请帮我用python搭建一个以pytorch为框架的神经网络,能够对cifar-10数据进行多分类
时间: 2024-05-11 21:15:43 浏览: 11
以下是一个以PyTorch为框架的神经网络,能够对CIFAR-10数据进行多分类的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# 定义数据预处理
transform = 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))
])
# 加载CIFAR-10数据
trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)
# 定义神经网络
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.relu1 = nn.ReLU()
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(128)
self.relu2 = nn.ReLU()
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(256)
self.relu3 = nn.ReLU()
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(256 * 8 * 8, 512)
self.fc2 = nn.Linear(512, 10)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.pool(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu2(x)
x = self.pool(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.relu1(x)
x = self.fc2(x)
return x
net = Net()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
# 训练网络
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
# 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 test images: %d %%' % (100 * correct / total))
```
这个神经网络包含了三个卷积层、三个池化层和两个全连接层,使用交叉熵损失函数和随机梯度下降优化器进行训练。在训练过程中,每个epoch都会输出训练集上的平均损失值;在测试过程中,计算分类准确率并输出。