手写识别pytorch
时间: 2023-10-06 16:11:05 浏览: 32
在 PyTorch 中进行手写识别可以使用深度学习模型来实现。以下是一个基本的手写识别的 PyTorch 代码示例:
首先,导入所需的库:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
```
定义一个手写数字识别的神经网络模型:
```python
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(784, 128)
self.fc2 = nn.Linear(128, 64)
self.fc3 = nn.Linear(64, 10)
self.relu = nn.ReLU()
def forward(self, x):
x = x.view(-1, 784) # 将输入展平成一维向量
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.fc3(x)
return x
```
创建网络实例并定义损失函数和优化器:
```python
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
```
加载手写数字数据集,并进行训练:
```python
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
for epoch in range(num_epochs):
for batch_idx, (data, targets) in enumerate(train_loader):
outputs = model(data)
loss = criterion(outputs, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (batch_idx + 1) % log_interval == 0:
print(f"Epoch [{epoch+1}/{num_epochs}], Step [{batch_idx+1}/{len(train_loader)}], Loss: {loss.item():.4f}")
```
通过以上代码,你可以使用 PyTorch 来实现手写数字识别的训练过程。请注意,你需要根据你的数据集和需求进行适当的修改。