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[:, :2, :, :], output_shape=lambda x: (None, 2, 22, 80))(input_img) # no map channel

        # whole map 10x1
        tower_1 = ZeroPadding2D(padding=(1, 0), data_format="channels_first")(input_2)
        tower_1 = Conv2D(32, (10, 1), data_format="channels_first", strides=(7, 1), kernel_initializer=initializer, padding="valid")(tower_1)
        tower_1 = Flatten()(tower_1)

        # whole map 1x10
        tower_2 = Conv2D(32, (1, 10), data_format="channels_first", strides=(1, 7), kernel_initializer=initializer, padding="valid")(input_2)
        tower_2 = Flatten()(tower_2)

        # whole map 3x3 then maxpool 22x80
        tower_3 = Conv2D(32, (3, 3), data_format="channels_first", strides=(1, 1), kernel_initializer=initializer, padding="same")(input_2)
        tower_3 = MaxPooling2D(pool_size=(22, 80), data_format="channels_first")(tower_3)
        tower_3 = Flatten()(tower_3)

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

        predictions = Dense(4, kernel_initializer=initializer)(merged_layers)
        model = Model(inputs=input_img, outputs=predictions)

        adam = Adam(lr=1e-6)
        model.compile(loss='mse', optimizer=adam)
        return model