双线性卷积神经网络代码

双线性卷积神经网络（Bilinear CNN）是一种用于图像分类和物体识别的深度学习模型。它使用双线性池化操作来捕捉两个不同卷积层之间的交互信息。以下是一个使用PyTorch实现的双线性CNN的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class BilinearCNN(nn.Module):
    def __init__(self, num_classes=10):
        super(BilinearCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(128 * 8 * 8, 512)
        self.fc2 = nn.Linear(512, num_classes)
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 128 * 8 * 8)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return x
    
class BilinearPooling(nn.Module):
    def __init__(self, num_channels):
        super(BilinearPooling, self).__init__()
        self.num_channels = num_channels
        
    def forward(self, x):
        batch_size = x.size(0)
        out_dim = self.num_channels ** 2
        x = x.view(batch_size, self.num_channels, -1)
        x_t = torch.transpose(x, 1, 2)
        bilinear_pool = torch.bmm(x, x_t) / (x.size(2))
        bilinear_pool = bilinear_pool.view(batch_size, -1)
        bilinear_pool = torch.sqrt(F.relu(bilinear_pool))
        bilinear_pool = F.normalize(bilinear_pool)
        return bilinear_pool

在这个模型中，我们定义了一个标准的卷积神经网络，包括两个卷积层和两个全连接层。然后，我们定义了一个BilinearPooling层，它将两个卷积层的输出作为输入，并使用双线性池化操作来捕捉它们之间的交互信息。最后，我们将BilinearPooling层的输出传递给全连接层进行分类。