C语言实现基于CNN的垃圾分类 - 代码示例

///'#include ///'stdio.h///'//n#include ///'stdlib.h///'//n#include ///'math.h///'//n//n#define INPUT_SIZE 784//n#define HIDDEN_SIZE 100//n#define OUTPUT_SIZE 10//n//ntypedef struct {//n double* weights;//n double* biases;//n} Layer;//n//ndouble sigmoid(double x) {//n return 1.0 / (1.0 + exp(-x));//n}//n//ndouble* dot_product(double* a, double* b, int a_size, int b_size) {//n double* result = malloc(sizeof(double) * a_size);//n for (int i = 0; i < a_size; i++) {//n result[i] = 0.0;//n for (int j = 0; j < b_size; j++) {//n result[i] += a[j] * b[j * a_size + i];//n }//n }//n return result;//n}//n//ndouble* add_bias(double* a, double* biases, int size) {//n double* result = malloc(sizeof(double) * size);//n for (int i = 0; i < size; i++) {//n result[i] = a[i] + biases[i];//n }//n return result;//n}//n//ndouble* apply_activation(double* a, int size) {//n double* result = malloc(sizeof(double) * size);//n for (int i = 0; i < size; i++) {//n result[i] = sigmoid(a[i]);//n }//n return result;//n}//n//nint predict(double* input, Layer layer1, Layer layer2) {//n double* hidden_output = apply_activation(//n add_bias(//n dot_product(input, layer1.weights, INPUT_SIZE, HIDDEN_SIZE),//n layer1.biases,//n HIDDEN_SIZE//n ),//n HIDDEN_SIZE//n );//n double* output = apply_activation(//n add_bias(//n dot_product(hidden_output, layer2.weights, HIDDEN_SIZE, OUTPUT_SIZE),//n layer2.biases,//n OUTPUT_SIZE//n ),//n OUTPUT_SIZE//n );//n//n int max_index = 0;//n double max_value = output[0];//n for (int i = 1; i < OUTPUT_SIZE; i++) {//n if (output[i] > max_value) {//n max_value = output[i];//n max_index = i;//n }//n }//n//n free(hidden_output);//n free(output);//n//n return max_index;//n}//n//nint main() {//n // 模型参数//n Layer layer1 = {//n .weights = { /* 第一层权重 / },//n .biases = { / 第一层偏置 / }//n };//n Layer layer2 = {//n .weights = { / 第二层权重 / },//n .biases = { / 第二层偏置 / }//n };//n//n // 输入图片//n double input[INPUT_SIZE] = { / 输入图片像素值 */ };//n//n int prediction = predict(input, layer1, layer2);//n printf(///'Prediction: %d//n///', prediction);//n//n return 0;//n}//n/