在C语言中实现一个完整的AlexNet神经网络是一个复杂的任务，涉及大量的矩阵运算和卷积操作。这个示例代码将涵盖AlexNet的主要组件，包括卷积层、ReLU激活函数、池化层和全连接层。

由于C语言本身没有像Python的NumPy那样的矩阵库，我们需要自己编写矩阵运算函数。另外，C语言不支持自动微分，因此我们不会实现反向传播和训练部分，只实现前向传播。

代码结构
1.定义数据结构
2.实现矩阵运算函数
3.实现卷积层
4.实现ReLU激活函数
5.实现池化层
6.实现全连接层
7.实现前向传播
示例代码

#include <stdio.h>
#include <stdlib.h>
#include <math.h>#define INPUT_SIZE 224
#define CONV1_FILTERS 96
#define CONV2_FILTERS 256
#define CONV3_FILTERS 384
#define CONV4_FILTERS 384
#define CONV5_FILTERS 256
#define FC1_SIZE 4096
#define FC2_SIZE 4096
#define OUTPUT_SIZE 1000// 卷积层
typedef struct {int num_filters;int filter_size;double ****filters; // 4D array for filters (num_filters x depth x height x width)
} ConvLayer;// 全连接层
typedef struct {int input_size;int output_size;double **weights; // 2D array for weights (output_size x input_size)double *biases; // 1D array for biases (output_size)
} FCLayer;// 初始化卷积层
ConvLayer* init_conv_layer(int num_filters, int depth, int filter_size) {ConvLayer *layer = malloc(sizeof(ConvLayer));layer->num_filters = num_filters;layer->filter_size = filter_size;layer->filters = malloc(num_filters * sizeof(double***));for (int i = 0; i < num_filters; i++) {layer->filters[i] = malloc(depth * sizeof(double**));for (int j = 0; j < depth; j++) {layer->filters[i][j] = malloc(filter_size * sizeof(double*));for (int k = 0; k < filter_size; k++) {layer->filters[i][j][k] = malloc(filter_size * sizeof(double));for (int l = 0; l < filter_size; l++) {layer->filters[i][j][k