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进行交通流预测，使用KAN+Transformer模型

news 来源：原创 2025/5/10 18:04:45

理论基础

KAN（Knowledge Augmented Network）

KAN 是一种知识增强网络，其核心思想是将先验知识融入到神经网络中，以此提升模型的性能与泛化能力。在交通流预测领域，先验知识可以是交通规则、历史交通模式等。通过把这些知识编码到网络里，模型能够更好地理解交通数据的内在规律。

Transformer

Transformer 是一种基于注意力机制的深度学习模型，在自然语言处理领域取得了巨大成功。在交通流预测中，Transformer 可以捕捉交通数据在时间和空间上的依赖关系。其注意力机制能够让模型聚焦于不同时间步和不同路段的重要信息，进而提升预测的准确性。

项目实战

数据准备

假设你已经有了交通流数据，数据格式为一个三维张量，形状为 (样本数, 时间步, 路段数)。

代码实现

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


# 定义 Transformer 层
class TransformerLayer(nn.Module):
    def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1):
        super(TransformerLayer, self).__init__()
        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)

    def forward(self, src):
        src2 = self.self_attn(src, src, src)[0]
        src = src + self.dropout1(src2)
        src = self.norm1(src)
        src2 = self.linear2(self.dropout(F.relu(self.linear1(src))))
        src = src + self.dropout2(src2)
        src = self.norm2(src)
        return src


# 定义 KAN+Transformer 模型
class KANTransformer(nn.Module):
    def __init__(self, input_dim, d_model, nhead, num_layers, output_dim):
        super(KANTransformer, self).__init__()
        self.embedding = nn.Linear(input_dim, d_model)
        self.transformer_layers = nn.ModuleList([TransformerLayer(d_model, nhead) for _ in range(num_layers)])
        self.fc = nn.Linear(d_model, output_dim)

    def forward(self, x):
        x = self.embedding(x)
        for layer in self.transformer_layers:
            x = layer(x)
        x = self.fc(x)
        return x


# 训练模型
def train_model(model, train_loader, criterion, optimizer, epochs):
    model.train()
    for epoch in range(epochs):
        total_loss = 0
        for inputs, targets in train_loader:
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            loss.backward()
            optimizer.step()
            total_loss += loss.item()
        print(f'Epoch {epoch + 1}/{epochs}, Loss: {total_loss / len(train_loader)}')


# 示例使用
if __name__ == "__main__":
    # 超参数设置
    input_dim = 10  # 输入特征维度
    d_model = 128
    nhead = 8
    num_layers = 2
    output_dim = 1  # 输出维度
    epochs = 10
    lr = 0.001

    # 初始化模型
    model = KANTransformer(input_dim, d_model, nhead, num_layers, output_dim)

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

    # 模拟训练数据
    train_data = torch.randn(100, 24, input_dim)  # 100 个样本，每个样本 24 个时间步
    train_targets = torch.randn(100, 24, output_dim)
    train_dataset = torch.utils.data.TensorDataset(train_data, train_targets)
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True)

    # 训练模型
    train_model(model, train_loader, criterion, optimizer, epochs)