x = torch . randn ( 2 , 1 , 7 , 3 ) conv = torch . nn . Conv2d ( 1 , 8 , ( 2 , 3 ) ) res = conv ( x ) print ( res . shape ) # shape = (2, 8, 6, 1) [ batch_size , channels , height_1, width_1 ]
batch_size 一个batch中样例的个数       2
channels 通道数，也就是当前层的深度 1
height_1, 图片的高                                 7
width_1, 图片的宽                                  3

Conv2d的参数
[ channels , output , height_2, width_2 ]

channels, 通道数，和上面保持一致，也就是当前层的深度  1
output 输出的深度                                                                 8
height_2, 过滤器filter的高                                                      2
width_2, 过滤器filter的宽                                                       3

[ batch_size , output , height_3, width_3 ]

batch_size, 一个batch中样例的个数，同上           2
output 输出的深度                                                  8
height_3, 卷积结果的高度                                      6 = height_1 - height_2 + 1 = 7-2+1
width_3, 卷积结果的宽度                                       1 = width_1 - width_2 +1 = 3-3+1

torch.nn.Conv2d
torch.nn.MaxPool2d

文章最后发布于: 2019-02-21