Keras中重复赋值变量x为何未覆盖中间层信息？

输入代码 #

encoder_input = keras.Input(shape=(28, 28, 1), name="img")
x = layers.Conv2D(16, 3, activation="relu")(encoder_input)
x = layers.Conv2D(32, 3, activation="relu")(x)
x = layers.MaxPooling2D(3)(x)
x = layers.Conv2D(32, 3, activation="relu")(x)
x = layers.Conv2D(16, 3, activation="relu")(x)
encoder_output = layers.GlobalMaxPooling2D()(x)

encoder = keras.Model(encoder_input, encoder_output, name="encoder")
encoder.summary()

x = layers.Reshape((4, 4, 1))(encoder_output)
x = layers.Conv2DTranspose(16, 3, activation="relu")(x)
x = layers.Conv2DTranspose(32, 3, activation="relu")(x)
x = layers.UpSampling2D(3)(x)
x = layers.Conv2DTranspose(16, 3, activation="relu")(x)
decoder_output = layers.Conv2DTranspose(1, 3, activation="relu")(x)

autoencoder = keras.Model(encoder_input, decoder_output, name="autoencoder")
autoencoder.summary()

输出结果 #

Model: "encoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
img (InputLayer)             [(None, 28, 28, 1)]       0         
_________________________________________________________________
conv2d (Conv2D)              (None, 26, 26, 16)        160       
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 24, 24, 32)        4640      
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 8, 8, 32)          0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 6, 6, 32)          9248      
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 4, 4, 16)          4624      
_________________________________________________________________
global_max_pooling2d (Global (None, 16)                0         
=================================================================
Total params: 18,672
Trainable params: 18,672
Non-trainable params: 0
_________________________________________________________________
Model: "autoencoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
img (InputLayer)             [(None, 28, 28, 1)]       0         
_________________________________________________________________
conv2d (Conv2D)              (None, 26, 26, 16)        160       
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 24, 24, 32)        4640      
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 8, 8, 32)          0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 6, 6, 32)          9248      
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 4, 4, 16)          4624      
_________________________________________________________________
global_max_pooling2d (Global (None, 16)                0         
_________________________________________________________________
reshape (Reshape)            (None, 4, 4, 1)           0         
_________________________________________________________________
conv2d_transpose (Conv2DTran (None, 6, 6, 16)          160       
_________________________________________________________________
conv2d_transpose_1 (Conv2DTr (None, 8, 8, 32)          4640      
_________________________________________________________________
up_sampling2d (UpSampling2D) (None, 24, 24, 32)        0         
_________________________________________________________________
conv2d_transpose_2 (Conv2DTr (None, 26, 26, 16)        4624      
_________________________________________________________________
conv2d_transpose_3 (Conv2DTr (None, 28, 28, 1)         145       
=================================================================
Total params: 28,241
Trainable params: 28,241
Non-trainable params: 0
_________________________________________________________________

问题 #

为何每次调用新层时，变量x未被覆盖？调用encoder.summary()时能追踪到完整的层结构，但重复赋值x理当丢失中间层信息，这些信息究竟存储在何处？

解答 #

这是TensorFlow Functional API的核心工作机制决定的：

• 当你执行layers.Conv2D(...)(input_tensor)这类操作时，**不是在修改x变量，而是创建了一个全新</think_never_used_51bce0c785ca2f68081bfa7d91973934>
• 变量x只是一个"指针"，每次赋值只是让x指向最新生成的那个张量节点而已，之前的节点并没有被销毁——因为它们已经被纳入了从encoder_input到encoder_output的完整计算路径中，这些连接关系和层信息都存在TensorFlow的内部计算图结构里，和x变量的引用无关。
• 当你创建keras.Model(encoder_input, encoder_output)时，Keras会从输出节点出发，反向追溯所有依赖的上游节点，把整个路径上的所有层和张量连接都提取出来，组成完整的模型结构，所以summary()能显示所有中间层。
• 后面重新给x赋值时，只是在从encoder_output开始构建新的计算分支，和之前的编码器路径互不影响，因为之前的连接关系已经被固定在计算图里了，不会因为x的引用改变而消失。

内容的提问来源于stack exchange，提问作者user19580074