基于pytorch多头注意力机制实现的数字预测python源码+模型+数据集

#!/usr/bin/env python3 # encoding: utf-8 """ @Time : 2021/7/7 19:52 @Author : Xie Cheng @File : transformer.py @Software: PyCharm @desc: transformer架构 https://zhuanlan.zhihu.com/p/370481790 """ import math import torch from torch import nn class PositionalEncoding(nn.Module): def __init__(self, d_model, dropout=0.1, max_len=5000): super(PositionalEncoding, self).__init__() self.dropout = nn.Dropout(p=dropout) pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0).transpose(0, 1) self.register_buffer('pe', pe) def forward(self, x): x = x + self.pe[:x.size(1), :].squeeze(1) return self.dropout(x) class TransformerTS(nn.Module): def __init__(self, input_dim, dec_seq_len, out_seq_len, d_model=512, nhead=8, num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, activation='relu', custom_encoder=None, custom_decoder=None): r"""A transformer model. User is able to modify the attributes as needed. The architecture is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000-6010. Users can build the BERT(https://arxiv.org/abs/1810.04805) model with corresponding parameters. Args: input_dim: dimision of imput series d_model: the number of expected features in the encoder/decoder inputs (default=512). nhead: the number of heads in the multiheadattention models (default=8). num_encoder_layers: the number of sub-encoder-layers in the encoder (default=6). num_decoder_layers: the number of sub-decoder-layers in the decoder (default=6). dim_feedforward: the dimension of the feedforward network model (default=2048). dropout: the dropout value (default=0.1). activation: the activation function of encoder/decoder intermediate layer, relu or gelu (default=relu). custom_encoder: custom encoder (default=None). custom_decoder: custom decoder (default=None). Examples:: >>> transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12) >>> src = torch.rand((10, 32, 512)) (time length, N, feature dim) >>> tgt = torch.rand((20, 32, 512)) >>> out = transformer_model(src, tgt) Note: A full example to apply nn.Transformer module for the word language model is available in https://github.com/pytorch/examples/tree/master/word_language_model """ super(TransformerTS, self).__init__() self.transform = nn.Transformer( d_model=d_model, nhead=nhead, num_encoder_layers=num_encoder_layers, num_decoder_layers=num_decoder_layers, dim_feedforward=dim_feedforward, dropout=dropout, activation=activation, custom_encoder=custom_encoder, custom_decoder=custom_decoder ) self.pos = PositionalEncoding(d_model) self.enc_input_fc = nn.Linear(input_dim, d_model) self.dec_input_fc = nn.Linear(input_dim, d_model) self.out_fc = nn.Linear(dec_seq_len * d_model, out_seq_len) self.dec_seq_len = dec_seq_len def forward(self, x): x = x.transpose(0, 1) # embedding embed_encoder_input = self.pos(self.enc_input_fc(x)) embed_decoder_input = self.dec_input_fc(x[-self.dec_seq_len:, :]) # transform x = self.transform(embed_encoder_input, embed_decoder_input) # output x = x.transpose(0, 1) x = self.out_fc(x.flatten(start_dim=1)) return x.squeeze()