Day 72-73 固定激活函数的BP神经网络 (1. 网络结构理解 2. 训练与测试过程理解)

代码：

package dl;

/**
 * Back-propagation neural networks. The code comes from
 */

public class SimpleAnn extends GeneralAnn{

    /**
     * The value of each node that changes during the forward process. The first
     * dimension stands for the layer, and the second stands for the node.
     */
    public double[][] layerNodeValues;

    /**
     * The error on each node that changes during the back-propagation process.
     * The first dimension stands for the layer, and the second stands for the
     * node.
     */
    public double[][] layerNodeErrors;

    /**
     * The weights of edges. The first dimension stands for the layer, the
     * second stands for the node index of the layer, and the third dimension
     * stands for the node index of the next layer.
     */
    public double[][][] edgeWeights;

    /**
     * The change of edge weights. It has the same size as edgeWeights.
     */
    public double[][][] edgeWeightsDelta;

    /**
     ********************
     * The first constructor.
     *
     * @param paraFilename
     *            The arff filename.
     * @param paraLayerNumNodes
     *            The number of nodes for each layer (may be different).
     * @param paraLearningRate
     *            Learning rate.
     * @param paraMobp
     *            Momentum coefficient.
     ********************
     */
    public SimpleAnn(String paraFilename, int[] paraLayerNumNodes, double paraLearningRate,
                     double paraMobp) {
        super(paraFilename, paraLayerNumNodes, paraLearningRate, paraMobp);

        // Step 1. Across layer initialization.
        layerNodeValues = new double[numLayers][];
        layerNodeErrors = new double[numLayers][];
        edgeWeights = new double[numLayers - 1][][];
        edgeWeightsDelta = new double[numLayers - 1][][];

        // Step 2. Inner layer initialization.
        for (int l = 0; l < numLayers; l++) {
            layerNodeValues[l] = new double[layerNumNodes[l]];
            layerNodeErrors[l] = new double[layerNumNodes[l]];

            // One less layer because each edge crosses two layers.
            if (l + 1 == numLayers) {
                break;
            } // of if

            // In layerNumNodes[l] + 1, the last one is reserved for the offset.
            edgeWeights[l] = new double[layerNumNodes[l] + 1][layerNumNodes[l + 1]];
            edgeWeightsDelta[l] = new double[layerNumNodes[l] + 1][layerNumNodes[l + 1]];
            for (int j = 0; j < layerNumNodes[l] + 1; j++) {
                for (int i = 0; i < layerNumNodes[l + 1]; i++) {
                    // Initialize weights.
                    edgeWeights[l][j][i] = random.nextDouble();
                } // Of for i
            } // Of for j
        } // Of for l
    }// Of the constructor

    /**
     ********************
     * Forward prediction.
     *
     * @param paraInput
     *            The input data of one instance.
     * @return The data at the output end.
     ********************
     */
    public double[] forward(double[] paraInput) {
        // Initialize the input layer.
        for (int i = 0; i < layerNodeValues[0].length; i++) {
            layerNodeValues[0][i] = paraInput[i];
        } // Of for i

        // Calculate the node values of each layer.
        double z;
        for (int l = 1; l < numLayers; l++) {
            for (int j = 0; j < layerNodeValues[l].length; j++) {
                // Initialize according to the offset, which is always +1
                z = edgeWeights[l - 1][layerNodeValues[l - 1].length][j];
                // Weighted sum on all edges for this node.
                for (int i = 0; i < layerNodeValues[l - 1].length; i++) {
                    z += edgeWeights[l - 1][i][j] * layerNodeValues[l - 1][i];
                } // Of for i

                // Sigmoid activation.
                // This line should be changed for other activation functions.
                layerNodeValues[l][j] = 1 / (1 + Math.exp(-z));
            } // Of for j
        } // Of for l

        return layerNodeValues[numLayers - 1];
    }// Of forward

    /**
     ********************
     * Back propagation and change the edge weights.
     *
     * @param paraTarget
     *            For 3-class data, it is [0, 0, 1], [0, 1, 0] or [1, 0, 0].
     ********************
     */
    public void backPropagation(double[] paraTarget) {
        // Step 1. Initialize the output layer error.
        int l = numLayers - 1;
        for (int j = 0; j < layerNodeErrors[l].length; j++) {
            layerNodeErrors[l][j] = layerNodeValues[l][j] * (1 - layerNodeValues[l][j])
                    * (paraTarget[j] - layerNodeValues[l][j]);
        } // Of for j

        // Step 2. Back-propagation even for l == 0
        while (l > 0) {
            l--;
            // Layer l, for each node.
            for (int j = 0; j < layerNumNodes[l]; j++) {
                double z = 0.0;
                // For each node of the next layer.
                for (int i = 0; i < layerNumNodes[l + 1]; i++) {
                    if (l > 0) {
                        z += layerNodeErrors[l + 1][i] * edgeWeights[l][j][i];
                    } // Of if

                    // Weight adjusting.
                    edgeWeightsDelta[l][j][i] = mobp * edgeWeightsDelta[l][j][i]
                            + learningRate * layerNodeErrors[l + 1][i] * layerNodeValues[l][j];
                    edgeWeights[l][j][i] += edgeWeightsDelta[l][j][i];
                    if (j == layerNumNodes[l] - 1) {
                        // Weight adjusting for the offset part.
                        edgeWeightsDelta[l][j + 1][i] = mobp * edgeWeightsDelta[l][j + 1][i]
                                + learningRate * layerNodeErrors[l + 1][i];
                        edgeWeights[l][j + 1][i] += edgeWeightsDelta[l][j + 1][i];
                    } // Of if
                } // Of for i

                // Record the error according to the differential of Sigmoid.
                // This line should be changed for other activation functions.
                layerNodeErrors[l][j] = layerNodeValues[l][j] * (1 - layerNodeValues[l][j]) * z;
            } // Of for j
        } // Of while
    }// Of backPropagation

    /**
     ********************
     * Test the algorithm.
     ********************
     */
    public static void main(String[] args) {
        int[] tempLayerNodes = { 4, 8, 8, 3 };
        SimpleAnn tempNetwork = new SimpleAnn("C:\\Users\\86183\\IdeaProjects\\deepLearning\\src\\main\\java\\resources\\iris.arff", tempLayerNodes, 0.01,
                0.6);

        for (int round = 0; round < 5000; round++) {
            tempNetwork.train();
        } // Of for n

        double tempAccuracy = tempNetwork.test();
        System.out.println("The accuracy is: " + tempAccuracy);
    }// Of main
}// Of class SimpleAnn

结果：