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    fann_train_epoch

    (PECL fann >= 1.0.0)

    fann_train_epoch — Effectue un entrainement avec un jeu de données d'entrainement

    Description

    fann_train_epoch(resource $ann, resource $data): float

    Effectue un entrainement avec un jeu de données d'entrainement. Une époque correspond au fait que toutes les données d'entrainement sont considérées comme étant exactement uniques.

    Cette fonction retourne l'erreur MSE sachant qu'elle est calculée soit avant, soit pendant l'entrainement actuel. Ce n'est pas le MSE actuel après l'époque d'entrainement, sachant que ce calcul nécessite le parcours de la totalité du jeu de données d'entrainement au moins une fois. Il est donc plus adéquate d'utiliser cette valeur pendant l'entrainement.

    L'algorithme d'entrainement utilisé par cette fonction est choisi par la fonction fann_set_training_algorithm().

    Liste de paramètres

    ann

    Ressource de réseau de neurones.

    data

    Ressource de données d'entrainement du réseau de neurones.

    Valeurs de retour

    Le MSE, ou false si une erreur survient.

    Voir aussi

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    User Contributed Notes 1 note

    up
    5
    geekgirljoy at gmail dot com
    6 years ago
    This code demonstrates training XOR using fann_train_epoch and will let you watch the training process by observing a psudo MSE (mean squared error).

    Other training functions: fann_train_on_data, fann_train_on_file, fann_train.

    fann_train_epoch is useful when you want to observe the ANN while it is training and perhaps save snapshots or compare competing networks during training.

    fann_train_epoch is different from fann_train in that it takes a data resource (training file) whereas fann_train takes an array of inputs and a separate array of outputs so use fann_train_epoch for observing training on data files (callback training resources) and use fann_train when observing manually specified data.

    Example code:

    <?php
    $num_input     = 2;
    $num_output = 1;
    $num_layers = 3;
    $num_neurons_hidden = 3;
    $desired_error = 0.0001;
    $max_epochs = 500000;
    $current_epoch = 0;
    $epochs_between_saves = 100; // Minimum number of epochs between saves
    $epochs_since_last_save = 0;
    $filename = dirname(__FILE__) . "/xor.data";

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

    // Initialize ANN
    $ann = fann_create_standard($num_layers, $num_input, $num_neurons_hidden, $num_output);

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

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

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


    // Check if psudo_mse_result is greater than our desired_error
    // if so keep training so long as we are also under max_epochs
    while(($psudo_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 with the training data stored in data.
    //
    // 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.
    $psudo_mse_result = fann_train_epoch ($ann , $train_data );
    echo     'Epoch ' . $current_epoch . ' : ' . $psudo_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 >= $epochs_between_saves) && ($psudo_mse_result < $best_mse)){

    $best_mse = $psudo_mse_result; // we have a new best_mse

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

    }     // While we're training

    echo 'Training Complete! Saving Final Network.' . PHP_EOL;

    // Save the final network
    fann_save($ann, dirname(__FILE__) . "/xor.net");
    fann_destroy($ann); // free memory
    }
    echo     'All Done!' . PHP_EOL;
    ?>
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