使用pytorch实现手写数字识别的案例。

使用pytorch实现手写数字识别的案例。

好的，下面是使用PyTorch实现手写数字识别的简单案例。

首先，导入必要的库：
```python
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
```
定义超参数：
```python
batch_size = 64
num_epochs = 10
learning_rate = 0.001
```
下载MNIST数据集并进行预处理：
```python
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)
```
定义CNN模型：
```python
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2)
self.relu1 = nn.ReLU()
self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2) self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2)
self.relu2 = nn.ReLU()
self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2) self.fc = nn.Linear(7*7*32, 10)
def forward(self, x):
out = self.conv1(x)
out = self.relu1(out)
out = self.maxpool1(out)
out = self.conv2(out)
out = self.relu2(out)
out = self.maxpool2(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
model = CNN()
```
定义损失函数和优化器：
```python
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate) ```
训练模型：
```python
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
outputs = model(images)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
```
测试模型：
```python
model.eval()
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
```
这样就完成了一个简单的手写数字识别的PyTorch实现。