二值化卷积神经网络例程

这里提供一个使用PyTorch实现二值化卷积神经网络的例程，供您参考：

import torch
import torch.nn as nn

# 定义二值化卷积层
class BinarizeConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True):
        super(BinarizeConv2d, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias)
        self.bn = nn.BatchNorm2d(out_channels, eps=1e-4, momentum=0.1, affine=True)
        self.activation = nn.Hardtanh(inplace=True)
    
    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.activation(x)
        return x

# 定义二值化卷积神经网络
class BinarizeCNN(nn.Module):
    def __init__(self):
        super(BinarizeCNN, self).__init__()
        self.conv1 = BinarizeConv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = BinarizeConv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.conv3 = BinarizeConv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(128*8*8, 512)
        self.fc2 = nn.Linear(512, 10)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = x.view(-1, 128*8*8)
        x = self.fc1(x)
        x = self.fc2(x)
        return x

# 加载数据集
train_set = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=torchvision.transforms.ToTensor())
test_set = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=torchvision.transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=False)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练模型
model = BinarizeCNN()
for epoch in range(10):
    for i, (images, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, loss.item()))

# 测试模型
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        outputs = model(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: {} %'.format(100 * correct / total))

以上代码实现了一个简单的二值化卷积神经网络，并使用CIFAR-10数据集进行训练和测试。您可以根据自己的需求进行修改和调整。