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<?php
/** layer class include
*
*
*
* @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
*/
require_once( 'rn_node.class.php' );
/* layer class
*
* @param int $num_nodes num of nodes that will have the layer
* @param float $default_weight default weight for each node
* @param float $default_bias default weight for bias
*/
class layer
{
public $num_nodes;
public $nodes;
public $previous_layer;
public $next_layer;
public $imFirst;
public $imLast;
public $activation_function;
public function __construct( $num_nodes = 2, $default_weight = [.5], $default_bias = .5) {
$this->nodes = [];
// Init nodes
for($i=0;$i<$num_nodes;$i++){
$this->nodes[] = new node( $default_weight, $default_bias);
}
$this->default_bias = $default_bias;
$this->num_nodes = $num_nodes;
$this->imFirst = false; // Input Data
$this->imLast = false; // Output Data
$this->activation_function = 'sigm'; // sigm | tanh | relu | bin
} // /__construct()
/**
* Get Delta for Last Layer
*/
public function deltaLastLayer($id_output_node, $arrInputValues, $arrOutputValues){
$Y = $this->Y( $id_output_node, $arrInputValues );
$Derivate = $this->DerivateFunctionActivation( $id_output_node, $arrInputValues, $Y );
$RealError = $this->RealError( $id_output_node, $arrOutputValues, $Y );
return $Derivate*$RealError;
} // /deltaLastLayer()
/**
* Get Delta for Layer
*/
public function deltaLayer($id_previous_node, $id_output_node, $arrInputValues, $arrOutputValues){
$next_layer = $this->next_layer;
$next_layer_num_nodes = $next_layer->num_nodes;
$previous_layer = $this->previous_layer;
$previous_layer_num_nodes = $previous_layer->num_nodes;
$num_nodes = $this->num_nodes;
$outputNode = $this->nodes[$id_output_node];
$Y = $this->Y( $id_output_node, $arrInputValues );
$sum=0;
for($i=0; $i<$next_layer_num_nodes; $i++){
$NextWeight = $this->nodes[$id_output_node]->arr_weights_to_next_layer[$i];
$DeltaLayer = (($next_layer->imLast)?$next_layer->nodes[$i]->delta:$next_layer->deltaLayer($id_output_node, $i, $arrInputValues, $arrOutputValues));
$sum += ($NextWeight*$DeltaLayer);
}
$Derivate = $this->DerivateFunctionActivation( $id_output_node, $arrInputValues, $Y );
return $Derivate * $sum;
} // /deltaLastLayer()
/**
* Get Real Error for Last Layer
*/
public function RealError($id_output_node, $arrOutputValues, $Y){
return ($Y-$arrOutputValues[$id_output_node]);
} // /deltaLastLayer()
/**
* Set the activation function for this layer
* sigm = sigmoidal
* tanh = hyperbolic tangent
*
* We can set different activation funcion for each layer
*
* @param string $activation_function ['sigm'|'tanh']
*/
public function fSet_activation_function( $activation_function ){
$this->activation_function = $activation_function;
} // /fSet_activation_function()
/**
* Set this layer as first layer in neural network
*/
public function fSetImFirst( ){
$this->imFirst = true;
} // /fSetImFirst()
/**
* Set this layer as last layer in neural network
*/
public function fSetImLast( ){
$this->imLast = true;
} // /fSetImLast()
/**
* Set the weights to next layer
*
* @param array array of weights
*/
public function fInitNodeWeightsToNextLayer( $arrNewWeights, $bias_weight = .5 ){
// Init nodes
for($i=0;$i<$this->num_nodes;$i++){
$this->nodes[$i]->setNodeWeightsToNextLayer ( $arrNewWeights );
$this->nodes[$i]->bias = $bias_weight;
}
} // /fInitNodeWeightsToNextLayer()
/**
* Get the derivate function of activacion function
*
* @param int $id_output_node
* @param array $arrInputValues
* @return float
*/
public function DerivateFunctionActivation( $id_output_node, $arrInputValues, $Y=null ){
if(! $Y ){
$f = $this->Y( $id_output_node, $arrInputValues );
} else {
$f = $Y;
}
switch($this->activation_function){
case 'relu': return $f > 0;
break;
case 'tanh': $tanh = tanh($f);
return (1 - $tanh) * (1 + $tanh);
break;
case 'sigm':
default: return $f*(1-$f);
break;
}
}// /DerivateFunctionActivation()
/** Activation function
*
* @param float
*/
function f($x){
switch( $this->activation_function ){
case 'relu': return $x * ($x > 0); // or max(0, $x);
break;
case 'tanh': return tanh($x);
break;
case 'sigm':
default: return 1 / (1 + exp(-$x));
break;
}
} // /f()
/** Output value
*
* @param int $id_output_node
* @param array $arrInputValues
* @return float
*/
public function Y( $id_output_node, $arrInputValues ){
if( $this->imFirst ){
return $arrInputValues[$id_output_node];
} else {
$Y = 0;
$PreviousLayer = $this->previous_layer;
$NumNodesPrevLayer = $PreviousLayer->num_nodes;
for($i=0;$i<$NumNodesPrevLayer;$i++){
$sumPreviousLayer = $PreviousLayer->Y( $i, $arrInputValues );
$NextWeight = $PreviousLayer->nodes[$i]->arr_weights_to_next_layer[$id_output_node];
$Y += ( $sumPreviousLayer*$NextWeight );
}
$Y += $this->nodes[$id_output_node]->bias;
return $this->f($Y);
} // if !imFirst
} // /Y()
/**
* Export the data of layer into jSON object
*
* @return json $arrJSON
*/
public function exportData2Json(){
$arrJSONNodes = [];
// Create an array of jSON'S data layers
foreach($this->nodes as $node){
$arrJSONNodes[] = json_decode( $node->exportData2Json() );
}
$arrJSON = [ 'Layer' =>
[ 'num_nodes' => $this->num_nodes,
'imFirst' => $this->imFirst,
'imLast' => $this->imLast,
'activation_function' => $this->activation_function,
'Nodes' => $arrJSONNodes
]
];
return json_encode($arrJSON);
} // /exportData2Json()
/**
* Import the data of jSON object into layer
*
* @param json $JsonData
*/
public function importJson2Data($JsonData){
$JsonDataLayer = $JsonData->Layer;
$this->num_nodes = $JsonDataLayer->num_nodes;
$this->imFirst = $JsonDataLayer->imFirst;
$this->imLast = $JsonDataLayer->imLast;
$this->activation_function = $JsonDataLayer->activation_function;
$this->nodes = [];
$i = 0;
foreach($JsonDataLayer->Nodes as $Node){
$this->nodes[] = new node( );
$this->nodes[$i++]->importJson2Data($Node);
}
} // /importJson2Data()
} // /class
?>