使用pytorch动手实现LSTM模块

原文

import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.nn import init
from torch import Tensor
import mathclass NaiveLSTM(nn.Module):"""Naive LSTM like nn.LSTM"""def __init__(self, input_size: int, hidden_size: int):super(NaiveLSTM, self).__init__()self.input_size = input_sizeself.hidden_size = hidden_size# input gateself.w_ii = Parameter(Tensor(hidden_size, input_size))self.w_hi = Parameter(Tensor(hidden_size, hidden_size))self.b_ii = Parameter(Tensor(hidden_size, 1))self.b_hi = Parameter(Tensor(hidden_size, 1))# forget gateself.w_if = Parameter(Tensor(hidden_size, input_size))self.w_hf = Parameter(Tensor(hidden_size, hidden_size))self.b_if = Parameter(Tensor(hidden_size, 1))self.b_hf = Parameter(Tensor(hidden_size, 1))# output gateself.w_io = Parameter(Tensor(hidden_size, input_size))self.w_ho = Parameter(Tensor(hidden_size, hidden_size))self.b_io = Parameter(Tensor(hidden_size, 1))self.b_ho = Parameter(Tensor(hidden_size, 1))# cellself.w_ig = Parameter(Tensor(hidden_size, input_size))self.w_hg = Parameter(Tensor(hidden_size, hidden_size))self.b_ig = Parameter(Tensor(hidden_size, 1))self.b_hg = Parameter(Tensor(hidden_size, 1))self.reset_weigths()def reset_weigths(self):"""reset weights"""stdv = 1.0 / math.sqrt(self.hidden_size)for weight in self.parameters():init.uniform_(weight, -stdv, stdv)def forward(self, inputs: Tensor, state: Tuple[Tensor]) \-> Tuple[Tensor, Tuple[Tensor, Tensor]]:"""ForwardArgs:inputs: [1, 1, input_size]state: ([1, 1, hidden_size], [1, 1, hidden_size])"""
#         seq_size, batch_size, _ = inputs.size()if state is None:h_t = torch.zeros(1, self.hidden_size).t()c_t = torch.zeros(1, self.hidden_size).t()else:(h, c) = stateh_t = h.squeeze(0).t()c_t = c.squeeze(0).t()hidden_seq = []seq_size = 1for t in range(seq_size):x = inputs[:, t, :].t()# input gatei = torch.sigmoid(self.w_ii @ x + self.b_ii + self.w_hi @ h_t +self.b_hi)# forget gatef = torch.sigmoid(self.w_if @ x + self.b_if + self.w_hf @ h_t +self.b_hf)# cellg = torch.tanh(self.w_ig @ x + self.b_ig + self.w_hg @ h_t+ self.b_hg)# output gateo = torch.sigmoid(self.w_io @ x + self.b_io + self.w_ho @ h_t +self.b_ho)c_next = f * c_t + i * gh_next = o * torch.tanh(c_next)c_next_t = c_next.t().unsqueeze(0)h_next_t = h_next.t().unsqueeze(0)hidden_seq.append(h_next_t)hidden_seq = torch.cat(hidden_seq, dim=0)return hidden_seq, (h_next_t, c_next_t)def reset_weigths(model):"""reset weights"""for weight in model.parameters():init.constant_(weight, 0.5)### test
inputs = torch.ones(1, 1, 10)
h0 = torch.ones(1, 1, 20)
c0 = torch.ones(1, 1, 20)
print(h0.shape, h0)
print(c0.shape, c0)
print(inputs.shape, inputs)# test naive_lstm with input_size=10, hidden_size=20
naive_lstm = NaiveLSTM(10, 20)
reset_weigths(naive_lstm)output1, (hn1, cn1) = naive_lstm(inputs, (h0, c0))print(hn1.shape, cn1.shape, output1.shape)
print(hn1)
print(cn1)
print(output1)

# Use official lstm with input_size=10, hidden_size=20
lstm = nn.LSTM(10, 20)
reset_weigths(lstm)
output2, (hn2, cn2) = lstm(inputs, (h0, c0))
print(hn2.shape, cn2.shape, output2.shape)
print(hn2)
print(cn2)
print(output2)

使用pytorch动手实现LSTM模块相关推荐

【深度学习】在PyTorch中使用 LSTM 进行新冠病例预测
时间序列数据,顾名思义是一种随时间变化的数据.例如,24 小时时间段内的温度,一个月内各种产品的价格,特定公司一年内的股票价格.长短期记忆网络(LSTM)等高级深度学习模型能够捕捉时间序列数据中的模式 ...
【Pytorch学习笔记2】Pytorch的主要组成模块
个人笔记,仅用于个人学习与总结感谢DataWhale开源组织提供的优秀的开源Pytorch学习文档:原文档链接本文目录 1. Pytorch的主要组成模块 1.1 完成深度学习的必要部分 1.2 ...
运用PyTorch动手搭建一个共享单车预测器
本文摘自 <深度学习原理与PyTorch实战> 我们将从预测某地的共享单车数量这个实际问题出发,带领读者走进神经网络的殿堂,运用PyTorch动手搭建一个共享单车预测器,在实战过程中掌握神 ...
【Pytorch】BERT+LSTM+多头自注意力（文本分类）
[Pytorch]BERT+LSTM+多头自注意力(文本分类) 2018年Google提出了BERT[1](Bidirectional Encoder Representations from Tra ...
lstm 根据前文预测词_干货 | Pytorch实现基于LSTM的单词检测器
Pytorch实现基于LSTM的单词检测器字幕组双语原文: Pytorch实现基于LSTM的单词检测器英语原文: LSTM Based Word Detectors 翻译: 雷锋字幕组(Icar ...
PyTorch基础-使用LSTM神经网络实现手写数据集识别-08
import numpy as np import torch from torch import nn,optim from torch.autograd import Variable from ...
PyTorch基础(part8)--LSTM
学习笔记,仅供参考,有错必纠文章目录代码问题分析初始设置导包载入数据模型模型持久化模型的保存模型的载入代码问题分析我们将图片看成是一种序列问题,比如将28*28的图像中的每行 ...
深度学习实战（2）用Pytorch搭建双向LSTM
用Pytorch搭建双向LSTM 应最近的课程实验要求,要做LSTM和GRU的实验效果对比.LSTM的使用和GRU十分相似,欢迎参考我的另外一篇介绍搭建双向GRU的Blog:https://blog. ...
Pytorch实现的LSTM模型结构
LSTM模型结构 1.LSTM模型结构 2.LSTM网络 3.LSTM的输入结构 4.Pytorch中的LSTM 4.1.pytorch中定义的LSTM模型 4.2.喂给LSTM的数据格式 4.3.L ...

使用pytorch动手实现LSTM模块

使用pytorch动手实现LSTM模块相关推荐

最新文章

热门文章