交叉熵Loss多分类问题实战（手写数字）

1、import所需要的torch库和包

2、加载mnist手写数字数据集，划分训练集和测试集，转化数据格式，batch_size设置为200
3、定义三层线性网络参数w，b，设置求导信息

4、初始化参数，这一步比较关键，是否初始化影响到数据质量以及后续网络学习效果

5、自定义三层线性网络

6、选定优化器激活函数和loss函数

7、训练及测试，并记录每轮训练的loss变化和在测试集上的效果。第一轮就达到了98的准确度，判断是初始化效果较好，在前几次测试中根据初始化的情况不同，初始准确率为50%-85%不等

完整代码：

import torch
import torchvision
import torch.nn.functional as F

train_loader = torch.utils.data.DataLoader(
    torchvision.datasets.MNIST('mnist_data', train=True, download=True,
                          transform=torchvision.transforms.Compose([
                              torchvision.transforms.ToTensor(),
                              torchvision.transforms.Normalize(
                                  (0.1307, ), (0.3081, ))
                              ])
                          ),
    batch_size=200, shuffle=True)

test_loader = torch.utils.data.DataLoader(
    torchvision.datasets.MNIST('mnist_data', train=False, download=True,
                          transform=torchvision.transforms.Compose([
                              torchvision.transforms.ToTensor(),
                              torchvision.transforms.Normalize(
                                  (0.1307, ), (0.3081, ))
                              ])
                          ),
    batch_size=200, shuffle=True)

w1 = torch.randn(200, 784, requires_grad=True)
b1 = torch.randn(200, requires_grad=True)
w2 = torch.randn(200, 200, requires_grad=True)
b2 = torch.randn(200, requires_grad=True)
w3 = torch.randn(10, 200, requires_grad=True)
b3 = torch.randn(10, requires_grad=True)

torch.nn.init.kaiming_normal_(w1)
torch.nn.init.kaiming_normal_(w2)
torch.nn.init.kaiming_normal_(w3)

def forward(x):
    x = x@w1.t() +b1
    x = F.relu(x)
    x = x@w2.t() +b2
    x = F.relu(x)
    x = x@w3.t() +b3
    x = F.relu(x)
    
    return x
    
optimizer = torch.optim.Adam([w1, b1, w2, b2, w3, b3], lr=0.001)
criterion = torch.nn.CrossEntropyLoss()

for epoch in range(10):
    for batch_idx, (data, target) in enumerate(train_loader):
        data = data.view(-1, 28*28)
        logits = forward(data)
        loss = criterion(logits, target)
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        if (batch_idx+1) % 150 == 0:
            print('Train Epoch:{} [{}/{}({:.0f}%)]\tLoss:{:.6f}'.format(
                epoch, (batch_idx+1) * len(data), len(train_loader.dataset),
                100. * (batch_idx+1) / len(train_loader), loss.item())
            )
            
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        data = data.view(-1, 28*28)
        logits = forward(data)
        test_loss += criterion(logits, target).item()
        pred = logits.data.max(1)[1]
        correct += pred.eq(target.data).sum()
    
    test_loss /= len(test_loader)
    print('\nTest Set:Average Loss:{:.4f}, Accuracy:{}/{}({:.0f}%)\n'.format(
         test_loss, correct, len(test_loader.dataset),
         100. * correct / len(test_loader.dataset))
    )