{"cells":[{"metadata":{},"cell_type":"markdown","source":"# SIIM-ISIC Melanoma","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"## The Competition\n\nSkin cancer is common cancer type and despite beign mostly non malignant, due to high case numbers it's pretty serious diasease and can lead serious cases if not detected, treated in time. It's usually diagnosed by eye for primarily and followed by further clinical analysis if needed. Even though the rares outcome is called melanoma it's the most deadly one, so early detection is pretty important. For this task using computer aided diagnosis might be helpful for primarily steps and early detections. Better detection might save thousands of lives.\n\nThis competition might help reaching that goal and I hope it can help people around the world...[](http://)","execution_count":null},{"metadata":{},"cell_type":"markdown","source":"## First Impressions and Getting Tools Ready\n\nLet's buckle up and get our tools ready for our work! We start with importing neccesary libraries. Since we going to do mostly EDA our libraries are going to be related with tabular data and visualization.","execution_count":null},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install -q efficientnet","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import os\nimport re\nimport seaborn as sns\nimport numpy as np\nimport pandas as pd\nimport math\nimport tensorflow_hub as hub\nfrom matplotlib import pyplot as plt\n\nfrom sklearn import metrics\nfrom sklearn.model_selection import train_test_split\nimport efficientnet.tfkeras as efn\nimport tensorflow as tf\nimport tensorflow.keras.layers as L\n\n\nfrom kaggle_datasets import KaggleDatasets\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"try:\n    # TPU detection. No parameters necessary if TPU_NAME environment variable is\n    # set: this is always the case on Kaggle.\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n    print('Running on TPU ', tpu.master())\nexcept ValueError:\n    tpu = None\n\nif tpu:\n    tf.config.experimental_connect_to_cluster(tpu)\n    tf.tpu.experimental.initialize_tpu_system(tpu)\n    strategy = tf.distribute.experimental.TPUStrategy(tpu)\nelse:\n    # Default distribution strategy in Tensorflow. Works on CPU and single GPU.\n    strategy = tf.distribute.get_strategy()\n\nprint(\"REPLICAS: \", strategy.num_replicas_in_sync)\ndef seed_everything(seed=0):\n    np.random.seed(seed)\n    tf.random.set_seed(seed)\n    os.environ['PYTHONHASHSEED'] = str(seed)\n    os.environ['TF_DETERMINISTIC_OPS'] = '1'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# For tf.dataset\nAUTO = tf.data.experimental.AUTOTUNE\n\n# Data access\nGCS_PATH = KaggleDatasets().get_gcs_path('siim-isic-melanoma-classification')\n\n# Configuration\n#EPOCHS = 1\n#BATCH_SIZE = 16 * strategy.num_replicas_in_sync\n#IMAGE_SIZE = [224, 224]\nEPOCHS = 12\nBATCH_SIZE = 16 * strategy.num_replicas_in_sync\nIMAGE_SIZE = [1024, 1024]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def append_path(pre):\n    return np.vectorize(lambda file: os.path.join(GCS_DS_PATH, pre, file))\nsub = pd.read_csv('/kaggle/input/siim-isic-melanoma-classification/sample_submission.csv')\ntrain = pd.read_csv('/kaggle/input/siim-isic-melanoma-classification/train.csv')\nsns.countplot(train['target'])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nTRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/tfrecords/train*.tfrec')\nTEST_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/tfrecords/test*.tfrec')\n\nCLASSES = [0,1]  ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"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        \"target\": 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    label = tf.cast(example['target'], 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        \"image_name\": 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['image_name']\n    return image, idnum # returns a dataset of image(s)\n\ndef load_dataset(filenames, labeled=True, ordered=False):\n    # Read from TFRecords. For optimal performance, reading from multiple files at once and\n    # disregarding data order. Order does not matter since we will be shuffling the data anyway.\n\n    ignore_order = tf.data.Options()\n    if not ordered:\n        ignore_order.experimental_deterministic = False # disable order, increase speed\n\n    dataset = tf.data.TFRecordDataset(filenames, 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 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    #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_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)\nNUM_TEST_IMAGES = count_data_items(TEST_FILENAMES)\nSTEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE\nprint('Dataset: {} training images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES, NUM_TEST_IMAGES))\n\n\n\"\"\"\"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    image = tf.image.resize(image, IMAGE_SIZE)\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        \"target\": 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    label = tf.cast(example['target'], 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        \"image_name\": 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['image_name']\n    return image, idnum # returns a dataset of image(s)\n\ndef load_dataset(filenames, labeled=True, ordered=False):\n    # Read from TFRecords. For optimal performance, reading from multiple files at once and\n    # disregarding data order. Order does not matter since we will be shuffling the data anyway.\n\n    ignore_order = tf.data.Options()\n    if not ordered:\n        ignore_order.experimental_deterministic = False # disable order, increase speed\n\n    dataset = tf.data.TFRecordDataset(filenames, 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 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    image = tf.image.random_flip_up_down(image)\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_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\n\nNUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)\nNUM_VALID_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,{} vaid images, {} unlabeled test images'.format(NUM_TRAINING_IMAGES,NUM_VALID_IMAGES, NUM_TEST_IMAGES))\"\"\"\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def build_lrfn(lr_start=0.00001, lr_max=0.0001, \n               lr_min=0.000001, lr_rampup_epochs=20, \n               lr_sustain_epochs=0, lr_exp_decay=.8):\n    lr_max = lr_max * strategy.num_replicas_in_sync\n\n    def 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    \n    return lrfn","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lr = 0.003 * BATCH_SIZE / 512 \n\nlr_schedule = tf.keras.optimizers.schedules.PiecewiseConstantDecay(boundaries=[5,10,15], \n                                                                   values=[lr, lr*0.1, lr*0.001, lr*0.0001])\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with strategy.scope():\n    get_model = tf.keras.Sequential([\n        efn.EfficientNetB5(\n            input_shape=(*IMAGE_SIZE, 3),\n            #weights='imagenet',\n            weights='imagenet',\n            include_top=False\n        ),\n        L.GlobalAveragePooling2D(),\n        L.Dense(1024, activation = 'relu'), \n        L.Dropout(0.3), \n        L.Dense(512, activation= 'relu'), \n        L.Dropout(0.2), \n        L.Dense(256, activation='relu'), \n        L.Dropout(0.2), \n        L.Dense(128, activation='relu'), \n        L.Dropout(0.1), \n        L.Dense(1, activation='sigmoid')\n    ])\n    ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with strategy.scope():\n    model= get_model\n    with strategy.scope():\n        model = tf.keras.Sequential([\n        efn.EfficientNetB0(\n            input_shape=(*IMAGE_SIZE, 3),\n            weights='noisy-student',\n            include_top=False\n        ),\n        L.GlobalAveragePooling2D(),\n        L.Dense(1, activation='sigmoid')\n    ])\n    \n    \nmodel.compile(\n    optimizer='adam',\n    #loss = 'binary_crossentropy',\n    loss = tf.keras.losses.BinaryCrossentropy(label_smoothing = 0.01),\n    metrics=['binary_crossentropy']\n)\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lrfn = build_lrfn()\nlr_schedule = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=1)\nSTEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit(\n    get_training_dataset(), \n    epochs=EPOCHS, \n    callbacks=[lr_schedule],\n    steps_per_epoch=STEPS_PER_EPOCH,\n)\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_ds = get_test_dataset(ordered=True)\n\nprint('Computing predictions...')\ntest_images_ds = test_ds.map(lambda image, idnum: image)\nprobabilities = model.predict(test_images_ds)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print('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","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred_df = pd.DataFrame({'image_name': test_ids, 'target': np.concatenate(probabilities)})\npred_df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"del sub['target']\nsub = sub.merge(pred_df, on='image_name')\n#sub.to_csv('submission_label_smoothing.csv', index=False)\nsub.to_csv('submission.csv', index=False)\nsub.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}