基于pytorch的CNN代码
时间: 2023-09-17 15:08:52 浏览: 35
以下是一个基于PyTorch实现的简单卷积神经网络(CNN)的代码示例:
```
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# 定义卷积层、池化层和全连接层
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
# 前向传播
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 训练网络
for epoch in range(2):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
```
在这个代码示例中,我们定义了一个简单的CNN,包括两个卷积层和三个全连接层。我们使用PyTorch内置的交叉熵损失函数(`nn.CrossEntropyLoss`)作为损失函数,使用随机梯度下降(SGD)作为优化器(`optim.SGD`)。我们将模型训练了两个epoch,并输出了每2000个batch的训练损失。
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