用java 语言实现 卷积神经网络的代码

以下是使用Java语言实现卷积神经网络的代码示例：

import java.util.Random;

public class ConvolutionalNeuralNetwork {

    private int inputWidth;
    private int inputHeight;
    private int inputDepth;

    private int filterSize;
    private int numFilters;
    private int stride;
    private int padding;

    private double[][][] input;
    private double[][][] filters;
    private double[] biases;

    private int outputWidth;
    private int outputHeight;
    private int outputDepth;

    public ConvolutionalNeuralNetwork(int inputWidth, int inputHeight, int inputDepth,
                                      int filterSize, int numFilters, int stride, int padding) {
        this.inputWidth = inputWidth;
        this.inputHeight = inputHeight;
        this.inputDepth = inputDepth;
        this.filterSize = filterSize;
        this.numFilters = numFilters;
        this.stride = stride;
        this.padding = padding;

        Random random = new Random();

        filters = new double[numFilters][filterSize][filterSize];
        biases = new double[numFilters];

        for (int i = 0; i < numFilters; i++) {
            biases[i] = random.nextDouble();
            for (int j = 0; j < filterSize; j++) {
                for (int k = 0; k < filterSize; k++) {
                    filters[i][j][k] = random.nextDouble();
                }
            }
        }

        outputWidth = (inputWidth - filterSize + 2 * padding) / stride + 1;
        outputHeight = (inputHeight - filterSize + 2 * padding) / stride + 1;
        outputDepth = numFilters;
    }

    public double[][][] forward(double[][][] inputData) {
        input = inputData;
        double[][][] output = new double[outputDepth][outputWidth][outputHeight];

        for (int i = 0; i < numFilters; i++) {
            for (int j = 0; j < outputHeight; j++) {
                for (int k = 0; k < outputWidth; k++) {
                    double sum = 0;
                    for (int l = 0; l < filterSize; l++) {
                        for (int m = 0; m < filterSize; m++) {
                            int x = k * stride + l - padding;
                            int y = j * stride + m - padding;
                            if (x >= 0 && x < inputWidth && y >= 0 && y < inputHeight) {
                                sum += input[l][m][i] * filters[i][l][m];
                            }
                        }
                    }
                    output[i][k][j] = sum + biases[i];
                }
            }
        }

        return output;
    }
}

该代码实现了一个简单的卷积神经网络，包括输入数据的维度、卷积核的大小和数量、步长和填充等参数。在构造函数中，随机初始化了卷积核和偏置。在前向传播方法中，使用卷积操作计算输出数据。