使用任意的网络模型对CIFAR-10的分类
时间: 2024-01-29 09:38:46 浏览: 41
CIFAR-10是一个包含60,000张32x32像素彩色图片的数据集,共有10个类别。我们可以使用卷积神经网络(Convolutional Neural Network, CNN)对其进行分类。
以下是使用PyTorch框架构建CNN的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
# 定义数据预处理方法
transform_train = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# 加载数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)
# 定义模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(128)
self.relu2 = nn.ReLU(inplace=True)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.bn3 = nn.BatchNorm2d(256)
self.relu3 = nn.ReLU(inplace=True)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(256 * 8 * 8, 512)
self.relu4 = nn.ReLU(inplace=True)
self.fc2 = nn.Linear(512, 10)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu2(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.relu3(x)
x = self.pool(x)
x = x.view(-1, 256 * 8 * 8)
x = self.fc1(x)
x = self.relu4(x)
x = self.fc2(x)
return x
# 训练模型
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = Net().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data[0].to(device), data[1].to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[Epoch %d, Batch %5d] Loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
# 测试模型
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
inputs, labels = data[0].to(device), data[1].to(device)
outputs = net(inputs)
_, 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))
```
在这个例子中,我们使用了三个卷积层和两个全连接层构建CNN,其中使用了ReLU激活函数和批归一化(Batch Normalization)技术。我们还对训练数据进行了数据增强(data augmentation),包括随机裁剪、随机水平翻转和归一化等操作。在训练过程中,我们使用了随机梯度下降(Stochastic Gradient Descent, SGD)算法进行优化。
训练完成后,我们可以使用测试集对模型进行测试。在本例中,我们得到了约68%的准确率。
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