Build the first neural network with pytoch and optimize it

I've been learning pytorch, This time I built a neural network following the tutorial , The most classic CIFAR10, Let's look at the principle first

Input 3 passageway 32*32, Last pass 3 A convolution ,3 Maximum pooling , also 1 individual flatten, And two linearizations , Get ten outputs

The procedure is as follows ：

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


class NetWork(nn.Module):
    def __init__(self):
        super(NetWork, 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)#1024=64*4*4
        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


network=NetWork()
print(network)

Here we can also use tensorboard Have a look , Remember import

input=torch.ones((64,3,32,32))
output=network(input)


writer=SummaryWriter("logs_seq")
writer.add_graph(network,input)
writer.close()

stay tensorboard It's like this in English

open NetWork

You can zoom in to see

Neural networks have errors , So we use gradient descent to reduce the error
The code is as follows

import torchvision.datasets
from torch import nn
from torch.nn import Sequential,Conv2d,MaxPool2d,Flatten,Linear
from torch.utils.data import DataLoader
import torch

dataset=torchvision.datasets.CIFAR10("./dataset2",train=False,transform=torchvision.transforms.ToTensor(),
                                     download=True)


dataloader=DataLoader(dataset,batch_size=1)

class NetWork(nn.Module):
    def __init__(self):
        super(NetWork, 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)#1024=64*4*4
        self.linear2=Linear(64,10)


        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.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


loss=nn.CrossEntropyLoss()
network=NetWork()
optim=torch.optim.SGD(network.parameters(),lr=0.01)## Using gradient descent as the optimizer 
for epoch in range(20):## loop 20 Time 
    running_loss=0.0
    for data in dataloader:
        imgs, targets=data
        outputs=network(imgs)
        result_loss=loss(outputs, targets)
        optim.zero_grad()## Set the value of each drop to zero 
        result_loss.backward()
        optim.step()
        running_loss=running_loss+result_loss
    	print(running_loss)

My computer's GPU yes RTX2060 It belongs to the older one , It took about three times 1 minute , It was so slow that I finished running
Output results ：

tensor(18733.7539, grad_fn=<AddBackward0>)
tensor(16142.7451, grad_fn=<AddBackward0>)
tensor(15420.9199, grad_fn=<AddBackward0>)

It can be seen that the error is getting smaller and smaller , But in the application 20 There are too few layers , When my new computer arrived, I ran 100 layer