我有以下顺序模型：
model = models.Sequential()
model.add(Reshape(([1]+in_shp), input_shape=in_shp))
model.add(ZeroPadding2D((0, 2)))
model.add(Conv2D(256, (1, 3),padding='valid', activation="relu", name="conv1",data_format="channels_first", kernel_initializer='glorot_uniform'))
model.add(Dropout(dr))
model.add(ZeroPadding2D((0, 2)))
model.add(Conv2D(80, (2, 3), padding="valid", activation="relu", name="conv2",data_format="channels_first", kernel_initializer='glorot_uniform'))
model.add(Dropout(dr))
model.add(Flatten())
model.add(Dense(256, activation='relu', kernel_initializer='he_normal', name="dense1"))
model.add(Dropout(dr))
model.add(Dense( len(classes), kernel_initializer='he_normal', name="dense2" ))
model.add(Activation('softmax'))
model.add(Reshape([len(classes)]))
model.compile(loss='categorical_crossentropy', optimizer='adam')
model.summary()
我得到了以下摘要：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
reshape_1 (Reshape) (None, 1, 2, 128) 0
_________________________________________________________________
zero_padding2d_1 (ZeroPaddin (None, 1, 6, 128) 0
_________________________________________________________________
conv1 (Conv2D) (None, 256, 6, 126) 1024
_________________________________________________________________
dropout_1 (Dropout) (None, 256, 6, 126) 0
_________________________________________________________________
zero_padding2d_2 (ZeroPaddin (None, 256, 10, 126) 0
_________________________________________________________________
conv2 (Conv2D) (None, 80, 9, 124) 122960
_________________________________________________________________
dropout_2 (Dropout) (None, 80, 9, 124) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 89280) 0
_________________________________________________________________
dense1 (Dense) (None, 256) 22855936
_________________________________________________________________
dropout_3 (Dropout) (None, 256) 0
_________________________________________________________________
dense2 (Dense) (None, 8) 2056
_________________________________________________________________
activation_1 (Activation) (None, 8) 0
_________________________________________________________________
reshape_2 (Reshape) (None, 8) 0
=================================================================
Total params: 22,981,976
Trainable params: 22,981,976
Non-trainable params: 0
该模型工作正常。但是，我想了解有关conv1图层的一些信息。为什么宽度值从 128 减少到 126？我真的很困惑，它不应该与上一层的值相同吗？
此外，对于conv2高度和宽度从 (10,126) 减小到 (9,124) 的图层也是如此。
有人可以解释我为什么吗？