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);
}
}
}
?>
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
Descrizione
fann_create_train_from_callback(
int
int
int
callable
): resource
int
$num_data,int
$num_input,int
$num_output,callable
$user_function): resource
Creates the training data struct from a user supplied function. As the training data are numerable (data 1, data 2...), the user must write a function that receives the number of the training data set (input, output) and returns the set.
Elenco dei parametri
-
num_data -
The number of training data
-
num_input -
The number of inputs per training data
-
num_output -
The number of ouputs per training data
-
user_function -
The user supplied function with following parameters:
num- The number of the training data setnum_input- The number of inputs per training datanum_output- The number of ouputs per training data
The function should return an associative array with keys
inputandoutputand two array values of input and output.
Valori restituiti
Returns a train data resource on success, or false on error.
Esempi
Example #1 fann_create_train_from_callback() example
<?php
function create_train_callback($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
Nota:
This function is only available if the fann extension has been build against libfann >= 2.2.
Vedere anche:
- 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
geekgirljoy at gmail dot com ¶
8 years ago
