Conv2D 사이즈 관련 질문드립니다

[Kcrong]

keraspp/ex6_2_ae_conv_mnist_mc.py

Line 29 in 4090fcc

z = Conv2D(1, (7, 7))(x)

리포지토리 코드와 책에 따르면 인코딩 모델의 마지막 레이어는 7x7 사이즈를 갖도록 설정되었는데요,

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_12 (InputLayer)        (None, 28, 28, 1)         0         
_________________________________________________________________
cnn1 (Conv2D)                (None, 26, 26, 4)         40        
_________________________________________________________________
max_pooling1 (MaxPooling2D)  (None, 13, 13, 4)         0         
_________________________________________________________________
cnn3 (Conv2D)                (None, 11, 11, 8)         296       
_________________________________________________________________
max_pooling2 (MaxPooling2D)  (None, 6, 6, 8)           0         # <--- 이 부분
_________________________________________________________________
final_cnn (Conv2D)           (None, 1, 1, 1)           289       
=================================================================
Total params: 625
Trainable params: 625
Non-trainable params: 0
_________________________________________________________________

max_pooling2 레이어의 출력값이 6x6 인데 CNN 7x7 을 사용할 수 있나요?

위 summary() 결과를 얻기 위해 사용한 모델 코드는 아래와 같습니다.

        input_layer = Input(shape=self.input_shape)
        
        x = Conv2D(4, (3, 3), name='cnn1')(input_layer)
        x = MaxPooling2D((2, 2), padding='same', name='max_pooling1')(x)
        x = Conv2D(8, (3, 3), name='cnn3')(x)
        x = MaxPooling2D((2, 2), padding='same', name='max_pooling2')(x)
        
        output_layer = Conv2D(1, (6, 6), name='final_cnn')(x)

또한 7x7 사이즈의 CNN 을 사용했을 때, 나오는 에러는 아래와 같습니다.

ValueError: Negative dimension size caused by subtracting 7 from 6 for 'final_cnn_4/convolution' (op: 'Conv2D') with input shapes: [?,6,6,8], [7,7,8,1].