使用FANN时，我无法成功运行从FANN网站上复制粘贴的代码。我使用的是Windows 7上的FANN版本2.2.0和MS Visual Studio 2008。我的XOR示例训练程序的代码如下所示：
#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 coutint 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++ wrappervoid 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;}我注释掉了以下代码：
//net.set_activation_steepness_hidden(0.5);//net.set_activation_steepness_output(0.5);否则程序会崩溃。xor.data文件内容如下：
4 2 11 1 -1 -1 -1 -1 -1 1 1 1 -1 1 输出看起来很奇怪：
XOR test started.Creating network.Network Type : LAYERInput layer : 2 neurons, 1 bias Hidden layer : 3 neurons, 1 biasOutput layer : 1 neuronsTotal neurons and biases : 8Total connections : 13Connection rate : 1.000Network type : FANN_NETTYPE_LAYERTraining algorithm : FANN_TRAIN_RPROPTraining error function : FANN_ERRORFUNC_TANHTraining stop function : FANN_STOPFUNC_MSEBit fail limit : 0.350Learning rate : 0.700Learning momentum : 0.000Quickprop decay : -0.000100Quickprop mu : 1.750RPROP increase factor : 1.200RPROP decrease factor : 0.500RPROP delta min : 0.000RPROP delta max : 50.000Cascade output change fraction : 0.010000Cascade candidate change fraction : 0.010000Cascade output stagnation epochs : 12Cascade candidate stagnation epochs : 12Cascade max output epochs : 150Cascade min output epochs : 50Cascade max candidate epochs : 150Cascade min candidate epochs : 50Cascade weight multiplier : 0.400Cascade candidate limit :1000.000Cascade activation functions[0] : FANN_SIGMOIDCascade activation functions[1] : FANN_SIGMOID_SYMMETRICCascade activation functions[2] : FANN_GAUSSIANCascade activation functions[3] : FANN_GAUSSIAN_SYMMETRICCascade activation functions[4] : FANN_ELLIOTCascade activation functions[5] : FANN_ELLIOT_SYMMETRICCascade activation functions[6] : FANN_SIN_SYMMETRICCascade activation functions[7] : FANN_COS_SYMMETRICCascade activation functions[8] : FANN_SINCascade activation functions[9] : FANN_COSCascade activation steepnesses[0] : 0.250Cascade activation steepnesses[1] : 0.500Cascade activation steepnesses[2] : 0.750Cascade activation steepnesses[3] : 1.000Cascade candidate groups : 2Cascade no. of candidates : 80Training network.*****************Printing read data (2):XOR test (0.000000,1.875000) -> 0.000000XOR test (0.000000,-1.875000) -> 0.000000*****************Max Epochs 300000. Desired Error: 1e-005Epochs 1. Current Error: 0.260461Epochs 36. Current Error: 7.15071e-006Testing network.XOR test (+0, +1.875) -> +5.295e-035, should be +0, difference = 5.295e-035XOR test (+0, -1.875) -> +0, should be +0, difference = -0XOR test (+0, -1.875) -> +0, should be +0, difference = -0XOR test (+0, +1.875) -> +0, should be +0, difference = -0Saving network.XOR test completed.在Testing network.之后的输出看起来像是：
- 训练数据和测试数据都被解释为(0, +/- 1.875)，如
Printing read data (2)和Testing network.之后的行所示。 Printing read data后的(2)是从data.num_input_train_data()获取的，我期望得到(4)，因为我有四组训练数据。- “目标”似乎总是“0”（见输出），尽管训练数据从不为零，而是总是+/- 1。
一个不同的问题也有同样的奇怪输出，暗示训练数据被解释为(0,+/-1.875)->0.0。使用这个示例进行训练（如我的XOR示例）似乎也成功了，但即使在用于训练的数据上执行ANN，也返回了看似随机的数字。
回答：
我在FANN – I get incorrect results (near 0) at simply task中找到了答案。上面说在包含”doublefann.h”时，也应该链接doublefann库。这显然也适用于”floatfann.h”和floatfann库。
