递归神经网络LSTM原理——结合实例MATLAB实现

最近正在看递归神经网络，看了网上很多博文，算是鱼龙混杂，并且基本都是使用Python实现，要不就是使用Matlab中的函数库等。对于使用Matlab的同学，甚为不方便。所以我将结合实例，使用matlab语言，完成递归神经网络程序的编写（LSTM）。本人菜鸡一枚，如有错误还望各路大神，指正教导。文章的问题和数据和我之前写的递归神经网络BPTT文章中一致，方便大家比较两种方法的差异，文章链接递归神经网络BPTT的MATLAB实现。另外，关于理论推导算法步骤，等我过几天有时间更新。
一、问题描述
问题描述

二、相关数据
相关数据

三、程序代码
LSTM_mian.m

%%% LSTM网络结合实例仿真%%% 作者：xd.wp%%% 时间：2016.10.08  12:06%% 程序说明%  1、数据为7天，四个时间点的空调功耗，用前三个推测第四个训练，依次类推。第七天作为检验%  2、LSTM网络输入结点为12，输出结点为4个，隐藏结点18个clear all;clc;%% 数据加载，并归一化处理[train_data,test_data]=LSTM_data_process();data_length=size(train_data,1);data_num=size(train_data,2);%% 网络参数初始化% 结点数设置input_num=12;cell_num=18;output_num=4;% 网络中门的偏置bias_input_gate=rand(1,cell_num);bias_forget_gate=rand(1,cell_num);bias_output_gate=rand(1,cell_num);% ab=1.2;% bias_input_gate=ones(1,cell_num)/ab;% bias_forget_gate=ones(1,cell_num)/ab;% bias_output_gate=ones(1,cell_num)/ab;%网络权重初始化ab=20;weight_input_x=rand(input_num,cell_num)/ab;weight_input_h=rand(output_num,cell_num)/ab;weight_inputgate_x=rand(input_num,cell_num)/ab;weight_inputgate_c=rand(cell_num,cell_num)/ab;weight_forgetgate_x=rand(input_num,cell_num)/ab;weight_forgetgate_c=rand(cell_num,cell_num)/ab;weight_outputgate_x=rand(input_num,cell_num)/ab;weight_outputgate_c=rand(cell_num,cell_num)/ab;%hidden_output权重weight_preh_h=rand(cell_num,output_num);%网络状态初始化cost_gate=1e-6;h_state=rand(output_num,data_num);cell_state=rand(cell_num,data_num);%% 网络训练学习for iter=1:3000    yita=0.01;            %每次迭代权重调整比例    for m=1:data_num        %前馈部分        if(m==1)            gate=tanh(train_data(:,m)'*weight_input_x);            input_gate_input=train_data(:,m)'*weight_inputgate_x+bias_input_gate;            output_gate_input=train_data(:,m)'*weight_outputgate_x+bias_output_gate;            for n=1:cell_num                input_gate(1,n)=1/(1+exp(-input_gate_input(1,n)));                output_gate(1,n)=1/(1+exp(-output_gate_input(1,n)));            end            forget_gate=zeros(1,cell_num);            forget_gate_input=zeros(1,cell_num);            cell_state(:,m)=(input_gate.*gate)';        else            gate=tanh(train_data(:,m)'*weight_input_x+h_state(:,m-1)'*weight_input_h);            input_gate_input=train_data(:,m)'*weight_inputgate_x+cell_state(:,m-1)'*weight_inputgate_c+bias_input_gate;            forget_gate_input=train_data(:,m)'*weight_forgetgate_x+cell_state(:,m-1)'*weight_forgetgate_c+bias_forget_gate;            output_gate_input=train_data(:,m)'*weight_outputgate_x+cell_state(:,m-1)'*weight_outputgate_c+bias_output_gate;            for n=1:cell_num                input_gate(1,n)=1/(1+exp(-input_gate_input(1,n)));                forget_gate(1,n)=1/(1+exp(-forget_gate_input(1,n)));                output_gate(1,n)=1/(1+exp(-output_gate_input(1,n)));            end            cell_state(:,m)=(input_gate.*gate+cell_state(:,m-1)'.*forget_gate)';           end        pre_h_state=tanh(cell_state(:,m)').*output_gate;        h_state(:,m)=(pre_h_state*weight_preh_h)';        %误差计算        Error=h_state(:,m)-test_data(:,m);        Error_Cost(1,iter)=sum(Error.^2);        if(Error_Cost(1,iter)<cost_gate)            flag=1;            break;        else            [   weight_input_x,...                weight_input_h,...                weight_inputgate_x,...                weight_inputgate_c,...                weight_forgetgate_x,...                weight_forgetgate_c,...                weight_outputgate_x,...                weight_outputgate_c,...                weight_preh_h ]=LSTM_updata_weight(m,yita,Error,...                                                   weight_input_x,...                                                   weight_input_h,...                                                   weight_inputgate_x,...                                                   weight_inputgate_c,...                                                   weight_forgetgate_x,...                                                   weight_forgetgate_c,...                                                   weight_outputgate_x,...                                                   weight_outputgate_c,...                                                   weight_preh_h,...                                                   cell_state,h_state,...                                                   input_gate,forget_gate,...                                                   output_gate,gate,...                                                   train_data,pre_h_state,...                                                   input_gate_input,...                                                   output_gate_input,...                                                   forget_gate_input);        end    end    if(Error_Cost(1,iter)<cost_gate)        break;    endend%% 绘制Error-Cost曲线图% for n=1:1:iter%     text(n,Error_Cost(1,n),'*');%     axis([0,iter,0,1]);%     title('Error-Cost曲线图');   % endfor n=1:1:iter    semilogy(n,Error_Cost(1,n),'*');    hold on;    title('Error-Cost曲线图');   end%% 使用第七天数据检验%数据加载test_final=[0.4557 0.4790 0.7019 0.8211 0.4601 0.4811 0.7101 0.8298 0.4612 0.4845 0.7188 0.8312]';test_final=test_final/sqrt(sum(test_final.^2));test_output=test_data(:,4);%前馈m=4;gate=tanh(test_final'*weight_input_x+h_state(:,m-1)'*weight_input_h);input_gate_input=test_final'*weight_inputgate_x+cell_state(:,m-1)'*weight_inputgate_c+bias_input_gate;forget_gate_input=test_final'*weight_forgetgate_x+cell_state(:,m-1)'*weight_forgetgate_c+bias_forget_gate;output_gate_input=test_final'*weight_outputgate_x+cell_state(:,m-1)'*weight_outputgate_c+bias_output_gate;for n=1:cell_num    input_gate(1,n)=1/(1+exp(-input_gate_input(1,n)));    forget_gate(1,n)=1/(1+exp(-forget_gate_input(1,n)));    output_gate(1,n)=1/(1+exp(-output_gate_input(1,n)));endcell_state_test=(input_gate.*gate+cell_state(:,m-1)'.*forget_gate)';pre_h_state=tanh(cell_state_test').*output_gate;h_state_test=(pre_h_state*weight_preh_h)'test_output

LSTM_data_process.m

function [train_data,test_data]=LSTM_data_process()%% 数据加载并完成初始归一化train_data_initial= [0.4413 0.4707 0.6953 0.8133 0.4379 0.4677 0.6981 0.8002 0.4517 0.4725 0.7006 0.8201;                     0.4379 0.4677 0.6981 0.8002 0.4517 0.4725 0.7006 0.8201 0.4557 0.4790 0.7019 0.8211;                     0.4517 0.4725 0.7006 0.8201 0.4557 0.4790 0.7019 0.8211 0.4601 0.4911 0.7101 0.8298]';% train_data_initial=[ 0.4413 0.4707 0.6953 0.8133;%                      0.4379 0.4677 0.6981 0.8002;%                      0.4517 0.4725 0.7006 0.8201;%                      0.4557 0.4790 0.7019 0.8211;%                      0.4601 0.4811 0.7101 0.8298;%                      0.4612 0.4845 0.7188 0.8312]';test_data_initial=[0.4557 0.4790 0.7019 0.8211;                   0.4612 0.4845 0.7188 0.8312;                   0.4601 0.4811 0.7101 0.8298;                   0.4615 0.4891 0.7201 0.8330]';data_length=size(train_data_initial,1);            %每个样本的长度data_num=size(train_data_initial,2);               %样本数目  %%归一化过程for n=1:data_num    train_data(:,n)=train_data_initial(:,n)/sqrt(sum(train_data_initial(:,n).^2));  endfor m=1:size(test_data_initial,2)    test_data(:,m)=test_data_initial(:,m)/sqrt(sum(test_data_initial(:,m).^2));end

LSTM_updata_weight.m

function [   weight_input_x,weight_input_h,weight_inputgate_x,weight_inputgate_c,weight_forgetgate_x,weight_forgetgate_c,weight_outputgate_x,weight_outputgate_c,weight_preh_h ]=LSTM_updata_weight(n,yita,Error,...                                                   weight_input_x, weight_input_h, weight_inputgate_x,weight_inputgate_c,weight_forgetgate_x,weight_forgetgate_c,weight_outputgate_x,weight_outputgate_c,weight_preh_h,...                                                   cell_state,h_state,input_gate,forget_gate,output_gate,gate,train_data,pre_h_state,input_gate_input, output_gate_input,forget_gate_input)%%% 权重更新函数input_num=12;cell_num=18;output_num=4;data_length=size(train_data,1);data_num=size(train_data,2);weight_preh_h_temp=weight_preh_h;%% 更新weight_preh_h权重for m=1:output_num    delta_weight_preh_h_temp(:,m)=2*Error(m,1)*pre_h_state;endweight_preh_h_temp=weight_preh_h_temp-yita*delta_weight_preh_h_temp;%% 更新weight_outputgate_xfor num=1:output_num    for m=1:data_length        delta_weight_outputgate_x(m,:)=(2*weight_preh_h(:,num)*Error(num,1).*tanh(cell_state(:,n)))'.*exp(-output_gate_input).*(output_gate.^2)*train_data(m,n);    end    weight_outputgate_x=weight_outputgate_x-yita*delta_weight_outputgate_x;end%% 更新weight_inputgate_xfor num=1:output_numfor m=1:data_length    delta_weight_inputgate_x(m,:)=2*(weight_preh_h(:,num)*Error(num,1))'.*output_gate.*(ones(size(cell_state(:,n)))-tanh(cell_state(:,n)).^2)'.*gate.*exp(-input_gate_input).*(input_gate.^2)*train_data(m,n);endweight_inputgate_x=weight_inputgate_x-yita*delta_weight_inputgate_x;endif(n~=1)    %% 更新weight_input_x    temp=train_data(:,n)'*weight_input_x+h_state(:,n-1)'*weight_input_h;    for num=1:output_num    for m=1:data_length        delta_weight_input_x(m,:)=2*(weight_preh_h(:,num)*Error(num,1))'.*output_gate.*(ones(size(cell_state(:,n)))-tanh(cell_state(:,n)).^2)'.*input_gate.*(ones(size(temp))-tanh(temp.^2))*train_data(m,n);    end    weight_input_x=weight_input_x-yita*delta_weight_input_x;    end    %% 更新weight_forgetgate_x    for num=1:output_num    for m=1:data_length        delta_weight_forgetgate_x(m,:)=2*(weight_preh_h(:,num)*Error(num,1))'.*output_gate.*(ones(size(cell_state(:,n)))-tanh(cell_state(:,n)).^2)'.*cell_state(:,n-1)'.*exp(-forget_gate_input).*(forget_gate.^2)*train_data(m,n);    end    weight_forgetgate_x=weight_forgetgate_x-yita*delta_weight_forgetgate_x;    end    %% 更新weight_inputgate_c    for num=1:output_num    for m=1:cell_num        delta_weight_inputgate_c(m,:)=2*(weight_preh_h(:,num)*Error(num,1))'.*output_gate.*(ones(size(cell_state(:,n)))-tanh(cell_state(:,n)).^2)'.*gate.*exp(-input_gate_input).*(input_gate.^2)*cell_state(m,n-1);    end    weight_inputgate_c=weight_inputgate_c-yita*delta_weight_inputgate_c;    end    %% 更新weight_forgetgate_c    for num=1:output_num    for m=1:cell_num        delta_weight_forgetgate_c(m,:)=2*(weight_preh_h(:,num)*Error(num,1))'.*output_gate.*(ones(size(cell_state(:,n)))-tanh(cell_state(:,n)).^2)'.*cell_state(:,n-1)'.*exp(-forget_gate_input).*(forget_gate.^2)*cell_state(m,n-1);    end    weight_forgetgate_c=weight_forgetgate_c-yita*delta_weight_forgetgate_c;    end    %% 更新weight_outputgate_c    for num=1:output_num    for m=1:cell_num        delta_weight_outputgate_c(m,:)=2*(weight_preh_h(:,num)*Error(num,1))'.*tanh(cell_state(:,n))'.*exp(-output_gate_input).*(output_gate.^2)*cell_state(m,n-1);    end    weight_outputgate_c=weight_outputgate_c-yita*delta_weight_outputgate_c;    end    %% 更新weight_input_h    temp=train_data(:,n)'*weight_input_x+h_state(:,n-1)'*weight_input_h;    for num=1:output_num    for m=1:output_num        delta_weight_input_h(m,:)=2*(weight_preh_h(:,num)*Error(num,1))'.*output_gate.*(ones(size(cell_state(:,n)))-tanh(cell_state(:,n)).^2)'.*input_gate.*(ones(size(temp))-tanh(temp.^2))*h_state(m,n-1);    end    weight_input_h=weight_input_h-yita*delta_weight_input_h;    endelse    %% 更新weight_input_x    temp=train_data(:,n)'*weight_input_x;    for num=1:output_num    for m=1:data_length        delta_weight_input_x(m,:)=2*(weight_preh_h(:,num)*Error(num,1))'.*output_gate.*(ones(size(cell_state(:,n)))-tanh(cell_state(:,n)).^2)'.*input_gate.*(ones(size(temp))-tanh(temp.^2))*train_data(m,n);    end    weight_input_x=weight_input_x-yita*delta_weight_input_x;    endendweight_preh_h=weight_preh_h_temp;end

四、程序结果图
Error_Cost图

第七天预测值与理论值，第一组为预测值，第二组为实际值