使用c语言实现手写数字识别的代码

手写数字识别的代码比较复杂，需要用到神经网络等机器学习算法，以下是一个简单的示例代码，用于演示基本的流程和思路：

// 定义神经网络模型
typedef struct {
    int n_input;    // 输入层节点数
    int n_hidden;   // 隐藏层节点数
    int n_output;   // 输出层节点数
    double **w_ih;  // 输入层到隐藏层的权重
    double **w_ho;  // 隐藏层到输出层的权重
    double *b_h;    // 隐藏层的偏置
    double *b_o;    // 输出层的偏置
} NeuralNetwork;

// 初始化神经网络
void nn_init(NeuralNetwork *nn, int n_input, int n_hidden, int n_output) {
    int i, j;
    nn->n_input = n_input;
    nn->n_hidden = n_hidden;
    nn->n_output = n_output;
    nn->w_ih = (double **)malloc(n_input * sizeof(double *));
    nn->w_ho = (double **)malloc(n_hidden * sizeof(double *));
    for (i = 0; i < n_input; i++) {
        nn->w_ih[i] = (double *)malloc(n_hidden * sizeof(double));
        for (j = 0; j < n_hidden; j++) {
            nn->w_ih[i][j] = (double)rand() / RAND_MAX - 0.5;
        }
    }
    for (i = 0; i < n_hidden; i++) {
        nn->w_ho[i] = (double *)malloc(n_output * sizeof(double));
        for (j = 0; j < n_output; j++) {
            nn->w_ho[i][j] = (double)rand() / RAND_MAX - 0.5;
        }
    }
    nn->b_h = (double *)malloc(n_hidden * sizeof(double));
    nn->b_o = (double *)malloc(n_output * sizeof(double));
    for (i = 0; i < n_hidden; i++) {
        nn->b_h[i] = (double)rand() / RAND_MAX - 0.5;
    }
    for (i = 0; i < n_output; i++) {
        nn->b_o[i] = (double)rand() / RAND_MAX - 0.5;
    }
}

// 前向传播
void nn_forward(NeuralNetwork *nn, double *input, double *output) {
    int i, j;
    double *hidden = (double *)malloc(nn->n_hidden * sizeof(double));
    for (i = 0; i < nn->n_hidden; i++) {
        hidden[i] = 0.0;
        for (j = 0; j < nn->n_input; j++) {
            hidden[i] += input[j] * nn->w_ih[j][i];
        }
        hidden[i] += nn->b_h[i];
        hidden[i] = 1.0 / (1.0 + exp(-hidden[i]));
    }
    for (i = 0; i < nn->n_output; i++) {
        output[i] = 0.0;
        for (j = 0; j < nn->n_hidden; j++) {
            output[i] += hidden[j] * nn->w_ho[j][i];
        }
        output[i] += nn->b_o[i];
        output[i] = 1.0 / (1.0 + exp(-output[i]));
    }
    free(hidden);
}

// 训练神经网络
void nn_train(NeuralNetwork *nn, double **inputs, double **outputs, int n_samples, int n_epochs, double learning_rate) {
    int i, j, k, epoch, sample;
    double *input = (double *)malloc(nn->n_input * sizeof(double));
    double *output = (double *)malloc(nn->n_output * sizeof(double));
    double *error = (double *)malloc(nn->n_output * sizeof(double));
    double *delta_o = (double *)malloc(nn->n_output * sizeof(double));
    double *delta_h = (double *)malloc(nn->n_hidden * sizeof(double));
    double **grad_ih = (double **)malloc(nn->n_input * sizeof(double *));
    double **grad_ho = (double **)malloc(nn->n_hidden * sizeof(double *));
    for (i = 0; i < nn->n_input; i++) {
        grad_ih[i] = (double *)malloc(nn->n_hidden * sizeof(double));
    }
    for (i = 0; i < nn->n_hidden; i++) {
        grad_ho[i] = (double *)malloc(nn->n_output * sizeof(double));
    }
    for (epoch = 0; epoch < n_epochs; epoch++) {
        for (sample = 0; sample < n_samples; sample++) {
            for (i = 0; i < nn->n_input; i++) {
                input[i] = inputs[sample][i];
            }
            for (i = 0; i < nn->n_output; i++) {
                output[i] = outputs[sample][i];
            }
            nn_forward(nn, input, output);
            for (i = 0; i < nn->n_output; i++) {
                error[i] = output[i] - outputs[sample][i];
                delta_o[i] = error[i] * output[i] * (1.0 - output[i]);
            }
            for (i = 0; i < nn->n_hidden; i++) {
                delta_h[i] = 0.0;
                for (j = 0; j < nn->n_output; j++) {
                    delta_h[i] += delta_o[j] * nn->w_ho[i][j];
                }
                delta_h[i] *= nn->b_h[i] * (1.0 - nn->b_h[i]);
            }
            for (i = 0; i < nn->n_input; i++) {
                for (j = 0; j < nn->n_hidden; j++) {
                    grad_ih[i][j] = input[i] * delta_h[j];
                    nn->w_ih[i][j] -= learning_rate * grad_ih[i][j];
                }
            }
            for (i = 0; i < nn->n_hidden; i++) {
                for (j = 0; j < nn->n_output; j++) {
                    grad_ho[i][j] = nn->b_h[i] * delta_o[j];
                    nn->w_ho[i][j] -= learning_rate * grad_ho[i][j];
                }
            }
            for (i = 0; i < nn->n_hidden; i++) {
                nn->b_h[i] -= learning_rate * delta_h[i];
            }
            for (i = 0; i < nn->n_output; i++) {
                nn->b_o[i] -= learning_rate * delta_o[i];
            }
        }
    }
    free(input);
    free(output);
    free(error);
    free(delta_o);
    free(delta_h);
    for (i = 0; i < nn->n_input; i++) {
        free(grad_ih[i]);
    }
    for (i = 0; i < nn->n_hidden; i++) {
        free(grad_ho[i]);
    }
    free(grad_ih);
    free(grad_ho);
}

// 测试神经网络
void nn_test(NeuralNetwork *nn, double **inputs, double **outputs, int n_samples) {
    int i, j, correct = 0;
    double *input = (double *)malloc(nn->n_input * sizeof(double));
    double *output = (double *)malloc(nn->n_output * sizeof(double));
    for (i = 0; i < n_samples; i++) {
        for (j = 0; j < nn->n_input; j++) {
            input[j] = inputs[i][j];
        }
        nn_forward(nn, input, output);
        int prediction = -1;
        double max_output = -1.0;
        for (j = 0; j < nn->n_output; j++) {
            if (output[j] > max_output) {
                prediction = j;
                max_output = output[j];
            }
        }
        if (prediction == -1) {
            printf("Error: Prediction failed!\n");
            return;
        }
        if (outputs[i][prediction] == 1.0) {
            correct++;
        }
    }
    printf("Accuracy: %.2f%%\n", (double)correct / n_samples * 100.0);
    free(input);
    free(output);
}

这段代码实现了一个简单的三层神经网络：输入层、隐藏层和输出层，使用反向传播算法进行训练和优化。具体的流程和算法细节可以参考代码中的注释。需要注意的是，这段代码仅作为示例，实际的手写数字识别任务需要更加复杂和精细的算法和实现。