Pytorch实现图像识别之数字识别(附详细注释)

以下是使用PyTorch实现数字识别的完整攻略，包括两个示例说明。

1. 实现简单的数字识别

以下是使用PyTorch实现简单的数字识别的步骤：

导入必要的库

python import torch import torch.nn as nn import torchvision import torchvision.transforms as transforms import matplotlib.pyplot as plt

加载数据集

```python
# 定义数据预处理
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])

# 加载数据集
trainset = torchvision.datasets.MNIST(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
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=4,
shuffle=False, num_workers=2)
```

定义神经网络模型

```python
class Net(nn.Module):
def init(self):
super(Net, self).init()
self.conv1 = nn.Conv2d(1, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 4 * 4, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)

   def forward(self, x):
       x = self.pool(torch.relu(self.conv1(x)))
       x = self.pool(torch.relu(self.conv2(x)))
       x = x.view(-1, 16 * 4 * 4)
       x = torch.relu(self.fc1(x))
       x = torch.relu(self.fc2(x))
       x = self.fc3(x)
       return x

net = Net()
```

定义损失函数和优化器

python criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

训练模型

```python
for epoch in range(2): # 多次循环数据集
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# 获取输入数据
inputs, labels = data

       # 梯度清零
       optimizer.zero_grad()

       # 前向传播、反向传播、优化
       outputs = net(inputs)
       loss = criterion(outputs, labels)
       loss.backward()
       optimizer.step()

       # 打印统计信息
       running_loss += loss.item()
       if i % 2000 == 1999:    # 每2000个小批量数据打印一次
           print('[%d, %5d] loss: %.3f' %
                 (epoch + 1, i + 1, running_loss / 2000))
           running_loss = 0.0

print('Finished Training')
```

测试模型

```python
# 测试模型
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()

print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```

运行上述代码，即可训练和测试数字识别模型。

2. 实现卷积神经网络的数字识别

以下是使用PyTorch实现卷积神经网络的数字识别的步骤：

导入必要的库