115 lines
3.9 KiB
PHP
115 lines
3.9 KiB
PHP
<?php
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namespace neuralnetwork\core;
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use filemanager\core\FileManager;
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class NeuralNetworkCore implements \Serializable{
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/************************************************
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**** GLOBAL ATTRIBUTES ****
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************************************************/
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private $maxGnr; // Maximum generation iterations
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private $maxGnm; // Maximum genomes per generation
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private $kptGnm; // Number of genomes kept for each generation
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private $mutThr; // Mutation threshold
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private $fitEnd; // Fitness range to end process
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private $numHid; // Number of hidden layer(s)
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private $numNeu; // Number of neurons for each hidden layer
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private $max; // max values for input and output neurons
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private $storage; // path to storage
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/************************************************
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**** LOCAL ATTRIBUTES ****
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************************************************/
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private $gnr; // Current generation index
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private $gmns; // Current generation's genomes
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private $gnm; // Current genome index
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private $fit; // Current fitness
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}
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/************************************************
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**** USE CASE ****
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************************************************/
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$use_case = false;
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if( $use_case ){
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/* (1) Instanciate and configure neural network
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---------------------------------------------------------*/
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/* (1) Initialize neural network to try 100 genomes over 50 generations (max) */
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$neunet = NeuralNetwork::create(100, 50);
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/* (2) Specifies that a fitness greater than 0.89 will stop the neural network */
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$neunet->setFitnessEnd(0.89);
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/* (3) Specifies that it will keep the 2 best genomes of each generation */
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$neunet->setKeptGenomes(2);
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/* (4) Specifies that for each mutation, it must be at maximum 30% different */
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$neunet->setMutationThreshold(0.4);
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/* (5) Specifies the number of hidden layers */
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$neunet->setHiddenLayersCount(2);
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/* (6) Specifies the number of neurons per hidden layer */
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$neunet->setHiddenLayerNeuronsCount(4);
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/* (2) Let's learn
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---------------------------------------------------------*/
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/* (1) Set dataset sample maximum values */
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$neunet->setMaxValues([2,2,7], [10, 10]);
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// input output
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/* (2) Setting our example dataset */
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$neunet->addSample([1.9, 2, 5.3], [6, 0]);
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$neunet->addSample([1.2, 1.2, 0.1], [0, 2]);
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$neunet->addSample([0, 0, 0], [5, 5]);
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/* (3) Launch learning routine with callback function */
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$neunet->learn(function($ctx){ // callback with @ctx the current context
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echo 'generation '. $ctx['generation'] .'/'. $ctx['generations'] ."\n";
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echo 'genome '. $ctx['genome'] .'/'. $ctx['genomes'] ."\n";
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echo 'fitness: '. $ctx['fitness'] .'/'. $ctx['fitness_end']. "\n";
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});
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/* (3) What to do next ?
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---------------------------------------------------------*/
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/* (1) Store your data */
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$neunet->store('/some/path/to/storage');
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/* (2) [HIDDEN] will create those 2 files */
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'/some/path/to/storage.nn'; // containing neural network configuration
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'/somt/path/to/storage.ln'; // containing neural network learnt values & weights
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'/some/path/to/storage.ex'; // containing neural network samples
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/* (4) And next ?
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---------------------------------------------------------*/
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/* (1) Load your stored neural network */
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$trainednn = NeuralNetwork::load('/some/path/to/storage');
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/* (2) Use it to guess output, with well-formed input */
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$guessed = $trainednn->guess([1.2, 0.9, 6.1]);
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/* (3) And correct it if needed*/
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echo "1: ". $guessed[0] ."\n";
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echo "2: ". $guessed[2] ."\n";
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echo "is it correct ?\n";
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// .. some code to manage and read correct output if it was wrong
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$trainednn->addSample([1.2, 0.9, 6.1], [9, 0]);
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// You can now relaunch a new neural network's learning
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// or You can just add a few generations to evolve your neural network
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}
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?>
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