写一段python代码,用神经网络的方式对cifar-10彩色图像数据集进行分类
时间: 2024-05-14 10:16:07 浏览: 13
好的,这是一段使用PyTorch框架实现的神经网络代码,用于对CIFAR-10数据集进行图像分类。
```python
import torch
import torch.nn as nn
from torchvision import datasets, transforms
# 定义神经网络模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(128 * 4 * 4, 512)
self.fc2 = nn.Linear(512, 10)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = self.pool(torch.relu(self.conv3(x)))
x = x.view(-1, 128 * 4 * 4)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# 加载数据集
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)
# 定义训练函数
def train(model, device, train_loader, optimizer, criterion):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
# 定义测试函数
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)
test_loss += nn.functional.cross_entropy(output, target, reduction='sum').item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
accuracy = 100. * correct / len(test_loader.dataset)
print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
test_loss, correct, len(test_loader.dataset), accuracy))
# 定义设备和超参数
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
epochs = 10
lr = 0.001
# 初始化模型、损失函数和优化器
model = Net().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
# 训练模型
for epoch in range(1, epochs + 1):
train(model, device, trainloader, optimizer, criterion)
test(model, device, testloader)
```
这段代码中,我们定义了一个包含三个卷积层和两个全连接层的神经网络模型,并使用PyTorch内置的CIFAR-10数据集进行训练和测试。训练过程使用了Adam优化器和交叉熵损失函数,测试过程计算了模型在测试集上的准确率。