIDATION=False\n    \nprint('Best alpha: ' + str(best_alpha))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Confusion Matrix","metadata":{}},{"cell_type":"code","source":"if not SKIP_VALIDATION:\n    cmat = confusion_matrix(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)))\n    score = f1_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro')\n    precision = precision_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro')\n    recall = recall_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro')\n    #cmat = (cmat.T / cmat.sum(axis=1)).T # normalized\n    display_confusion_matrix(cmat, score, precision, recall)\n    print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Test time augmentation(TTA)","metadata":{}},{"cell_type":"code","source":"def predict_tta(model, n_iter):\n    probs  = []\n    for i in range(n_iter):\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        probs.append(model.predict(test_images_ds,verbose=0))\n        \n    return probs","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"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('Calculating predictions...')\ntest_images_ds = test_ds.map(lambda image, idnum: image)\nprobs1 = np.mean(predict_tta(model1, TTA_NUM), axis=0)\nprobs2 = np.mean(predict_tta(model2, TTA_NUM), axis=0)\nprobabilities = best_alpha*probs1 + (1-best_alpha)*probs2\npredictions = np.argmax(probabilities, axis=-1)\n\nprint('Generating submission 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","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"[submission](submission.csv)","metadata":{}},{"cell_type":"markdown","source":"### References\n\n- https://codelabs.developers.google.com/codelabs/keras-flowers-tpu/#0\n- https://arxiv.org/abs/1905.11946\n- https://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html\n- https://medium.com/@lessw/efficientnet-from-google-optimally-scaling-cnn-model-architectures-with-compound-scaling-e094d84d19d4\n- https://medium.com/@nainaakash012/efficientnet-rethinking-model-scaling-for-convolutional-neural-networks-92941c5bfb95\n- https://www.kaggle.com/mmmarchetti/flowers-on-tpu-ii","metadata":{}}]}