C# FANN example — распознавание с использованием нейронной сети
http://leenissen.dk/fann/wp/ - сайт библиотеки FANN
http://code.google.com/p/fanndotnetwrapper/ - обертка под .NET
http://leenissen.dk/fann/fann.html - php
http://code.google.com/p/fanntool/ - тул по работе с библиотекой FANN - самое классное в нем - что он может подобрать оптимальные параметры нейронной сети.
В примере стандартная задача по операции xor, и еще одна по определению прямоугольника - на вход подается сто бит (грубо говоря рисунок - 10х10 пикселей) - сеть пытается определить нарисован ли на ней прямоугольник.
Пример:
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using FANN.Net;
using System.IO;
namespace fann
{
class Program
{
static void Main(string[] args)
{
Console.WriteLine("XOR");
NeuralNet xornet = xor.get_xor_net();
xor.test(xornet, 0, 0);
xor.test(xornet, 0, 1);
xor.test(xornet, 1, 0);
xor.test(xornet, 1, 1);
Console.WriteLine("\nBOX");
NeuralNet boxnet = box.get_box_net();
box.test(boxnet, box.randombox());
box.test(boxnet, box.randombox());
box.test(boxnet, box.randombox_with_some_elements());
box.test(boxnet, box.randombox_without_some_elements());
box.test(boxnet, box.rand());
Console.ReadKey();
}
}
class xor
{
public static NeuralNet get_xor_net()
{
NeuralNet net = new NeuralNet();
if (!File.Exists("xor.net"))
{
if (!File.Exists("xor.data"))
{
create_training_file();
Console.WriteLine("xor.data generated");
}
List<uint> layers = new List<uint>();
layers.Add(2); // inputs
layers.Add(3); // hidden
layers.Add(1); // output
net.CreateStandardArray(layers.ToArray());
net.SetLearningRate((float)0.7);
net.SetActivationSteepnessHidden(1.0);
net.SetActivationSteepnessOutput(1.0);
net.SetActivationFunctionHidden(ActivationFunction.SigmoidSymmetric);
net.SetActivationFunctionOutput(ActivationFunction.SigmoidSymmetric);
net.SetTrainStopFunction(StopFunction.Bit);
net.SetBitFailLimit(0.01f);
// Set additional properties such as the training algorithm
//net.SetTrainingAlgorithm(FANN::TRAIN_QuickProp);
TrainingData data = new TrainingData();
data.ReadTrainFromFile("xor.data");
net.InitWeights(data);
net.TrainOnData(data,
1000, // max iterations
10, // iterations between report
0 //desired error
);
net.Save("xor.net");
}
net.CreateFromFile("xor.net");
return net;
}
public static void create_training_file()
{
StringBuilder sb = new StringBuilder();
sb.AppendLine("4 2 1");
sb.AppendLine("0 0");
sb.AppendLine("1");
sb.AppendLine("0 1");
sb.AppendLine("0");
sb.AppendLine("1 0");
sb.AppendLine("0");
sb.AppendLine("1 1");
sb.AppendLine("0");
File.WriteAllText("xor.data", sb.ToString());
}
public static void test(NeuralNet net, double a, double b)
{
double r = net.Run(new double[] { a, b })[0];
r = Math.Round(r, 1);
Console.WriteLine(string.Format("{0} xor {1} = {2}", a, b, r));
}
}
class box
{
public const int WIDTH = 10;
public static NeuralNet get_box_net()
{
NeuralNet net = new NeuralNet();
if (!File.Exists("box.net"))
{
if (!File.Exists("box.data"))
{
box.create_training_file();
Console.WriteLine("box.data generated");
}
List<uint> layers = new List<uint>();
layers.Add(100); // inputs
layers.Add(300); // hidden
layers.Add(1); // output
net.CreateStandardArray(layers.ToArray());
net.SetLearningRate((float)0.7);
net.SetActivationSteepnessHidden(1.0);
net.SetActivationSteepnessOutput(1.0);
net.SetActivationFunctionHidden(ActivationFunction.SigmoidSymmetric);
net.SetActivationFunctionOutput(ActivationFunction.SigmoidSymmetric);
net.SetTrainStopFunction(StopFunction.Bit);
net.SetBitFailLimit(0.01f);
// Set additional properties such as the training algorithm
//net.SetTrainingAlgorithm(FANN::TRAIN_QuickProp);
TrainingData data = new TrainingData();
data.ReadTrainFromFile("box.data");
net.InitWeights(data);
net.TrainOnData(data,
1000, // max iterations
10, // iterations between report
0 //desired error
);
net.Save("box.net");
}
net.CreateFromFile("box.net");
return net;
}
public static void create_training_file()
{
File.WriteAllText("box.data", "5000 100 1" + Environment.NewLine);
for (int i = 0; i < 1000; i++)
{
File.AppendAllText("box.data", box.tostring(box.randombox(), true));
File.AppendAllText("box.data", box.tostring(box.randombox_without_some_elements(), false));
File.AppendAllText("box.data", box.tostring(box.randombox_with_some_elements(), false));
File.AppendAllText("box.data", box.tostring(box.rand(), false));
File.AppendAllText("box.data", box.tostring(box.randombox(), true));
}
}
public static int[,] rand()
{
int[,] input = get_empty();
Random rnd = new Random();
for (int x = 0; x < WIDTH; x++)
{
for (int y = 0; y < WIDTH; y++)
{
input[x, y] = (rnd.Next(0, 1000) > 500) ? 1 : 0;
}
}
return input;
}
public static int[,] randombox_with_some_elements()
{
int[,] input = randombox();
Random rnd = new Random();
for (int x = 0; x < WIDTH; x++)
{
for (int y = 0; y < WIDTH; y++)
{
if (rnd.Next(0, 1000) > 500)
{
input[x, y] = 1;
}
}
}
return input;
}
public static int[,] randombox_without_some_elements()
{
int[,] input = randombox();
Random rnd = new Random();
for (int x = 0; x < WIDTH; x++)
{
for (int y = 0; y < WIDTH; y++)
{
if (input[x, y] == 1 && rnd.Next(0, 1000) > 500)
{
input[x, y] = 0;
}
}
}
return input;
}
public static int[,] randombox()
{
int[,] input = get_empty();
Random rnd = new Random();
int x1 = rnd.Next(0, WIDTH - 3);
int y1 = rnd.Next(0, WIDTH - 3);
int x2 = rnd.Next(x1 + 2, WIDTH - 1);
int y2 = rnd.Next(y1 + 2, WIDTH - 1);
for (int x = x1; x <= x2; x++)
{
input[x, y1] = 1;
input[x, y2] = 1;
}
for (int y = y1; y <= y2; y++)
{
input[x1, y] = 1;
input[x2, y] = 1;
}
return input;
}
public static void print(int[,] input)
{
for (int x = 0; x < WIDTH; x++)
{
for (int y = 0; y < WIDTH; y++)
{
Console.Write(input[x, y]);
Console.Write(" ");
}
Console.WriteLine();
}
}
public static string tostring(int[,] input, bool isbox)
{
List<string> items = new List<string>();
for (int x = 0; x < WIDTH; x++)
{
for (int y = 0; y < WIDTH; y++)
{
items.Add(input[x, y].ToString());
}
}
string b = isbox ? "1" : "0";
return string.Join(" ", items.ToArray()) + Environment.NewLine + b + Environment.NewLine;
}
public static int[,] get_empty()
{
int[,] input = new int[WIDTH, WIDTH];
for (int x = 0; x < WIDTH; x++)
{
for (int y = 0; y < WIDTH; y++)
{
input[x, y] = 0;
}
}
return input;
}
public static void test(NeuralNet net, int[,] input)
{
List<double> items = new List<double>();
for (int x = 0; x < WIDTH; x++)
{
for (int y = 0; y < WIDTH; y++)
{
items.Add((double)input[x, y]);
}
}
double r = net.Run(items.ToArray())[0];
r = Math.Round(r, 1);
print(input);
Console.WriteLine((r == 1) ? "IS box" : "NOT box");
}
}
class Example
{
#region example from sources
// Test function that demonstrates usage of the fann C++ wrapper
private static void xor_test()
{
System.Console.WriteLine("XOR test started.");
const float LearningRate = 0.7f;
const uint numInput = 2;
const uint numHidden = 3;
const uint numOutput = 1;
const float desired_error = 0;
const uint max_iterations = 1000;
const uint iterations_between_reports = 10;
System.Console.WriteLine("Creating network.");
NeuralNet net = new NeuralNet();
List<uint> layers = new List<uint>();
layers.Add(numInput);
layers.Add(numHidden);
layers.Add(numOutput);
net.CreateStandardArray(layers.ToArray());
net.SetLearningRate(LearningRate);
net.SetActivationSteepnessHidden(1.0);
net.SetActivationSteepnessOutput(1.0);
net.SetActivationFunctionHidden(ActivationFunction.SigmoidSymmetric);
net.SetActivationFunctionOutput(ActivationFunction.SigmoidSymmetric);
net.SetTrainStopFunction(StopFunction.Bit);
net.SetBitFailLimit(0.01f);
// Set additional properties such as the training algorithm
//net.SetTrainingAlgorithm(FANN::TRAIN_QuickProp);
// Output network type and parameters
System.Console.WriteLine("Network Type : ");
switch (net.GetNetworkType())
{
case NetworkType.Layer:
System.Console.WriteLine("LAYER");
break;
case NetworkType.ShortCut:
System.Console.WriteLine("SHORTCUT");
break;
default:
System.Console.WriteLine("UNKNOWN");
break;
}
net.PrintParameters();
System.Console.WriteLine("Training network.");
TrainingData data = new TrainingData();
if (data.ReadTrainFromFile("xor.data"))
{
// Initialize and train the network with the data
net.InitWeights(data);
System.Console.WriteLine("Max Epochs " + max_iterations + ". "
+ "Desired Error: " + desired_error);
/*
net.Callback += (nn, train, max_epochs, epochs_between_reports, de, epochs)
=> {
System.Console.WriteLine("Epochs " + epochs + ". " + "Current Error: " + nn.GetMSE() + "\n");
return 0;
};
*/
net.TrainOnData(data, max_iterations,
iterations_between_reports, desired_error);
System.Console.WriteLine("Testing network.");
for (uint i = 0; i < data.TrainingDataLength; ++i)
{
// Run the network on the test data
double calcOut = net.Run(data.Input[i])[0];
System.Console.WriteLine("XOR test (" + data.Input[i][0] + ", "
+ data.Input[i][1] + ") -> " + calcOut
+ ", should be " + data.Output[i][0] + ", "
+ "difference = "
+ Math.Abs(calcOut - data.Output[i][0]));
}
System.Console.WriteLine("Saving network.");
// Save the network in floating point and fixed point
net.Save("xor_float.net");
uint decimal_point = (uint)net.SaveToFixed("xor_fixed.net");
data.SaveTrainToFixed("xor_fixed.data", decimal_point);
System.Console.WriteLine("XOR test completed.");
}
}
#endregion
}
}На выходе дает такое:
XOR
0 xor 0 = 1
0 xor 1 = 0
1 xor 0 = 0
1 xor 1 = 0
BOX
0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 0 0
1 0 0 0 0 0 0 1 0 0
1 0 0 0 0 0 0 1 0 0
1 1 1 1 1 1 1 1 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
IS box
0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 0 0
1 0 0 0 0 0 0 1 0 0
1 0 0 0 0 0 0 1 0 0
1 1 1 1 1 1 1 1 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
IS box
0 0 0 1 0 0 1 1 0 0
1 1 1 1 1 1 1 1 1 0
1 0 0 0 1 0 1 1 0 0
1 1 1 0 1 1 0 1 1 0
1 1 1 1 1 1 1 1 1 1
1 0 0 1 1 1 1 1 0 0
1 1 1 1 0 1 0 1 1 0
1 1 1 1 0 0 1 0 1 1
1 0 1 1 1 1 1 1 1 1
1 0 1 1 0 1 1 1 1 1
NOT box
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0
0 0 0 0 1 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
NOT box
1 1 0 0 1 0 1 1 0 1
0 1 1 1 1 1 0 0 0 0
1 0 1 0 0 0 1 1 1 0
1 0 0 1 0 0 0 1 1 0
0 1 1 1 1 0 0 1 0 1
1 0 1 1 1 0 1 0 0 0
1 0 0 0 0 0 0 0 0 1
0 0 1 1 0 1 1 0 0 0
0 1 0 1 1 0 0 0 1 1
0 0 1 0 0 0 0 1 0 1
NOT boxПример на php из исходников pecl модуля (сохраняю на память):
<?php
/* demo.php
* $Id: demo.php,v 1.3 2004/01/19 22:33:51 tadpole9 Exp $
*
* This file should help explain the FANN API from a PHP perspective. It
* is basically a PHP port of the simple_test/simple_train examples from
* the FANN distribution. The original C versions can be found in
* $FANN_DIR/examples
*
* There are a few functions that aren't demonstrated in this file, here
* are their prototypes:
*
* void fann_randomize_weights(resource ann [, double min [, double max]])
* void fann_set_learning_rate(resource ann, float learning_rate)
* void fann_set_activation_function_hidden(resource ann, int activation_function)
* void fann_set_activation_function_output(resource ann, int activation_function)
* void fann_set_activation_hidden_steepness(resource ann, int activation_function)
* void fann_set_activation_output_steepness(resource ann, int activation_function)
* double fann_get_MSE(resource ann)
* double fann_get_learning_rate(resource ann)
* long fann_get_num_input(resource ann)
* long fann_get_num_output(resource ann)
* long fann_get_activation_function_hidden(resource ann)
* long fann_get_activation_function_output(resource ann)
* double fann_get_activation_hidden_steepness(resource ann)
* double fann_get_activation_output_steepness(resource ann)
* long fann_get_total_neurons(resource ann)
* long fann_get_total_connections(resource ann)
*
* If you have any questions or comments, please e-mail Evan Nemerson
* <[email protected]>
*/
/* If you don't want to compile FANN into PHP... */
if ( !extension_loaded('fann') ) {
if ( !dl('fann.so') ) {
exit("You must install the FANN extension. You can get it from http://fann.sf.net/\n");
}
}
/* Create an artificial neural network */
$ann = fann_create(
array(2, 4, 1), /* layers. in this case, three layers- two input neurons, 4 neurons in a hidden layer, and one output neuron */
1.0, /* learning rate */
0.7 /* connection rate */
);
/* To load from a file, you can use. If your version of PHP includes the streams API (4.3.0+ ?),
* this can be anything accessible through streams (http, ftp, https, etc) */
// $ann = fann_create("http://example.com/xor_float.net");
/* Train the network using the same data as is in the xor.data file */
fann_train($ann,
array(
array(
array(0,0), /* Input(s) */
array(0) /* Output(s) */
),
array(
array(0,1), /* Input(s) */
array(1) /* Output(s) */
),
array(
array(1,0), /* Input(s) */
array(1) /* Output(s) */
),
array(array(1,1), /* Input(s) */
array(0) /* Output(s) */
)
),
100000, /* Maximum number of epochs */
0.00001, /* Desired error. */
1000 /* Number of epochs between reports */
);
/* To achieve the same effect as the above with the data stored in an external file... Also works
* with the streams API, when available. */
// fann_train($ann, '/home/tadpole/local/src/fann/examples/xor.data', 100000, 0.00001, 1000);
print_r(fann_run($ann, array(0,0))); // Should be ~ 0
print_r(fann_run($ann, array(0,1))); // Should be ~ 1
print_r(fann_run($ann, array(1,0))); // Should be ~ 1
print_r(fann_run($ann, array(1,1))); // Should be ~ 0
/* This function is pretty simple. It will use the streams API if available. */
fann_save($ann, 'xor_float.net');
?>Наметки по установке fann на linux'е:
sudo pear install http://pecl.php.net/get/fann-0.1.1.tgz
sudo pecl install fann-devel
pecl install fann-0.1.1без этого не конфигурится
sudo apt-get install libfann-dev
sudo apt-get install libfann1-dev
libfann1-dev libfann1 libfann2
wget http://pecl.php.net/get/fann-0.1.1.tgz
tar zxf fann-0.1.1.tgz
cd fann-0.1.1
sudo phpize
sudo ./configure
sudo make
sudo make installпосле make install скажет:
Installing shared extensions: /usr/lib/php5/20090626+lfs/sudo touch /etc/php5/apache2/conf.d/fann.ini
fann_extension=/usr/lib/php5/20090626+lfs/fann.soпока ничего не получилось... (