pytorch cnn
时间: 2023-09-12 16:03:20 浏览: 41
PyTorch是一个流行的深度学习框架,它提供了许多用于构建卷积神经网络(CNN)的工具和函数。以下是使用PyTorch构建CNN的一些基本步骤:
1.导入必要的库和模块:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as dsets
import torchvision.transforms as transforms
```
2.定义超参数:
```python
batch_size = 100
num_epochs = 10
learning_rate = 0.001
```
3.加载数据集并进行预处理:
```python
train_dataset = dsets.MNIST(root='./data',
train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = dsets.MNIST(root='./data',
train=False,
transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
```
4.定义CNN模型:
```python
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.layer2 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.fc = nn.Linear(7 * 7 * 64, 10)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.reshape(out.size(0), -1)
out = self.fc(out)
return out
model = CNN()
```
5.定义损失函数和优化器:
```python
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
```
6.训练模型:
```python
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
outputs = model(images)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
```
7.测试模型:
```python
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('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
```
这就是使用PyTorch构建CNN的基本步骤。当然,这只是一个简单的例子,您可以根据自己的需求进行修改和扩展。