{"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# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"train_df = pd.read_csv('../input/train.csv')\nprint(train_df.info())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df['diagnosis'] = train_df['diagnosis'].astype('str')\ntrain_df['id_code'] = train_df['id_code'].astype(str)+'.png'\nfrom keras.preprocessing.image import ImageDataGenerator\n\ndatagen=ImageDataGenerator(\n    rescale=1./255, \n    validation_split=0.2)\n\nbatch_size = 32\n\ntrain_gen=datagen.flow_from_dataframe(\n    dataframe=train_df,\n    directory=\"../input/train_images\",\n    x_col=\"id_code\",\n    y_col=\"diagnosis\",\n    batch_size=batch_size,\n    shuffle=True,\n    class_mode=\"categorical\",\n    target_size=(224,224),\n    subset='training')\n\ntest_gen=datagen.flow_from_dataframe(\n    dataframe=train_df,\n    directory=\"../input/train_images\",\n    x_col=\"id_code\",\n    y_col=\"diagnosis\",\n    batch_size=batch_size,\n    shuffle=True,\n    class_mode=\"categorical\", \n    target_size=(224,224),\n    subset='validation')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_train = train_df['diagnosis']\nfrom keras.utils import np_utils\ny_train = np_utils.to_categorical(y_train)\nnum_classes = y_train.shape[1]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.models import Sequential\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.layers.core import Activation\nfrom keras.layers.core import Flatten\nfrom keras.layers.core import Dropout\nfrom keras.layers.core import Dense\nfrom keras import backend as K","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\nmodel.add(Conv2D(64, (3, 3), padding=\"same\",input_shape=(224,224,3)))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(Conv2D(64, (3, 3), padding=\"same\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\n\nmodel.add(MaxPooling2D(pool_size=(3, 3),strides=2))\nmodel.add(Dropout(0.25))\nmodel.add(Conv2D(128, (3, 3), padding=\"same\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(Conv2D(128, (3, 3), padding=\"same\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(MaxPooling2D(pool_size=(3, 3),strides=2))\nmodel.add(Dropout(0.25))\nmodel.add(Conv2D(256, (3, 3), padding=\"same\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(Conv2D(256, (3, 3), padding=\"valid\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\n\nmodel.add(Conv2D(256, (3, 3), padding=\"valid\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(Conv2D(256, (3, 3), padding=\"valid\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(MaxPooling2D(pool_size=(3, 3),strides=2))\nmodel.add(Dropout(0.25))\nmodel.add(Conv2D(512, (3, 3), padding=\"same\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(Conv2D(512, (3, 3), padding=\"same\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(Conv2D(512, (3, 3), padding=\"same\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(Conv2D(512, (3, 3), padding=\"same\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(MaxPooling2D(pool_size=(3,3),strides=2))\nmodel.add(Dropout(0.25))\n\nmodel.add(Conv2D(512, (3, 3), padding=\"same\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(Conv2D(512, (3, 3), padding=\"same\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(Conv2D(512, (3, 3), padding=\"same\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(Conv2D(512, (3, 3), padding=\"same\"))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization(axis=-1))\nmodel.add(MaxPooling2D(pool_size=(3,3),strides=2))\nmodel.add(Dropout(0.25))\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.add(Flatten())\nmodel.add(Dense(1024))\nmodel.add(Activation(\"relu\"))\nmodel.add(BatchNormalization())\nmodel.add(Dense(num_classes, activation='softmax'))\nmodel.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.callbacks import EarlyStopping, ModelCheckpoint\nes= EarlyStopping(monitor='val_loss', mode ='min', verbose = 0, patience = 5)\nmc = ModelCheckpoint('model.h5', monitor='val_loss', save_best_only = True, mode ='min', verbose = 0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit_generator(generator=train_gen,              \n                                    steps_per_epoch=len(train_gen),\n                                    validation_data=test_gen,                    \n                                    validation_steps=len(test_gen),\n                                    epochs=10,\n                                    callbacks = [es, mc], \n                                    use_multiprocessing = True,\n                                    verbose=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_df = pd.read_csv('../input/sample_submission.csv')\n#submission_df['diagnosis'] = submission_df['diagnosis'].astype('str')\nsubmission_df['id_code'] = submission_df['id_code'].astype(str)+'.png'\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_datagen=ImageDataGenerator(rescale=1./255)\nsubmission_gen=submission_datagen.flow_from_dataframe(\n    dataframe=submission_df,\n    directory=\"../input/test_images\",\n    x_col=\"id_code\",    \n    batch_size=batch_size,\n    shuffle=False,\n    class_mode=None, \n    target_size=(224,224)\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions=model.predict_generator(submission_gen, steps = len(submission_gen))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"max_probability = np.argmax(predictions,axis=1) \nsubmission_df1 = pd.read_csv('../input/sample_submission.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_df['diagnosis'] = max_probability\nsubmission_df.drop('id_code',axis=1)\nsubmission_df['id_code']=submission_df1['id_code']\nsubmission_df.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}