{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import os\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport keras as ks\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Conv2D, Dense, MaxPooling2D, Flatten, Dropout\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom PIL import Image\nfrom tensorflow.keras.applications.densenet import preprocess_input\n\n\n\n\ntrain_original = pd.read_csv(\"../input/siim-isic-melanoma-classification/train.csv\", dtype=str)\ntest_original = pd.read_csv('../input/siim-isic-melanoma-classification/test.csv', dtype=str)\n\n\n\n\npart_true = train_original[train_original[\"target\"] == '1']\npart_false = train_original[train_original[\"target\"] == '0'].sample(len(part_true) * 2)\ntrain_balanced = pd.concat([part_true, part_false])\n\n\n\n\nt = train_balanced\nt[\"file_name\"] = t[\"image_name\"] + \".png\"\n            \ntrain_set = t.sample(round(len(t) * 0.7))\nval_set = t[~t[\"image_name\"].isin(train_set[\"image_name\"])]\n\n\ntest_set = test_original\ntest_set[\"file_name\"] = test_set[\"image_name\"] + \".png\"\n\n\n\n\n\nimg_width, img_height = 224, 224\nnb_validation_samples = len(val_set)\nepochs = 10\nbatch_size = 128\n\n\n\n\n\n\ntrain_datagen = ImageDataGenerator(\n    rescale=1. / 255,\n    zoom_range=0.2,\n    rotation_range = 5,\n    horizontal_flip=True)\n\ntrain_generator = train_datagen.flow_from_dataframe(\n    dataframe = train_set, \n    directory = \"../input/siic-isic-224x224-images/train\", \n    x_col = \"file_name\", \n    # y_col = \"diagnosis\", \n    y_col = \"target\",\n    # class_mode = \"categorical\", \n    class_mode = \"binary\", \n    target_size = (img_width, img_height), \n    batch_size = batch_size,\n    validate_filenames = False)\n\n\n\n\n\n\n\n\nval_datagen = ImageDataGenerator(rescale=1. / 255)\nval_generator = val_datagen.flow_from_dataframe(\n    dataframe = val_set, \n    directory = \"../input/siic-isic-224x224-images/train\", \n    x_col = \"file_name\",\n    # y_col = \"diagnosis\",\n    y_col = \"target\",\n    # class_mode = \"categorical\", \n    class_mode = \"binary\", \n    target_size = (img_width, img_height), \n    batch_size = batch_size,\n    validate_filenames = False)\n\n\n\n\n\n\n\n\ntest_datagen = ImageDataGenerator(rescale=1. / 255)\ntest_generator = test_datagen.flow_from_dataframe(\n    dataframe=test_set,\n    directory=\"../input/siic-isic-224x224-images/test\",\n    x_col=\"file_name\",\n    y_col=None,\n    batch_size=1,\n    seed=420,\n    class_mode=None,\n    target_size=(img_width, img_height),\n    validate_filenames = False\n)\n\n\n\n\n\n\n\nbase_model = ks.applications.densenet.DenseNet169(\n    include_top=False, \n    input_shape=(img_width,img_height,3), \n    weights = '../input/densenet-keras/DenseNet-BC-169-32-no-top.h5')\n\nbase_model.trainable = False\n\n\n\nadd_model = Sequential()\nadd_model.add(Conv2D(32, kernel_size=(3,3), activation='relu'))\nadd_model.add(MaxPooling2D(pool_size=(2, 2)))\nadd_model.add(Flatten(input_shape=base_model.output_shape[1:]))\nadd_model.add(Dense(256, activation='relu'))\nadd_model.add(Dropout(0.2))\nadd_model.add(Dense(1, activation='softmax'))\n\n\n\nmodel = Model(inputs = base_model.input, outputs = add_model(base_model.output))\nmodel.compile(optimizer='adam',\n              loss='binary_crossentropy', \n              metrics=['accuracy'])\n\n\n\n\n\n\n\n\n\nhistory = model.fit_generator(\n    train_generator,\n    epochs=epochs,\n    validation_data=val_generator, \n    validation_steps=nb_validation_samples // batch_size)\n\n\n\n\n\n\npredict=model.predict_generator(test_generator)\n\n\nsubmission = pd.DataFrame(\n    {'image_name': test_set[\"image_name\"], 'target': predict.flatten()},\n    columns = ['image_name', 'target']\n)\nsubmission.to_csv('submission_file.csv', index=False)\n\n\n","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}