MATLAB 中的神经网络 [英] Neural network in MATLAB
问题描述
我已经在 MATLAB 中训练了异或神经网络并获得了这些权重:
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]
出于好奇,我尝试编写 MATLAB 代码来计算该网络的输出(隐藏层中的两个神经元,输出中的一个神经元,TANSIG 激活函数).
Just from curiosity I have tried to write MATLAB code which computes the output of this network (two neurons in the hidden layer, and one in the 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))
问题是当输入是 [1,1] 时,它输出 -0.9989,当 [0,1] 0.4902.充分模拟用 MATLAB 生成的网络输出为 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
我写了一个简单的异或网络示例.我使用了 newpr
,它默认为隐藏层和输出层的 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中的矩阵乘积:
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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