Pytorch——优化器

优化器的使用

1.创建优化器
2.循环内梯度清零
3.计算梯度
4.根据梯度优化loss

例子：

dataset_transform_compose = transforms.Compose([transforms.ToTensor()])
train_set = torchvision.datasets.CIFAR10(root="./dataset", train=True, transform=dataset_transform_compose, download=True)
test_set = torchvision.datasets.CIFAR10(root="./dataset", train=False, transform=dataset_transform_compose, download=True)

dataloader = DataLoader(test_set, batch_size=64, shuffle=True)

class model(nn.Module):
    def __init__(self):
        super(model,self).__init__()
        self.model1 = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 32, (5, 5), padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, (5, 5), padding=2),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(1024, 64),
            nn.Linear(64, 10)
        )

    def forward(self, input):
        input = self.model1(input)
        return input

model1 = model()
bloss = nn.CrossEntropyLoss()
opti = torch.optim.Adam(model1.parameters(), lr=0.01)##第一步，创建优化器
for epoch in range(5):
    epoch_loss = 0.0
    for data in dataloader:
        imgs, targets = data
        output = model1(imgs)
        loss = bloss(output, targets)
        opti.zero_grad() ##第二步：梯度清零
        loss.backward()##第三步：计算梯度
        opti.step()##第四步：优化
        epoch_loss = epoch_loss + loss
    print(epoch_loss)

输出：

tensor(1807.5908, grad_fn=<AddBackward0>)
tensor(276.8998, grad_fn=<AddBackward0>)
tensor(253.0088, grad_fn=<AddBackward0>)
tensor(241.0754, grad_fn=<AddBackward0>)
tensor(225.8897, grad_fn=<AddBackward0>)