Previously, Matlab Geeks discussed a simple perceptron, which involves feed-forward learning based on two layers: inputs and outputs. Today we’re going to add a little more complexity by including a third layer, or a hidden layer into the network. A reason for doing so is based on the concept of linear separability. While logic gates like “OR”, “AND” or “NAND” can have 0′s and 1′s separated by a single line (or hyperplane in multiple dimensions), this linear separation is not possible for “XOR” (exclusive OR).

As the two images above demonstrate, a single line can separate values that return 1 and 0 for the “OR” gate, but no such line can be drawn for the “XOR” logic. Therefore, a simple perceptron cannot solve the XOR problem. What we need is a nonlinear means of solving this problem, and that is where multi-layer perceptrons can help.

First let’s initialize all of our variables, including the input, desired output, bias, learning coefficient, iterations and randomized weights.

% XOR input for x1 and x2 input = [0 0; 0 1; 1 0; 1 1]; % Desired output of XOR output = [0;1;1;0]; % Initialize the bias bias = [-1 -1 -1]; % Learning coefficient coeff = 0.7; % Number of learning iterations iterations = 10000; % Calculate weights randomly using seed. rand('state',sum(100*clock)); weights = -1 +2.*rand(3,3);

Similar to biological neurons which are activated when a certain threshold is reached, we will once again use a sigmoid transfer function to provide a nonlinear activation of our neural network. As we mentioned in our previous lesson, the sigmoid function 1/(1+e^(-x)) will squash all values between the range of 0 and 1. Also a requirement of the function in multilayer perceptrons, which use backpropagation to learn, is that this sigmoid activation function is continuously differentiable.

Lets set up our network to have 5 total neurons (if you are interested you can change the number of hidden nodes, change the learning rate, change the learning algorithm, change the activation functions as needed. In fact the artificial neural network toolbox in Matlab allows you to modify all these as well.)

This is how the network will look like, with the subscript numbers utilized as indexing in the Matlab code as well.

Whereas before we stated the delta rule as delta = (desired out)-(network output), we will use a modification, which is nicely explained by generation5 .

Back-propagation with the delta rule will allow us to modify the weights at each node in the network based on the error at the current level n and at the n+1 level.

Now for the code with back propagation.

for i = 1:iterations out = zeros(4,1); numIn = length (input(:,1)); for j = 1:numIn % Hidden layer H1 = bias(1,1)*weights(1,1) + input(j,1)*weights(1,2) + input(j,2)*weights(1,3); % Send data through sigmoid function 1/1+e^-x % Note that sigma is a different m file % that I created to run this operation x2(1) = sigma(H1); H2 = bias(1,2)*weights(2,1) + input(j,1)*weights(2,2) + input(j,2)*weights(2,3); x2(2) = sigma(H2); % Output layer x3_1 = bias(1,3)*weights(3,1) + x2(1)*weights(3,2) + x2(2)*weights(3,3); out(j) = sigma(x3_1); % Adjust delta values of weights % For output layer: % delta(wi) = xi*delta, % delta = (1-actual output)*(desired output - actual output) delta3_1 = out(j)*(1-out(j))*(output(j)-out(j)); % Propagate the delta backwards into hidden layers delta2_1 = x2(1)*(1-x2(1))*weights(3,2)*delta3_1; delta2_2 = x2(2)*(1-x2(2))*weights(3,3)*delta3_1; % Add weight changes to original weights % And use the new weights to repeat process. % delta weight = coeff*x*delta for k = 1:3 if k == 1 % Bias cases weights(1,k) = weights(1,k) + coeff*bias(1,1)*delta2_1; weights(2,k) = weights(2,k) + coeff*bias(1,2)*delta2_2; weights(3,k) = weights(3,k) + coeff*bias(1,3)*delta3_1; else % When k=2 or 3 input cases to neurons weights(1,k) = weights(1,k) + coeff*input(j,1)*delta2_1; weights(2,k) = weights(2,k) + coeff*input(j,2)*delta2_2; weights(3,k) = weights(3,k) + coeff*x2(k-1)*delta3_1; end end end end

Try to go through each step individually, and there are some additionally great tutorials online, including the generation5 website.

As for the final results?

Well the Weights are:

weights = -9.8050 -6.0907 -7.0623 -2.4839 -5.3249 -6.9537 5.7278 12.1571 -12.8941

These weights represent just one possible solution to this problem, but based on these results, what is the output? Drumroll please…

out = 0.0042 0.9961 0.9956 0.0049

Not bad, as the expected is [0; 1; 1; 0], which gives us a mean squared error (MSE) of 1.89*10^-5. Of course we ran this using 100,000 iterations, and while this could be optimized further, stopped earlier modified to have different architecture as mentioned previously, I’ll leave that to you or for a further lesson down the road.

hi

i need your help!!!!

how can i found out use perceptron with one layer or multi layers for solving a problem

Hello Sir,

I just need the code for Multilayer Perceptron feed forward network.

i need a script on matlab using the multilayer perceptron in predection , thank you

%Created by Arghya Pal

%Date 09/03/2014

%M.Tech, Goa University

% a program for back propagation network for EX-OR gate

%Machine Learning

close all, clear all, clc

x = [0 0 1 1; 0 1 0 1]

t = [0 1 1 0]

[ni N] = size(x)

[no N] = size(t)

nh = 2

% wih = .1*ones(nh,ni+1);

% who = .1*ones(no,nh+1);

wih = 0.01*randn(nh,ni+1);

who = 0.01*randn(no,nh+1);

c = 0;

while(c < 3000)

c = c+1;

% %for i = 1:length(x(1,:))

for i = 1:N

for j = 1:nh

netj(j) = wih(j,1:end-1)*x(:,i)+wih(j,end);

% %outj(j) = 1./(1+exp(-netj(j)));

outj(j) = tansig(netj(j));

end

% hidden to output layer

for k = 1:no

netk(k) = who(k,1:end-1)*outj' + who(k,end);

outk(k) = 1./(1+exp(-netk(k)));

delk(k) = outk(k)*(1-outk(k))*(t(k,i)-outk(k));

end

% back propagation

for j = 1:nh

s=0;

for k = 1:no

s = s + who(k,j)*delk(k);

end

delj(j) = outj(j)*(1-outj(j))*s;

% %s=0;

end

for k = 1:no

for l = 1:nh

who(k,l) = who(k,l)+.5*delk(k)*outj(l);

end

who(k,l+1) = who(k,l+1)+1*delk(k)*1;

end

for j = 1:nh

for ii = 1:ni

wih(j,ii) = wih(j,ii)+.5*delj(j)*x(ii,i);

end

wih(j,ii+1) = wih(j,ii+1)+1*delj(j)*1;

end

end

end

h = tansig(wih*[x;ones(1,N)])

y = logsig(who*[h;ones(1,N)])

e = t-round(y)

hi,pleaze

i neede source code matlab neural network MLP for character recognition.

i neede source code matlabe backpropagation.

size character input is 5*7

pleaze me help

mr30

%Created by Arghya Pal

%Date 09/03/2014

%M.Tech, Goa University

% a program for back propagation network for EX-OR gate

%Machine Learning

close all, clear all, clc

x = [0 0 1 1; 0 1 0 1]

t = [0 1 1 0]

[ni N] = size(x)

[no N] = size(t)

nh = 2

% wih = .1*ones(nh,ni+1);

% who = .1*ones(no,nh+1);

wih = 0.01*randn(nh,ni+1);

who = 0.01*randn(no,nh+1);

c = 0;

while(c < 3000)

c = c+1;

% %for i = 1:length(x(1,:))

for i = 1:N

for j = 1:nh

netj(j) = wih(j,1:end-1)*x(:,i)+wih(j,end);

% %outj(j) = 1./(1+exp(-netj(j)));

outj(j) = tansig(netj(j));

end

% hidden to output layer

for k = 1:no

netk(k) = who(k,1:end-1)*outj' + who(k,end);

outk(k) = 1./(1+exp(-netk(k)));

delk(k) = outk(k)*(1-outk(k))*(t(k,i)-outk(k));

end

% back propagation

for j = 1:nh

s=0;

for k = 1:no

s = s + who(k,j)*delk(k);

end

delj(j) = outj(j)*(1-outj(j))*s;

% %s=0;

end

for k = 1:no

for l = 1:nh

who(k,l) = who(k,l)+.5*delk(k)*outj(l);

end

who(k,l+1) = who(k,l+1)+1*delk(k)*1;

end

for j = 1:nh

for ii = 1:ni

wih(j,ii) = wih(j,ii)+.5*delj(j)*x(ii,i);

end

wih(j,ii+1) = wih(j,ii+1)+1*delj(j)*1;

end

end

end

h = tansig(wih*[x;ones(1,N)])

y = logsig(who*[h;ones(1,N)])

e = t-round(y)

and also i want to know what the function of this line:

rand(‘state’,sum(100*clock));

it was very good. but i think a book that after calculating the delta3_1 it goes toward weight and after that calculate the delta2_1 and delta2_2

are ther both of them corect or not??

Hi,

I also get these values for the output, instead of the expected ones.

0.499524196804705

0.477684866785518

0.500475803195295

0.522315133214482

I replaced sigma by x2(1) = 1/(1+exp(-x)); as everyone else did. Any suggestions? Is it possible to mail me the sigma.m file to rerun the code.Good work and thanks in advance

just change the bias to [1 1 1] you will get good results

can you please email me the sigma.m too ? Thank you..

function [ y ] = sigma( x )

y=1/(1+exp(-x));

end

good work..

it helps me a lot if u post sigma.m file.

Bravo! Good approach.

But you forgot something to attach, that makes your work incomplete. Can you add here or send me the sigma.m file? Then I can have a look.

hi,please can you send me the sigma.m ?

thank you a lot for your program it works well, i want to use a linear function in the out node but when i integrate th purelin function or my own linear function the execution fail and i have nan nan nan in out if you can help me for this problem i’m waiting your answer

thanks

Write the pseudocode of an Multi-Layer Perceptron (NN) with Back-Propagation Learning algorithm

Hi,

I am user of artificial neural nets, I am looking for multi-layer perceptron and backpropagation. Is there possibility to help me to write an incremental multilayer perceptron matlab code

thank you

Hi,

I am user of neural nets, I am looking for backpropagation with incremental or stochastic mode, Is there possibility to help me to write an incremental multilayer perceptron matlab code for input/output regression

thank you

Hi,

I am user of neural nets, I am looking for backpropagation with incremental or stochastic mode, Is there possibility to help me to write an incremental multilayer perceptron matlab code

thank you

I’m trying to adapt this code to work in a character recognition program. A letter is converted to a binary array of 1′s and 0′s. The dimensions are 1×100. I have 4 letters that I would like to train the network to recognize. So the size of input would be 4×100. How can I adapt the code to work for my input size? Also, I would like the network to get trained so that the output is 1 2 3 4 instead of a 0 or 1 like your code does.

hey when i try to plot the error curve,i get 2 curves .. y is that .please help

This is very informative and thanks a lot. What if I want to just evolve a neural network to approximate a function. I dont want to train or use backpropagation, just a straight forward evolution of a nueral network. I understand that soem people call it NeuroEvolution.

Thanks in advance

I am very new in neural network.how can i train the network.how can i use the codes?

I am very new in neural network.how can I train a network?how can I use the codes?

Hey ,

Thanks for the tutorial. How can this be applied for stock market data fetched with y=yahoo ?

thax mate

hi…nice work but first as i read in the previous comments the scheme differ than the code& the answer is not c correct for me

0.499524196804705

0.477684866785518

0.500475803195295

0.522315133214482

this the out value idk why????

i replaced sigma by x2(1) = 1/(1+exp(-H1));

thx in advanced

Hi,

thanks for the tutorial, but how to implementation matlab program MLP NN to analisis Network Intrusion detection System with input Protocol ID, DNS, Source MAC,Source IP, Source Port, Dest MAC, Dest IP, Dest Port, ICMP type, ICMP Code, Raw Data Length, Raw Data Size and Output Attac or Not Attack and how to build hiden layer for that…. Thks

I seem to have the same problem as Don as well. The output is converging to 0.5;0.5;0.5;0.5 instead of 0;1;1;0. I use the exact code as posted above, but replaced the sigma function

function x = sigma(net)

x = 1/(1+exp(-net));

end

hi behnaz, i think i have found a problem in the backpropagation part of this code. I havent received any replies from the site admin to a few of my questions but reply to this if you want to collaborate! cheers Jonathan

hi,

where is the problem please

can you please email me the sigma.m? Thank you..

can you please email me the sigma.m too ? Thank you..

amini_r89@yahoo.com

Hi,

thanks for the intro to MLP’s and the example code – its been very helpful.

Out of interest, if I am building a net to predict, say efficiency of a component (in percent i.e. 0 – 100%) based of input parameters, how would I change the code to allow for outputs greater that the standard range [0 1] (for the sigmoid function)?

In other words, all the examples i have seen so far, give an output of either 0 or 1 or a value inbetween, but cannot give outputs outside of this range.

Kind regards

Jonathan

Jonathan,

Have you figure out how to get values outside of [0 1]? I was thinking of scaling down the output values by the maximum number in outputs. So if I have [1 2 3 4], new outputs would be [.25 .5 .75 1].

Please i was told to go and look what sigma means and its value,can you help me

Hi

Could you mail me the sigma.m?Thank you!

Hi,

Thanx for code, it’s cool. But I have problems with “weights=…” and “out=…”, I just can’t see it. Could u pls help me, it’s really urgent since I have an exam in few days.

When I run the code, it looks like this

H1 =

-2.7332

ans =

-0.8847

H2 =

-2.6884

ans =

-3.3642

x3_1 =

0.0019

ans =

-0.0041

H1 =

-2.7336

ans =

-0.8851

H2 =

-2.6884

ans =

-3.3642

x3_1 =

0.0893

ans =

and so on…((

if you type your code like this your problem would be solved:

H1 = bias(1,1)*weights(1,1) + input(j,1)*weights(1,2) + input(j,2)*weights(1,3);

(type in one line)

sir,

i need the code for back propagation

How could I divide with 2 lines? 4 data sets?

Thank you in advanced!

Hi . This is good explanation of MLPNN . I have some problem in my code. I have a matrix of 30×11 and i want to classify this in three categories in 2-d plane . In this way i trained 60% data 10% validate and 30% for test. The three categories are

1- x<-1 -1<y<1

2- 01.8 y>0

3- x0

how can this achieve by MLPNN.

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Hello, I don’t really understand why the weight matrix has 9 values in it. There are 6 in the diagram, are there not? I’m trying to figure out how to create a 4-input hidden layer but I am having difficulty understanding how the weights are derived within the code.

There is a weight applied to each of the 3 biases: 4 Input layer weights, 2 hidden layer weights, and 3 bias weights.

i also didn’t get it. why do you need to initiate a 3X3 matrix for the random weights? Was that supposed to be for the 3 biases?

so for any NN i’ll make that has 3biases? i’ll always have a 3X3matrix for the weight?

so for any NN i’ll make that has 3biases, i should always have a 3X3matrix for the randomized weights?

Hi.

I had recently try your network. Even if i had set the iteration to 100,000 times, the result is still far from the target values. I also try with shorter and larger iteration times to reconfirm the result. It seems that the error that propagate back to the network is not working very well as i am getting a similar results even with different iteration times. I hope you can revise it back or I am the only one have this problem??. FYI i am using Matlab 2010b.

Thanks.

I believe you’re the only one having problem. It works fine over here. You may want to;

clear all; close all; clc

then copy and paste code to try it again.

Hi,

I am a newby when it comes to ANNs, but your site has been of great help! However, I seem to have the same problem as Don as well. The output is converging to 0.5;0.5;0.5;0.5 instead of 0;1;1;0. I use the exact code as posted above, but replaced the sigma function as suggested by Vipul. Here is the code I am using:

input = [0 0; 0 1; 1 0; 1 1];

% Desired output of XOR

output = [0;1;1;0];

% Initialize the bias

bias = [-1 -1 -1];

% Learning coefficient

coeff = 0.7;

% Number of learning iterations

iterations = 10000;

% Calculate weights randomly using seed.

rand(‘state’,sum(100*clock));

weights = -1 +2.*rand(3,3);

for i = 1:iterations

out = zeros(4,1);

numIn = length (input(:,1));

for j = 1:numIn

% Hidden layer

H1 = bias(1,1)*weights(1,1)

+ input(j,1)*weights(1,2)

+ input(j,2)*weights(1,3);

% Send data through sigmoid function 1/1+e^-x

% Note that sigma is a different m file

% that I created to run this operation

x2(1) = 1/(1+exp(-H1));

H2 = bias(1,2)*weights(2,1)

+ input(j,1)*weights(2,2)

+ input(j,2)*weights(2,3);

x2(2) = 1/(1+exp(-H2));

% Output layer

x3_1 = bias(1,3)*weights(3,1)

+ x2(1)*weights(3,2)

+ x2(2)*weights(3,3);

out(j) = 1/(1+exp(-x3_1));

% Adjust delta values of weights

% For output layer:

% delta(wi) = xi*delta,

% delta = (1-actual output)*(desired output – actual output)

delta3_1 = out(j)*(1-out(j))*(output(j)-out(j));

% Propagate the delta backwards into hidden layers

delta2_1 = x2(1)*(1-x2(1))*weights(3,2)*delta3_1;

delta2_2 = x2(2)*(1-x2(2))*weights(3,3)*delta3_1;

% Add weight changes to original weights

% And use the new weights to repeat process.

% delta weight = coeff*x*delta

for k = 1:3

if k == 1 % Bias cases

weights(1,k) = weights(1,k) + coeff*bias(1,1)*delta2_1;

weights(2,k) = weights(2,k) + coeff*bias(1,2)*delta2_2;

weights(3,k) = weights(3,k) + coeff*bias(1,3)*delta3_1;

else % When k=2 or 3 input cases to neurons

weights(1,k) = weights(1,k) + coeff*input(j,1)*delta2_1;

weights(2,k) = weights(2,k) + coeff*input(j,2)*delta2_2;

weights(3,k) = weights(3,k) + coeff*x2(k-1)*delta3_1;

end

end

end

end

Hopefully someone can help me with this. Thanks.

Berry,

Your ‘out’ variable is converging to [0.5; 0.5; 0.5; 0.5]? I copy and pasted your code, and was getting close to [0; 1; 1; 0]. Not sure what the difference is though…

Can you tell me what your weights end up being? Maybe change the iterations to just 2 or 3 or something small, and then track how the weights and out change over the course of just a couple iterations. The math should be easy to follow using just a couple iterations as well, so you can see where it might be failing.

Sorry its not working out properly. If anyone else has suggestions, we’re all ears.

Vipul

i have looked at this problem and can replicate both using the code above. My understanding is that, sometimes the gradient descent algorithm which is used to find the weights for min error, finds a local minimum in the error function which results in 0.5 / 0.5 / 0.5 / 0.5 rather than the weights for the global min of the error function which for the 0utput 0 / 1 / 1 / 0.

I had the same problem the first time I ran it; check that the equations for H1, H2 and x3_1 fit in one line each; that solved it for me. Thanks a lot for the material, Vitpul!!

Buen aporte

Hi,

Why is the equation for calculating H1 (and H2) look different with your network representation in the graphic? Shouldn’t H1 be calculated as H1 = bias(1,1)*weights(1,1) + input(j,1)*weights(1,2) + input(j,2)*weights(2,2)?

And also H2 = bias(1,2)*weights(2,1) + input(j,1)*weights(1,3) + input(j,2)*weights(2,3)?

Of course your code still works, but maybe it’s good for keeping the consistency with your picture.

Hey good catch, i’ll try to fix that when i get a chance.

Did you ever change that?

Find of the day! Great work!

Very helpful.

Thanks

Hi. Nice posting. Thank you very much for this material. I am doing NN for my study. So this is very helpful. Hope you can post more on NN.

The sigma code is a simple function that squashes the data between a certain range. One such sigmoid function example could be something like the following:

function x = sigma(net)

x = 1/(1+exp(-net));

Hi,

thanks for the tutorial, but aren’t you missing the sigma code? I am referring to this in-code comment:

% Note that sigma is a different m file

% that I created to run this operation