fcn16s_conv_layer = keras.layers.Conv2D(filters=NUM_CLASSES,kernel_size=(1, 1),
activation="softmax",padding="same",strides=(1, 1),)

fcn16s_upsample_layer = keras.layers.UpSampling2D(size=(16, 16),data_format=keras.
backend.image_data_format(),interpolation="bilinear",)

final_fcn16s_pool = keras.layers.Add()([pool4, pool5])
final_fcn16s_output = fcn16s_conv_layer(final_fcn16s_pool)
final_fcn16s_output = fcn16s_upsample_layer(final_fcn16s_output)

fcn16s_model = keras.models.Model(inputs=input_layer, outputs=final_fcn16s_output)

pool3 = keras.layers.Conv2D( filters=NUM_CLASSES, kernel_size=(1, 1), padding="same", strides=(1, 1), activation="linear", kernel_initializer=keras.initializers.Zeros(),)(pool3_output)

intermediate_pool_output = keras.layers.UpSampling2D(size=(2, 2),data_format=keras.
backend.image_data_format(),interpolation="bilinear",)(final_fcn16s_pool)

fcn8s_conv_layer = keras.layers.Conv2D(filters=NUM_CLASSES,kernel_size=(1, 1),
activation="softmax",padding="same",strides=(1, 1),)