PHP Classes

File: TEST/TrainingTest/p285_Optimizer_SGD_with_momentum.php

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  Classes of Cuthbert Martin Lwinga   PHP Neural Net Library   TEST/TrainingTest/p285_Optimizer_SGD_with_momentum.php   Download  
File: TEST/TrainingTest/p285_Optimizer_SGD_with_momentum.php
Role: Example script
Content type: text/plain
Description: Example script
Class: PHP Neural Net Library
Build, train, evaluate, and use neural networks
Author: By
Last change: Removed existing rapidjson directory from index
Date: 5 months ago
Size: 2,180 bytes
 

Contents

Class file image Download
<?php
include_once("../../CLASSES/Headers.php");
use
NameSpaceNumpyLight\NumpyLight;
use
NameSpaceRandomGenerator\RandomGenerator;
use
NameSpaceActivationRelu\Activation_Relu;
use
NameSpaceOptimizerSGD\Optimizer_SGD;
list(
$X, $y) = NumpyLight::spiral_data(100, 3);

// Create layers and activations
$dense1 = new Layer_Dense(2, 64);
$activation1 = new Activation_ReLU();
$dense2 = new Layer_Dense(64, 3);
$loss_activation = new Activation_Softmax_Loss_CategoricalCrossentropy();
$optimizer = new Optimizer_SGD($learning_rate=1,$decay = 0.01 , $momentum = 0.9);


$lossTrend = [];
$accTrend = [];
$lrTrend = [];

$plotterTemp = new LinePlotter(500, 500);
$plotterTemp->plotPoints($X, $y);
$plotterTemp->save('spiral_data.png');

// // Train the network
for ($epoch = 0; $epoch <= 10000; $epoch++) {
   
// echo "$epoch \n";

   
$dense1->forward($X);
   
$activation1->forward($dense1->output);
   
$dense2->forward($activation1->output);
   
$loss = $loss_activation->forward($dense2->output, $y);
   
$predictions = NumpyLight::accuracy($loss_activation->output, $y);
   
    if ((
$epoch%100==0)) {
       
$lossTrend[] = $loss;
       
$accTrend[] = $predictions;
       
$lrTrend[] = $optimizer->current_learning_rate;
        echo
"epoc: $epoch ,\tacc: $predictions\t,loss: $loss,\t lr: $optimizer->current_learning_rate \n";
    }

   
# Backward pass

   
$loss_activation->backward($loss_activation->output, $y);
   
$dense2->backward($loss_activation->dinputs);
   
$activation1->backward($dense2->dinputs);
   
$dense1->backward($activation1->dinputs);
   
   
// # Update weights and biases
   
$optimizer->pre_update_params();
   
$optimizer->update_params($dense1);
   
$optimizer->update_params($dense2);
   
   
$optimizer->post_update_params();

   

}


$plotter = new LinePlotter(500, 500);
$plotter->setColor('red', 255, 0, 0);
$plotter->plotLine($lossTrend, 'red');
$plotter->save('images/p285_Loss_stat.png');

$plotter = new LinePlotter(500, 500);
$plotter->setColor('green', 0, 255, 0);
$plotter->plotLine($accTrend, 'green');
$plotter->save('images/p285_Acc_stat.png');

$plotter = new LinePlotter(500, 500);
$plotter->setColor('blue', 0, 0, 255);
$plotter->plotLine($lrTrend, 'blue');
$plotter->save('images/p285_lr_stat.png');


?>