<?php
include_once("../../CLASSES/Headers.php");
use NameSpaceNumpyLight\NumpyLight;
use NameSpaceRandomGenerator\RandomGenerator;
use NameSpaceActivationRelu\Activation_Relu;
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();
$optimizer = new Optimizer_Adam(0.05, 5e-7);
// // Train the network
for ($epoch = 0; $epoch <= 10000; $epoch++) {
$dense1->forward($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)) {
echo "epoc: $epoch ,\tacc: $predictions\t,loss: $loss,\tlr: ".$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();
}
?>
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