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

二、手撕 CIFAR 10 model structure

手撕代码：

python 复制代码

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.tensorboard import SummaryWriter


class 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：

使用nn.Sequential的代码：

python 复制代码

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.tensorboard import SummaryWriter


class 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()