1.引子

Sequential的使用：将网络结构放入其中即可，可以简化代码。
找了一个对CIFAR10进行分类的模型。

2.代码实战

from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, 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

tudui=Tudui()
print(tudui)

nn.Flatten()和torch.flatten()有相同的效果。

3.Sequential

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential


class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.model1=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.model1(x)
        return x

tudui=Tudui()
print(tudui)
## 创建一个指定形状的 ones 张量
input=torch.ones((64,3,32,32))
output=tudui(input)
print(output.shape)

使用Sequential可以很大程度地简化代码。

4.利用TensorBoard进行数据可视化

使用SummaryWriter的add_graph()方法进行数据可视化。

writer=SummaryWriter("logs_sqe")
writer.add_graph(tudui,input)
writer.close()

基本的网络搭建到此结束。