pytorch学习9-优化器学习

一、优化器使用

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

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

class Mynn(nn.Module):
    #这是使用序列的方法：
    def __init__(self):
        super(Mynn,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 x

mynn=Mynn()
loss=nn.CrossEntropyLoss()
optim=torch.optim.SGD(mynn.parameters(),lr=0.01)#调用SGD优化器。第一个参数是把模型的参数全输入进去

for epoch in range(20):
    runing_loss=0.0
    for data in dataloader:
        imgs,target=data
        outputs=mynn(imgs)
        result_loss=loss(outputs,target)
        optim.zero_grad()#将上一次的梯度设置为0，这一步必须做
        result_loss.backward()#反向传播，计算出模型的参数
        optim.step()#进行完反向传播之后，模型的参数就计算出来了，就可以调用优化器了
        runing_loss=runing_loss+result_loss
    print(runing_loss)#查看每一轮的损失之和