我就廢話不多說了，直接上程式碼吧！

# -*- coding: utf-8 -*-
#@Time  :2019/7/1 13:34
#@Author :XiaoMa
 
import torch as t
from torch import nn
#Sequential的三種寫法
net1=nn.Sequential()
net1.add_module('conv',nn.Conv2d(3,3,3))  #Conv2D(輸入通道數，輸出通道數，卷積核大小)
net1.add_module('batchnorm',nn.BatchNorm2d(3))  #BatchNorm2d(特徵數)
net1.add_module('activation_layer',nn.ReLU())
 
net2=nn.Sequential(nn.Conv2d(3,3),nn.BatchNorm2d(3),nn.ReLU()
          )
 
from collections import OrderedDict
net3=nn.Sequential(OrderedDict([
  ('conv1',3)),('bh1',nn.BatchNorm2d(3)),('al',nn.ReLU())
]))
 
print('net1',net1)
print('net2',net2)
print('net3',net3)
 
#可根據名字或序號取出子module
print(net1.conv,net2[0],net3.conv1)

輸出結果：

net1 Sequential(
 (conv): Conv2d(3,kernel_size=(3,stride=(1,1))
 (batchnorm): BatchNorm2d(3,eps=1e-05,momentum=0.1,affine=True,track_running_stats=True)
 (activation_layer): ReLU()
)
 
net2 Sequential(
 (0): Conv2d(3,1))
 (1): BatchNorm2d(3,track_running_stats=True)
 (2): ReLU()
)
 
net3 Sequential(
 (conv1): Conv2d(3,1))
 (bh1): BatchNorm2d(3,track_running_stats=True)
 (al): ReLU()
)
 
Conv2d(3,1)) 
Conv2d(3,1))

以上這篇pytorch實現特殊的Module--Sqeuential三種寫法就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。