神经网络——优化器

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 DataLoader

dataset=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 x

loss=nn.CrossEntropyLoss()
tudui=Tudui()

optim=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)
        #清零
        optim.zero_grad()
        result_loss.backward()
        #调优
        optim.step()
        running_loss=running_loss+result_loss
    print(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