在C語言中實現一個完整的AlexNet神經網絡是一個復雜的任務，涉及大量的矩陣運算和卷積操作。這個示例代碼將涵蓋AlexNet的主要組件，包括卷積層、ReLU激活函數、池化層和全連接層。

由于C語言本身沒有像Python的NumPy那樣的矩陣庫，我們需要自己編寫矩陣運算函數。另外，C語言不支持自動微分，因此我們不會實現反向傳播和訓練部分，只實現前向傳播。

代碼結構
1.定義數據結構
2.實現矩陣運算函數
3.實現卷積層
4.實現ReLU激活函數
5.實現池化層
6.實現全連接層
7.實現前向傳播
示例代碼

#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// 卷積層
typedef struct {int num_filters;int filter_size;double ****filters; // 4D array for filters (num_filters x depth x height x width)
} ConvLayer;// 全連接層
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;// 初始化卷積層
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