目录

• 程序流程分析图：
• 传播过程：
• 代码展示：
  • 创建环境
  • 准备数据集
  • 下载数据集
  • 下载测试集
  • 绘制图像
  • 搭建神经网络
  • 训练模型
  • 测试模型
  • 保存训练模型
• 运行结果展示：

BATCH_SIZE = 512EPOCHS = 8device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

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

test_loader = torch.utils.data.DataLoader(    datasets.MNIST("data", train=False,                   transform=transforms.Compose([                       transforms.ToTensor(),                       transforms.Normalize((0.1307,), (0.3081,))                   ])),    batch_size=BATCH_SIZE, shuffle=True)

examples = enumerate(test_loader)batch_idx, (example_data, example_targets) = next(examples)print(example_targets)print(example_data.shape)print(example_data) import matplotlib.pyplot as pltfig = plt.figure()for i in range(6):  plt.subplot(2,3,i+1)  plt.tight_layout()  plt.imshow(example_data[i][0], cmap="gray", interpolation="none")  plt.title("Ground Truth: {}".format(example_targets[i]))  plt.xticks([])  plt.yticks([])plt.show()

class linearNet(nn.Module):    def __init__(self):        super().__init__()        self.fc1 = nn.Linear(784, 128)        self.fc2 = nn.Linear(128, 64)        self.fc3 = nn.Linear(64, 10)    def forward(self, x):        x = x.view(-1, 784)        x = self.fc1(x)        x = F.relu(x)        x = self.fc2(x)        x = F.relu(x)        x = self.fc3(x)        x = F.log_softmax(x, dim=1)        return x

def train(model, device, train_loader, optimizer, epoch):    model.train()    for batch_idx, (data, target) in enumerate(train_loader):         data, target = data.to(device), target.to(device)        optimizer.zero_grad()        output = model(data)        loss = F.nll_loss(output, target)        loss.backward()        optimizer.step()        if (batch_idx) % 30 == 0:            print("Train Epoch: {} [{}/{} ({:.0f}%)]	Loss: {:.6f}".format(                epoch, batch_idx * len(data), len(train_loader.dataset),                       100. * batch_idx / len(train_loader), loss.item()))

def test(model, device, test_loader):    model.eval()    test_loss = 0    correct = 0    with torch.no_grad():        for data, target in test_loader:            data, target = data.to(device), target.to(device)            output = model(data)            test_loss += F.nll_loss(output, target, reduction="sum").item() # 将一批的损失相加            pred = output.max(1, keepdim=True)[1] # 找到概率最大的下标            correct += pred.eq(target.view_as(pred)).sum().item()     test_loss /= len(test_loader.dataset)    print("Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(        test_loss, correct, len(test_loader.dataset),        100. * correct / len(test_loader.dataset)))

if __name__ == "__main__":    model = linearNet()    optimizer = optim.Adam(model.parameters())     for epoch in range(1, EPOCHS + 1):        train(model, device, train_loader, optimizer, epoch)        test(model, device, test_loader)

torch.save(model, "MNIST.pth")