Matlab神经网络工具箱，关于提取重量和重量来自前馈网的偏见 [英] Matlab neural network tool box,on extract weight & bias from feedforwardnet

问题描述

我的问题很简单.我已经训练了一个前馈网络.现在，我想提取其权重和偏差，以便可以在另一种编程语言上对其进行测试.但是当我用自己的代码测试那些经过训练的权重时，与神经工具箱相比，它总是返回不同的结果.这是我的代码

My problem is simple. I have trained a feedforwardnet. now I want to extract its weights and biases so i can test it on another programming language. but while i tested those trained weights by my own code, it always returns different results compare with neural tool box.here is my code

close all 
RandStream.setGlobalStream (RandStream ('mrg32k3a','Seed', 1234));
[x,t] = simplefit_dataset;
plot(t)
hold on 
topo = [2]
net = feedforwardnet(topo);
net = train(net,x,t);
view(net)
y = net(x);
plot(y)

%rewrite net

BI = net.B{1};
WI = net.IW{1};

BO = net.B{2};
WO = net.LW{2};

% input layer

Z = WI*x + BI*ones(1,length(x));
Z = 2./(1+exp(-2*Z))-1;

Y = WO*Z + BO*ones(1,length(x));

plot(Y)
legend('target','tool box result','my result')

它是一个只有两层的简单神经网络. 没有暗示缩放或归一化 这是结果

it is a simple neural network only have two layer. no scaling or normalization be implied here is the result

推荐答案

默认情况下，神经网络输入和输出映射到[-1;1]范围，因此Z和Y的计算对于映射值是正确的，而不是实际的输入和输出.为避免这种情况，您可以取消设置processFcns属性:

Neural network inputs and outputs are mapped to the [-1;1] range by default, so the calculation of Z and Y is correct for the mapped values, not for the actual inputs and outputs. To avoid this, you can unset the processFcns properties:

 ....
net = feedforwardnet(topo);
net.inputs{1}.processFcns= {};
net.outputs{2}.processFcns= {};
net = train(net,x,t);
 ....

或者，您可以手动映射输入和输出值:

Alternatively, you can map the input and output values manually:

x= mapminmax(x,-1,1);
Z = WI*x + BI*ones(1,length(x));
Z = 2./(1+exp(-2*Z))-1;
Y = WO*Z + BO*ones(1,length(x));
Y= mapminmax(Y,min(y),max(y));

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