import json from keras.models import Sequential from keras.layers import Convolution2D, MaxPooling2D from keras.layers import Activation, Dropout, Flatten, Dense def get_tutorial_model(): model = Sequential() model.add(Convolution2D(32, 3, 3, input_shape=(150, 150, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering='tf')) model.add(Convolution2D(32, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering='tf')) model.add(Convolution2D(64, 3, 3)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering='tf')) # the model so far outputs 3D feature maps (height, width, features) model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors model.add(Dense(64)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(1)) model.add(Activation('sigmoid')) return model