卷积神经网络代码
时间: 2023-06-30 22:11:31 浏览: 40
下面是一个简单的卷积神经网络(CNN)的代码示例,用于图像分类任务。这个示例使用了Python语言和PyTorch深度学习框架。
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# 定义CNN模型
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
self.pool1 = nn.MaxPool2d(2)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
self.pool2 = nn.MaxPool2d(2)
self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
self.pool3 = nn.MaxPool2d(2)
self.fc1 = nn.Linear(64 * 4 * 4, 256)
self.fc2 = nn.Linear(256, 10)
def forward(self, x):
x = self.pool1(torch.relu(self.conv1(x)))
x = self.pool2(torch.relu(self.conv2(x)))
x = self.pool3(torch.relu(self.conv3(x)))
x = x.view(-1, 64 * 4 * 4)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# 加载数据集
train_dataset = datasets.CIFAR10(root='data/', train=True, transform=transforms.ToTensor(), download=True)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
test_dataset = datasets.CIFAR10(root='data/', train=False, transform=transforms.ToTensor(), download=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False)
# 定义损失函数和优化器
model = ConvNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
for epoch in range(10):
for i, (images, labels) in enumerate(train_loader):
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print(f'Epoch [{epoch+1}/{10}], Step [{i+1}/{len(train_loader)}], Loss: {loss.item():.4f}')
# 测试模型
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 of the model on the test images: {:.2f}%'.format(100 * correct / total))
```
这个示例定义了一个包含三个卷积层和两个全连接层的CNN模型,用于CIFAR-10数据集的图像分类任务。训练循环中使用交叉熵损失函数和Adam优化器进行模型训练。最后,在测试循环中计算模型的准确率。