这里主要涉及一些nn.Sequential()的用法，nn.Sequential()是一个顺序容器，将神经网络的相关操作进行封装。

1. nn.Sequential()容器定义

从nn.Sequential()的定义来看，输入要么是orderdict,要么是一系列的模型，遇到list，必须用*号进行转化，否则会报错 TypeError: list is not a Module subclass

2. nn.Sequential()的几种构造方法

1. 简单的顺序构造

import torch.nn as nn
model = nn.Sequential(
                  nn.Conv2d(1,20,5),
                  nn.ReLU(),
                  nn.Conv2d(20,64,5),
                  nn.ReLU()
                )
 
print(model)
print(model[2]) # 通过索引获取第几个层
'''运行结果为：
Sequential(
  (0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
  (1): ReLU()
  (2): Conv2d(20, 64, kernel_size=(5, 5), stride=(1, 1))
  (3): ReLU()
)
Conv2d(20, 64, kernel_size=(5, 5), stride=(1, 1))
'''

2. 为各层添加名称

import torch.nn as nn
from collections import OrderedDict
model = nn.Sequential(OrderedDict([
                  ('conv1', nn.Conv2d(1,20,5)),
                  ('relu1', nn.ReLU()),
                  ('conv2', nn.Conv2d(20,64,5)),
                  ('relu2', nn.ReLU())
                ]))
 
print(model)
print(model[2]) # 通过索引获取第几个层
'''运行结果为：
Sequential(
  (conv1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
  (relu1): ReLU()
  (conv2): Conv2d(20, 64, kernel_size=(5, 5), stride=(1, 1))
  (relu2): ReLU()
)
Conv2d(20, 64, kernel_size=(5, 5), stride=(1, 1))
'''

3. 利用Sequential的父类Module中的方法add_module()

import torch.nn as nn
from collections import OrderedDict
model = nn.Sequential()
model.add_module("conv1",nn.Conv2d(1,20,5))
model.add_module('relu1', nn.ReLU())
model.add_module('conv2', nn.Conv2d(20,64,5))
model.add_module('relu2', nn.ReLU())
 
print(model)
print(model[2]) # 通过索引获取第几个层

参考：添加链接描述