降噪自编码器C语言实现

降噪自编码器是一种无监督学习算法，用于学习输入数据的低维表示。它通过在输入数据中引入噪声，并尝试从噪声数据中重构原始输入数据来训练模型。以下是一个简单的降噪自编码器的C语言实现示例：

#include <stdio.h>
#include <stdlib.h>
#include <math.h>

#define INPUT_SIZE 10
#define HIDDEN_SIZE 5
#define OUTPUT_SIZE 10
#define LEARNING_RATE 0.01
#define EPOCHS 1000

// 定义降噪自编码器的结构体
typedef struct {
    double input[INPUT_SIZE];
    double hidden[HIDDEN_SIZE];
    double output[OUTPUT_SIZE];
    double weights_ih[INPUT_SIZE][HIDDEN_SIZE];
    double weights_ho[HIDDEN_SIZE][OUTPUT_SIZE];
} Autoencoder;

// 初始化降噪自编码器的权重
void initialize_weights(Autoencoder *ae) {
    for (int i = 0; i < INPUT_SIZE; i++) {
        for (int j = 0; j < HIDDEN_SIZE; j++) {
            ae->weights_ih[i][j] = ((double)rand() / RAND_MAX) * 2 - 1;
        }
    }
    for (int i = 0; i < HIDDEN_SIZE; i++) {
        for (int j = 0; j < OUTPUT_SIZE; j++) {
            ae->weights_ho[i][j] = ((double)rand() / RAND_MAX) * 2 - 1;
        }
    }
}

// 前向传播
void forward(Autoencoder *ae) {
    // 计算隐藏层输出
    for (int i = 0; i < HIDDEN_SIZE; i++) {
        double sum = 0.0;
        for (int j = 0; j < INPUT_SIZE; j++) {
            sum += ae->input[j] * ae->weights_ih[j][i];
        }
        ae->hidden[i] = 1.0 / (1.0 + exp(-sum));
    }
    // 计算输出层输出
    for (int i = 0; i < OUTPUT_SIZE; i++) {
        double sum = 0.0;
        for (int j = 0; j < HIDDEN_SIZE; j++) {
            sum += ae->hidden[j] * ae->weights_ho[j][i];
        }
        ae->output[i] = 1.0 / (1.0 + exp(-sum));
    }
}

// 反向传播
void backward(Autoencoder *ae) {
    // 计算输出层误差
    double output_errors[OUTPUT_SIZE];
    for (int i = 0; i < OUTPUT_SIZE; i++) {
        output_errors[i] = ae->input[i] - ae->output[i];
    }
    // 更新隐藏层到输出层的权重
    for (int i = 0; i < HIDDEN_SIZE; i++) {
        for (int j = 0; j < OUTPUT_SIZE; j++) {
            ae->weights_ho[i][j] += LEARNING_RATE * ae->hidden[i] * output_errors[j] * ae->output[j] * (1.0 - ae->output[j]);
        }
    }
    // 更新输入层到隐藏层的权重
    for (int i = 0; i < INPUT_SIZE; i++) {
        for (int j = 0; j < HIDDEN_SIZE; j++) {
            double sum = 0.0;
            for (int k = 0; k < OUTPUT_SIZE; k++) {
                sum += output_errors[k] * ae->weights_ho[j][k];
            }
            ae->weights_ih[i][j] += LEARNING_RATE * ae->input[i] * ae->hidden[j] * (1.0 - ae->hidden[j]) * sum;
        }
    }
}

// 训练降噪自编码器
void train(Autoencoder *ae, double input_data[][INPUT_SIZE], int num_samples) {
    for (int epoch = 0; epoch < EPOCHS; epoch++) {
        for (int sample = 0; sample < num_samples; sample++) {
            // 添加噪声到输入数据
            for (int i = 0; i < INPUT_SIZE; i++) {
                ae->input[i] = input_data[sample][i] + ((double)rand() / RAND_MAX) * 0.1 - 0.05;
            }
            // 执行前向传播和反向传播
            forward(ae);
            backward(ae);
        }
    }
}

int main() {
    Autoencoder ae;
    initialize_weights(&ae);

    // 假设有一些输入数据用于训练
    double input_data[][INPUT_SIZE] = {
        {0, 1, 1, 0, 0, 0, 0, 1, 1, 0},
        {1, 1, 1, 1, 0, 0, 0, 1, 1, 1},
        {0, 0, 0, 1, 1, 1, 1, 0, 0, 0}
    };
    int num_samples = sizeof(input_data) / sizeof(input_data[0]);

    // 训练降噪自编码器
    train(&ae, input_data, num_samples);

    // 测试降噪自编码器
    double test_input[INPUT_SIZE] = {0, 1, 1, 0, 0, 0, 0, 1, 1, 0};
    for (int i = 0; i < INPUT_SIZE; i++) {
        ae.input[i] = test_input[i];
    }
    forward(&ae);

    // 打印输出结果
    printf("Input: ");
    for (int i = 0; i < INPUT_SIZE; i++) {
        printf("%.2f ", ae.input[i]);
    }
    printf("\nOutput: ");
    for (int i = 0; i < OUTPUT_SIZE; i++) {
        printf("%.2f ", ae.output[i]);
    }
    printf("\n");

    return 0;
}

这是一个简单的降噪自编码器的C语言实现示例，其中包括了初始化权重、前向传播、反向传播和训练过程。你可以根据自己的需求进行修改和扩展。希望对你有所帮助！