人工神经网络框架AForge学习(一)
本示例演示了单层人工神经网络的学习过程.
示例中采用了16条样本数据,3个分类,在经过7-20次的迭代后,误差值与期望最小误差(示例中为0)的差距变为最小.
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using System;
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using System.Collections.Generic;
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using System.Text;
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using System.IO;
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using AForge;
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using AForge.Neuro;
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using AForge.Neuro.Learning;
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using AForge.Controls;
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namespace AspxOn.AForgeNeuroTest
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{
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class Program
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{
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static void Main(string[] args)
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{
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double[,] data = null;
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int[] classes = null;
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double[,] tempData = new double[200, 2];
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int[] tempClass = new int[200];
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double learnintRate = 0.1;
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//最大和最小的X值
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double minX = double.MaxValue;
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double maxX = double.MinValue;
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//样本数目统计
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int samples = 0;
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//分类数目统计
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int classCount = 0;
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int[] samplesPerClass = new int[10];
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try
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{
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//准备训练样本数据
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string s = "0.1,0.1,0;0.2,0.3,0;0.3,0.4,0;0.1,0.3,0;0.2,0.5,0;0.1,1,1;0.2,1.1,1;0.3,0.9,1;";
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s+="0.4,0.8,1;0.2,0.9,1;1,0.4,2;0.9,0.5,2;0.8,0.6,2;0.9,0.4,2;1,0.5,2;1,0.3,2";
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string[] ss = s.Split(';');
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for (int i = 0; i < ss.Length; i++)
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{
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string str = ss[i];
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string[] strs = str.Split(',');
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//检查数组长度
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if (strs.Length != 3)
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throw new Exception("length error!");
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//生成单词
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tempData[samples, 0] = double.Parse(strs[0]);
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tempData[samples, 1] = double.Parse(strs[1]);
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//生成分类
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tempClass[samples] = int.Parse(strs[2]);
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//跳过分类如果分类值大于等于10
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if (tempClass[samples] >= 10)
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{
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continue;
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}
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//计算不同分类的总数
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if (tempClass[samples] >= classCount)
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classCount += tempClass[samples] + 1;
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//统计每个分类的样本数目
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samplesPerClass[tempClass[samples]]++;
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//寻找最小值
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if (tempData[samples, 0] < minX)
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minX = tempData[samples, 0];
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//寻找最大值
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if (tempData[samples, 0] > maxX)
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maxX = tempData[samples, 0];
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//样本数目累加
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samples++;
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}
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data = new double[samples, 2];
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Array.Copy(tempData, 0, data, 0, samples * 2);
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classes = new int[samples];
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Array.Copy(tempClass, 0, classes, 0, samples);
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}
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catch
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{
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throw new Exception("failed loading data!");
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}
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/*=============================开始训练=================================*/
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//准备学习样本数据
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double[][] input = new double[samples][];
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double[][] output = new double[samples][];
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for (int i = 0; i < samples; i++)
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{
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input[i] = new double[2];
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output[i] = new double[classCount];
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//设置输入值
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input[i][0] = data[i, 0];
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input[i][1] = data[i, 1];
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//设置输出值
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output[i][classes[i]] = 1;
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}
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//神经网络初始化,创建激活神经网络
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ActivationNetwork network = new ActivationNetwork(new ThresholdFunction(), 2, classCount);
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ActivationLayer layer = network[0];
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//创建导师
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PerceptronLearning teacher = new PerceptronLearning(network);
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//设置学习比率
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teacher.LearningRate = learnintRate;
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//迭代次数
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int iteration = 1;
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//停止迭代标志
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bool neetToStop = false;
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//是否输出每次权重
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bool showWeight = true;
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try
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{
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//误差值
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List<double> errors = new List<double>();
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while (!neetToStop)
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{
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输出权重
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//将本次误差添加到误差列表
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double error = teacher.RunEpoch(input, output);
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errors.Add(error);
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//输出本次训练误差
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Console.WriteLine("误差:" + error.ToString());
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//输出当前迭代次数
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Console.WriteLine("迭代次数:" + iteration);
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Console.WriteLine("---------------------------------------------");
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//如果误差等于0,停止训练
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//此处可预设一个最小值,不一定非是0.当误差小于此最小值时,结束训练。
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if (error == 0)
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break;
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iteration++;
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}
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}
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catch
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{
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throw new Exception("error!");
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}
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/*=============================结束训练=================================*/
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Console.ReadKey();
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}
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}
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}
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输出结果如下: