利用softmax网络实现手写数字识别的代码

以下是使用Python和PyTorch框架实现手写数字识别的代码，其中包括了softmax回归模型的训练和测试过程。

首先，我们需要导入必要的库和模块：

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

然后，我们需要定义数据集和数据加载器：

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5,), (0.5,))])

trainset = torchvision.datasets.MNIST(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.MNIST(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64,
                                         shuffle=False, num_workers=2)

接下来，我们需要定义softmax回归模型：

class SoftmaxRegression(nn.Module):
    def __init__(self):
        super(SoftmaxRegression, self).__init__()
        self.linear = nn.Linear(784, 10)

    def forward(self, x):
        x = x.view(x.size(0), -1)
        x = self.linear(x)
        return x

然后，我们需要定义损失函数和优化器：

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

接下来，我们需要定义训练函数和测试函数：

def train(net, trainloader, criterion, optimizer):
    net.train()
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    return running_loss / len(trainloader)

def test(net, testloader, criterion):
    net.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    return correct / total

最后，我们可以开始训练和测试模型：

net = SoftmaxRegression()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

for epoch in range(10):
    train_loss = train(net, trainloader, criterion, optimizer)
    test_acc = test(net, testloader, criterion)
    print('Epoch %d: Train Loss: %.3f, Test Acc: %.3f' % (epoch+1, train_loss, test_acc))

训练过程中，我们可以看到每个epoch的训练损失和测试准确率。最后，我们可以使用训练好的模型对新的手写数字图像进行分类。