pytorch cifa10
时间: 2023-09-24 12:14:07 浏览: 38
PyTorch是一个流行的深度学习框架,CIFAR-10是一个常用的图像分类数据集,其中包含10个不同类别的60000个32x32彩色图像。在PyTorch中,可以使用torchvision库中的CIFAR-10数据集加载器来加载数据集。
以下是一个简单的PyTorch CIFAR-10分类器的示例:
```
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
# 定义转换器
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
# 加载训练集和测试集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
shuffle=False, num_workers=2)
# 定义类别标签
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
# 定义神经网络模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 训练网络
for epoch in range(2): # 多次遍历数据集
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# 获取输入
inputs, labels = data
# 清零参数梯度
optimizer.zero_grad()
# 前向传递,后向传递,优化
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 打印统计信息
running_loss += loss.item()
if i % 2000 == 1999: # 每2000个小批量打印一次状态
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
# 测试网络
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
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))
```
这个示例中,我们定义了一个简单的卷积神经网络模型,使用SGD优化器和交叉熵损失函数进行训练。我们还使用了数据增强技术,例如随机裁剪和水平翻转,以提高模型的泛化能力。最后,我们测试了模型在测试集上的准确性。
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