给我一个简单的基于pytorch的CNN代码
时间: 2024-03-23 14:42:49 浏览: 13
以下是一个基于PyTorch的CNN代码示例,用于图像分类任务:
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# 定义CNN模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 3)
self.conv2 = nn.Conv2d(6, 16, 3)
self.fc1 = nn.Linear(16 * 6 * 6, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = x.view(-1, self.num_flat_features(x))
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def num_flat_features(self, x):
size = x.size()[1:]
num_features = 1
for s in size:
num_features *= s
return num_features
# 加载数据
train_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST('/files/', train=True, download=True,
transform=torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST('/files/', train=False, download=True,
transform=torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=64, shuffle=True)
# 初始化模型和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.5)
# 训练模型
for epoch in range(10):
for i, (inputs, labels) in enumerate(train_loader, 0):
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if i % 100 == 0:
print('[%d, %5d] loss: %.3f' % (epoch+1, i+1, loss.item()))
print('Finished Training')
# 在测试集上验证模型
correct = 0
total = 0
with torch.no_grad():
for (images, labels) in test_loader:
outputs = net(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: %d %%' % (
100 * correct / total))
```
此代码使用PyTorch库定义了一个简单的CNN模型,用于手写数字识别。首先定义了模型的结构,然后加载数据,并初始化模型和优化器。接着,使用训练数据训练模型,并在测试数据上验证模型。