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广西网站建设开发团队,开发网上商城多少钱,部门门户网站建设的目的,微信运营模式理论基础 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…

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 = 0for 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 = 128nhead = 8num_layers = 2output_dim = 1  # 输出维度epochs = 10lr = 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)