python实现一个简单CNN网络的训练
时间: 2023-05-17 21:04:37 浏览: 45
可以使用PyTorch或TensorFlow等深度学习框架来实现一个简单的CNN网络的训练。以下是一个使用PyTorch实现的简单CNN网络的训练代码示例:
```
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网络
class SimpleCNN(nn.Module):
def __init__(self):
super(SimpleCNN, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.relu2 = nn.ReLU()
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(32 * 8 * 8, 10)
def forward(self, x):
x = self.conv1(x)
x = self.relu1(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.relu2(x)
x = self.pool2(x)
x = x.view(-1, 32 * 8 * 8)
x = self.fc1(x)
return x
# 加载数据集
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.ToTensor())
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.ToTensor())
# 定义训练参数
batch_size = 64
learning_rate = 0.001
num_epochs = 10
# 定义优化器和损失函数
model = SimpleCNN()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()
# 开始训练
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
for epoch in range(num_epochs):
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 ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))
# 测试模型
model.eval()
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 network on the 10000 test images: {} %'.format(100 * correct / total))
```
这个简单的CNN网络包含两个卷积层和一个全连接层,用于对CIFAR-10数据集进行分类。训练过程中使用Adam优化器和交叉熵损失函数。