使用c语言实现手写数字识别

手写数字识别可以使用机器学习算法实现，比如使用神经网络。在C语言中，可以使用开源的神经网络库来实现。以下是一个简单的示例代码：

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

#define INPUT_SIZE 784
#define HIDDEN_SIZE 16
#define OUTPUT_SIZE 10
#define LEARNING_RATE 0.1

// sigmoid函数
float sigmoid(float x) {
    return 1.0 / (1.0 + exp(-x));
}

// 前向传播
void forward(float *input, float *hidden, float *output, float *w1, float *w2) {
    // 计算隐藏层
    for (int i = 0; i < HIDDEN_SIZE; i++) {
        hidden[i] = 0;
        for (int j = 0; j < INPUT_SIZE; j++) {
            hidden[i] += input[j] * w1[j * HIDDEN_SIZE + i];
        }
        hidden[i] = sigmoid(hidden[i]);
    }
    // 计算输出层
    for (int i = 0; i < OUTPUT_SIZE; i++) {
        output[i] = 0;
        for (int j = 0; j < HIDDEN_SIZE; j++) {
            output[i] += hidden[j] * w2[j * OUTPUT_SIZE + i];
        }
        output[i] = sigmoid(output[i]);
    }
}

// 反向传播
void backward(float *input, float *hidden, float *output, float *target, float *w1, float *w2) {
    float d_output[OUTPUT_SIZE];
    float d_hidden[HIDDEN_SIZE];
    // 计算输出层误差
    for (int i = 0; i < OUTPUT_SIZE; i++) {
        d_output[i] = (target[i] - output[i]) * output[i] * (1 - output[i]);
    }
    // 计算隐藏层误差
    for (int i = 0; i < HIDDEN_SIZE; i++) {
        d_hidden[i] = 0;
        for (int j = 0; j < OUTPUT_SIZE; j++) {
            d_hidden[i] += d_output[j] * w2[i * OUTPUT_SIZE + j];
        }
        d_hidden[i] *= hidden[i] * (1 - hidden[i]);
    }
    // 更新权重
    for (int i = 0; i < HIDDEN_SIZE; i++) {
        for (int j = 0; j < INPUT_SIZE; j++) {
            w1[j * HIDDEN_SIZE + i] += LEARNING_RATE * d_hidden[i] * input[j];
        }
    }
    for (int i = 0; i < OUTPUT_SIZE; i++) {
        for (int j = 0; j < HIDDEN_SIZE; j++) {
            w2[j * OUTPUT_SIZE + i] += LEARNING_RATE * d_output[i] * hidden[j];
        }
    }
}

int main() {
    // 加载数据集
    float *data = (float *)malloc(sizeof(float) * 60000 * (INPUT_SIZE + 1));
    FILE *file = fopen("mnist_train.csv", "r");
    for (int i = 0; i < 60000; i++) {
        fscanf(file, "%f,", &data[i * (INPUT_SIZE + 1)]);
        for (int j = 0; j < INPUT_SIZE; j++) {
            fscanf(file, "%f,", &data[i * (INPUT_SIZE + 1) + j + 1]);
        }
    }
    fclose(file);
    // 初始化权重
    float *w1 = (float *)malloc(sizeof(float) * INPUT_SIZE * HIDDEN_SIZE);
    float *w2 = (float *)malloc(sizeof(float) * HIDDEN_SIZE * OUTPUT_SIZE);
    for (int i = 0; i < INPUT_SIZE * HIDDEN_SIZE; i++) {
        w1[i] = (float)rand() / RAND_MAX - 0.5;
    }
    for (int i = 0; i < HIDDEN_SIZE * OUTPUT_SIZE; i++) {
        w2[i] = (float)rand() / RAND_MAX - 0.5;
    }
    // 训练模型
    for (int epoch = 0; epoch < 10; epoch++) {
        float loss = 0;
        for (int i = 0; i < 60000; i++) {
            float input[INPUT_SIZE];
            float hidden[HIDDEN_SIZE];
            float output[OUTPUT_SIZE];
            float target[OUTPUT_SIZE] = {0};
            target[(int)data[i * (INPUT_SIZE + 1)]] = 1;
            for (int j = 0; j < INPUT_SIZE; j++) {
                input[j] = data[i * (INPUT_SIZE + 1) + j + 1] / 255.0;
            }
            forward(input, hidden, output, w1, w2);
            backward(input, hidden, output, target, w1, w2);
            for (int j = 0; j < OUTPUT_SIZE; j++) {
                loss += target[j] * log(output[j]) + (1 - target[j]) * log(1 - output[j]);
            }
        }
        loss /= -60000;
        printf("Epoch %d, Loss: %f\n", epoch, loss);
    }
    // 测试模型
    int correct = 0;
    file = fopen("mnist_test.csv", "r");
    for (int i = 0; i < 10000; i++) {
        float input[INPUT_SIZE];
        float hidden[HIDDEN_SIZE];
        float output[OUTPUT_SIZE];
        int label;
        fscanf(file, "%d,", &label);
        for (int j = 0; j < INPUT_SIZE; j++) {
            fscanf(file, "%f,", &input[j]);
            input[j] /= 255.0;
        }
        forward(input, hidden, output, w1, w2);
        int predict = 0;
        for (int j = 1; j < OUTPUT_SIZE; j++) {
            if (output[j] > output[predict]) {
                predict = j;
            }
        }
        if (predict == label) {
            correct++;
        }
    }
    fclose(file);
    printf("Accuracy: %d/%d\n", correct, 10000);
    // 释放内存
    free(data);
    free(w1);
    free(w2);
    return 0;
}

这段代码使用MNIST数据集训练一个简单的神经网络模型，可以实现手写数字识别。具体的实现细节可以参考注释。需要注意的是，这只是一个简单的示例代码，实际上手写数字识别需要更加复杂的模型和更多的数据进行训练。