Pytorch学习--神经网络--搭建小实战（手撕CIFAR 10 model structure）和 Sequential 的使用

一、Sequential 的使用方法

在手撕代码中进一步体现
torch.nn.Sequential
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二、手撕 CIFAR 10 model structure

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手撕代码：

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.tensorboard import SummaryWriterclass Mary(nn.Module):def __init__(self):super(Mary,self).__init__()self.conv1 = Conv2d(3,32,5,padding=2)self.maxpool1 = MaxPool2d(2)self.conv2 = Conv2d(32,32,5,padding=2)self.maxpool2 = MaxPool2d(2)self.conv3 = Conv2d(32,64,5,padding=2)self.maxpool3 = MaxPool2d(2)self.flatten = Flatten()self.linear1 = Linear(1024,64)self.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 x
Yorelee = Mary()
print(Yorelee)
# 检测
input = torch.ones((64,3,32,32))
output = Yorelee(input)
print(output.shape)  #如果是[64,10]即为正确#用Tensorboard去检测
writer = SummaryWriter("logs")
writer.add_graph(Yorelee,input)
writer.close()

Tensorboard 输出：
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使用nn.Sequential的代码：

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.tensorboard import SummaryWriterclass Mary(nn.Module):def __init__(self):super(Mary,self).__init__()# self.conv1 = Conv2d(3,32,5,padding=2)# self.maxpool1 = MaxPool2d(2)# self.conv2 = Conv2d(32,32,5,padding=2)# self.maxpool2 = MaxPool2d(2)# self.conv3 = Conv2d(32,64,5,padding=2)# self.maxpool3 = MaxPool2d(2)# self.flatten = Flatten()# self.linear1 = Linear(1024,64)# self.linear2 = Linear(64,10)self.model1 = nn.Sequential(Conv2d(3, 32, 5, padding=2),MaxPool2d(2),Conv2d(32, 32, 5, padding=2),MaxPool2d(2),Conv2d(32, 64, 5, padding=2),MaxPool2d(2),Flatten(),Linear(1024, 64),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)x = self.model1(x)return x
Yorelee = Mary()
print(Yorelee)
# 检测
input = torch.ones((64,3,32,32))
output = Yorelee(input)
print(output.shape)  #如果是[64,10]即为正确#用Tensorboard去检测
writer = SummaryWriter("logs")
writer.add_graph(Yorelee,input)
writer.close()