Can I export my (Matlab-based) neural network to PHP? - matlab

Question

I have trained a neural network in Matlab (Using the neural network toolbox). Now I would like to export the calculated weights and biases to another platform (PHP) in order to make calculations with them. Is there a way to create a function or equation to do this?
I found this related question: Equation that compute a Neural Network in Matlab.
Is there a way to do what I want and port the results of my NN (29 inputs, 10 hidden layers, 1 output) to PHP?

Answer 1

Yes, the net properties also referenced in the other question are simple matrices:
W1=net.IW{1,1};
W2=net.LW{2,1};
b1=net.b{1,1};
b2=net.b{2,1};
So you can write them to a file, say, as comma-separated-values.
csvwrite('W1.csv',W1)
Then, in PHP read this data and convert or use it as you like.
<?php
if (($handle = fopen("test.csv", "r")) !== FALSE) {
$data = fgetcsv($handle, 1000, ",");
}
?>
Than, to process the weights, you can use the formula from the other question by replacing the tansig function, which is calculated according to:
n = 2/(1+exp(-2*n))-1
This is mathematically equivalent to tanh(N)
Which exists in php as well.
source: http://dali.feld.cvut.cz/ucebna/matlab/toolbox/nnet/tansig.html

Answer 2

Transferring all of these is pretty trivial. You will need:
Write the code for matrix multiplication, which are a pretty simple couple of for loops.
Second, observe that according to the Matlab documentation tansig(n) = 2/(1+exp(-2*n))-1. I'm pretty sure that PHP has exp (and if not, it is has a pretty simple polynomial expansion which you can write yourself)
Read, understand and apply Jasper van den Bosch's excellent answer to your question.

Answer 3

Hence the solution becomes (after correcting all wrong parts)
Here I am giving a solution in Matlab, but if you have tanh() function, you may easily convert it to any programming language. For PHP, tanh() function exists: php tanh(). It is for just showing the fields from network object and the operations you need.
Assume you have a trained ann (network object) that you want to export
Assume that the name of the trained ann is trained_ann
Here is the script for exporting and testing.
Testing script compares original network result with my_ann_evaluation() result
% Export IT
exported_ann_structure = my_ann_exporter(trained_ann);
% Run and Compare
% Works only for single INPUT vector
% Please extend it to MATRIX version by yourself
input = [12 3 5 100];
res1 = trained_ann(input')';
res2 = my_ann_evaluation(exported_ann_structure, input')';
where you need the following two functions
First my_ann_exporter:
function [ my_ann_structure ] = my_ann_exporter(trained_netw)
% Just for extracting as Structure object
my_ann_structure.input_ymax = trained_netw.inputs{1}.processSettings{1}.ymax;
my_ann_structure.input_ymin = trained_netw.inputs{1}.processSettings{1}.ymin;
my_ann_structure.input_xmax = trained_netw.inputs{1}.processSettings{1}.xmax;
my_ann_structure.input_xmin = trained_netw.inputs{1}.processSettings{1}.xmin;
my_ann_structure.IW = trained_netw.IW{1};
my_ann_structure.b1 = trained_netw.b{1};
my_ann_structure.LW = trained_netw.LW{2};
my_ann_structure.b2 = trained_netw.b{2};
my_ann_structure.output_ymax = trained_netw.outputs{2}.processSettings{1}.ymax;
my_ann_structure.output_ymin = trained_netw.outputs{2}.processSettings{1}.ymin;
my_ann_structure.output_xmax = trained_netw.outputs{2}.processSettings{1}.xmax;
my_ann_structure.output_xmin = trained_netw.outputs{2}.processSettings{1}.xmin;
end
Second my_ann_evaluation:
function [ res ] = my_ann_evaluation(my_ann_structure, input)
% Works with only single INPUT vector
% Matrix version can be implemented
ymax = my_ann_structure.input_ymax;
ymin = my_ann_structure.input_ymin;
xmax = my_ann_structure.input_xmax;
xmin = my_ann_structure.input_xmin;
input_preprocessed = (ymax-ymin) * (input-xmin) ./ (xmax-xmin) + ymin;
% Pass it through the ANN matrix multiplication
y1 = tanh(my_ann_structure.IW * input_preprocessed + my_ann_structure.b1);
y2 = my_ann_structure.LW * y1 + my_ann_structure.b2;
ymax = my_ann_structure.output_ymax;
ymin = my_ann_structure.output_ymin;
xmax = my_ann_structure.output_xmax;
xmin = my_ann_structure.output_xmin;
res = (y2-ymin) .* (xmax-xmin) /(ymax-ymin) + xmin;
end