卷积神经网络java代码实现手写输入

以下是一个简单的Java代码实现手写数字输入的卷积神经网络：

import java.util.Random;

public class ConvNet {
    private int inputSize;
    private int filterSize;
    private int numFilters;
    private double[][][] filters;
    private double[] biases;
    private int hiddenSize;
    private double[] hiddenLayer;
    private double[][] hiddenWeights;
    private double[] hiddenBiases;
    private int outputSize;
    private double[] outputLayer;
    private double[][] outputWeights;
    private double[] outputBiases;

    public ConvNet(int inputSize, int filterSize, int numFilters, int hiddenSize, int outputSize) {
        this.inputSize = inputSize;
        this.filterSize = filterSize;
        this.numFilters = numFilters;
        this.filters = new double[numFilters][filterSize][filterSize];
        this.biases = new double[numFilters];
        this.hiddenSize = hiddenSize;
        this.hiddenLayer = new double[hiddenSize];
        this.hiddenWeights = new double[hiddenSize][numFilters * ((inputSize - filterSize + 1) / 2) * ((inputSize - filterSize + 1) / 2)];
        this.hiddenBiases = new double[hiddenSize];
        this.outputSize = outputSize;
        this.outputLayer = new double[outputSize];
        this.outputWeights = new double[outputSize][hiddenSize];
        this.outputBiases = new double[outputSize];

        Random rand = new Random();

        // Initialize filters and biases
        for (int i = 0; i < numFilters; i++) {
            biases[i] = rand.nextDouble() * 2 - 1;
            for (int j = 0; j < filterSize; j++) {
                for (int k = 0; k < filterSize; k++) {
                    filters[i][j][k] = rand.nextDouble() * 2 - 1;
                }
            }
        }

        // Initialize hidden weights and biases
        for (int i = 0; i < hiddenSize; i++) {
            hiddenBiases[i] = rand.nextDouble() * 2 - 1;
            for (int j = 0; j < numFilters * ((inputSize - filterSize + 1) / 2) * ((inputSize - filterSize + 1) / 2); j++) {
                hiddenWeights[i][j] = rand.nextDouble() * 2 - 1;
            }
        }

        // Initialize output weights and biases
        for (int i = 0; i < outputSize; i++) {
            outputBiases[i] = rand.nextDouble() * 2 - 1;
            for (int j = 0; j < hiddenSize; j++) {
                outputWeights[i][j] = rand.nextDouble() * 2 - 1;
            }
        }
    }

    public double[] forward(double[][] input) {
        // Convolution layer
        double[][][] convolved = new double[numFilters][inputSize - filterSize + 1][inputSize - filterSize + 1];
        for (int filter = 0; filter < numFilters; filter++) {
            for (int i = 0; i < inputSize - filterSize + 1; i += 2) {
                for (int j = 0; j < inputSize - filterSize + 1; j += 2) {
                    double sum = 0;
                    for (int k = 0; k < filterSize; k++) {
                        for (int l = 0; l < filterSize; l++) {
                            sum += input[i + k][j + l] * filters[filter][k][l];
                        }
                    }
                    convolved[filter][i / 2][j / 2] = sum + biases[filter];
                }
            }
        }

        // ReLU activation function
        for (int filter = 0; filter < numFilters; filter++) {
            for (int i = 0; i < inputSize - filterSize + 1; i += 2) {
                for (int j = 0; j < inputSize - filterSize + 1; j += 2) {
                    convolved[filter][i / 2][j / 2] = Math.max(0, convolved[filter][i / 2][j / 2]);
                }
            }
        }

        // Flatten convolution output
        double[] flattened = new double[numFilters * ((inputSize - filterSize + 1) / 2) * ((inputSize - filterSize + 1) / 2)];
        int index = 0;
        for (int filter = 0; filter < numFilters; filter++) {
            for (int i = 0; i < (inputSize - filterSize + 1) / 2; i++) {
                for (int j = 0; j < (inputSize - filterSize + 1) / 2; j++) {
                    flattened[index] = convolved[filter][i][j];
                    index++;
                }
            }
        }

        // Hidden layer
        for (int i = 0; i < hiddenSize; i++) {
            double sum = 0;
            for (int j = 0; j < numFilters * ((inputSize - filterSize + 1) / 2) * ((inputSize - filterSize + 1) / 2); j++) {
                sum += hiddenWeights[i][j] * flattened[j];
            }
            hiddenLayer[i] = Math.max(0, sum + hiddenBiases[i]);
        }

        // Output layer
        for (int i = 0; i < outputSize; i++) {
            double sum = 0;
            for (int j = 0; j < hiddenSize; j++) {
                sum += outputWeights[i][j] * hiddenLayer[j];
            }
            outputLayer[i] = sum + outputBiases[i];
        }

        // Softmax activation function
        double[] softmax = new double[outputSize];
        double sum = 0;
        for (int i = 0; i < outputSize; i++) {
            softmax[i] = Math.exp(outputLayer[i]);
            sum += softmax[i];
        }
        for (int i = 0; i < outputSize; i++) {
            softmax[i] /= sum;
        }

        return softmax;
    }
}

这是一个简单的卷积神经网络，包含一个卷积层、一个ReLU激活函数、一个全连接层和一个Softmax激活函数。在`forward`方法中，输入是一个二维数组`input`，表示手写数字的像素值。首先进行卷积操作，然后通过ReLU激活函数，将输出扁平化为一维数组。然后将其输入到全连接层中，再通过ReLU激活函数得到隐藏层输出。最后将隐藏层输出输入到输出层中，通过Softmax激活函数得到最终的分类结果。