1.优化器介绍：
优化器集中在torch.optim中。
- Constructing it
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
optimizer = optim.Adam([var1, var2], lr=0.0001)
- Taking an optimization step
for input, target in dataset:optimizer.zero_grad()output = model(input)loss = loss_fn(output, target)loss.backward()optimizer.step()
2.代码实战：
import torch
import torchvision
from torch import nn
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.data import DataLoaderdataset=torchvision.datasets.CIFAR10("data",train=False,transform=torchvision.transforms.ToTensor(),download=True)#每个批次中加载的数据项数量
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=nn.CrossEntropyLoss()
tudui=Tudui()optim=torch.optim.SGD(tudui.parameters(),lr=0.01)for epoch in range(20):running_loss=0.0for data in dataloader:imgs,targets = dataoutputs =tudui(imgs)result_loss=loss(outputs,targets)#清零optim.zero_grad()result_loss.backward()#调优optim.step()running_loss=running_loss+result_lossprint(running_loss)
后面loss又升高，为反向优化
3.总结：
优化器的基本使用
- 如果要知道各个优化器的详细用法
- 需要对其有一定了解
- 注意要多训练几轮
