{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport os\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\n!pip install efficientnet\nfrom efficientnet import tfkeras as efn","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"train = pd.read_csv(\"../input/siim-isic-melanoma-classification/train.csv\")\ntrain.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"size  = (256,256)\ndef get_model():\n    model_input = tf.keras.Input(shape=(*size, 3), name='imgIn')\n    tab_input = tf.keras.Input(shape=(3,),name=\"tabIn\")\n    dummy = tf.keras.layers.Lambda(lambda x:x)(model_input)\n    outputs = []    \n    for i in range(8):\n        constructor = getattr(efn, f'EfficientNetB{i}')\n \n        x = constructor(include_top=False, weights='imagenet', \n                        input_shape=(*size, 3), \n                        pooling='avg')(dummy)\n \n        x = tf.keras.layers.Dense(100, activation='relu')(x)\n        x = tf.keras.layers.Dropout(0.4)(x)\n        x = tf.keras.layers.Dense(50, activation='relu')(x)\n        y = tf.keras.layers.Dense(100,activation=\"relu\")(tab_input)\n        y = tf.keras.layers.Dense(50,activation=\"relu\")(y)\n        concatenated = tf.keras.layers.concatenate([x, y], axis=-1)\n        output = tf.keras.layers.Dense(1, activation='sigmoid')(concatenated)\n        outputs.append(output)\n \n    model = tf.keras.Model([model_input,tab_input], outputs, name='aNetwork')\n    model.compile(optimizer='adam',loss = tf.keras.losses.BinaryCrossentropy(\n    label_smoothing = 0.05),metrics=[tf.keras.metrics.AUC(name='auc')])\n    return model\nmodel = get_model()\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import sys\nsys.getsizeof(model)","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}