神经网络——CIFAR10小实战

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, Linearclass 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 xtudui=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, Sequentialclass 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 xtudui=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。