基于pytorch卷积神经网络手写数字识别 自适应调整学习率的实现 代码

以下是基于PyTorch实现的手写数字识别卷积神经网络，包含自适应调整学习率的代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms

# 定义卷积神经网络模型
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 32, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.fc1 = nn.Linear(7*7*64, 1000)
        self.fc2 = nn.Linear(1000, 10)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc1(out)
        out = self.fc2(out)
        return out

# 加载MNIST数据集
train_dataset = datasets.MNIST(root='./', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./', train=False, transform=transforms.ToTensor())

# 定义训练参数
batch_size = 100
num_epochs = 5

# 定义损失函数和优化器
model = ConvNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)

# 训练模型
total_step = len(train_dataset) // batch_size
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_dataset):
        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # 每100步打印一次训练状态
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, total_step, loss.item()))

    # 动态调整学习率
    if (epoch+1) % 2 == 0:
        for param_group in optimizer.param_groups:
            param_group['lr'] /= 2

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_dataset:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += 1
        correct += (predicted == labels).sum().item()

    print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))

在训练过程中，每训练2个epoch，会将学习率除以2进行自适应调整。可以根据实际情况进行调整。