BP神经网络的Java实现

课程作业要求实现一个BPNN。此前只用Matlab实现过，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。

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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