MATLAB中的神经网络 [英] Neural network in MATLAB

问题描述

我已经在Matlab中训练了xor神经网络，并获得了以下权重:

I have trained xor neural network in Matlab and got these weights:

iw: [-2.162 2.1706; 2.1565 -2.1688]

lw: [-3.9174 -3.9183]

b{1} [2.001; 2.0033]

b{2} [3.8093]

仅出于好奇，我尝试编写MATLAB代码来计算该网络的输出(隐藏层中2个神经元，TANSIG激活函数中1个输出).

Just from curiosity I have tried to write MATLAB code which computes the output of this network (2 neurons in hidden layer, and 1 in output, TANSIG activation function).

我得到的代码:

l1w = [-2.162 2.1706; 2.1565 -2.1688];
l2w = [-3.9174 -3.9183];
b1w = [2.001 2.0033];
b2w = [3.8093];

input = [1, 0];

out1 = tansig (input(1)*l1w(1,1) + input(2)*l1w(1,2) + b1w(1));
out2 = tansig (input(1)*l1w(2,1) + input(2)*l1w(2,2) + b1w(2));
out3 = tansig (out1*l2w(1) + out2*l2w(2) + b2w(1))

问题是当输入为[1,1]时，输出为 -0.9989 ，而当输入[0,1] 0.4902 时，输出为 -0.9989 .在模拟使用MATLAB输出生成的网络时，分别为 0.00055875 和 0.99943 .

The problem is when input is lets say [1,1], it outputs -0.9989, when [0,1] 0.4902. While simulating network generated with MATLAB outputs adequately are 0.00055875 and 0.99943.

我做错了什么?

推荐答案

我写了一个XOR网络的简单示例.我使用了newpr，对于隐藏层和输出层，默认为tansig传递函数.

I wrote a simple example of an XOR network. I used newpr, which defaults to tansig transfer function for both hidden and output layers.

input = [0 0 1 1; 0 1 0 1];               %# each column is an input vector
ouputActual = [0 1 1 0];

net = newpr(input, ouputActual, 2);       %# 1 hidden layer with 2 neurons
net.divideFcn = '';                       %# use the entire input for training

net = init(net);                          %# initialize net
net = train(net, input, ouputActual);     %# train
outputPredicted = sim(net, input);        %# predict

然后我们通过自己计算输出来检查结果.要记住的重要一点是，默认情况下，输入/输出会缩放到[-1,1]范围:

then we check the result by computing the output ourselves. The important thing to remember is that by default, inputs/outputs are scaled to the [-1,1] range:

scaledIn = (2*input - 1);           %# from [0,1] to [-1,1]
for i=1:size(input,2)
    in = scaledIn(:,i);             %# i-th input vector
    hidden(1) = tansig( net.IW{1}(1,1)*in(1) + net.IW{1}(1,2)*in(2) + net.b{1}(1) );
    hidden(2) = tansig( net.IW{1}(2,1)*in(1) + net.IW{1}(2,2)*in(2) + net.b{1}(2) );
    out(i) = tansig( hidden(1)*net.LW{2,1}(1) + hidden(2)*net.LW{2,1}(2) + net.b{2} );
end
scaledOut = (out+1)/2;              %# from [-1,1] to [0,1]

或更有效地表示为一行的矩阵乘积:

or more efficiently expressed as matrix product in one line:

scaledIn = (2*input - 1);           %# from [0,1] to [-1,1]
out = tansig( net.LW{2,1} * tansig( net.IW{1}*scaledIn + repmat(net.b{1},1,size(input,2)) ) + repmat(net.b{2},1,size(input,2)) );
scaledOut = (1 + out)/2;            %# from [-1,1] to [0,1]

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