fann_create_train_from_callback

(PECL fann >= 1.0.0)

fann_create_train_from_callback — Creates the training data struct from a user supplied function

Descrierea

fann_create_train_from_callback ( int $num_data , int $num_input , int $num_output , callable $user_function ) : resource

Creates the training data struct from a user supplied function. As the training data are numerable (data 1, data 2...), the user must write a function that receives the number of the training data set (input, output) and returns the set.

Parametri

num_data

The number of training data

num_input

The number of inputs per training data

num_output

The number of ouputs per training data

user_function

The user supplied function with following parameters:

num - The number of the training data set
num_input - The number of inputs per training data
num_output - The number of ouputs per training data

The function should return an associative array with keys input and output and two array values of input and output.

Valorile întoarse

Întoarce o resource a datelor pentru antrenarea rețelei neuronale în caz de succes, sau false în caz de eroare.

Exemple

Example #1 fann_create_train_from_callback() example


<?php
function create_train_callback($num_data, $num_input, $num_output) {
    return array(
        "input" => array_fill(0, $num_input, 1),
        "output" => array_fill(0, $num_output, 1),
    );
}

$num_data = 3;
$num_input = 2;
$num_output = 1;
$train_data = fann_create_train_from_callback($num_data, $num_input, $num_output, "create_train_callback");
if ($train_data) {
    // Do something with $train_data
}
?>

Note

Notă:
Această funcție este disponibilă doar dacă extensia fann a fost compilată cu biblioteca libfann >= 2.2.

A se vedea și

fann_read_train_from_file() - Reads a file that stores training data
fann_train_on_data() - Trains on an entire dataset for a period of time
fann_destroy_train() - Destructs the training data
fann_save_train() - Save the training structure to a file

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User Contributed Notes 1 note

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geekgirljoy at gmail dot com ¶

8 years ago


This code can be used to read training data from MySQL rather than a text file.

<?php

// MySQL for This Example:
/*
CREATE TABLE `TrainingSets` (
  `ID` int(11) NOT NULL,
  `Name` varchar(150) COLLATE utf8mb4_unicode_ci NOT NULL,
  `TrainingData` text COLLATE utf8mb4_unicode_ci NOT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;

ALTER TABLE `TrainingSets` ADD PRIMARY KEY (`ID`);
  
INSERT INTO `TrainingSets` (`ID`, `Name`, `TrainingData`) VALUES(1, 'XOR', '-1 -1\n-1\n-1 1\n1\n1 -1\n1\n1 1\n-1');

ALTER TABLE `TrainingSets` MODIFY `ID` int(11) NOT NULL AUTO_INCREMENT, AUTO_INCREMENT=2;
*/

// This function calls pulls the TrainingData from MySQL
function get_training_data_from_db($id) {
    $table_name = "TrainingSets";
    $field = "TrainingData";
    $connection=mysqli_connect("host","username","password","database"); // change to your DB credentials
    $result=mysqli_query($connection,"SELECT $field FROM $table_name");
    $data=mysqli_fetch_assoc($result);
    mysqli_close($connection);

    return $data[$field];
}

// This function prepares the newline delimited data to be handed off to FANN
/*
Example of "newline delimited data" (like XOR in a Plain Text File) stored in MySQL:
-1 -1
-1
-1 1
1
1 1
-1
1 -1
1
*/
function prepare_data_from_db($training_data) {
    $training_data = explode( "\n", $training_data ); // convert training data rows to array
    $num_data = count($training_data);
    
    // Sift the data and split inputs and outputs 
    for($i=0;$i<$num_data;$i++) {
      if($i % 2) { // $training_data[$i] is Output
       $training_data['outputs'][] = explode( " ", $training_data[$i]);
      }else{ // $training_data[$i] is Input
       $training_data['inputs'][] = explode( " ", $training_data[$i]);
      }
    }
    // remove the unsifted data
    foreach ($training_data as $key => $value) {
        if (is_numeric($key)) {
            unset($training_data[$key]);
        }
    }
    return $training_data; // returned the prepaired associative array
}

// This function hands the prepared data over to FANN
function create_train_callback($num_data, $num_input, $num_output) {
    global $training_data;
    global $current_dataset;
   
    $dataset = array("input" => $training_data['inputs'][$current_dataset],
                    "output" => $training_data['outputs'][$current_dataset]);
    $current_dataset++;

    return $dataset;
}

// Initialize the program variables
$record_id = 1; // the 'ID' for the training data in MySQL
$current_dataset = 0;
$num_input = 2;
$num_output = 1;
$num_layers = 3;
$num_neurons = 3;
$desired_error = 0.001;
$max_epochs = 500000;
$epochs_between_reports = 1000;

$training_data = get_training_data_from_db($record_id); // Get the Training Data from MySQL
$training_data = prepare_data_from_db($training_data); // Prepare the data
$num_data = count($training_data["input"]); // How many sets are there?

// Hand the data over to FANN
$train_data = fann_create_train_from_callback($num_data, $num_input, $num_output, "create_train_callback");

// Test for $train_data 
if ($train_data) {
    
    // Create $ann
    $ann = fann_create_standard($num_layers, $num_input, $num_neurons, $num_output);

    // Test for $ann
    if ($ann) {
        fann_set_activation_function_hidden($ann, FANN_SIGMOID_SYMMETRIC);
        fann_set_activation_function_output($ann, FANN_SIGMOID_SYMMETRIC);

        // Train XOR ANN with training data obtainied from MySQL
        if (fann_train_on_data($ann, $train_data, $max_epochs, $epochs_between_reports, $desired_error)){
           print('XOR trained.<br>' . PHP_EOL);

           // Test $ann
           $input = array(-1, 1);
           $calc_out = fann_run($ann, $input);
           printf("xor test (%f,%f) -> %f\n", $input[0], $input[1], $calc_out[0]);
           
           // destore $ann 
           fann_destroy($ann);
        }
    }
}
?>

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