fann_scale_input
(PECL fann >= 1.0.0)
fann_scale_input — Scale data in input vector before feed it to ann based on previously calculated parameters
説明
Scale data in input vector before feed it to ann based on previously calculated parameters.
パラメータ
ann-
ニューラルネットワークリソース。
input_vector-
Input vector that will be scaled
参考
- fann_descale_input() - Scale data in input vector after get it from ann based on previously calculated parameters
- fann_scale_output() - Scale data in output vector before feed it to ann based on previously calculated parameters
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User Contributed Notes 4 notes
geekgirl dot joy at gmail dot com ¶
3 years ago
<?php
// This example will use the XOR dataset with negative one represented
// as zero and one represented as one-hundred and demonstrate how to
// scale those values so that FANN can understand them and then how
// to de-scale the value FANN returns so that you can understand them.
// Scaling allows you to take raw data numbers like -1234.975 or 4502012
// in your dataset and convert them into an input/output range that
// your neural network can understand.
// De-scaling lets you take the scaled data and convert it back into
// the original range.
// scale_test.data
// Note the values are "raw" or un-scaled.
/*
4 2 1
0 0
0
0 100
100
100 0
100
100 100
0
*/
////////////////////
// Configure ANN //
////////////////////
// New ANN
$ann = fann_create_standard_array(3, [2,3,1]);
// Set activation functions
fann_set_activation_function_hidden($ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output($ann, FANN_SIGMOID_SYMMETRIC);
// Read raw (un-scaled) training data from file
$train_data = fann_read_train_from_file("scale_test.data");
// Scale the data range to -1 to 1
fann_set_input_scaling_params($ann , $train_data, -1, 1);
fann_set_output_scaling_params($ann , $train_data, -1, 1);
///////////
// Train //
///////////
// Presumably you would train here (uncomment to perform training)...
// fann_train_on_data($ann, $train_data, 100, 10, 0.01);
// But it's not needed to test the scaling because the training file
// in this case is just used to compute/derive the scale range.
// However, doing the training will improve the answer the ANN gives
// in correlation to the training data.
//////////
// Test //
//////////
$raw_input = array(0, 100); // test XOR (0,100) input
$scaled_input = fann_scale_input ($ann , $raw_input); // scaled XOR (-1,1) input
$descaled_input = fann_descale_input ($ann , $scaled_input); // de-scaled XOR (0,100) input
$raw_output = fann_run($ann, $scaled_input); // get the answer/output from the ANN
$output_descale = fann_descale_output($ann, $raw_output); // de-scale the output
////////////////////
// Report Results //
////////////////////
echo 'The raw_input:' . PHP_EOL;
var_dump($raw_input);
echo 'The raw_input Scaled then De-Scaled (values are unchanged/correct):' . PHP_EOL;
var_dump($descaled_input);
echo 'The Scaled input:' . PHP_EOL;
var_dump($scaled_input);
echo "The raw_output of the ANN (Scaled input):" . PHP_EOL;
var_dump($raw_output);
echo 'The De-Scaled output:' . PHP_EOL;
var_dump($output_descale);
////////////////////
// Example Output //
////////////////////
/*
The raw_input:
array(2) {
[0]=>
float(0)
[1]=>
float(100)
}
The raw_input Scaled then De-Scaled (values are unchanged/correct):
array(2) {
[0]=>
float(0)
[1]=>
float(100)
}
The Scaled input:
array(2) {
[0]=>
float(-1)
[1]=>
float(1)
}
The raw_output of the ANN (Scaled input):
array(1) {
[0]=>
float(1)
}
The De-Scaled output:
array(1) {
[0]=>
float(100)
}
*/
geekgirl dot joy at gmail dot com ¶
3 years ago
<?php
// This example will use the XOR dataset with negative one represented
// as zero and one represented as one-hundred and demonstrate how to
// scale those values so that FANN can understand them and then how
// to de-scale the value FANN returns so that you can understand them.
// Scaling allows you to take raw data numbers like -1234.975 or 4502012
// in your dataset and convert them into an input/output range that
// your neural network can understand.
// De-scaling lets you take the scaled data and convert it back into
// the original range.
// scale_test.data
// Note the values are "raw" or un-scaled.
/*
4 2 1
0 0
0
0 100
100
100 0
100
100 100
0
*/
////////////////////
// Configure ANN //
////////////////////
// New ANN
$ann = fann_create_standard_array(3, [2,3,1]);
// Set activation functions
fann_set_activation_function_hidden($ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output($ann, FANN_SIGMOID_SYMMETRIC);
// Read raw (un-scaled) training data from file
$train_data = fann_read_train_from_file("scale_test.data");
// Scale the data range to -1 to 1
fann_set_input_scaling_params($ann , $train_data, -1, 1);
fann_set_output_scaling_params($ann , $train_data, -1, 1);
///////////
// Train //
///////////
// Presumably you would train here (uncomment to perform training)...
// fann_train_on_data($ann, $train_data, 100, 10, 0.01);
// But it's not needed to test the scaling because the training file
// in this case is just used to compute/derive the scale range.
// However, doing the training will improve the answer the ANN gives
// in correlation to the training data.
//////////
// Test //
//////////
$raw_input = array(0, 100); // test XOR (0,100) input
$scaled_input = fann_scale_input ($ann , $raw_input); // scaled XOR (-1,1) input
$descaled_input = fann_descale_input ($ann , $scaled_input); // de-scaled XOR (0,100) input
$raw_output = fann_run($ann, $scaled_input); // get the answer/output from the ANN
$output_descale = fann_descale_output($ann, $raw_output); // de-scale the output
////////////////////
// Report Results //
////////////////////
echo 'The raw_input:' . PHP_EOL;
var_dump($raw_input);
echo 'The raw_input Scaled then De-Scaled (values are unchanged/correct):' . PHP_EOL;
var_dump($descaled_input);
echo 'The Scaled input:' . PHP_EOL;
var_dump($scaled_input);
echo "The raw_output of the ANN (Scaled input):" . PHP_EOL;
var_dump($raw_output);
echo 'The De-Scaled output:' . PHP_EOL;
var_dump($output_descale);
////////////////////
// Example Output //
////////////////////
/*
The raw_input:
array(2) {
[0]=>
float(0)
[1]=>
float(100)
}
The raw_input Scaled then De-Scaled (values are unchanged/correct):
array(2) {
[0]=>
float(0)
[1]=>
float(100)
}
The Scaled input:
array(2) {
[0]=>
float(-1)
[1]=>
float(1)
}
The raw_output of the ANN (Scaled input):
array(1) {
[0]=>
float(1)
}
The De-Scaled output:
array(1) {
[0]=>
float(100)
}
*/
saakyanalexandr at gmail dot com ¶
5 years ago
fann_scale_input and fann_scale_output return not bool value. This function return scaling vector.
Example
$r = fann_scale_input($ann, $input);
$output = fann_run($ann, $input);
$s = fann_scale_output ( $ann, $output);
$r and $s is array