Method to get trained genome + anti-regression management + some stuff to do (output neurons issue) + manage `mutThr` decreasing + ...
This commit is contained in:
xdrm-brackets
parent
ece00a7688
commit
7aefda0bf0
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@ -4,12 +4,14 @@
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use filemanager\core\FileManager;
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// FIXME: Propagation of outputNeurons from NeuralNetwork to Genome
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class Genome implements \Serializable{
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/************************************************
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**** Constants ****
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************************************************/
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const MIN = 0;
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const MIN = -1e9;
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const MAX = 1e9;
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/************************************************
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@ -295,10 +297,8 @@
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// echo "result: ${neurons[$n]}\n\n";
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}
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if( $l == 1 )
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$neurons[$n] /= $this->inputN;
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else
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$neurons[$n] /= $this->neurons;
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// if( $l == 1 ) $neurons[$n] /= $this->inputN;
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// else $neurons[$n] /= $this->neurons;
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}
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@ -309,6 +309,7 @@
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/* [3] Callback the output layer's values
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=========================================================*/
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echo $this->outputN;
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call_user_func($this->callback, $input, array_slice($neurons, -$this->outputN) );
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}
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@ -24,6 +24,7 @@
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/************************************************
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**** LOCAL ATTRIBUTES ****
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************************************************/
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private $maxFit; // Maximum fitness of the previous generation
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public $gnr; // Current generation index
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public $gnm; // Current genome index
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private $genome; // Current genome instance
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@ -123,7 +124,6 @@
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$this->callback = function(){}; // default value
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}
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/************************************************
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**** Attributes Setters ****
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************************************************/
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@ -315,6 +315,27 @@
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}
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/* RETURNS THE TRAINED GENOME IF IT EXISTS
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*
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* @return trained<Genome> The trained genome
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*
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*/
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public function getTrainedGenome(){
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/* (1) Read file */
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$serialized = FileManager::readline($this->storage['ln']['filename'], 0);
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/* (2) If no data */
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if( is_null($serialized) )
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throw new \Exception('No trained genome found.');
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/* (3) Unserializes it */
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$genome = new Genome(2, 2, 2, 2);
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$genome->unserialize($serialized);
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/* (4) Return genome */
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return $genome;
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}
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/* INITIALIZES THE LEARNING ROUTINE
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*
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* @callback<Function> Callback function to display current state
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@ -360,15 +381,20 @@
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/* (1) Initializes data & storage */
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$this->gnr = 0;
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$this->gnm = 0;
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$this->maxFit = null;
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FileManager::write($this->storage['gn']['filename'], '');
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FileManager::write($this->storage['ft']['filename'], '');
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/* (2) Fetch learnt best genomes */
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/* (2.1) Fetch learnt best genomes */
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$loadedGenomes = [
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FileManager::readline($this->storage['ln']['filename'], 0),
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FileManager::readline($this->storage['ln']['filename'], 1)
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];
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/* (2.2) Checks content */
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if( is_null($loadedGenomes[0]) || is_null($loadedGenomes[1]) )
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return $this->initLearningRoutine($callback);
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/* (3) Unserializes them */
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$father = new Genome(2, 2, 2, 2);
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$father->unserialize($loadedGenomes[0]);
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@ -398,7 +424,6 @@
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}
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/* RETURNS THE CURRENT GENOME
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*
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* @return genome<Genome> Returns the current genome
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@ -458,21 +483,47 @@
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$ftRead = FileManager::read($this->storage['ft']['filename']);
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$fitnesses = explode("\n", trim($ftRead) );
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/* (3) Extract @mother & @father indexes */
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/* (3) Checks if theres a fitness evolution */
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$fitnessEvolution = is_null($this->maxFit) || max($fitnesses) > $this->maxFit;
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/* (3.1) If evolution -> choose best + cross-over ... */
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if( $fitnessEvolution ){
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// {1} Extract @mother & @father indexes //
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$iBest = $this->bestFitnesses($fitnesses);
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/* (4) Extract best 2 genomes */
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// {2} Extract best 2 genomes //
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$sFather = FileManager::readline($this->storage['gn']['filename'], $iBest[0]);
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$sMother = FileManager::readline($this->storage['gn']['filename'], $iBest[1]);
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/* (5) Unserializes them */
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// {3} Unserializes them //
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$father = new Genome(2, 2, 2, 2);
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$father->unserialize($sFather);
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$mother = new Genome(2, 2, 2, 2);
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$mother->unserialize($sMother);
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/* (6) Create new generation */
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$this->maxFit = max($fitnesses);
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/* (3.2) If regression -> renew generation */
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}else{
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// {1} Extract 2 parents of the current generation //
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$sFather = FileManager::readline($this->storage['gn']['filename'], 0);
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$sMother = FileManager::readline($this->storage['gn']['filename'], 1);
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// {3} Unserializes them //
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$father = new Genome(2, 2, 2, 2);
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$father->unserialize($sFather);
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$mother = new Genome(2, 2, 2, 2);
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$mother->unserialize($sMother);
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/* (4) Stay on the current genertion */
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$this->gnr--;
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}
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/* (7) Create new generation */
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FileManager::write($this->storage['gn']['filename'], '');
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FileManager::write($this->storage['ft']['filename'], '');
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@ -495,14 +546,13 @@
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}
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/* (3) If end of process
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---------------------------------------------------------*/
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}else{
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/* (1) Get the 2 best genomes */
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$best = FileManager::readline($this->storage['ln']['filename'], 0);
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$best.= FileManager::readline($this->storage['ln']['filename'], 1);
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$best = FileManager::readline($this->storage['gn']['filename'], 0);
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$best.= FileManager::readline($this->storage['gn']['filename'], 1);
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/* (2) Stores data to learnt data */
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FileManager::write($this->storage['ln']['filename'], $best);
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@ -514,6 +564,8 @@
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}
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}
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// TODO: Manage @mutThr decreasing to be more precise
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/************************************************
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**** Utility ****
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************************************************/
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@ -1,16 +1,16 @@
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0,0,0;0,0
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|
||||
-0.26291432434374
|
||||
-0.24253701374514
|
||||
-0.24379596365793
|
||||
-0.23996414424896
|
||||
-0.22123873931464
|
||||
-0.27001852389648
|
||||
-0.25111384165343
|
||||
-0.24175301813362
|
||||
-0.26529997159751
|
||||
-0.23852883258585
|
||||
-0.22210609410012
|
||||
-0.22823595168029
|
||||
-0.2300380623709
|
||||
-0.23896167033627
|
||||
-0.26509436775629
|
||||
-0.26328307763079
|
||||
-0.26247886873884
|
||||
-0.21495589397895
|
||||
-0.23967246175787
|
||||
-0.23839653632849
|
||||
-0.25995519705069
|
||||
-0.24627053693291
|
||||
-0.28207214320164
|
||||
-0.22499059687071
|
||||
-0.23338432454985
|
||||
-0.21582926394003
|
||||
-0.24272426226148
|
||||
-0.22659467028497
|
||||
-0.26097887720452
|
||||
-0.2355457341177
|
||||
-0.24979157249363
|
||||
-0.23307403271554
|
||||
-0.24097631233955
|
||||
-0.25950346473092
|
||||
-0.24422640236537
|
||||
-0.21881174427133
|
||||
-0.25138914226329
|
||||
-0.27032074467422
|
||||
-0.22096845560221
|
||||
-0.27190881634849
|
||||
-0.220023331219
|
||||
-0.29367165210062
|
||||
-0.26982749475642
|
||||
-0.23299693741808
|
||||
-0.26749006305528
|
||||
-0.23971615242697
|
||||
-0.24511407028025
|
||||
-0.21672379113928
|
||||
-0.27777722826163
|
||||
-0.2534721449125
|
||||
-0.2484361544963
|
||||
-0.21306825458823
|
||||
-0.24421454714016
|
||||
-0.26239812741934
|
||||
-0.27751629379262
|
||||
-0.27253134534381
|
||||
-0.23566019695354
|
||||
-0.24144314658119
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,2 @@
|
|||
3,3,3,3;-0.673048732,-0.412463881,-0.736532526,0.829005319,-0.813510365,-0.502031047,0.950266852,0.818343199,0.03824395,0.219597033,-0.339935295,-0.181847521,-0.820436519,-0.762547916,0.165912707,0.799397635,-0.883427944,0.304251916,-0.414343129,0.25450569,-0.472308773,-0.804309556,0.137957493,0.576775783,-0.004366962,-0.494472245,-0.518802319,-0.819242309,0.447490839,0.506983895,0.452629618,-0.248047378,0.468150892,0.291926346,0.947744258,0.536874463,-0.73260191,0.834541621,0.177465943
|
||||
3,3,3,3;-0.673048732,-0.412463881,-0.736532526,0.829005319,-0.813510365,-0.502031047,0.950266852,0.818343199,0.03824395,0.219597033,-0.339935295,-0.181847521,-0.820436519,-0.762547916,0.165912707,0.799397635,-0.883427944,0.304251916,-0.414343129,0.25450569,-0.472308773,-0.804309556,0.137957493,0.576775783,-0.004366962,-0.494472245,-0.518802319,-0.819242309,0.447490839,0.506983895,0.452629618,-0.248047378,0.468150892,0.291926346,0.947744258,0.536874463,-0.73260191,0.834541621,0.177465943
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
3,3,3,3;-0.673048732,-0.412463881,-0.736532526,0.829005319,-0.813510365,-0.502031047,0.950266852,0.818343199,0.03824395,0.219597033,-0.339935295,-0.181847521,-0.820436519,-0.762547916,0.165912707,0.799397635,-0.883427944,0.304251916,-0.414343129,0.25450569,-0.472308773,-0.804309556,0.137957493,0.576775783,-0.004366962,-0.494472245,-0.518802319,-0.819242309,0.447490839,0.506983895,0.452629618,-0.248047378,0.468150892,0.291926346,0.947744258,0.536874463,-0.73260191,0.834541621,0.177465943
|
||||
3,3,3,3;-0.673048732,-0.412463881,-0.736532526,0.829005319,-0.813510365,-0.502031047,0.950266852,0.818343199,0.03824395,0.219597033,-0.339935295,-0.181847521,-0.820436519,-0.762547916,0.165912707,0.799397635,-0.883427944,0.304251916,-0.414343129,0.25450569,-0.472308773,-0.804309556,0.137957493,0.576775783,-0.004366962,-0.494472245,-0.518802319,-0.819242309,0.447490839,0.506983895,0.452629618,-0.248047378,0.468150892,0.291926346,0.947744258,0.536874463,-0.73260191,0.834541621,0.177465943
|
||||
|
|
@ -1 +1 @@
|
|||
{"maxGnr":50,"maxGnm":1000,"mutThr":0.5,"fitEnd":1,"numHid":4,"numNeu":4,"inpNeu":3,"outNeu":2,"storage":{"nn":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.nn","exists":false},"ex":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.ex","exists":false},"gn":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.gn","exists":false},"ft":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.ft","exists":false},"ln":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.ln","exists":false}}}
|
||||
{"maxGnr":50,"maxGnm":100,"mutThr":0.005,"fitEnd":1,"numHid":3,"numNeu":3,"inpNeu":3,"outNeu":1,"storage":{"nn":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.nn","exists":false},"ex":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.ex","exists":false},"gn":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.gn","exists":false},"ft":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.ft","exists":false},"ln":{"filename":"\/home\/xdrm-brackets\/Desktop\/git.xdrm.io\/neural-network.php\/build\/neuralnetwork\/storage\/test\/test1.ln","exists":false}}}
|
||||
|
|
|
|||
|
|
@ -7,10 +7,10 @@
|
|||
use \filemanager\core\FileManager;
|
||||
|
||||
function behaviour($abc){
|
||||
return [($abc[0] & $abc[1]), $abc[1] | $abc[2]];
|
||||
return [($abc[0] + $abc[1] - $abc[2])];
|
||||
}
|
||||
|
||||
if( true && 'test_creating_dataset' ){
|
||||
if( false && 'learning_process' ){
|
||||
|
||||
$part = 1;
|
||||
|
||||
|
|
@ -29,13 +29,13 @@
|
|||
=========================================================*/
|
||||
}catch(\Exception $e){
|
||||
|
||||
$nn = NeuralNetwork::create(50, 1000);
|
||||
$nn = NeuralNetwork::create(50, 100);
|
||||
|
||||
$nn->setHiddenLayersCount(4);
|
||||
$nn->setHiddenLayerNeuronsCount(4);
|
||||
$nn->setHiddenLayersCount(3);
|
||||
$nn->setHiddenLayerNeuronsCount(3);
|
||||
$nn->setInputLayerCount(3);
|
||||
$nn->setOutputLayerCount(2);
|
||||
$nn->setMutationThreshold(0.5);
|
||||
$nn->setOutputLayerCount(1);
|
||||
$nn->setMutationThreshold(0.3);
|
||||
|
||||
echo "$part. NeuralNetwork configured\n"; $part++;
|
||||
|
||||
|
|
@ -69,10 +69,7 @@
|
|||
$max_fit = 0;
|
||||
$nn->loadLearningRoutine(function($input, $output){
|
||||
global $fitness;
|
||||
$diff = [ abs($output[0] - behaviour($input)[0]), abs($output[1] - behaviour($input)[1]) ];
|
||||
|
||||
if( $diff[0] > 0 ) $fitness += 1 / $diff[0];
|
||||
if( $diff[1] > 0 ) $fitness += 1 / $diff[1];
|
||||
$fitness -= abs($output[0] - behaviour($input)[0]);
|
||||
});
|
||||
echo "$part. Learning routine initialized.\n"; $part++;
|
||||
|
||||
|
|
@ -80,18 +77,23 @@
|
|||
/* [3] Learning through generations and genomes
|
||||
=========================================================*/
|
||||
/* (1) For each generation */
|
||||
for( $gnr = 0 ; $gnr < 50 ; $gnr++ ){
|
||||
$last_gnr = -1;
|
||||
while( true ){
|
||||
|
||||
$max_fit = 0;
|
||||
if( $nn->gnr > $last_gnr)
|
||||
$start = microtime(true);
|
||||
|
||||
$last_gnr = $nn->gnr;
|
||||
|
||||
$max_fit = -1e9;
|
||||
|
||||
/* (2) For each genome */
|
||||
for( $gnm = 0 ; $gnm < 1000 ; $gnm++ ){
|
||||
while( true ){
|
||||
$fitness = 0;
|
||||
|
||||
/* (2.1) Get current genome */
|
||||
$g = $nn->getGenome();
|
||||
echo "\r[x] genome ".($nn->gnm+1)."/1000 on generation ".($nn->gnr+1)."/50 - max fitness: $max_fit ";
|
||||
echo "\r[x] gnm ".($nn->gnm+1)."/100 on gnr ".($nn->gnr+1)."/50 - max_fit: $max_fit ";
|
||||
|
||||
/* (2.2) Train genome with random samples */
|
||||
for( $r = 0 ; $r < 100 ; $r++ )
|
||||
|
|
@ -101,50 +103,66 @@
|
|||
if( $fitness > $max_fit ) $max_fit = $fitness;
|
||||
|
||||
$g->setFitness($fitness);
|
||||
|
||||
if( $nn->gnm >= 100-1 )
|
||||
break;
|
||||
|
||||
$nn->nextGenome();
|
||||
}
|
||||
|
||||
$nn->nextGenome();
|
||||
|
||||
// If generation evolution, notify
|
||||
if( $nn->gnr > $last_gnr)
|
||||
echo "\n\t".((microtime(true)-$start))."s\n";
|
||||
|
||||
if( $nn->gnr == 50-1 )
|
||||
break;
|
||||
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
if( true && 'guessing_process' ){
|
||||
$part = 1;
|
||||
|
||||
|
||||
echo "Welcome to neural-network.php\n";
|
||||
echo "-----------------------------\n\n";
|
||||
|
||||
if( false && 'load_neural_network' ){
|
||||
/* [1] Trying to load neural network
|
||||
=========================================================*/
|
||||
try{
|
||||
|
||||
$nn = NeuralNetwork::load('test/test1');
|
||||
echo "$part. NeuralNetwork loaded from 'test/test1'\n"; $part++;
|
||||
|
||||
/* [2] Else, creates it
|
||||
=========================================================*/
|
||||
}catch(\Exception $e){
|
||||
|
||||
echo "You must create/train your neural network before using it.\n";
|
||||
exit();
|
||||
}
|
||||
|
||||
if( false && 'test_genomes' ){
|
||||
/* (1) Basic Creation */
|
||||
$a = new Genome(2, 3, 3, 2); // 2 layers of 3 neurons each -> randomly filled
|
||||
|
||||
echo "A : ".$a->serialize()."\n";
|
||||
/* [2] Fetch trained genome
|
||||
=========================================================*/
|
||||
$genome = $nn->getTrainedGenome();
|
||||
$genome->setCallback(function($in, $out){
|
||||
echo "callback input: ".implode(',', $in)."\n";
|
||||
echo "callback output: ".round($out[0])."\n";
|
||||
echo "callback result: ".implode(',', behaviour($in))."\n";
|
||||
});
|
||||
|
||||
/* (2) Inheritance */
|
||||
$b = new Genome($a); // Clone of @aecho "A neurons\n";
|
||||
echo "cloning A to B\n";
|
||||
echo "B : ".$b->serialize()."\n";
|
||||
$genome->train([rand(0,10), rand(0,10), rand(0,10)]);
|
||||
|
||||
/* (3) Section Title */
|
||||
$b->mutation(0.3); // @b has now mutated with a threshold of 30%
|
||||
echo "mutate B\n";
|
||||
echo "B : ".$b->serialize()."\n";
|
||||
|
||||
/* (4) Cross-over (father+mother) */
|
||||
$c = new Genome($a, $b); // @c is a randomly-done mix of @a and @b
|
||||
echo "crossover : A+B -> C\n";
|
||||
echo "C : ".$c->serialize()."\n";
|
||||
|
||||
}
|
||||
|
||||
|
||||
if( false ){
|
||||
|
||||
$g = new Genome(2, 3, 3, 2);
|
||||
|
|
|
|||
Loading…
Reference in New Issue