<?php
ini_set('memory_limit', '1024M'); // Increase the memory limit to 1024MB
include_once("../../CLASSES/Headers.php");
use NameSpaceNumpyLight\NumpyLight;
use NameSpaceRandomGenerator\RandomGenerator;
use NameSpaceActivationRelu\Activation_Relu;
use NameSpaceOptimizerSGD\Optimizer_SGD;
use NameSpaceOptimizerAdagrad\Optimizer_Adagrad;
use NameSpaceOptimizerRMSprop\Optimizer_RMSprop;
// use NameSpaceActivationSigmoid\Activation_Sigmoid;
// use NameSpaceLossBinaryCrossentropy\Loss_BinaryCrossentropy;
$dataAnalyzed = "spiral_data";
list($X, $y) = NumpyLight::spiral_data(100, 2);
$y = NumpyLight::reshape($y,[-1,1]);
# Create test dataset
list($X_test, $y_test) = NumpyLight::spiral_data(100, 2);
$y_test = NumpyLight::reshape($y_test,[-1,1]);
// Create layers and activations,,$weight_regularizer_l2 = 5e-4 ,$bias_regularizer_l2 = 5e-4
$dense1 = new Layer_Dense(2, 64,$weight_regularizer_l2 = 5e-4 ,$bias_regularizer_l2 = 5e-4);
$activation1 = new Activation_ReLU();
$dense2 = new Layer_Dense(64, 1);
$activation2 = new Activation_Sigmoid();
$loss_function = new Loss_BinaryCrossentropy();
$optimizer = new Optimizer_Adam($learning_rate = 0.001 , $decay = 5e-7 );
$lossTrend = [];
$accTrend = [];
$lrTrend = [];
$data_loss_arr = [];
$regularization_loss_arr = [];
// Train the network
for ($epoch = 0; $epoch <= 10000; $epoch++) {
$dense1->forward($X);
$activation1->forward($dense1->output);
# Perform a forward pass through Dropout layer
$dense2->forward($activation1->output);
$activation2->forward($dense2->output);
$data_loss = $loss_function->calculate($activation2->output, $y,false);
$regularization_loss = $loss_function->regularization_loss($dense1)+$loss_function->regularization_loss($dense2);
# Calculate overall loss
$loss = $data_loss + $regularization_loss;
$predictions = NumpyLight::BinaryClassificationAccuracy($activation2->output, $y);
if (($epoch%100==0)) {
$lossTrend[] = $loss;
$accTrend[] = $predictions;
$lrTrend[] = $optimizer->current_learning_rate;
$data_loss_arr[] = $data_loss;
echo "epoc: $epoch ,\tacc: $predictions\t,loss: $loss, (data_loss: $data_loss, reg_loss: $regularization_loss),\t lr: $optimizer->current_learning_rate\n";
}
# Backward pass
$loss_function->backward($activation2->output, $y);
$activation2->backward($loss_function->dinputs);
$dense2->backward($activation2->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();
}
$dense1->forward($X_test);
$activation1->forward($dense1->output);
$dense2->forward($activation1->output);
$loss = $loss_function->forward($activation2->output, $y_test);
NumpyLight::displayMatrix($activation2->output);
$acc = NumpyLight::BinaryClassificationAccuracy($activation2->output, $y_test);
echo "\n\n validation, acc: $acc , loss: $loss \n\n";
// $plotter = new LinePlotter(500, 500);
// $plotter->setColor('red', 255, 0, 0);
// $plotter->plotLine($lossTrend, 'red');
// $plotter->save("images/".$filename."_".$dataAnalyzed."_Loss_stat.png");
// $plotter = new LinePlotter(500, 500);
// $plotter->setColor('green', 0, 255, 0);
// $plotter->plotLine($accTrend, 'green');
// $plotter->save("images/".$filename."_".$dataAnalyzed."_Acc_stat.png");
// $plotter = new LinePlotter(500, 500);
// $plotter->setColor('blue', 0, 0, 255);
// $plotter->plotLine($lrTrend, 'blue');
// $plotter->save("images/".$filename."_".$dataAnalyzed."_lr_stat.png");
// $plotter = new LinePlotter(500, 500);
// $plotter->setColor('blue', 0, 0, 255);
// $plotter->plotLine($data_loss_arr, 'blue');
// $plotter->save("images/".$filename."_".$dataAnalyzed."_data_loss.png");
// $plotter = new LinePlotter(500, 500);
// $plotter->setColor('blue', 0, 0, 255);
// $plotter->plotLine($regularization_loss_arr, 'blue');
// $plotter->save("images/".$filename."_".$regularization_loss_arr."_regularization_loss.png");
?>
|
Download
