<?php
/** example rn class. red neuronal (neural network)
*
* BASIC USAGE:
*
* Requeriments:
* - A minimum (minimum, minimum, minimum requeriments is needed). Tested on:
* - Simple Raspberry pi (B + 512MB 700 MHz ARM11) with Raspbian Lite PHP7.3 ^_^
* - VirtualBox Ubuntu Server 20.04.2 LTS (Focal Fossa) with PHP7.4.3
* - Needed 1 hidden layer at least
*
*
* INSTALLATION:
* A lot of easy :). It is written in PURE PHP. Only need to inclue the files. Tested on basic PHP installation
*
* require_once( 'rn.class.php' );
*
*
* - Define train input items array
*
* $arrTrainInputItems = [
* [0, 0],
* [0, 1],
* [1, 0],
* [1, 1]
* ];
*
*
* - Define desired output values array
*
* $arrTrainOutputItems = [
* [0.1, 0.2],
* [0.3, 0.4],
* [0.5, 0.6],
* [0.7, 0.8]
* ];
*
*
* - Create neural network object
*
* $rn = new rn( [3, 1, 2] ); // 3x1x2 = 3 layers. 3 input neurons, hidden layer with 1 neuron, 2 output neurons
*
* If you want for example 4 layers (3x12x8x2): 3 input neurons, hidden layer with 12 neurons, hidden layer with 8 neurons, output layer with 2 neurons, simply do:
* $rn = new rn( [3, 12, 8, 2] );
*
*
* - Print All Train Input data, Neural Network Output data & Train Desired Data
*
* $num_sample_data = count($arrTrainInputItems);
*
* echo "Default Values: ".PHP_EOL;
*
* for($i=0;$i<$num_sample_data;$i++){
* $rn->EchoOutputValues( $arrTrainInputItems[$i], $arrTrainOutputItems[$i] );
* }
*
*
* - Do learn process:
*
* $rn->Learn($arrTrainInputItems, $arrTrainOutputItems);
*
*
* For full configuration, please, read the file readme.txt
*
*
*
* @author Rafael Martin Soto
* @author {@link http://www.inatica.com/ Inatica}
* @since July 2021
* @version 1.0
* @license GNU General Public License v3.0
*
* Thanks to:
* - https://github.com/infostreams/neural-network/blob/master/class_neuralnetwork.php
* - https://gist.github.com/ikarius6/26851fb7220837e8016fe0c425d34dd6
* - https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
* - https://www.youtube.com/channel/UCy5znSnfMsDwaLlROnZ7Qbg
*/
require_once( 'rn.class.php' );
// Prepare configuration of our Neural Network
// Train Values
$arrTrainInputItems = [
[0, 0],
[0, 1],
[1, 0],
[1, 1]
];
$arrTrainOutputItems = [
[0.1, 0.2],
[0.3, 0.4],
[0.5, 0.6],
[0.7, 0.8]
];
// Some variables for use later
$num_sample_data = count($arrTrainInputItems);
$NumEpochs = 1000;
// Most important part of Neural Network
$rn = new rn( [2, 1, 2] ); // 2x1x2 = 3 layers. 2 input neurons, hidden layer with 1 neuron, 2 output neurons.
$rn->fSet_num_epochs( $NumEpochs ); // Set rn Num Epochs (1000 by default config if not set).
$rn->fSet_activation_function( 'sigm' ); // Set the default activation function ('sigm' if not set).
$rn->set_alpha( 1 ); // Set the default activation function ('sigm' if not set).
$rn->InformEachXBlock = 10;
// Print Not trained Neural Network Input data, Output data & Desired Values
echo 'Default Values: '.PHP_EOL;
for($i=0;$i<$num_sample_data;$i++){
$rn->EchoOutputValues( $arrTrainInputItems[$i], $arrTrainOutputItems[$i] );
}
// Process of learn
echo 'Learning '.$NumEpochs.' Epochs....'.PHP_EOL;
$rn->Learn($arrTrainInputItems, $arrTrainOutputItems);
// Print trained Neural Network Input data, Output data & Desired Values
echo 'Final Values: '.PHP_EOL;
for($i=0;$i<$num_sample_data;$i++){
$rn->EchoOutputValues( $arrTrainInputItems[$i], $arrTrainOutputItems[$i] );
}
// We can export the data to export the trained model to use it on other sites, as for example, a simple Production Web Server :)
echo $rn->exportData2Json().PHP_EOL;
?>
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