model.py 文件源码

def GeneratorDeconv(image_size = 64): 

    L = int(image_size)

    inputs = Input(shape = (100, ))
    x = Dense(512*int(L/16)**2)(inputs) #shape(512*(L/16)**2,)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Reshape((int(L/16), int(L/16), 512))(x) # shape(L/16, L/16, 512)
    x = Conv2DTranspose(256, (4, 4), strides = (2, 2),
                        kernel_initializer = init,
                        padding = 'same')(x) # shape(L/8, L/8, 256)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2DTranspose(128, (4, 4), strides = (2, 2),
                        kernel_initializer = init,
                        padding = 'same')(x) # shape(L/4, L/4, 128)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2DTranspose(64, (4, 4), strides = (2, 2),
                        kernel_initializer = init,
                        padding = 'same')(x) # shape(L/2, L/2, 64)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2DTranspose(3, (4, 4), strides= (2, 2),
                        kernel_initializer = init,
                        padding = 'same')(x) # shape(L, L, 3)
    images = Activation('tanh')(x)

    model = Model(inputs = inputs, outputs = images)
    model.summary()
    return model