(PECL fann >= 1.0.0)

fann_train_epochEntrenar una época con un conjunto de datos de entrenamiento


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

Entrena una época con los datos de entrenamiento almacenados en data. Una época es donde todos los datos de entrenamiento son considerados exactamente una vez.

Esta función devuelve el ECM tal como es calculado antes o durante el entrenamiento real. No es el ECM real después de la época de entrenamiento, ya que calcularlo requerirá atravesar el conjunto de entrenamiento completo una vez más. El empleo de este valor durante el entrenamiento es más que adecuado.

El algoritmo de entrenaiento empleado por esta función se elige mediante la función fann_set_training_algorithm().



Un resource de red neuronal.


Un resource de datos de entrenamiento de red neuronal.

Valores devueltos

El ECM, o false en caso de error.

Ver también

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User Contributed Notes 1 note

geekgirljoy at gmail dot com
5 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:

= 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__) . "/";

// 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) {
'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)){

// See:
// 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 );
'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__) . "/");
'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($ann, dirname(__FILE__) . "/");
fann_destroy($ann); // free memory
'All Done!' . PHP_EOL;
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