课程作业要求实现一个BPNN。这次尝试使用Java实现了一个。现共享之。版权属于大家。关于BPNN的原理,就不赘述了。
下面是BPNN的实现代码。类名为BP。
- package ml;
-
- import java.util.Random;
-
- /**
- * BPNN.
- *
- * @author RenaQiu
- *
- */
- public class BP {
- /**
- * input vector.
- */
- private final double[] input;
- /**
- * hidden layer.
- */
- private final double[] hidden;
- /**
- * output layer.
- */
- private final double[] output;
- /**
- * target.
- */
- private final double[] target;
-
- /**
- * delta vector of the hidden layer .
- */
- private final double[] hidDelta;
- /**
- * output layer of the output layer.
- */
- private final double[] optDelta;
-
- /**
- * learning rate.
- */
- private final double eta;
- /**
- * momentum.
- */
- private final double momentum;
-
- /**
- * weight matrix from input layer to hidden layer.
- */
- private final double[][] iptHidWeights;
- /**
- * weight matrix from hidden layer to output layer.
- */
- private final double[][] hidOptWeights;
-
- /**
- * previous weight update.
- */
- private final double[][] iptHidPrevUptWeights;
- /**
- * previous weight update.
- */
- private final double[][] hidOptPrevUptWeights;
-
- public double optErrSum = 0d;
-
- public double hidErrSum = 0d;
-
- private final Random random;
-
- /**
- * Constructor.
- * <p>
- * <strong>Note:</strong> The capacity of each layer will be the parameter
- * plus 1. The additional unit is used for smoothness.
- * </p>
- *
- * @param inputSize
- * @param hiddenSize
- * @param outputSize
- * @param eta
- * @param momentum
- * @param epoch
- */
- public BP(int inputSize, int hiddenSize, int outputSize, double eta,
- double momentum) {
-
- input = new double[inputSize 1];
- hidden = new double[hiddenSize 1];
- output = new double[outputSize 1];
- target = new double[outputSize 1];
-
- hidDelta = new double[hiddenSize 1];
- optDelta = new double[outputSize 1];
-
- iptHidWeights = new double[inputSize 1][hiddenSize 1];
- hidOptWeights = new double[hiddenSize 1][outputSize 1];
-
- random = new Random(19881211);
- randomizeWeights(iptHidWeights);
- randomizeWeights(hidOptWeights);
-
- iptHidPrevUptWeights = new double[inputSize 1][hiddenSize 1];
- hidOptPrevUptWeights = new double[hiddenSize 1][outputSize 1];
-
- this.eta = eta;
- this.momentum = momentum;
- }
-
- private void randomizeWeights(double[][] matrix) {
- for (int i = 0, len = matrix.length; i != len; i )
- for (int j = 0, len2 = matrix[i].length; j != len2; j ) {
- double real = random.nextDouble();
- matrix[i][j] = random.nextDouble() > 0.5 ? real : -real;
- }
- }
-
- /**
- * Constructor with default eta = 0.25 and momentum = 0.3.
- *
- * @param inputSize
- * @param hiddenSize
- * @param outputSize
- * @param epoch
- */
- public BP(int inputSize, int hiddenSize, int outputSize) {
- this(inputSize, hiddenSize, outputSize, 0.25, 0.9);
- }
-
- /**
- * Entry method. The train data should be a one-dim vector.
- *
- * @param trainData
- * @param target
- */
- public void train(double[] trainData, double[] target) {
- loadInput(trainData);
- loadTarget(target);
- forward();
- calculateDelta();
- adjustWeight();
- }
-
- /**
- * Test the BPNN.
- *
- * @param inData
- * @return
- */
- public double[] test(double[] inData) {
- if (inData.length != input.length - 1) {
- throw new IllegalArgumentException("Size Do Not Match.");
- }
- System.arraycopy(inData, 0, input, 1, inData.length);
- forward();
- return getNetworkOutput();
- }
-
- /**
- * Return the output layer.
- *
- * @return
- */
- private double[] getNetworkOutput() {
- int len = output.length;
- double[] temp = new double[len - 1];
- for (int i = 1; i != len; i )
- temp[i - 1] = output[i];
- return temp;
- }
-
- /**
- * Load the target data.
- *
- * @param arg
- */
- private void loadTarget(double[] arg) {
- if (arg.length != target.length - 1) {
- throw new IllegalArgumentException("Size Do Not Match.");
- }
- System.arraycopy(arg, 0, target, 1, arg.length);
- }
-
- /**
- * Load the training data.
- *
- * @param inData
- */
- private void loadInput(double[] inData) {
- if (inData.length != input.length - 1) {
- throw new IllegalArgumentException("Size Do Not Match.");
- }
- System.arraycopy(inData, 0, input, 1, inData.length);
- }
-
- /**
- * Forward.
- *
- * @param layer0
- * @param layer1
- * @param weight
- */
- private void forward(double[] layer0, double[] layer1, double[][] weight) {
- // threshold unit.
- layer0[0] = 1.0;
- for (int j = 1, len = layer1.length; j != len; j) {
- double sum = 0;
- for (int i = 0, len2 = layer0.length; i != len2; i)
- sum = weight[i][j] * layer0[i];
- layer1[j] = sigmoid(sum);
- }
- }
-
- /**
- * Forward.
- */
- private void forward() {
- forward(input, hidden, iptHidWeights);
- forward(hidden, output, hidOptWeights);
- }
-
- /**
- * Calculate output error.
- */
- private void outputErr() {
- double errSum = 0;
- for (int idx = 1, len = optDelta.length; idx != len; idx) {
- double o = output[idx];
- optDelta[idx] = o * (1d - o) * (target[idx] - o);
- errSum = Math.abs(optDelta[idx]);
- }
- optErrSum = errSum;
- }
-
- /**
- * Calculate hidden errors.
- */
- private void hiddenErr() {
- double errSum = 0;
- for (int j = 1, len = hidDelta.length; j != len; j) {
- double o = hidden[j];
- double sum = 0;
- for (int k = 1, len2 = optDelta.length; k != len2; k)
- sum = hidOptWeights[j][k] * optDelta[k];
- hidDelta[j] = o * (1d - o) * sum;
- errSum = Math.abs(hidDelta[j]);
- }
- hidErrSum = errSum;
- }
-
- /**
- * Calculate errors of all layers.
- */
- private void calculateDelta() {
- outputErr();
- hiddenErr();
- }
-
- /**
- * Adjust the weight matrix.
- *
- * @param delta
- * @param layer
- * @param weight
- * @param prevWeight
- */
- private void adjustWeight(double[] delta, double[] layer,
- double[][] weight, double[][] prevWeight) {
-
- layer[0] = 1;
- for (int i = 1, len = delta.length; i != len; i) {
- for (int j = 0, len2 = layer.length; j != len2; j) {
- double newVal = momentum * prevWeight[j][i] eta * delta[i]
- * layer[j];
- weight[j][i] = newVal;
- prevWeight[j][i] = newVal;
- }
- }
- }
-
- /**
- * Adjust all weight matrices.
- */
- private void adjustWeight() {
- adjustWeight(optDelta, hidden, hidOptWeights, hidOptPrevUptWeights);
- adjustWeight(hidDelta, input, iptHidWeights, iptHidPrevUptWeights);
- }
-
- /**
- * Sigmoid.
- *
- * @param val
- * @return
- */
- private double sigmoid(double val) {
- return 1d / (1d Math.exp(-val));
- }
- }
为了验证正确性,我写了一个测试用例,目的是对于任意的整数(int型),BPNN在经过训练之后,能够准确地判断出它是奇数还是偶数,正数还是负数。首先对于训练的样本(是随机生成的数字),将它转化为一个32位的向量,向量的每个分量就是其二进制形式对应的位上的0或1。将目标输出视作一个4维的向量,[1,0,0,0]代表正奇数,[0,1,0,0]代表正偶数,[0,0,1,0]代表负奇数,[0,0,0,1]代表负偶数。
训练样本为1000个,学习200次。
- package ml;
-
- import java.io.IOException;
- import java.util.ArrayList;
- import java.util.List;
- import java.util.Random;
-
- public class Test {
-
- /**
- * @param args
- * @throws IOException
- */
- public static void main(String[] args) throws IOException {
- BP bp = new BP(32, 15, 4);
-
- Random random = new Random();
- List<Integer> list = new ArrayList<Integer>();
- for (int i = 0; i != 1000; i ) {
- int value = random.nextInt();
- list.add(value);
- }
-
- for (int i = 0; i != 200; i ) {
- for (int value : list) {
- double[] real = new double[4];
- if (value >= 0)
- if ((value & 1) == 1)
- real[0] = 1;
- else
- real[1] = 1;
- else if ((value & 1) == 1)
- real[2] = 1;
- else
- real[3] = 1;
- double[] binary = new double[32];
- int index = 31;
- do {
- binary[index--] = (value & 1);
- value >>>= 1;
- } while (value != 0);
-
- bp.train(binary, real);
- }
- }
-
- System.out.println("训练完毕,下面请输入一个任意数字,神经网络将自动判断它是正数还是复数,奇数还是偶数。");
-
- while (true) {
- byte[] input = new byte[10];
- System.in.read(input);
- Integer value = Integer.parseInt(new String(input).trim());
- int rawVal = value;
- double[] binary = new double[32];
- int index = 31;
- do {
- binary[index--] = (value & 1);
- value >>>= 1;
- } while (value != 0);
-
- double[] result = bp.test(binary);
-
- double max = -Integer.MIN_VALUE;
- int idx = -1;
-
- for (int i = 0; i != result.length; i ) {
- if (result[i] > max) {
- max = result[i];
- idx = i;
- }
- }
-
- switch (idx) {
- case 0:
- System.out.format("%d是一个正奇数\n", rawVal);
- break;
- case 1:
- System.out.format("%d是一个正偶数\n", rawVal);
- break;
- case 2:
- System.out.format("%d是一个负奇数\n", rawVal);
- break;
- case 3:
- System.out.format("%d是一个负偶数\n", rawVal);
- break;
- }
- }
- }
-
- }
运行结果截图如下:
这个测试的例子非常简单。大家可以根据自己的需要去使用BP这个类。
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