pytorch 学习率衰减策略

##学习率衰减策略
import torch.nn.functional as F
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
 
#初始化模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)
    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return x
model=Net()
input=torch.randn(1,1,28,28)
output=model(input)
print(output.shape)
#初始化优化器
optimizer = torch.optim.SGD(model.parameters(), lr=1)
 
# scheduler = torch.opti