如果border_mode选择为same,那么卷积操作的输入和输出尺寸会保持一致。如果选择valid,那卷积过后,尺寸会变小
# apply a 3x3 convolution with 64 output filters on a 256x256 image:
model = Sequential()
model.add(Convolution2D(64, 3, 3, border_mode='same', input_shape=(3, 256, 256)))
# now model.output_shape == (None, 64, 256, 256) >> definition in keras:
def conv_output_length(input_length, filter_size, border_mode, stride):
if input_length is None:
return None
assert border_mode in {'same', 'valid'}
if border_mode == 'same':
output_length = input_length
elif border_mode == 'valid':
output_length = input_length - filter_size + 1
return (output_length + stride - 1) // stride