Train.php

<?php

// Training Variables
$desired_error = 0.0001;
$max_epochs = 5000;
$current_epoch = 0;
$epochs_between_saves = 1; // Minimum number of epochs between saves
$epochs_since_last_save = 0;


// Training Data
$data = dirname(__FILE__) . "/MyVisualTraningData.data";


// Initialize pseudo mse (mean squared error) to a number greater than the desired_error
// this is what the network is trying to minimize.
$pseudo_mse_result = $desired_error * 10000; // 1
$best_mse = $pseudo_mse_result; // keep the last best seen MSE network score here

// Initialize ANN
$num_input = 96;
$num_output = 96;
$num_neurons_hidden_1 = 160;
$layers = array($num_input, $num_neurons_hidden_1, $num_output);
$num_layers = count($layers);

// Create ANN
$ann = fann_create_standard_array ($num_layers , $layers);

if ($ann) {
    echo 'Training ANN... ' . PHP_EOL;

    $log = fopen('traning_mse_save_log.txt', 'w');

    // Configure the ANN
    fann_set_training_algorithm ($ann , FANN_TRAIN_RPROP);
    fann_set_activation_function_hidden($ann, FANN_SIGMOID_SYMMETRIC);
    fann_set_activation_function_output($ann, FANN_SIGMOID_SYMMETRIC);

    // Read training data
    $train_data = fann_read_train_from_file($data);

 
    // Check if pseudo_mse_result is greater than our desired_error
    // if so keep training so long as we are also under max_epochs
    while(($pseudo_mse_result > $desired_error) && ($current_epoch <= $max_epochs)){

        $current_epoch++;
        $epochs_since_last_save++; 

        // See: http://php.net/manual/en/function.fann-train-epoch.php
        // Train one epoch
        //
        // One epoch is where all of the training data is considered
        // exactly once.
        //
        // This function returns the MSE error as it is calculated
        // either before or during the actual training. This is not the
        // actual MSE after the training epoch, but since calculating this
        // will require to go through the entire training set once more.
        // It is more than adequate to use this value during training.
        $pseudo_mse_result = fann_train_epoch ($ann , $train_data );
        echo 'Epoch ' . $current_epoch . ' : ' . $pseudo_mse_result . PHP_EOL; // report

        // If we haven't saved the ANN in a while...
        // and the current network is better then the previous best network
        // as defined by the current MSE being less than the last best MSE
        // Save it!
        if(($epochs_since_last_save >= $epochs_between_saves) && ($pseudo_mse_result < $best_mse)){
            $best_mse = $pseudo_mse_result; // we have a new best_mse

            // Save a Snapshot of the ANN
            fann_save($ann, dirname(__FILE__) . "/MyVisualANN.train.net");
            echo "Saved MyVisualANN.train.net ANN." . PHP_EOL; // report the save
            $epochs_since_last_save = 0; // reset the count

            // Log the MSE
            fwrite($log, $pseudo_mse_result . PHP_EOL);
        } 
    } // While we're training

    // Save the final network
    echo 'Training Complete! Saving Final Network.'  . PHP_EOL;
    fann_save($ann, dirname(__FILE__) . "/MyVisualANN.finished.net"); 
    fann_destroy($ann); // free memory

    // Log final MSE
    fwrite($log, $pseudo_mse_result . PHP_EOL);
    fclose($log);
}
echo 'All Done!' . PHP_EOL;