以下例子展示了怎么训练数据来实现 XOR (异或)功能。
Example #1 xor.data file
4 2 1 -1 -1 -1 -1 1 1 1 -1 1 1 1 -1
Example #2 一般训练
<?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);
}
?>
这个例子展示怎么读取神经网络并且使用 XOR (异或)功能来运行数据。
Example #3 一般测试
<?php
$train_file = (dirname(__FILE__) . "/xor_float.net");
if (!is_file($train_file))
die("The file xor_float.net has not been created! Please run simple_train.php to generate it");
$ann = fann_create_from_file($train_file);
if (!$ann)
die("ANN could not be created");
$input = array(-1, 1);
$calc_out = fann_run($ann, $input);
printf("xor test (%f,%f) -> %f\n", $input[0], $input[1], $calc_out[0]);
fann_destroy($ann);
?>
If you wan't your result to be saved after the time limit, you will need to add this to your code.
<?php
function shutdown()
{
global $ann;
fann_save($ann, dirname(__FILE__) . "/result.net");
fann_destroy($ann);
}
register_shutdown_function('shutdown');
?>
where $ann is your neural network var and 'result.net' your neural network config file.
A helpful reference for FANN is available here:
http://leenissen.dk/fann/html/files2/theory-txt.html
Here is an explanation for the input file for training, as it might be obvious to everyone and you must understand it to write your own:
4 2 1 <- header file saying there are 4 sets to read, with 2 inputs and 1 output
-1 -1 <- the 2 inputs for the 1st group
-1 <- the 1 output for the 1st group
-1 1 <- the 2 inputs for the 2nd group
1 <- the 1 output for the 2nd group
1 -1 <- the 2 inputs for the 3rd group
1 <- the 1 output for the 3rd group
1 1 <- the 2 inputs for the 4th group
-1 <- the 1 output for the 4th group