我尝试用下面的VggBlock实现Vgg网络。

class VggBlock(tf.keras.Model):
  def __init__(self, filters, repetitions):
    super(VggBlock, self).__init__()
    self.repetitions = repetitions

    self.conv_layers = [Conv2D(filters=filters, kernel_size=(3, 3), padding='same', activation='relu') for _ in range(repetitions)]
    self.max_pool = MaxPool2D(pool_size=(2, 2))

  def call(self, inputs):
    x = inputs
    for layer in self.conv_layers:
      x = layer(x)
    return self.max_pool(x)

test_block = VggBlock(filters=64, repetitions=2)
temp_inputs = Input(shape=(224, 224, 3))
test_block(temp_inputs)
test_block.summary()

然后，上面的代码将打印：

Model: "vgg_block"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 conv2d (Conv2D)             multiple                  1792      
                                                                 
 conv2d_1 (Conv2D)           multiple                  36928     
                                                                 
 max_pooling2d (MaxPooling2D  multiple                 0         
 )                                                               
                                                                 
=================================================================
Total params: 38,720
Trainable params: 38,720
Non-trainable params: 0
_________________________________________________________________

而且如果我用这些块构建Vgg，它的summary()也会打印“multiple”。
有一些问题与我的问题类似，例如：https://github.com/keras-team/keras/issues/13782，model.summary() can't print output shape while using subclass model
但是，我不能把答案延伸到第二个环节：以变化的input_shape表示。
我如何处理summary()，以便使“多个”成为一个适当的形状。