PyTorch(13)---优化器_随机梯度下降法

优化器：随机梯度下降法

反向传播—梯度下降

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


dataset=torchvision.datasets.CIFAR10("dataset_CIFAR10",train=False,transform=torchvision.transforms.ToTensor(),download=True)
dataloader=DataLoader(dataset,batch_size=1)


class Demo(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.model1=Sequential(
            Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2, dilation=1, ),
            MaxPool2d(kernel_size=2, ),
            Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2, ),
            MaxPool2d(kernel_size=2),
            Conv2d(32, 64, 5, 1, 2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10),
        )
    def forward(self,x):
        x=self.model1(x)
        return x

demo=Demo()
loss=nn.CrossEntropyLoss()
'''优化器：随机梯度下降'''
optim=torch.optim.SGD(params=demo.parameters(),lr=0.01,)

for epoch in range(20):#通常训练很多轮
    running_loss=0.0
    for data in dataloader:
        imgs,targets=data
        '''送入网络进行训练'''
        outputs=demo(imgs) 
        '''损失函数'''
        result_loss=loss(outputs,targets)
        '''优化器进行调优 1、调用一个优化器：optim=torch.optim.SGD(params=demo.parameters(),lr=0.01,) 2、将梯度初始化为0，在循环里这一步都要清0 3、损失函数调用反向传播 4、optim.step() '''
        optim.zero_grad()#首先将梯度设置为0
        result_loss.backward()#反向传播，算出梯度（梯度下降法），目的：求出最小的loss 得到需要调节的梯度
        optim.step()
        running_loss=running_loss+result_loss#在每一轮开始之前，将loss都设置为0，整体误差的总和
        #print(result_loss)
    print(running_loss)