fann_train_on_file

(PECL fann >= 1.0.0)

fann_train_on_file — Trains on an entire dataset, which is read from file, for a period of time

Опис

fann_train_on_file(
    resource $ann,
    string $filename,
    int $max_epochs,
    int $epochs_between_reports,
    float $desired_error
): bool

Trains on an entire dataset, which is read from file, for a period of time.

This training uses the training algorithm chosen by fann_set_training_algorithm() and the parameters set for these training algorithms.

Параметри

ann: Ресурс (resource) нейронної мережі.
filename: The file containing train data
max_epochs: The maximum number of epochs the training should continue
epochs_between_reports: The number of epochs between calling a user function. A value of zero means that user function is not called.
desired_error: The desired fann_get_MSE() or fann_get_bit_fail(), depending on the stop function chosen by fann_set_train_stop_function()

Значення, що повертаються

Повертає true у разі успіху або false у разі невдачі.

Прогляньте також

fann_train_on_data() - Trains on an entire dataset for a period of time
fann_train_epoch() - Train one epoch with a set of training data
fann_get_bit_fail() - The number of fail bits
fann_get_MSE() - Reads the mean square error from the network
fann_set_train_stop_function() - Sets the stop function used during training
fann_set_training_algorithm() - Sets the training algorithm
fann_set_callback() - Sets the callback function for use during training

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User Contributed Notes 1 note

down

geekgirljoy at gmail dot com ¶

6 years ago

Training File (xor.data):
4 2 1
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<?php
$num_input = 2;
$num_output = 1;
$num_layers = 3;
$num_neurons_hidden = 3;
$desired_error = 0.001;
$max_epochs = 500000;
$epochs_between_reports = 1000;
$training_data = dirname(__FILE__) . "/xor.data";  // training data file
$ann_save_file = dirname(__FILE__) . "/xor_float.net";  // training data file

// Create ANN object using
$ann = fann_create_standard($num_layers, $num_input, $num_neurons_hidden, $num_output);

if ($ann) {
    
    // Configure the ANN Activation Function 
    fann_set_activation_function_hidden($ann, FANN_SIGMOID_SYMMETRIC);
    fann_set_activation_function_output($ann, FANN_SIGMOID_SYMMETRIC);
    
    // Try to train using fann_train_on_file()
    if (fann_train_on_file($ann, $training_data, $max_epochs, $epochs_between_reports, $desired_error)){
        echo 'xor trained.' . PHP_EOL);
    }
    
    // Try to save
    if (fann_save($ann, $ann_save_file)){
        echo 'xor saved.' . PHP_EOL);
    }
    
    // Destroy the $ann object
    fann_destroy($ann);
}
?>

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