问神经网络反向传播无法正确计算权重

EN

    // main function - it runs the algorithm
     private void runBackpropagationAlgorithm() {
        for (int i = 0; i < 900; ++i) {
            for (ImageUnit iu : images) {
                double [] error = calcOutputError(iu.getRatioMatrix(), iu.getClassification());
                changeHiddenUnitsOutWeights(error);
                error = calcHiddenError(error);
                changeHiddenUnitsInWeights(error,iu.getRatioMatrix());
            }
        }
    }

  // it creates the neural network
    private void createNeuroneNetwork() {
            Random generator = new Random();
            for (int i = 0; i < inHiddenUnitsWeights.length; ++i) {
                for (int j = 0; j < hiddenUnits; ++j) {
                    inHiddenUnitsWeights[i][j] = generator.nextDouble();
                }
            }
            for (int i = 0; i < hiddenUnits; ++i) {
                for (int j = 0; j < 4; ++j) {
                    outHddenUnitsWeights[i][j] = generator.nextDouble();
                }
            }
        }
   // Calculates the error in the network. It runs through the whole network.
private double [] calcOutputError(double[][] input, double [] expectedOutput) {
        int currentEdge = 0;
        Arrays.fill(hiddenUnitNodeValue, 0.0);
        for (int i = 0; i < input.length; ++i) {
            for (int j = 0; j < input[0].length; ++j) {
                for (int k = 0; k < hiddenUnits; ++k) {
                    hiddenUnitNodeValue[k] += input[i][j] * inHiddenUnitsWeights[currentEdge][k];
                }
                ++currentEdge;
            }
        }
        double[] out = new double[4];
        for (int j = 0; j < 4; ++j) {
            for (int i = 0; i < hiddenUnits; ++i) {
                out[j] += outHddenUnitsWeights[i][j] * hiddenUnitNodeValue[i];
            }
        }
        double [] error = new double [4];
        Arrays.fill(error, 4);
        for (int i = 0; i < 4; ++i) {
            error[i] = ((expectedOutput[i] - out[i])*(1.0-out[i])*out[i]);
            //System.out.println((expectedOutput[i] - out[i]) + " " + expectedOutput[i] + " " +  out[i]);
        }
        return error;
    }

// Changes the weights of the outgoing edges of the hidden neurons
private void changeHiddenUnitsOutWeights(double [] error) {
        for (int i = 0; i < hiddenUnits; ++i) {
            for (int j = 0; j < 4; ++j) {
                outHddenUnitsWeights[i][j] += learningRate*error[j]*hiddenUnitNodeValue[i];
            }
        }
    }

// goes back to the hidden units to calculate their error.
private double [] calcHiddenError(double [] outputError) {
        double [] error = new double[hiddenUnits];
        for (int i = 0; i < hiddenUnits; ++i) {
            double currentHiddenUnitErrorSum = 0.0;
            for (int j = 0; j < 4; ++j) {
                currentHiddenUnitErrorSum += outputError[j]*outHddenUnitsWeights[i][j];
            }
            error[i] = hiddenUnitNodeValue[i] * (1.0 - hiddenUnitNodeValue[i]) * currentHiddenUnitErrorSum;
        }
        return error;
    }

// changes the weights of the incomming edges to the hidden neurons. input is the matrix of ratios
private void changeHiddenUnitsInWeights(double [] error, double[][] input) {
        int currentEdge = 0;
        for (int i = 0; i < input.length; ++i) {
            for (int j = 0; j < input[0].length; ++j) {
                for (int k = 0; k < hiddenUnits; ++k) {
                    inHiddenUnitsWeights[currentEdge][k] += learningRate*error[k]*input[i][j];
                }
                ++currentEdge;
            }
        }
    }