<?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)
}
*/
fann_scale_input
(PECL fann >= 1.0.0)
fann_scale_input — 在以前计算参数的基础上，在训练之前放大输入向量中的数据
说明
fann_scale_input(resource
$ann, array $input_vector): bool在以前计算参数的基础上，在训练之前放大输入向量中的数据。
参数
-
ann -
神经网络 资源。
-
input_vector -
将要被缩放的输入向量。
返回值
成功时返回 true，其它情况下返回 false。
参见
- fann_descale_input() - 在获取基于先前计算的参数之后，在输入向量中缩小数据
- fann_scale_output() - 在以前计算参数的基础上，在训练之前放大输出向量中的数据
add a note
User Contributed Notes 4 notes
geekgirl dot joy at gmail dot com ¶
4 months ago
geekgirl dot joy at gmail dot com ¶
4 months 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 ¶
1 year 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
