test_nn_optimizer.py

import torch
import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Linear, Sequential
from torch.nn.modules.flatten import Flatten
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset = torchvision.datasets.CIFAR10("torchvision_dataset", train=False,
                                       transform=torchvision.transforms.ToTensor(), download=True)
dataLoader = DataLoader(dataset, batch_size=1)

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        # padding = 2 是对着公式算出来的，其他几个参数的设置按照网上给的结构对着抄
        self.conv1 = Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2)
        self.maxpool1 = MaxPool2d(kernel_size=2)
        self.conv2 = Conv2d(in_channels=32, out_channels=32 ,kernel_size=5, padding=2)
        self.maxpool2 = MaxPool2d(kernel_size=2)
        self.conv3 = Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2)
        self.maxpool3 = MaxPool2d(kernel_size=2)
        self.flatten = Flatten()
        self.linear1 = Linear(1024, 64)
        self.linear2 = Linear(64, 10)
        # 将以上换成sequential写法：
        self.model1 = Sequential(
            Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2),
            MaxPool2d(kernel_size=2),
            Conv2d(in_channels=32, out_channels=32, kernel_size=5, padding=2),
            MaxPool2d(kernel_size=2),
            Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2),
            MaxPool2d(kernel_size=2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10),
        )


    def forward(self, x):
        # #冗余写法
        # 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)

        # seq写法
        x = self.model1(x)
        return x


loss =nn.CrossEntropyLoss()
tudui = Tudui()
optimizer = torch.optim.SGD(tudui.parameters(), lr=0.01)
for epoch in range(20):
    running_loss = 0.0
    for data in dataLoader:
        imgs, targets = data
        outputs = tudui(imgs)
        result_loss = loss(outputs, targets)
        # 每次优化前，调0优化器已存过的数字
        optimizer.zero_grad()
        result_loss.backward()
        #优化器优化
        optimizer.step()
        running_loss = running_loss +result_loss
    print(running_loss)