diff --git a/cnn.c b/cnn.c
index ae7ceda..1833785 100644
--- a/cnn.c
+++ b/cnn.c
@@ -165,13 +165,14 @@ float* hidden(const float* input_matrix){
     return affine1_rslt;
 }
 
-float* output(const float* input_matrix){
+float* output(Model model_w, const float* input_matrix){
+    u8 num = model_w.num_kernels;
     float affine2_temp; // 临时变量，用于存储输出层的中间结果
-    float *affine2_rslt = (float *) malloc(sizeof(float)*7);
-    memset(affine2_rslt, 0, sizeof(float)*7);
+    float *affine2_rslt = (float *) malloc(sizeof(float)*num);
+    memset(affine2_rslt, 0, sizeof(float)*num);
 
 // 遍历10个输出神经元（假设有10个类别）
-    for(int n=0; n<7; n++)
+    for(int n=0; n<num; n++)
     {
         affine2_temp = 0; // 当前神经元的输出初始化为0
 
@@ -252,10 +253,10 @@ float* generateMatrix(Model model, const float* value)
     return CNN_data;
 }
 
-float calculate_probabilities(float *input_array)
+float calculate_probabilities(Model model_w, float *input_array)
 {
     float sum = 0;
-    u8 input_num = 7;
+    u8 input_num = model_w.num_kernels;
     float *result = (float *) malloc(sizeof(float)*input_num);
     memset(result, 0, sizeof(float)*input_num);
 
@@ -293,8 +294,8 @@ float calculate_probabilities(float *input_array)
 }
 
 
-u8 calculate_layer(float *input_array){
-    u8 input_num = 7;
+u8 calculate_layer(Model model_w, float *input_array){
+    u8 input_num = model_w.num_kernels;
     u8 predict_num = 0;
     float max_temp = -100;
     for(int n=0; n<input_num; n++)
@@ -305,8 +306,8 @@ u8 calculate_layer(float *input_array){
             predict_num = n; // 记录最大值对应的类别索引
         }
     }
-    //print_rslt(input_array,7,7);
-    return predict_num+1;
+    print_rslt(input_array,7,7);
+    return predict_num+0;
 }
 
 
@@ -354,10 +355,10 @@ void cnn_run(){
         conv_rlst_3 = NULL;
 
         float* affine1_rslt = hidden(pool_rslt_3);
-        float* affine2_rslt = output(affine1_rslt);
+        float* affine2_rslt = output(fc2_weight, affine1_rslt);
 
-        printf("概率：%f\r\n",calculate_probabilities(affine2_rslt));
-        printf("Label is:%d\r\n",calculate_layer(affine2_rslt));
+        printf("概率：%f\r\n",calculate_probabilities(fc2_weight, affine2_rslt));
+        printf("Label is:%d\r\n",calculate_layer(fc2_weight, affine2_rslt));
 
         free(pool_rslt_3);
         pool_rslt_3 = NULL;
@@ -404,10 +405,10 @@ void cnn_run(){
         conv_rlst_3 = NULL;
 
         float* affine1_rslt = hidden(pool_rslt_3);
-        float* affine2_rslt = output(affine1_rslt);
+        float* affine2_rslt = output(fc2_weight, affine1_rslt);
 
-        printf("概率：%f\r\n",calculate_probabilities(affine2_rslt));
-        printf("Label is:%d\r\n",calculate_layer(affine2_rslt));
+        printf("概率：%f\r\n",calculate_probabilities(fc2_weight, affine2_rslt));
+        printf("Label is:%d\r\n",calculate_layer(fc2_weight, affine2_rslt));
 
         free(pool_rslt_3);
         pool_rslt_3 = NULL;
diff --git a/cnn_model.c b/cnn_model.c
index df9108b..a867304 100644
--- a/cnn_model.c
+++ b/cnn_model.c
@@ -383,6 +383,7 @@ void model_init(){
 	fc2_weight.name = "fc2_weight";
 	fc2_weight.array = modelmym_init(fc2_weight.name);
 	fc2_weight.maxlength = FC2_WEIGHT_ARRSIZE;
+    fc2_weight.num_kernels = FC2_WEIGHT_KERNELS;
 
 	data.name = "data";
 	data.array = modelmym_init(data.name);
diff --git a/cnn_model.h b/cnn_model.h
index 8a0c8f3..233e1e3 100644
--- a/cnn_model.h
+++ b/cnn_model.h
@@ -35,6 +35,8 @@ typedef struct {
 #define FC2_BIAS_ARRSIZE (7)
 #define FC2_WEIGHT_ARRSIZE (7*128)			//896
 
+#define FC2_WEIGHT_KERNELS 4                //4个卷积核
+
 #define is1250000 1
 #if is1250000
     #define DATA_ARRSIZE (1250000)
diff --git a/main.c b/main.c
index 51247e9..b65481d 100644
--- a/main.c
+++ b/main.c
@@ -99,10 +99,10 @@ void run_dataset(){
 int main(){
     model_init();
     model_write("all");
-//    run_dataset();
+    run_dataset();
 
-    model_switchdata("C1autosave00095_right_new_2");
-    cnn_run();
+//    model_switchdata("7E29181C 2024-11-12 19-13-55");
+//    cnn_run();
 
     DEBUG_PRINTF("\r\nEnd结束");
 }