如果用pytorch代码如何实现
时间: 2024-04-08 17:30:43 浏览: 13
以下是使用PyTorch实现CNN手写算式识别的代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import transforms
# 设置随机种子
torch.manual_seed(42)
# 定义CNN模型
class CNNModel(nn.Module):
def __init__(self):
super(CNNModel, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)
self.relu1 = nn.ReLU()
self.maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.relu2 = nn.ReLU()
self.maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(7 * 7 * 32, 128)
self.relu3 = nn.ReLU()
self.fc2 = nn.Linear(128, 10) # 假设有10个类别(0-9的数字)
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.fc1(out)
out = self.relu3(out)
out = self.fc2(out)
return out
# 加载并预处理数据集
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transform, download=True)
test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transform)
# 创建数据加载器
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)
# 初始化模型和优化器
model = CNNModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# 前向传播
outputs = model(images)
loss = criterion(outputs, labels)
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))
# 模型评估
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('Accuracy on the test images: {} %'.format(100 * correct / total))
```
以上代码使用了PyTorch构建了一个简单的CNN模型,并使用MNIST数据集进行训练和测试。你可以根据需要进行调整和扩展,例如更改模型结构、调整超参数等。