范恩训练不当

FANN doesn't train properly

本文关键字：范恩训      更新时间：2023-10-16

我正在尝试用FANN近似平方函数。代码如下：

#include "../FANN-2.2.0-Source/src/include/doublefann.h"
#include "../FANN-2.2.0-Source/src/include/fann_cpp.h"
#include <cstdlib>
#include <iostream>
using namespace std;
using namespace FANN;
//Remember: fann_type is double!
int main(int argc, char** argv) {
    //create a test network: [1,2,1] MLP
    neural_net * net = new neural_net;
    const unsigned int layers[3] = {1,3,1};
    net->create_standard_array(3,layers);
    //net->create_standard(num_layers, num_input, num_hidden, num_output);
    net->set_learning_rate(0.7f);
    net->set_activation_steepness_hidden(0.7);
    net->set_activation_steepness_output(0.7);
    net->set_activation_function_hidden(SIGMOID_SYMMETRIC_STEPWISE);
    net->set_activation_function_output(SIGMOID_SYMMETRIC_STEPWISE);
    net->set_training_algorithm(TRAIN_QUICKPROP);
    //cout<<net->get_train_error_function()
    //exit(0);
    //test the number 2
    fann_type * testinput = new fann_type;
    *testinput = 2;
    fann_type * testoutput = new fann_type;
    *testoutput = *(net->run(testinput));
    double outputasdouble = (double) *testoutput;
    cout<<"Test output: "<<outputasdouble<<endl;
    //make a training set of x->x^2
    training_data * squaredata = new training_data;
    squaredata->read_train_from_file("trainingdata.txt");
    net->train_on_data(*squaredata,1000,100,0.001);
    cout<<endl<<"Easy!";
    return 0;
}

trainingdata.txt是这样的：

10 1 1
1 1
2 4
3 9
4 16
5 25
6 36
7 49
8 64
9 81
10 100

我觉得我在API方面做得很好。然而，当我运行它时，我会遇到巨大的错误，这种错误似乎永远不会随着训练而减少。

Test output: -0.0311087
Max epochs     1000. Desired error: 0.0010000000.
Epochs            1. Current error: 633.9928588867. Bit fail 10.
Epochs          100. Current error: 614.3250122070. Bit fail 9.
Epochs          200. Current error: 614.3250122070. Bit fail 9.
Epochs          300. Current error: 614.3250122070. Bit fail 9.
Epochs          400. Current error: 614.3250122070. Bit fail 9.
Epochs          500. Current error: 614.3250122070. Bit fail 9.
Epochs          600. Current error: 614.3250122070. Bit fail 9.
Epochs          700. Current error: 614.3250122070. Bit fail 9.
Epochs          800. Current error: 614.3250122070. Bit fail 9.
Epochs          900. Current error: 614.3250122070. Bit fail 9.
Epochs         1000. Current error: 614.3250122070. Bit fail 9.
Easy!

我做错了什么？

如果您对输出层使用sigmoid函数，则输出将提供（0,1）的范围。

你可以有两个选择，（1）用一个常数除所有输出，比如1e4。当测试数据到来时，您还可以将其除以1e4。问题是，你可能无法预测大于100的平方数（100^2=1e4）；（2） 将隐藏层和输出层都设置为线性，网络将自动学习权重，以给出您所拥有的任何输出值。

相关文章：