From c948bd7c174c8d90d8094c162f582bc8be835c2f Mon Sep 17 00:00:00 2001
From: Qiea <1310371422@qq.com>
Date: Mon, 11 Nov 2024 00:45:13 +0800
Subject: [PATCH] tmp

---
 cnn.c | 61 ++++++++++++++++++++++++++++++++---------------------------
 1 file changed, 33 insertions(+), 28 deletions(-)

diff --git a/cnn.c b/cnn.c
index caf79f5..49873a2 100644
--- a/cnn.c
+++ b/cnn.c
@@ -3,12 +3,12 @@
 
 
 // 将原始矩阵复制到填充后的矩阵中央
-float* expand(const float* old_matrix, u8 old_matrix_length, u8 layer){
+float* expand(const float* old_matrix, int old_matrix_length, int layer){
     float* new_matrix = (float *)malloc(sizeof(float)*layer*(old_matrix_length+2)*(old_matrix_length+2));
     memset(new_matrix, 0, sizeof(float)*layer*(old_matrix_length+2)*(old_matrix_length+2));
-    for(u8 l=0; l < layer; l++){
-        for (u8 i = 0; i < old_matrix_length; i++) {
-            for (u8 j = 0; j < old_matrix_length; j++) {
+    for(int l=0; l < layer; l++){
+        for (int i = 0; i < old_matrix_length; i++) {
+            for (int j = 0; j < old_matrix_length; j++) {
                 new_matrix[(i + 1) * (old_matrix_length+2) + (j + 1) +
                            l * (old_matrix_length+2) * (old_matrix_length+2)]
                 = old_matrix[i * old_matrix_length + j +
@@ -24,28 +24,33 @@ float* expand(const float* old_matrix, u8 old_matrix_length, u8 layer){
 //input_matrix_length 输入图像的边长：102
 //c_rl 输出图像的边长：100
 //返回卷积的结果
-float* convolution(Model model_w, Model model_b, const float* input_matrix, u8 input_matrix_length){
+float* convolution(Model model_w, Model model_b, const float* input_matrix, int input_matrix_length){
     // 初始化卷积层参数
-    u8 im_l = input_matrix_length;
-    u8 cr_l = input_matrix_length - 2;
+    int _debug=0;
+    int im_l = input_matrix_length;
+    int cr_l = input_matrix_length - 2;
     float conv_temp; // 临时变量，用于存储卷积计算的中间结果
     float* conv_rlst = (float *) malloc(sizeof (float) * model_w.num_kernels * (cr_l * cr_l));
     memset(conv_rlst, 0, sizeof (float) * model_w.num_kernels * (cr_l * cr_l));
     // 遍历30个卷积核（假设有30个通道）
 
-    for(u8 l=0;l<model_w.layer;l++){
-        for(u8 n=0; n<model_w.num_kernels; n++){
-            for(u8 row = 0; row < cr_l; row++) {
-                for (u8 col = 0; col < cr_l; col++) {
+    for(int l=0;l<model_w.layer;l++){
+        for(int n=0; n<model_w.num_kernels; n++){
+            for(int row = 0; row < cr_l; row++) {
+                for (int col = 0; col < cr_l; col++) {
                     conv_temp = 0; // 每个输出像素初始化为0
                     // 进行3x3的卷积操作
-                    for (u8 x = 0; x < 3; x++) {
-                        for (u8 y = 0; y < 3; y++) {
+                    for (int x = 0; x < 3; x++) {
+                        for (int y = 0; y < 3; y++) {
                             // 将输入图像的对应像素与卷积核权重相乘，并累加到conv_temp
-                            conv_temp += input_matrix[(l*(im_l*im_l)) + (row*im_l) + (col) + (x*im_l) + (y)] *
-                                    model_w.array[(x*3) + y + (n*(3*3))];
+                            conv_temp += input_matrix[(l*(im_l*im_l)) + (row*im_l+col) + (x*im_l+y)] *
+                                    model_w.array[(l+n*(model_w.layer))*(3*3) + (x*3) + y];
                         }
                     }
+                    if(_debug < 10 && strcmp(model_w.name, "conv2_weight") == 0){
+                        printf("[%d]%f\r\n",_debug,conv_temp);
+                    }
+                    _debug++;
                     // 加上对应卷积核的偏置
                     conv_temp += model_b.array[n];
                     // 激活函数：ReLU（将小于0的值设为0）
@@ -127,15 +132,15 @@ int main(){
     float* expand_matrix_1 = expand(data.array, 100, 1);
 //        print_rslt(expand_matrix_1, 102, (1*102*102));
     float* conv_rlst_1 = convolution(conv1_weight,conv1_bias,expand_matrix_1, 102);
-//        print_rslt(conv_rlst_1, 100, (32*100*100));
+//        print_rslt(conv_rlst_1, 100*0.01, (0.01*10*100));
     float* pool_rslt_1 = pooling(conv1_weight, conv_rlst_1, 100);
-//        print_rslt(pool_rslt_1, 50, (32*50*50));
+//        print_rslt(pool_rslt_1, 50, (1*50*50));
 
 //第二层：填充32 * 52 * 52
     float* expand_matrix_2 = expand(pool_rslt_1, 50, 32);
 //        print_rslt(expand_matrix_2, 52, (1*52*52));
     float* conv_rlst_2 = convolution(conv2_weight,conv2_bias,expand_matrix_2, 52);
-//        print_rslt(conv_rlst_2, 50, (1*50*50));
+//        print_rslt(conv_rlst_2, 50*0.02, (0.02*10*50));
     float* pool_rslt_2 = pooling(conv2_weight, conv_rlst_2, 50);
 //        print_rslt(pool_rslt_2, 25, (1*25*25));
 
@@ -143,9 +148,9 @@ int main(){
     float* expand_matrix_3 = expand(pool_rslt_2, 25, 64);
 //        print_rslt(expand_matrix_2, 52, (1*52*52));
     float* conv_rlst_3 = convolution(conv3_weight,conv3_bias,expand_matrix_3, 27);
-            print_rslt(conv_rlst_3, 25, (1*25*25));
+//        print_rslt(conv_rlst_3, 25, (1*25*25));
     float* pool_rslt_3 = pooling(conv3_weight, conv_rlst_3, 25);
-            print_rslt(pool_rslt_3, 12, (1*12*12));
+//        print_rslt(pool_rslt_3, 12, (1*12*12));
 
 
 
@@ -167,7 +172,7 @@ int main(){
     float affine1_temp; // 临时变量，用于存储全连接层的中间结果
 
 // 遍历128个神经元（假设隐藏层有128个神经元）
-    for(int n=0; n<128; n++)
+    for(u8 n=0; n<128; n++)
     {
         affine1_temp = 0; // 每个神经元的输出初始化为0
 
@@ -187,24 +192,25 @@ int main(){
             affine1_rslt[n] = 0; // 否则存入0
     }
 
-    print_rslt(affine1_rslt,1,128);
+//    print_rslt(affine1_rslt,1,128);
 
 
 
     float affine2_temp; // 临时变量，用于存储输出层的中间结果
-    float affine2_rslt[7]; // 存储输出层的结果（假设输出层有7个神经元）
+    float* affine2_rslt = (float *) malloc(sizeof(float)*7);
+    memset(affine2_rslt, 0, 7);
 
 // 比较输出层的最大值
     float temp = -100; // 用于存储最大值的临时变量，初始化为一个非常小的值
-    int predict_num; // 用于存储预测的数字（对应最大值的索引）
+    u8 predict_num; // 用于存储预测的数字（对应最大值的索引）
 
 // 遍历7个输出神经元（假设有7个类别）
-    for(int n=0; n<7; n++)
+    for(u8 n=0; n<7; n++)
     {
         affine2_temp = 0; // 当前神经元的输出初始化为0
 
         // 进行矩阵乘法，将隐藏层的输出与输出层权重进行点积
-        for(int i=0; i<128; i++)
+        for(u8 i=0; i<128; i++)
         {
             affine2_temp = affine2_temp + fc2_weight.array[i+100*n] * affine1_rslt[i];
         }
@@ -221,8 +227,7 @@ int main(){
         }
     }
 
-    print_rslt(affine2_rslt,1,7);
-
+    print_rslt(affine2_rslt,7,7);
     printf("RES is:%d",predict_num+1);
 
     return 0;