python_garbage_collection/model_def.py at master · mass2010chromium/python_garbage_collection · GitHub

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from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense, LeakyReLU
from keras import regularizers

def create_model(input_shape):
    model = Sequential()

    model.add(Conv2D(8, (1, 1), input_shape=input_shape))
    model.add(LeakyReLU(0.1))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(8, (1, 1)))
    model.add(LeakyReLU(0.1))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(16, (1, 1)))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(16, (1, 1)))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('tanh'))
    model.add(MaxPooling2D(pool_size=(2, 2)))

    # model.add(Conv2D(32, (3, 3)))
    # model.add(Activation('tanh'))
    # model.add(MaxPooling2D(pool_size=(2, 2)))


    model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
    model.add(Dense(64))#, kernel_regularizer=regularizers.l2(0.01), activity_regularizer=regularizers.l1(0.01)))
    model.add(Dense(64))#, kernel_regularizer=regularizers.l2(0.01), activity_regularizer=regularizers.l1(0.01)))
    model.add(Dense(64))#, kernel_regularizer=regularizers.l2(0.01), activity_regularizer=regularizers.l1(0.01)))
    model.add(Dense(64))#, kernel_regularizer=regularizers.l2(0.01), activity_regularizer=regularizers.l1(0.01)))
    model.add(Dense(64, kernel_regularizer=regularizers.l2(0.01), activity_regularizer=regularizers.l1(0.01)))
    model.add(Activation('tanh'))
    #model.add(Dense(4))

    # model.compile(loss='categorical_crossentropy',
    #               optimizer='rmsprop',
    #               metrics=['accuracy'])

    # model.add(Dropout(0.5))
    model.add(Dense(1))
    model.add(Activation('sigmoid'))

    return model

def load_model(input_shape, path):
    model = create_model(input_shape)
    model.load_weights(path)
    return model