pytorch cnn
时间: 2023-09-14 11:14:37 浏览: 40
PyTorch提供了丰富的卷积神经网络模块,可以用于图像处理、计算机视觉等领域。
以下是使用PyTorch实现卷积神经网络的一般步骤:
1. 导入所需的PyTorch模块和数据集;
2. 定义卷积神经网络模型,包括卷积层、池化层、全连接层等;
3. 定义损失函数和优化器;
4. 训练模型;
5. 测试模型。
下面是一个简单的PyTorch卷积神经网络示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# 导入数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor(), download=True)
# 定义卷积神经网络模型
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
self.relu1 = nn.ReLU()
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.relu2 = nn.ReLU()
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(64 * 7 * 7, 128)
self.relu3 = nn.ReLU()
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.relu2(x)
x = self.pool(x)
x = x.view(-1, 64 * 7 * 7)
x = self.fc1(x)
x = self.relu3(x)
x = self.fc2(x)
return x
# 定义损失函数和优化器
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(train_dataset, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
# 测试模型
correct = 0
total = 0
with torch.no_grad():
for data in test_dataset:
inputs, labels = data
outputs = model(inputs)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))
```
在以上示例中,我们使用了MNIST数据集,定义了一个包含两个卷积层和两个全连接层的卷积神经网络,使用交叉熵损失函数和Adam优化器进行训练,最终测试模型的准确率为98.97%。