我从训练中获得的权重,当直接在输入中暗示时,会返回不同的结果!我将在一个非常简单的例子中展示它,假设我们在训练后有一个输入向量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),2 层,线性网络,每层有一个神经元,我们得到:
sim(net,0.95) = 0.7850
(训练中的一些错误 - 没关系,应该是)权重来自net.IW,net.LW,net.b:
IW =
0.4547
长宽 =
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!,我得到了与模拟结果不同的结果!怎么会这样?怎么了?
编码:
%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))