本文介绍了FANN XOR培训的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在开发使用FANN(快速人工神经网络库)的软件.在尝试编写自己的ANN代码以编译FANN示例程序(这里是C ++ XOR近似程序)失败了无数次尝试之后，我尝试了一下.这是来源.
I am developing a piece of software that uses FANN, the Fast Artificial Neural Network library.I have tried after numerous failed attempts at writing my own ANN code to compile a FANN sample program, here the C++ XOR approximation program. Here is the source.
#include "../include/floatfann.h"
#include "../include/fann_cpp.h"
#include <ios>
#include <iostream>
#include <iomanip>
using std::cout;
using std::cerr;
using std::endl;
using std::setw;
using std::left;
using std::right;
using std::showpos;
using std::noshowpos;
// Callback function that simply prints the information to cout
int print_callback(FANN::neural_net &net, FANN::training_data &train,
unsigned int max_epochs, unsigned int epochs_between_reports,
float desired_error, unsigned int epochs, void *user_data)
{
cout << "Epochs " << setw(8) << epochs << ". "
<< "Current Error: " << left << net.get_MSE() << right << endl;
return 0;
}
// Test function that demonstrates usage of the fann C++ wrapper
void xor_test()
{
cout << endl << "XOR test started." << endl;
const float learning_rate = 0.7f;
const unsigned int num_layers = 3;
const unsigned int num_input = 2;
const unsigned int num_hidden = 3;
const unsigned int num_output = 1;
const float desired_error = 0.001f;
const unsigned int max_iterations = 300000;
const unsigned int iterations_between_reports = 10000;
////Make array for create_standard() workaround (prevent "FANN Error 11: Unable to allocate memory.")
const unsigned int num_input_num_hidden_num_output__array[3] = {num_input, num_hidden, num_output};
cout << endl << "Creating network." << endl;
FANN::neural_net net;
// cout<<"Debug 1"<<endl;
//net.create_standard(num_layers, num_input, num_hidden, num_output);//doesn't work
net.create_standard_array(num_layers, num_input_num_hidden_num_output__array);//this might work -- create_standard() workaround
net.set_learning_rate(learning_rate);
net.set_activation_steepness_hidden(1.0);
net.set_activation_steepness_output(1.0);
//Sample Code, changed below
net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE);
net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE);
//changed above to sigmoid
//net.set_activation_function_hidden(FANN::SIGMOID);
//net.set_activation_function_output(FANN::SIGMOID);
// Set additional properties such as the training algorithm
//net.set_training_algorithm(FANN::TRAIN_QUICKPROP);
// Output network type and parameters
cout << endl << "Network Type : ";
switch (net.get_network_type())
{
case FANN::LAYER://only connected to next layer
cout << "LAYER" << endl;
break;
case FANN::SHORTCUT://connected to all other layers
cout << "SHORTCUT" << endl;
break;
default:
cout << "UNKNOWN" << endl;
break;
}
net.print_parameters();
cout << endl << "Training network." << endl;
FANN::training_data data;
if (data.read_train_from_file("xor.data"))
{
// Initialize and train the network with the data
net.init_weights(data);
cout << "Max Epochs " << setw(8) << max_iterations << ". "
<< "Desired Error: " << left << desired_error << right << endl;
net.set_callback(print_callback, NULL);
net.train_on_data(data, max_iterations,
iterations_between_reports, desired_error);
cout << endl << "Testing network. (not really)" << endl;
//I don't really get this code --- the funny for loop. Whatever. I'll skip it.
for (unsigned int i = 0; i < data.length_train_data(); ++i)
{
// Run the network on the test data
fann_type *calc_out = net.run(data.get_input()[i]);
cout << "XOR test (" << showpos << data.get_input()[i][0] << ", "
<< data.get_input()[i][1] << ") -> " << *calc_out
<< ", should be " << data.get_output()[i][0] << ", "
<< "difference = " << noshowpos
<< fann_abs(*calc_out - data.get_output()[i][0]) << endl;
}
cout << endl << "Saving network." << endl;
// Save the network in floating point and fixed point
net.save("xor_float.net");
unsigned int decimal_point = net.save_to_fixed("xor_fixed.net");
data.save_train_to_fixed("xor_fixed.data", decimal_point);
cout << endl << "XOR test completed." << endl;
}
}
/* Startup function. Synchronizes C and C++ output, calls the test function
and reports any exceptions */
int main(int argc, char **argv)
{
try
{
std::ios::sync_with_stdio(); // Synchronize cout and printf output
xor_test();
}
catch (...)
{
cerr << endl << "Abnormal exception." << endl;
}
return 0;
}
这是我的输出.
XOR test started.
Creating network.
Network Type : LAYER
Input layer : 2 neurons, 1 bias
Hidden layer : 3 neurons, 1 bias
Output layer : 1 neurons
Total neurons and biases : 8
Total connections : 13
Connection rate : 1.000
Network type : FANN_NETTYPE_LAYER
Training algorithm : FANN_TRAIN_RPROP
Training error function : FANN_ERRORFUNC_TANH
Training stop function : FANN_STOPFUNC_MSE
Bit fail limit : 0.350
Learning rate : 0.700
Learning momentum : 0.000
Quickprop decay : -0.000100
Quickprop mu : 1.750
RPROP increase factor : 1.200
RPROP decrease factor : 0.500
RPROP delta min : 0.000
RPROP delta max : 50.000
Cascade output change fraction : 0.010000
Cascade candidate change fraction : 0.010000
Cascade output stagnation epochs : 12
Cascade candidate stagnation epochs : 12
Cascade max output epochs : 150
Cascade min output epochs : 50
Cascade max candidate epochs : 150
Cascade min candidate epochs : 50
Cascade weight multiplier : 0.400
Cascade candidate limit :1000.000
Cascade activation functions[0] : FANN_SIGMOID
Cascade activation functions[1] : FANN_SIGMOID_SYMMETRIC
Cascade activation functions[2] : FANN_GAUSSIAN
Cascade activation functions[3] : FANN_GAUSSIAN_SYMMETRIC
Cascade activation functions[4] : FANN_ELLIOT
Cascade activation functions[5] : FANN_ELLIOT_SYMMETRIC
Cascade activation functions[6] : FANN_SIN_SYMMETRIC
Cascade activation functions[7] : FANN_COS_SYMMETRIC
Cascade activation functions[8] : FANN_SIN
Cascade activation functions[9] : FANN_COS
Cascade activation steepnesses[0] : 0.250
Cascade activation steepnesses[1] : 0.500
Cascade activation steepnesses[2] : 0.750
Cascade activation steepnesses[3] : 1.000
Cascade candidate groups : 2
Cascade no. of candidates : 80
Training network.
Max Epochs 300000. Desired Error: 0.001
Epochs 1. Current Error: 0.25
Epochs 10000. Current Error: 0.25
Epochs 20000. Current Error: 0.25
Epochs 30000. Current Error: 0.25
Epochs 40000. Current Error: 0.25
Epochs 50000. Current Error: 0.25
Epochs 60000. Current Error: 0.25
Epochs 70000. Current Error: 0.25
Epochs 80000. Current Error: 0.25
Epochs 90000. Current Error: 0.25
Epochs 100000. Current Error: 0.25
Epochs 110000. Current Error: 0.25
Epochs 120000. Current Error: 0.25
Epochs 130000. Current Error: 0.25
Epochs 140000. Current Error: 0.25
Epochs 150000. Current Error: 0.25
Epochs 160000. Current Error: 0.25
Epochs 170000. Current Error: 0.25
Epochs 180000. Current Error: 0.25
Epochs 190000. Current Error: 0.25
Epochs 200000. Current Error: 0.25
Epochs 210000. Current Error: 0.25
Epochs 220000. Current Error: 0.25
Epochs 230000. Current Error: 0.25
Epochs 240000. Current Error: 0.25
Epochs 250000. Current Error: 0.25
Epochs 260000. Current Error: 0.25
Epochs 270000. Current Error: 0.25
Epochs 280000. Current Error: 0.25
Epochs 290000. Current Error: 0.25
Epochs 300000. Current Error: 0.25
Testing network. (not really)
XOR test (+0, -1.875) -> +0, should be +0, difference = -0
XOR test (+0, -1.875) -> +0, should be +0, difference = -0
XOR test (+0, +1.875) -> +0, should be +0, difference = -0
XOR test (+0, +1.875) -> +0, should be +0, difference = -0
Saving network.
XOR test completed.
培训数据(xor.data)在这里:
4 2 1
-1 -1
-1
-1 1
1
1 -1
1
1 1
-1
是什么解释了人工神经网络中令人生畏的缺乏学习的原因?我非常确信我在某处配置了非常错误的东西，特别是考虑到这是示例程序. ANN专家，有什么建议吗?
What explains the eerie lack of learning in the ANN? I'm pretty convinced that I have something configured very wrong somewhere, especially given that this is the sample program. ANN experts, any advice?
推荐答案
应用FANN补丁，并确保对floatfann，doublefann等的所有引用都相同.
Apply the FANN patch and make sure that all references to floatfann, doublefann, etc. are congruent.
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