<?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=0.85);
$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,false);
$predictions = NumpyLight::accuracy($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('p276_Loss_stat.png');
$plotter = new LinePlotter(500, 500);
$plotter->setColor('green', 0, 255, 0);
$plotter->plotLine($accTrend, 'green');
$plotter->save('p276_Acc_stat.png');
$plotter = new LinePlotter(500, 500);
$plotter->setColor('blue', 0, 0, 255);
$plotter->plotLine($lrTrend, 'blue');
$plotter->save('p276_lr_stat.png');
?>
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