| Server IP : 79.136.114.73 / Your IP : 3.147.44.46 Web Server : Apache/2.4.7 (Ubuntu) PHP/5.5.9-1ubuntu4.29 OpenSSL/1.0.1f System : Linux b8009 3.13.0-170-generic #220-Ubuntu SMP Thu May 9 12:40:49 UTC 2019 x86_64 User : www-data ( 33) PHP Version : 5.5.9-1ubuntu4.29 Disable Function : pcntl_alarm,pcntl_fork,pcntl_waitpid,pcntl_wait,pcntl_wifexited,pcntl_wifstopped,pcntl_wifsignaled,pcntl_wexitstatus,pcntl_wtermsig,pcntl_wstopsig,pcntl_signal,pcntl_signal_dispatch,pcntl_get_last_error,pcntl_strerror,pcntl_sigprocmask,pcntl_sigwaitinfo,pcntl_sigtimedwait,pcntl_exec,pcntl_getpriority,pcntl_setpriority, MySQL : ON | cURL : ON | WGET : ON | Perl : ON | Python : ON | Sudo : ON | Pkexec : ON Directory : /var/www/appsrv.astacus.se/ai/ |
Upload File : |
<?php
/** rn class. red neuronal (neural network)
*
*
*
* @author Rafael Martin Soto
* @author {@link http://www.inatica.com/ Inatica}
* @since July 2021
* @version 1.0.1
* @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_layer.class.php' );
require_once( 'system_resources.class.php' );
/** rn class. red neuronal (neural network)
*
* Create a neural network class with one array with number of nodes each layer.
* Example: If we want a neural network with 4 layers (2 inputs neurons, 12 hidden neurons, 8 hidden neurons and 1 output neuron) as 2x12x8x1
* Only need to send as paramater an array as: [2, 12, 8, 1]
*
* $rn = new rn( [2, 12, 8, 1] ); // 2x12x8x1
*
* @param array $arrLayersNodes
*/
class rn
{
public $layers = [];
public $num_layers = 0;
public $alpha = 1;
public $InformEachXBlock = 100; // echoes actual error each 100 blocks of datasets
public $InformEachXEpoch = 100; // echoes actual error each 100 epochs
public $MaxItemsMeanSquareError = 1000000; // Max number of Train items to get the Mean Square Error
public $num_epochs = 1000;
public $MeanSquareError = 0;
public $system_resources = null;
public function __construct( $arrLayersNodes = [2, 12, 8, 1] ) {
$this->layers = [];
$this->num_layers = count( $arrLayersNodes );
$previous_layer = null;
$system_resources = new system_resources(); // Create obj class system_resources, needed for control CPU Temperature in process of BackPropagation of the Deep Learning
// Init layers
for($i=0;$i<$this->num_layers;$i++){
$NumOfNewNodes = $arrLayersNodes[$i];
$this->layers[] = new layer( $NumOfNewNodes );
if( $previous_layer != null ){
$previous_layer->next_layer = $this->layers[$i];
$arrNodeWeightsToNextLayer = array_fill(0, $NumOfNewNodes, .5);
$previous_layer->fInitNodeWeightsToNextLayer( $arrNodeWeightsToNextLayer, .5 );
}
$this->layers[$i]->previous_layer = $previous_layer;
$previous_layer = $this->layers[$i];
} // /for i
$this->layers[0]->fSetImFirst();
$this->layers[$this->num_layers-1]->fSetImLast();
} // / __construct
/**
* Set the activation function for all the layers
* sigm = sigmoidal
* tanh = hyperbolic tangent
*
* Optionaly, we can set different activation funcion for each layer
*
* @param string $activation_function ['sigm'|'tanh']
*/
public function fSet_activation_function( $activation_function ){
for($i=0;$i<$this->num_layers;$i++){
$this->layers[$i]->fSet_activation_function($activation_function);
}
} // /fSet_activation_function()
/**
* Set Num Epochs to model
*
* @param array $arrTrainInputItems
* @param array $arrTrainOutputItems
* @param int $Epochs (Optional. Default 1000 Epochs)
*/
public function fSet_num_epochs( $Epochs ){
$this->num_epochs = $Epochs;
} // /fSet_num_epochs
/**
* Set Num of max items to be calculated at Mean Square Error
*
* @param array $MaxItemsMeanSquareError
*/
public function fSet_MaxItemsMeanSquareError( $MaxItemsMeanSquareError ){
$this->MaxItemsMeanSquareError = $MaxItemsMeanSquareError;
} // /fSet_MaxItemsMeanSquareError
/**
* Run our model
*
* @param int $id_output_node
* @param array $arrInputValues
*/
public function run( $id_output_node, $arrInputValues ){
$idLastLayer = $this->num_layers-1;
return $this->layers[$idLastLayer]->Y( $id_output_node, $arrInputValues );
} // /run()
/**
* BackPropagation function
*
* @param array $arrInputValues
* @param array $arrOutputValues
*/
public function BackPropagation( $arrInputValues, $arrOutputValues ){
$idLastLayer = $this->num_layers-1;
$OutputLayer = $this->layers[$idLastLayer];
$previous_layer = $OutputLayer->previous_layer;
$previous_layer_num_nodes = $previous_layer->num_nodes;
$num_output_nodes = $OutputLayer->num_nodes;
$inputLayer = $this->layers[0];
$input_layer_num_nodes = $inputLayer->num_nodes;
for($i=0; $i<$num_output_nodes; $i++){
$OutputLayer->nodes[$i]->delta = $OutputLayer->deltaLastLayer( $i, $arrInputValues, $arrOutputValues );
}
// Set Error to the other layers
for($id_layer=0;$id_layer<($this->num_layers-1);$id_layer++){
$layer = $this->layers[$id_layer];
$next_layer = $layer->next_layer;
$next_layer_num_nodes = $next_layer->num_nodes;
$num_nodes = $layer->num_nodes;
for($i=0; $i<$num_nodes; $i++){
$layer_node = $layer->nodes[$i];
$Y = $layer->Y( $i, $arrInputValues );
for($j=0;$j<$next_layer_num_nodes;$j++){
$this->layers[$id_layer]->nodes[$i]->arr_weights_to_next_layer[$j] -= $this->alpha * $this->error($Y, $i, $j, $arrInputValues, $arrOutputValues, $layer->next_layer);
$next_layer->nodes[$j]->bias -= $this->alpha * (($next_layer->imLast)?$next_layer->nodes[$j]->delta:$next_layer->deltaLayer($i, $j, $arrInputValues, $arrOutputValues));
//exit(1);
}
}
}
} // /BackPropagation()
/**
* Set Alpha (Learning rate)
*
* @param float $alpha
*/
public function set_alpha($alpha){
$this->alpha = $alpha;
}
/**
* Get the error
*
* @param float $Y
* @param int $i
* @param int $j
* @param array $arrTrainInputItems
* @param array $arrTrainOutputItems
* @param object $next_layer
* @return float error
*/
public function error($Y, $i, $j, $arrInputValues, $arrOutputValues, $next_layer){
$Delta = (($next_layer->imLast)?$next_layer->nodes[$j]->delta:$next_layer->deltaLayer($i , $j, $arrInputValues, $arrOutputValues));
return $Y * $Delta;
}
/**
* Master function who calls BackPropagation process
* in the future it will be multithread
*
* @param array $arrTrainInputItems
* @param array $arrTrainOutputItems
* @param int $Epochs (Optional. Default 1000 Epochs)
*/
public function Learn($arrTrainInputItems, $arrTrainOutputItems, $arrValidationInputItems = NULL, $arrValidationOutputItems = NULL, $arrTestInputItems = NULL, $arrTestOutputItems = NULL, $Epochs = NULL){
if( !isset($arrValidationInputItems) || is_null($arrValidationInputItems) ){
$arrValidationInputItems = $arrTrainInputItems;
$arrValidationOutputItems = $arrTrainOutputItems;
}
if( !isset($arrTestInputItems) || is_null($arrTestInputItems) ){
$arrTestInputItems = $arrValidationInputItems;
$arrTestOutputItems = $arrValidationOutputItems;
}
if( isset($Epochs) && !is_null($Epochs) ){
$this->num_epochs = $Epochs;
}
$num_sample_data = count($arrTrainInputItems);
for($i = 0;$i<$this->num_epochs;$i++){
for($j=0;$j<$num_sample_data;$j++){
$this->BackPropagation($arrTrainInputItems[$j],$arrTrainOutputItems[$j]);
if( $j%$this->InformEachXBlock == 0 ){
$MeanSquareErrorValidationData = $this->MeanSquareError( $arrValidationInputItems, $arrValidationOutputItems );
$MeanSquareErrorTestData = $this->MeanSquareError( $arrTestInputItems, $arrTestOutputItems );
$StrEcho = 'Item '.$j.'/'.$num_sample_data.' . Epoch '.$i.'/'.$this->num_epochs;
$StrEcho .= '. Actual error: (Validaton Data: '.number_format($MeanSquareErrorValidationData, 4, '.', ','). ')';
$StrEcho .= '/ (Test Data: '.number_format($MeanSquareErrorTestData, 4, '.', ','). ')';
echo $StrEcho.PHP_EOL;
$this->MeanSquareError = $MeanSquareErrorValidationData;
}
}
if( $i%$this->InformEachXEpoch == 0 && $this->InformEachXEpoch > $this->InformEachXBlock ){
$MeanSquareErrorValidationData = $this->MeanSquareError( $arrValidationInputItems, $arrValidationOutputItems );
$MeanSquareErrorTestData = $this->MeanSquareError( $arrTestInputItems, $arrTestOutputItems );
$StrEcho = 'Epoch '.$i.'/'.$this->num_epochs;
$StrEcho .= '. Actual error: (Validaton Data: '.number_format($MeanSquareErrorValidationData, 4, '.', ','). ')';
$StrEcho .= '/ (Test Data: '.number_format($MeanSquareErrorTestData, 4, '.', ','). ')';
echo $StrEcho.PHP_EOL;
$this->MeanSquareError = $MeanSquareErrorValidationData;
}
}
} // /Learn()
/**
* Mean Square Error
*
* @param array $arrValidationInputItems
* @param array $arrValidationOutputItems
* @return float $MeanSquareError
*/
public function MeanSquareError($arrValidationInputItems, $arrValidationOutputItems){
$last_id_layer = $this->num_layers - 1;
$num_output_nodes = $this->layers[$last_id_layer]->num_nodes;
$ErrorSum = 0;
$num_sample_data = count($arrValidationInputItems);
if($num_sample_data > $this->MaxItemsMeanSquareError){
$num_sample_data = $this->MaxItemsMeanSquareError;
}
$NumItemsAdded = 0;
for($i=0;$i<$num_sample_data ;$i++){
for($j=0;$j<$num_output_nodes;$j++){
$ValueEstimated = $this->run($j, $arrValidationInputItems[$i]);
$Diff = ( $arrValidationOutputItems[$i][$j]-$ValueEstimated );
$ErrorSum += pow($Diff, 2);
++$NumItemsAdded;
}
}
$MeanSquareError = ( $ErrorSum / ($NumItemsAdded) );
return $MeanSquareError;
} // /MeanSquareError()
/**
* Echo output values of neural network with given values. If DesiredValues given, it will echo it too
*
* @param array $arrTrainInputItems
* @param array $arrTrainOutputItems
* @param int $Epochs (Optional. Default 1000 Epochs)
*/
public function EchoOutputValues($arrInputItems, $arrDesiredOutputItems = null){
$last_id_layer = $this->num_layers - 1;
$num_output_nodes = $this->layers[$last_id_layer]->num_nodes;
echo 'Input Values: '.implode(',', $arrInputItems).PHP_EOL;
for($j=0;$j<$num_output_nodes;$j++){
echo 'Output neuron ['.$j.']: '. $this->run($j, $arrInputItems);
if( isset($arrDesiredOutputItems) ){
echo '. Expect: '.$arrDesiredOutputItems[$j];
}
echo PHP_EOL;
}
echo PHP_EOL;
} // /EchoOutputValues()
/**
* Export the data of neural network into jSON object
*
* @return json $arrJSON
*/
public function exportData2Json(){
$FirstLayer = $this->layers[0];
$NumInputNodes = $FirstLayer->num_nodes;
$arrJSONLayers = [];
// Create an array of jSON'S data layers
foreach($this->layers as $layer){
$arrJSONLayers[] = json_decode( $layer->exportData2Json() );
}
$arrJSON = [ 'InaticaNeuralNetwork' =>
[ 'NumInputNeurons' => $this->layers[0]->num_nodes,
'NumOutputNeurons' => $this->layers[$this->num_layers-1]->num_nodes,
'CreationDate' => date("Y-m-d H:i:s"),
'NumTotalLayers' => $this->num_layers,
'NumHiddenLayers' => $this->num_layers -2, // All layers without input layer and without output layer
'NumEpochs' => $this->num_epochs, // number of epochs that the neural network has done to learn
'MeanSquareError' => $this->MeanSquareError,
'Layers' => $arrJSONLayers
]
];
return json_encode($arrJSON);
} // /exportData2Json()
/**
* Import the data of jSON object into rn
*
* @param json $JsonData
*/
public function importJson2Data($JsonData){
$this->num_layers = $JsonData->NumTotalLayers;
$this->NumEpochs = $JsonData->NumEpochs;
$this->MeanSquareError = $JsonData->MeanSquareError;
$previous_layer = null;
$this->layers = [];
$JsonLayers = $JsonData->Layers;
$i = 0;
foreach($JsonLayers as $Layer){
$this->layers[] = new layer( $Layer->Layer->num_nodes );
$this->layers[$i]->importJson2Data($Layer);
if( $previous_layer != null ){
$previous_layer->next_layer = $this->layers[$i];
}
$this->layers[$i]->previous_layer = $previous_layer;
$previous_layer = $this->layers[$i];
++$i;
} // /foreach
} // /importJson2Data()
} // /rn class
?>