使用 FANN，我无法成功从 FANN 的网站运行复制和粘贴的代码。我在 Windows 7 和 MS Visual Studio 2008 上使用 FANN 2.2.0 版。我的 XOR 示例训练程序代码如下所示：
#include "floatfann.h"
#include "fann_cpp.h"
#include <ios>
#include <iostream>
#include <iomanip>
#include <string>
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 = 0.00001f;
const unsigned int max_iterations = 300000;
const unsigned int iterations_between_reports = 1000;
cout << endl << "Creating network." << endl;
FANN::neural_net net;
net.create_standard(num_layers, num_input, num_hidden, num_output);
net.set_learning_rate(learning_rate);
//net.set_activation_steepness_hidden(0.5);
//net.set_activation_steepness_output(0.5);
net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE);
net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE);
// 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:
cout << "LAYER" << endl;
break;
case FANN::SHORTCUT:
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"))
{
// ***** MY INPUT
std::string fn;
fn = "xor_read.data";
data.save_train(fn);
fann_type **train_dat;
fann_type **out_dat;
train_dat = data.get_input();
out_dat = data.get_output();
printf("*****************\n");
printf("Printing read data (%d):\n", data.num_input_train_data());
for(unsigned int i = 0; i < data.num_input_train_data(); i++)
{
printf("XOR test (%f,%f) -> %f\n", train_dat[i][0], train_dat[i][1], out_dat[i][0]);
}
printf("*****************\n");
// END: MY INPUT **************
// 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." << endl;
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][2] << ") -> " << *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. Syncronizes C and C++ output, calls the test function
and reports any exceptions */
int main(int argc, char **argv)
{
try
{
std::ios::sync_with_stdio(); // Syncronize cout and printf output
xor_test();
}
catch (...)
{
cerr << endl << "Abnormal exception." << endl;
}
return 0;
}
我注释掉了：
//net.set_activation_steepness_hidden(0.5);
//net.set_activation_steepness_output(0.5);
否则它会崩溃。文件 xor.data ：
4 2 1
1 1
-1
-1 -1
-1
-1 1
1
1 -1
1
输出对我来说看起来很奇怪：
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.
*****************
Printing read data (2):
XOR test (0.000000,1.875000) -> 0.000000
XOR test (0.000000,-1.875000) -> 0.000000
*****************
Max Epochs 300000. Desired Error: 1e-005
Epochs 1. Current Error: 0.260461
Epochs 36. Current Error: 7.15071e-006
Testing network.
XOR test (+0, +1.875) -> +5.295e-035, should be +0, difference = 5.295e-035
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.
之后的输出Testing network.如下：
- 训练数据和测试数据被解释为 (0, +/- 1.875)，正如您在
Printing read data (2)和之后的行正下方看到的那样Testing network.。 (2)后面Printing read data是取自，我的data.num_input_train_data()期望是得到一个，(4)因为我有四组训练数据。- “目标”似乎总是“0”（见输出），虽然训练数据从不为零，但总是 +/- 1。
另一个问题具有相同的奇怪输出，暗示训练数据被解释为 (0,+/-1.875)->0.0。在这个示例中，训练（就像在我的 XOR 示例中一样）似乎也是成功的，但是 ANN 的执行（甚至在用于训练的数据上）返回了看似随机的数字。