给我一个用C++写一个CNN的代码

### 回答1：
以下是一个用C语言编写的CNN代码示例：

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

#define IMAGE_SIZE 28
#define FILTER_SIZE 3
#define STRIDE 1
#define PADDING 1
#define NUM_FILTERS 16
#define POOL_SIZE 2

float relu(float x) {
return fmaxf(.f, x);
}

float sigmoid(float x) {
return 1.f / (1.f + expf(-x));
}

float convolve(float *input, float *filter, int input_size, int filter_size, int stride, int padding, int i, int j, int k) {
float result = .f;
for (int c = ; c < input_size; c++) {
for (int p = ; p < filter_size; p++) {
for (int q = ; q < filter_size; q++) {
int x = i * stride + p - padding;
int y = j * stride + q - padding;
if (x >= && x < input_size && y >= && y < input_size) {
result += input[c * input_size * input_size + x * input_size + y] * filter[k * filter_size * filter_size + p * filter_size + q];
}
}
}
}
return result;
}

void conv2d(float *input, float *filter, float *output, int input_size, int filter_size, int stride, int padding, int num_filters) {
int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
for (int k = ; k < num_filters; k++) {
for (int i = ; i < output_size; i++) {
for (int j = ; j < output_size; j++) {
output[k * output_size * output_size + i * output_size + j] = relu(convolve(input, filter, input_size, filter_size, stride, padding, i, j, k));
}
}
}
}

void max_pool(float *input, float *output, int input_size, int pool_size) {
int output_size = input_size / pool_size;
for (int c = ; c < NUM_FILTERS; c++) {
for (int i = ; i < output_size; i++) {
for (int j = ; j < output_size; j++) {
float max_val = -INFINITY;
for (int p = ; p < pool_size; p++) {
for (int q = ; q < pool_size; q++) {
float val = input[c * input_size * input_size + (i * pool_size + p) * input_size + (j * pool_size + q)];
if (val > max_val) {
max_val = val;
}
}
}
output[c * output_size * output_size + i * output_size + j] = max_val;
}
}
}
}

int main() {
float input[IMAGE_SIZE * IMAGE_SIZE] = {.f};
float filter[FILTER_SIZE * FILTER_SIZE * NUM_FILTERS] = {.f};
float output1[NUM_FILTERS * (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE + 1 * (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE + 1] = {.f};
float output2[NUM_FILTERS * (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE + 1 * (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE + 1] = {.f};
float output3[NUM_FILTERS * (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE + 1 * (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE + 1] = {.f};
float pool_output[NUM_FILTERS * (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE / POOL_SIZE + 1 * (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE / POOL_SIZE + 1] = {.f};

// initialize input and filter
for (int i = ; i < IMAGE_SIZE * IMAGE_SIZE; i++) {
input[i] = (float)rand() / RAND_MAX;
}
for (int i = ; i < FILTER_SIZE * FILTER_SIZE * NUM_FILTERS; i++) {
filter[i] = (float)rand() / RAND_MAX;
}

// perform convolution and pooling
conv2d(input, filter, output1, IMAGE_SIZE, FILTER_SIZE, STRIDE, PADDING, NUM_FILTERS);
max_pool(output1, pool_output, (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE + 1, POOL_SIZE);
conv2d(pool_output, filter, output2, (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE / POOL_SIZE + 1, FILTER_SIZE, STRIDE, PADDING, NUM_FILTERS);
max_pool(output2, pool_output, (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE / POOL_SIZE + 1, POOL_SIZE);
conv2d(pool_output, filter, output3, (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE / POOL_SIZE / POOL_SIZE + 1, FILTER_SIZE, STRIDE, PADDING, NUM_FILTERS);

// print output
for (int k = ; k < NUM_FILTERS; k++) {
printf("Output for filter %d:\n", k);
for (int i = ; i < (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE / POOL_SIZE / POOL_SIZE + 1; i++) {
for (int j = ; j < (IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE / POOL_SIZE / POOL_SIZE + 1; j++) {
printf("%f ", output3[k * ((IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE / POOL_SIZE / POOL_SIZE + 1) * ((IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE / POOL_SIZE / POOL_SIZE + 1) + i * ((IMAGE_SIZE + 2 * PADDING - FILTER_SIZE) / STRIDE / POOL_SIZE / POOL_SIZE + 1) + j]);
}
printf("\n");
}
printf("\n");
}

return ;
}

### 回答2：
下面是一个基本的用C语言编写的简单卷积神经网络（CNN）的代码示例：

#include <stdio.h>

// 定义输入图像的大小
#define INPUT_SIZE 28

// 卷积层的核大小和数量
#define KERNEL_SIZE 3
#define NUM_KERNELS 16

// 定义池化层的大小和步幅
#define POOL_SIZE 2
#define STRIDE 2

// 定义全连接层的大小
#define FC_SIZE 128

// 定义卷积核
float kernels[NUM_KERNELS][KERNEL_SIZE][KERNEL_SIZE];

// 假设CNN的输入是一个28x28的图像
float input[INPUT_SIZE][INPUT_SIZE];

// 定义卷积层的输出和池化层的输出
float conv_output[NUM_KERNELS][(INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE][(INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE];
float pool_output[NUM_KERNELS][(INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE / POOL_SIZE][(INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE / POOL_SIZE];

// 定义全连接层的权重和偏差
float fc_weights[FC_SIZE][NUM_KERNELS * (INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE / POOL_SIZE * (INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE / POOL_SIZE];
float fc_bias[FC_SIZE];

// 定义全连接层的输出
float fc_output[FC_SIZE];

// 假设CNN的输出是10个类别的概率值
float output[10];

// 卷积操作
void convolution() {
    for (int k = 0; k < NUM_KERNELS; k++) {
        for (int i = 0; i < (INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE; i++) {
            for (int j = 0; j < (INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE; j++) {
                conv_output[k][i][j] = 0;
                for (int m = 0; m < KERNEL_SIZE; m++) {
                    for (int n = 0; n < KERNEL_SIZE; n++) {
                        conv_output[k][i][j] += kernels[k][m][n] * input[i*STRIDE+m][j*STRIDE+n];
                    }
                }
            }
        }
    }
}

// 池化操作
void pooling() {
    for (int k = 0; k < NUM_KERNELS; k++) {
        for (int i = 0; i < (INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE / POOL_SIZE; i++) {
            for (int j = 0; j < (INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE / POOL_SIZE; j++) {
                pool_output[k][i][j] = 0;
                for (int m = 0; m < POOL_SIZE; m++) {
                    for (int n = 0; n < POOL_SIZE; n++) {
                        if (conv_output[k][i*POOL_SIZE+m][j*POOL_SIZE+n] > pool_output[k][i][j]) {
                            pool_output[k][i][j] = conv_output[k][i*POOL_SIZE+m][j*POOL_SIZE+n];
                        }
                    }
                }
            }
        }
    }
}

// 全连接操作
void fullyConnected() {
    for (int i = 0; i < FC_SIZE; i++) {
        fc_output[i] = 0;
        for (int k = 0; k < NUM_KERNELS; k++) {
            for (int m = 0; m < (INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE / POOL_SIZE; m++) {
                for (int n = 0; n < (INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE / POOL_SIZE; n++) {
                    fc_output[i] += fc_weights[i][k*(INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE / POOL_SIZE / POOL_SIZE + m*(INPUT_SIZE - KERNEL_SIZE + 1) / STRIDE / POOL_SIZE + n] * pool_output[k][m][n];
                }
            }
        }
        fc_output[i] += fc_bias[i];
    }
}

int main() {
    // 初始化卷积核、权重和偏差等参数
    // ...

    // 假设输入图像已经读取到input数组中

    // 执行卷积操作
    convolution();

    // 执行池化操作
    pooling();

    // 执行全连接操作
    fullyConnected();

    // 输出结果
    for (int i = 0; i < 10; i++) {
        printf("Class %d: %f\n", i, output[i]);
    }

    return 0;
}

以上仅为简单卷积神经网络（CNN）的代码示例，其中包含了简单的卷积、池化和全连接操作。实际的CNN代码通常包含更多的层和更复杂的操作，还需要使用适当的激活函数、损失函数和优化算法等。

### 回答3：
以下是一个简单的用C语言编写的CNN（卷积神经网络）代码示例：

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

// 定义卷积神经网络结构
typedef struct {
    float** input_data;     // 输入数据
    float** filters;        // 卷积核
    float** output_data;    // 输出数据
    int input_size;         // 输入数据大小
    int filter_size;        // 卷积核大小
    int output_size;        // 输出数据大小
} CNN;

// 初始化卷积神经网络
CNN* initialize_cnn(int input_size, int filter_size) {
    CNN* cnn = (CNN*) malloc(sizeof(CNN));

    cnn->input_data = (float**) malloc(input_size * sizeof(float*));
    for (int i = 0; i < input_size; i++) {
        cnn->input_data[i] = (float*) malloc(input_size * sizeof(float));
    }

    cnn->filters = (float**) malloc(filter_size * sizeof(float*));
    for (int i = 0; i < filter_size; i++) {
        cnn->filters[i] = (float*) malloc(filter_size * sizeof(float));
    }

    cnn->output_size = input_size - filter_size + 1;
    cnn->output_data = (float**) malloc(cnn->output_size * sizeof(float*));
    for (int i = 0; i < cnn->output_size; i++) {
        cnn->output_data[i] = (float*) malloc(cnn->output_size * sizeof(float));
    }

    cnn->input_size = input_size;
    cnn->filter_size = filter_size;

    return cnn;
}

// 执行卷积运算
void convolution(CNN* cnn) {
    for (int i = 0; i < cnn->output_size; i++) {
        for (int j = 0; j < cnn->output_size; j++) {
            float value = 0.0;
            for (int m = 0; m < cnn->filter_size; m++) {
                for (int n = 0; n < cnn->filter_size; n++) {
                    value += cnn->input_data[i + m][j + n] * cnn->filters[m][n];
                }
            }
            cnn->output_data[i][j] = value;
        }
    }
}

int main() {
    int input_size = 5;
    int filter_size = 3;
    
    // 初始化CNN
    CNN* cnn = initialize_cnn(input_size, filter_size);

    // 初始化输入数据和卷积核
    for (int i = 0; i < input_size; i++) {
        for (int j = 0; j < input_size; j++) {
            cnn->input_data[i][j] = 1.0;  // 假设输入数据全为1
        }
    }

    for (int i = 0; i < filter_size; i++) {
        for (int j = 0; j < filter_size; j++) {
            cnn->filters[i][j] = 0.5;     // 假设卷积核全为0.5
        }
    }

    // 执行卷积运算
    convolution(cnn);

    // 输出卷积结果
    for (int i = 0; i < cnn->output_size; i++) {
        for (int j = 0; j < cnn->output_size; j++) {
            printf("%f ", cnn->output_data[i][j]);
        }
        printf("\n");
    }

    // 释放内存
    for (int i = 0; i < input_size; i++) {
        free(cnn->input_data[i]);
    }
    free(cnn->input_data);

    for (int i = 0; i < filter_size; i++) {
        free(cnn->filters[i]);
    }
    free(cnn->filters);

    for (int i = 0; i < cnn->output_size; i++) {
        free(cnn->output_data[i]);
    }
    free(cnn->output_data);

    free(cnn);

    return 0;
}

这段代码演示了如何使用C语言编写一个简单的卷积神经网络。首先，我们定义了一个CNN结构体，其中包含输入数据、卷积核和输出数据等信息。然后，通过`initialize_cnn()`函数初始化CNN，并分配内存空间。接着，我们通过`convolution()`函数执行卷积运算，使用输入数据和卷积核计算输出数据。最后，我们输出卷积结果，并在主函数中释放分配的内存。

请注意，这只是一个简单的示例代码，仅用于演示C语言中如何使用卷积神经网络。实际上，实现一个完整的CNN需要更多的代码和复杂的计算过程。