训练中的简单线性神经网络权重与训练结果不兼容 [英] Simple Linear Neural Network Weights from Training are not compatible with training results

问题描述

我直接从输入中得到的训练权重会返回不同的结果！ 我将在一个非常简单的示例中展示它 假设我们有一个输入向量x= 0:0.01:1; 和目标向量t=x^2(我知道最好使用非线性网络) 经过训练后的两层线性网络，每一层有一个神经元，我们得到:

The weights that I get from training, when implied directly on input, return different results! I'll show it on a very simple example let's say we have an input vector x= 0:0.01:1; and target vector t=x^2 (I know it better to use non linear network) after training, 2 layer, linear network, with one neuron at each layer, we get:

sim(net,0.95) = 0.7850(培训中的一些错误-没关系，应该这样) 来自net.IW,net.LW,net.b:

sim(net,0.95) = 0.7850 (some error in training - that's ok and should be) weights from net.IW,net.LW,net.b:

IW =

0.4547

LW =

2.1993

b =

0.3328   -1.0620

如果我使用权重:Out = purelin(purelin(0.95 * IW + b(1))* LW + b(2))= 0.6200！ ，我得到的结果与sim的结果不同！ 怎么会这样?怎么了?

if I use the weights: Out = purelin(purelin(0.95*IW+b(1))*LW+b(2)) = 0.6200! , I get different result from the result of the sim! how can it be? what's wrong?

代码:

%Main_TestWeights
close all
clear all
clc


t1 = 0:0.01:1;
x = t1.^2;

hiddenSizes = 1;
net = feedforwardnet(hiddenSizes);


[Xs,Xi,Ai,Ts,EWs,shift] = preparets(net,con2seq(t1),con2seq(x));
net.layers{1,1}.transferFcn = 'purelin';
[net,tr,Y,E,Pf,Af] = train(net,Xs,Ts,Xi,Ai);
view(net);


IW = cat(2,net.IW{1});
LW = cat(2,net.LW{2,1});
b = cat(2,[net.b{1,1},net.b{2,1}]);

%Result from Sim
t2=0.95;
Yk = sim(net,t2)

%Result from Weights
 x1 = IW*t2'+b(1)
 x1out = purelin(x1)
 x2 = purelin(x1out*(LW)+b(2))

推荐答案

神经网络工具箱将输入和输出重新缩放为[-1,1]范围.因此，您必须对其进行重新缩放和缩放，以使模拟输出与sim()的输出相同:

The neural network toolbox rescales inputs and outputs to the [-1,1] range. You must therefore rescale and unscale it so that your simulation output is the same sim()'s output:

 %Result from Weights
 x1 = 2*t2 - 1; # rescale 
 x1 = IW*x1+b(1);
 x1out = purelin(x1);
 x2 = purelin(x1out*(LW)+b(2));
 x2 = (x2+1)/2 # unscale

然后

>> x2 == Yk

ans =

     1

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