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。