给一个卷积神经网路模型的事例和代码事例
时间: 2023-04-08 17:01:49 浏览: 62
我可以回答这个问题。一个常见的卷积神经网络模型是LeNet-5,它由Yann LeCun等人在1998年提出。以下是一个使用PyTorch实现LeNet-5的代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
class LeNet5(nn.Module):
def __init__(self):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
self.pool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
self.pool2 = nn.MaxPool2d(kernel_size=2)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool1(torch.relu(self.conv1(x)))
x = self.pool2(torch.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
model = LeNet5()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))
# 测试模型
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy on test set: %d %%' % (100 * correct / total))
```
这个代码示例使用LeNet-5模型对MNIST手写数字数据集进行分类。在训练过程中,使用交叉熵损失函数和随机梯度下降优化器。在测试过程中,计算模型在测试集上的准确率。