Transformer回归预测

### 使用Transformer模型实现回归预测任务 #### 方法概述 为了使用Transformer模型执行回归预测任务，通常会采用一种特定架构来处理时间序列或多维输入数据。这种架构不仅能够捕捉到长时间依赖关系，还能够在不同位置之间建立联系，从而提高预测精度[^1]。 #### Python (PyTorch) 实现示例 以下是利用PyTorch框架构建的一个简单版本的Transformer用于回归预测的例子： ```python import torch from torch import nn, optim import numpy as np class PositionalEncoding(nn.Module): def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000): super().__init__() self.dropout = nn.Dropout(p=dropout) position = torch.arange(max_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2) * (-np.log(10000.0) / d_model)) pe = torch.zeros(max_len, 1, d_model) pe[:, 0, 0::2] = torch.sin(position * div_term) pe[:, 0, 1::2] = torch.cos(position * div_term) self.register_buffer('pe', pe) def forward(self, x): """ Args: x: Tensor, shape [seq_len, batch_size, embedding_dim] """ x = x + self.pe[:x.size(0)] return self.dropout(x) class TransformerModel(nn.Module): def __init__(self, input_dim, output_dim, nhead=8, num_encoder_layers=6, dim_feedforward=2048, dropout=0.1): super(TransformerModel, self).__init__() encoder_layer = nn.TransformerEncoderLayer(d_model=input_dim, nhead=nhead, dim_feedforward=dim_feedforward, dropout=dropout, batch_first=True) transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_encoder_layers) self.model_type = 'Transformer' self.src_mask = None self.pos_encoder = PositionalEncoding(input_dim) self.encoder = nn.Linear(output_dim, input_dim) self.transformer_encoder = transformer_encoder self.decoder = nn.Linear(input_dim, output_dim) self.init_weights() def init_weights(self): initrange = 0.1 self.encoder.weight.data.uniform_(-initrange, initrange) self.decoder.bias.data.zero_() self.decoder.weight.data.uniform_(-initrange, initrange) def forward(self, src): src = self.encoder(src) * math.sqrt(ninp) src = self.pos_encoder(src) output = self.transformer_encoder(src, self.src_mask) output = self.decoder(output) return output # Parameters and DataLoaders setup omitted for brevity. model = TransformerModel(input_dim=..., output_dim=...) criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=...) for epoch in range(num_epochs): model.train() total_loss = 0. for i_batch, sample_batched in enumerate(train_loader): data, targets = sample_batched['data'], sample_batched['target'] optimizer.zero_grad() output = model(data) loss = criterion(output.view(-1), targets) loss.backward() optimizer.step() ``` 此代码片段展示了如何创建一个简单的Transformer模型来进行回归分析。注意这里省略了一些参数设置以及数据加载器的具体配置细节[^2]。 #### MATLAB 实现示例 对于那些更倾向于MATLAB环境的研究者来说，也可以借助MATLAB内置的深度学习工具箱轻松搭建类似的结构: ```matlab % Define network architecture using layerGraph object layers = [ sequenceInputLayer(inputSize,'Name','input') % Add positional encoding here manually or use custom function bilstmLayer(hiddenUnits,'OutputMode','sequence',... 'Name','bilstm') %#ok<UNRCH> fullyConnectedLayer(size(targetSequence),'Name','fc') ]; lgraph = layerGraph(layers); % Create a dlnetwork from the layer graph net = dlnetwork(lgraph); % Specify training options options = trainingOptions('adam',... 'MaxEpochs',maxEpoch,... 'MiniBatchSize',miniBatch,... 'Plots','training-progress',... 'Verbose',false); % Train network with sequences of inputs XTrain and corresponding outputs YTrain [trainedNet, info] = trainNetwork(XTrain,YTrain,net,options); ``` 这段脚本定义了一个包含双向LSTM层的基础网络，并指定了训练选项。需要注意的是，在实际应用中可能还需要加入自定义的位置编码逻辑以及其他组件以完全模拟标准的Transformer设计。

Transformer模型可以用于回归预测。Transformer是一个Seq2seq（Sequence-to-sequence）模型，可以用于处理序列到序列的任务，包括回归预测。 Transformers是目前非常流行的模型，被广泛应用于许多机器学习任务，包括时间序列预测。因此，可以使用Transformer模型进行回归预测任务，例如预测时间序列中的连续数值变量。<span class="em">1</span><span class="em">2</span><span class="em">3</span> #### 引用[.reference_title] - *1* *2* [李宏毅《深度学习》- Transformer](https://blog.csdn.net/kkm09/article/details/120910020)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"] - *3* [利用 Transformer 网络建立预测模型](https://blog.csdn.net/weixin_38739735/article/details/118037452)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"] [ .reference_list ]