<?php
namespace MachineLearning;
include 'Util/Input.php';
include 'Util/Output.php';
$numLayers = 3;
$numInput = Util\Input::NUM_INPUT;
$numNeuronsHidden = 9;
$numOutput = Util\Output::NUM_OUTPUT;
$maxEpochs = 500000;
$epochsBetweenReports = 1000;
$desiredError = 0.001;
$ann = fann_create_standard($numLayers, $numInput, $numNeuronsHidden, $numOutput);
if ($ann) {
fann_set_activation_function_hidden($ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output($ann, FANN_SIGMOID_SYMMETRIC);
if (fann_train_on_file($ann, 'Data/training', $maxEpochs, $epochsBetweenReports, $desiredError)) {
fann_save($ann, 'Data/training_result');
}
fann_destroy($ann);
}
/**
* Train ANN with all data given from training file
* @param string filename with absolute path
* @param int max epochs to train
* @param float desired error
* @return bool true on success, false on error
*/
public function train_on_file($path, $max_epochs, $desired_error)
{
$this->checkIfAnnIsAssigned();
return fann_train_on_file($this->annId, $path, $max_epochs, $desired_error);
}
<?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;
$ann = fann_create_standard($num_layers, $num_input, $num_neurons_hidden, $num_output);
if ($ann) {
fann_set_activation_function_hidden($ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output($ann, FANN_SIGMOID_SYMMETRIC);
$filename = dirname(__FILE__) . "/xor.data";
if (fann_train_on_file($ann, $filename, $max_epochs, $epochs_between_reports, $desired_error)) {
fann_save($ann, dirname(__FILE__) . "/xor_float.net");
}
fann_destroy($ann);
}
/**
* Train On File.
*
* @param int $maxEpochs The maximum number of epochs the training should continue.
* @param int $epochsBetweenReports The number of epochs between calling a user function.
* A value of zero means that user function is not called.
* @param float $desiredError The desired fann_get_MSE() or fann_get_bit_fail(),
* depending on the stop function chosen by
* fann_set_train_stop_function().
* @return bool
*/
public function trainOnFile($maxEpochs, $epochsBetweenReports, $desiredError)
{
$result = fann_train_on_file($this->ann, $this->trainFile, $maxEpochs, $epochsBetweenReports, $desiredError);
return $result;
}
public function trainOnFile($filename, $maxEpochs, $epochsBetweenReports, $desiredError)
{
$this->updateTrainingsCount();
return fann_train_on_file($this->ann, $filename, $maxEpochs, $epochsBetweenReports, $desiredError);
}