models.py 文件源码

def build_model(self):

        initializer = initializers.random_normal(stddev=0.02)

        input_img = Input(shape=(self.layers, 22, 80))
        input_2 = Lambda(lambda x: x[:, 1:, :, :], output_shape=lambda x: (None, self.layers - 1, 22, 80))(input_img) # no map channel

        # whole map
        tower_1 = Conv2D(64, (3, 3), data_format="channels_first", strides=(1, 1), kernel_initializer=initializer, padding="same")(input_img)
        tower_1 = Conv2D(32, (3, 3), data_format="channels_first", strides=(1, 1), kernel_initializer=initializer, padding="same")(tower_1)
        tower_1 = MaxPooling2D(pool_size=(22, 80), data_format="channels_first")(tower_1)


        #tower2
        tower_2 = MaxPooling2D(pool_size=(2, 2), data_format="channels_first")(input_2)
        for _ in range(self.depth):
            tower_2 = Conv2D(32, (3, 3), data_format="channels_first", strides=(1, 1), kernel_initializer=initializer, padding="same", activation='relu')(tower_2)
        tower_2 = MaxPooling2D(pool_size=(11, 40), data_format="channels_first")(tower_2)

        #tower3
        tower_3 = MaxPooling2D(pool_size=(3, 6), data_format="channels_first", padding='same')(input_2)
        for _ in range(self.depth):
            tower_3 = Conv2D(32, (3, 3), data_format="channels_first", strides=(1, 1), kernel_initializer=initializer, padding="same", activation='relu')(tower_3)
        tower_3 = MaxPooling2D(pool_size=(8, 14), data_format="channels_first", padding='same')(tower_3)

        merged_layers = concatenate([tower_1, tower_2, tower_3], axis=1)

        flat_layer = Flatten()(merged_layers)

        predictions = Dense(5, kernel_initializer=initializer)(flat_layer)
        model = Model(inputs=input_img, outputs=predictions)

        rmsprop = RMSprop(lr=0.00025)
        model.compile(loss='mse', optimizer=rmsprop)
        return model