trandfoemer时间序列预测代码

一点儿不上心

已于 2023-01-20 11:54:13 修改

阅读量609

点赞数 1

文章标签： python transformer

于 2023-01-20 11:51:13 首次发布

本文链接：https://blog.youkuaiyun.com/wyx12121212/article/details/128740832

版权

该代码实现了一个基于Transformer的序列预测模型TransAm，包括PositionalEncoding层和TransformerEncoder。模型在给定数据上进行训练，使用均方误差作为损失函数，并在验证集上评估性能，展示预测结果与实际值的对比。训练过程中调整学习率并监控损失变化。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

import torch
import torch.nn as nn
import numpy as np
import time
import math
from matplotlib import pyplot
import matplotlib.pyplot as plt;
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
import tensorflow as tf
from math import sqrt
from sklearn import metrics

torch.manual_seed(0)
np.random.seed(0)

import warnings
warnings.filterwarnings('ignore')


class PositionalEncoding(nn.Module):

    def __init__(self, d_model, max_len=5000):
        super(PositionalEncoding, self).__init__()       
        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)
        #pe.requires_grad = False
        self.register_buffer('pe', pe)

    def forward(self, x):
        return x + self.pe[:x.size(0), :]
          

#构建transformer模型
class TransAm(nn.Module):
    def __init__(self,feature_size=250,num_layers=1,dropout=0.1):
        super(TransAm, self).__init__()
        self.model_type = 'Transformer'
        
        self.src_mask = None
        self.pos_encoder = PositionalEncoding(feature_size)
        self.encoder_layer = nn.TransformerEncoderLayer(d_model=feature_size, nhead=10, dropout=dropout)
        self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers)        
        self.decoder = nn.Linear(feature_size,1)
        self.init_weights()

    def init_weights(self):
        initrange = 0.1    
        self.decoder.bias.data.zero_()
        self.decoder.weight.data.uniform_(-initrange, initrange)

    def forward(self,src):
        if self.src_mask is None or self.src_mask.size(0) != len(src):
            device = src.device
            mask = self._generate_square_subsequent_mask(len(src)).to(device)
            self.src_mask = mask

        src = self.pos_encoder(src)
        output = self.transformer_encoder(src,

最低0.47元/天解锁文章