目录

神经网络-搭建小实战和Sequential的使用

版本1——未用Sequential 

版本2——用Sequential

---

神经网络-搭建小实战和Sequential的使用

1. torch.nn.Sequential的官方文档地址，模块将按照它们在构造函数中传递的顺序添加。
2. 代码实现的是下图： 

版本1——未用Sequential 

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linearclass Model(nn.Module):def __init__(self):super(Model, self).__init__()# 3,32,32 ---> 32,32,32self.conv1 = Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2)# 32,32,32 ---> 32,16,16self.maxpool1 = MaxPool2d(kernel_size=2, stride=2)# 32,16,16 ---> 32,16,16self.conv2 = Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2)# 32,16,16 ---> 32,8,8self.maxpool2 = MaxPool2d(kernel_size=2, stride=2)# 32,8,8 ---> 64,8,8self.conv3 = Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=2)# 64,8,8 ---> 64,4,4self.maxpool3 = MaxPool2d(kernel_size=2, stride=2)# 64,4,4 ---> 1024self.flatten = Flatten()  # 因为start_dim默认为1，所以可不再另外设置# 1024 ---> 64self.linear1 = Linear(1024, 64)# 64 ---> 10self.linear2 = Linear(64, 10)def forward(self, x):x = self.conv1(x)x = self.maxpool1(x)x = self.conv2(x)x = self.maxpool2(x)x = self.conv3(x)x = self.maxpool3(x)x = self.flatten(x)x = self.linear1(x)x = self.linear2(x)return xmodel = Model()
print(model)input = torch.ones((64, 3, 32, 32))
out = model(input)
print(out.shape)	# torch.Size([64, 10])

版本2——用Sequential

代码更简洁，而且会给每层自动从0开始编序。

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequentialclass Model(nn.Module):def __init__(self):super(Model, self).__init__()self.model = Sequential(Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2),MaxPool2d(kernel_size=2, stride=2),Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2),MaxPool2d(kernel_size=2, stride=2),Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1, padding=2),MaxPool2d(kernel_size=2, stride=2),Flatten(),Linear(1024, 64),Linear(64, 10))def forward(self, x):return self.model(x)model = Model()
print(model)input = torch.ones((64, 3, 32, 32))
out = model(input)
print(out.shape)	# torch.Size([64, 10])

 在代码最末尾加上writer.add_gragh(model, input)就可看到模型计算图，可放大查看。

writer = SummaryWriter('./logs/Seq')
writer.add_graph(model, input)
writer.close()