C # BP neural network and example (2) Improved Version

In the <C # BP Neural Network Class and example> Article, I reproduced a class provided by netizens. I am very grateful to this netizen for his selfless dedication, so that the author can quickly complete a function of the software.

It seems that I have found some shortcomings in the original version modified by netizens, And I have improved it accordingly.

1. The training sample result is not saved, so that the training data is re-trained every time you use it, which takes time:

Although methods for saving arrays W, V, B1, and b2 are provided in the class, the constructor is faulty, and the data used in the following sections are not saved: in_rate innum hidenum outnum.

The author has made the following improvements:

A new parameter-free constructor is created to predict the instantiated object when a new sample is created;

The saveparas and readparas methods are provided to save and read these parameters;

The initial method is provided to create a dynamic array used in the prediction process.

2. Use pseudo-random numbers in the class to initialize the W and V arrays, so that W and V are different each time a new object is created. I have not studied this algorithm in depth, and I don't know why it was designed.

The author puts

R = new random ();

This line of code has been modified

R = new random (32); // Add a parameter to make the generated pseudo-random sequence the same

The modified classes and examples are as follows:

Using system; using system. IO; using system. Text; namespace bpannet {/// <summary> /// bpnet abstract description. /// </Summary> public class bpnet {public int innum; // number of input nodes int hidenum; // number of hidden layer nodes public int outnum; // output layer node count public int samplenum; // total number of samples random R; double [] X; // input data of input nodes double [] x1; // The output double [] X2 of the hidden layer node; // The output double [] O1 of the output node; // The input double [] O2 of the hidden layer; // input public double [,] W in the output layer; // weight matrix W Public double [,] V; // weight matrix v Public double [,] dw; // weight matrix W Public double [,] DV; // weight matrix v Public double rate; // learning rate public doub Le [] B1; // hidden layer Threshold Matrix public double [] B2; // output layer Threshold Matrix public double [] db1; // hidden layer Threshold Matrix public double [] DB2; // output layer Threshold Matrix double [] pp; // output layer error double [] QQ; // hidden layer error double [] YD; // The Public Double E of the instructor data in the output layer; // The Mean Squared Error double in_rate; // The normalized proportional coefficient public int computehidenum (int m, int N) {double S = math. SQRT (0.43 * m * n + 0.12 * n + 2.54 * m + 0.77 * n + 0.35) + 0.51; int Ss = convert. toint32 (s); Return (S-(double) ss)> 0.5 )? SS + 1: SS;} public bpnet (double [,] P, double [,] T) {// constructor logic r = new random (32 ); // Add a parameter to make the pseudo-random sequence identical this. innum = P. getlength (1); // The second-dimensional size of the array is the number of input nodes. This. outnum = T. getlength (1); // number of output nodes this. hidenum = computehidenum (innum, outnum); // The number of hidden nodes. I do not know how it works. // This. hidenum = 18; this. samplenum = P. getlength (0); // the size of the first dimension of the array is console. writeline ("Number of input nodes:" + innum); console. writeline ("number of hidden layer nodes:" + hidenum); conso Le. writeline ("number of output layer nodes:" + outnum); console. readline (); X = new double [innum]; X1 = new double [hidenum]; x2 = new double [outnum]; O1 = new double [hidenum]; o2 = new double [outnum]; W = new double [innum, hidenum]; V = new double [hidenum, outnum]; DW = new double [innum, hidenum]; DV = new double [hidenum, outnum]; b1 = new double [hidenum]; b2 = new double [outnum]; db1 = new double [hidenum]; DB2 = new Double [outnum]; pp = new double [hidenum]; QQ = new double [outnum]; YD = new double [outnum]; // initialize W for (INT I = 0; I <innum; I ++) {for (Int J = 0; j 
Example:
// The main caller using system; namespace bpannet {/// <summary> /// class1 summary. /// </Summary> class class1 {/// <summary> /// main entry point of the application. /// </Summary> [stathread] Static void main (string [] ARGs) {// double [,] p1 = new double [,] {0.05, 0.02 }, {0.09, 0.11}, {0.12, 0.20}, {0.15, 0.22}, {0.20, 0.25}, {0.75, 0.75}, {0.80, 0.83}, {0.82, 0.80 },{ 0.90, 0.89 },{ 0.95, 0.89 },{ 0.09, 0.04 },{ 0.1, 0.1 },{ 0.14, 0.21 },{ 0.18, 0.24 }, {0.22, 0.28 },{ 0.77, 0.78 },{ 0.79, 0.81 },{ 0.84, 0.82 },{ 0.94, 0.93 },{ 0.98, 0.99 }}; // double [,] T1 = new double }, }, {0, 1}, {0, 1}, {0, 1}; double [,] p1 = new double [,] {0.1399, 0.1467, 0.1567, 0.1595, 0.1588, 0.1622}, {0.1467, 0.1567, 0.1595, 0.1588, 0.1622, 0.1611}, {0.1567, 0.1595, 0.1588, 0.1622, 0.1611}, {0.1615, 0.1595, 0.1588, 0.1622, 0.1611, 0.1615, 0.1685 },{ 0.1588, 0.1622, 0.1611, 0.1615, 0.1685, 0.1789 }}; double [,] T1 = new double [,] {0.1622}, {0.1611 }, {0.1615 },{ 0.1685 },{ 0.1789 },{ 0.1790 }}; bpnet BP = new bpnet (P1, T1); int study = 0; do {study ++; BP. train (P1, T1); // bp. rate = 0.95-(0.95-0.3) * study/50000; // console. write ("Number" + Study + "Times:"); // console. writeline ("mean variance is" + bp. e);} while (BP. e> 0.001 & Study <50000); console. write ("Number" + Study + "Times:"); console. writeline ("mean variance is" + bp. e); BP. savematrix (BP. w, "TXT"); BP. savematrix (BP. v, "v.txt"); BP. savematrix (BP. b1, "b1.txt"); BP. savematrix (BP. b2, "b2.txt"); BP. saveparas ("para.txt"); pretect (); // start prediction of new samples} public static void pretect () {console. writeline ("prediction starts... "); bpnet BP = new bpnet (); BP. readparas ("para.txt"); BP. initial (); BP. readmatrixw (BP. w, "TXT"); BP. readmatrixw (BP. v, "v.txt"); BP. readmatrixb (BP. b1, "b1.txt"); BP. readmatrixb (BP. b2, "b2.txt"); // double [,] P2 = new double [,] {0.05, 0.02}, {0.09, 0.11}, {0.12, 0.20 }, {0.15, 0.22}, {0.20, 0.25}, {0.75, 0.75}, {0.80, 0.83}, {0.82, 0.80}, {0.90, 0.89}, {0.95, 0.89 },{ 0.09, 0.04 },{ 0.1, 0.1 },{ 0.14, 0.21 },{ 0.18, 0.24 },{ 0.22, 0.28 },{ 0.77, 0.78 }, {0.79, 0.81 },{ 0.84, 0.82 },{ 0.94, 0.93 },{ 0.98, 0.99 }}; double [,] P2 = new double [,] {0.1399, 0.1467, 0.1567, 0.1595, 0.1588, 0.1622 },{ 0.1622, 0.1611, 0.1615, 0.1685, 0.1789 }}; int AA = bp. innum; int BB = bp. outnum; int cc = p2.getlength (0); double [] P2 = new double [AA]; double [] T2 = new double [BB]; for (INT n = 0; n <cc; n ++) {for (INT I = 0; I <AA; I ++) {p21' [I] = P2 [N, I];} t2 = bp. SIM (p21.); For (INT I = 0; I <t2.length; I ++) {console. writeline ("prediction data" + N. tostring () + ":" + T2 [I] + "") ;}} console. readline ();}}}