五、Optim 优化器

参考文档：https://pytorch.org/docs/stable/optim.html

1、SGD

参考文档：https://pytorch.org/docs/stable/generated/torch.optim.SGD.html#torch.optim.SGD

import torch.optim
import torchvision
from torch import nn
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear, CrossEntropyLoss
from torch.utils.data import DataLoaderdataset = torchvision.datasets.CIFAR10("../data", 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__()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 xloss = CrossEntropyLoss()tudui = Tudui()optim = torch.optim.SGD(tudui.parameters(), lr=0.01)  # 优化器

For循环1：

for data in dataloader:imgs, targets = dataoutput = tudui(imgs)result_loss = loss(output, targets)optim.zero_grad()  # 清零result_loss.backward()  # 反向传播optim.step()  # 调优print(result_loss)

Files already downloaded and verified
tensor(2.3287, grad_fn=<NllLossBackward0>)
tensor(2.3879, grad_fn=<NllLossBackward0>)
tensor(2.2987, grad_fn=<NllLossBackward0>)
...

For循环2：

for epoch in range(20):running_loss = 0.0for data in dataloader:imgs, targets = dataoutput = tudui(imgs)result_loss = loss(output, targets)optim.zero_grad()result_loss.backward()optim.step()running_loss += result_lossprint(running_loss)

Files already downloaded and verified
tensor(18592.4395, grad_fn=<AddBackward0>)
tensor(16118.4756, grad_fn=<AddBackward0>)
tensor(15450.5898, grad_fn=<AddBackward0>)
...