%%% 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(6);
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;
end
end
%% 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');
% end
for 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)));
end
cell_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
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