删除 CMakeLists.txt

.gitignore

@@ -1,14 +0,0 @@
-CMakeLists.txt.user
-CMakeCache.txt
-CMakeFiles
-CMakeScripts
-Testing
-Makefile
-cmake_install.cmake
-install_manifest.txt
-compile_commands.json
-CTestTestfile.cmake
-_deps
-.gitattributes
-/cmake-build-debug
-/.idea

CMakeLists.txt

@@ -1,13 +0,0 @@
-cmake_minimum_required(VERSION 3.26)
-project(c_cnn C)
-
-set(CMAKE_C_STANDARD 11)
-
-include_directories(.)
-
-add_executable(c_cnn
-        cnn.c
-        cnn.h
-        main.c
-        cnn_model.c
-        debug.c)

README.md

@@ -1,2 +0,0 @@
-# c-cnn
- c-cnn

cnn.c

@@ -2,7 +2,7 @@
 
 
 
-void print_rslt(float* rslt, u8 input_matrix_length, u32 length){
+void print_rslt(float* rslt, uint8_t input_matrix_length, uint32_t length){
     int _tmp = 0;
     printf("[0:0]");
     for (int i = 0; i < length; i++) {
@@ -14,7 +14,7 @@ void print_rslt(float* rslt, u8 input_matrix_length, u32 length){
     printf("\r\n");
 }
 
-// 将原始矩阵复制到填充后的矩阵中央
+// <EFBFBD><EFBFBD>ԭʼ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ƶ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ľ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
 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));
@@ -31,34 +31,34 @@ float* expand(const float* old_matrix, int old_matrix_length, int layer){
     return new_matrix;
 }
 
-//model 模型名字
-//input_matrix 输入图像
-//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
+//model ģ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+//input_matrix <EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><EFBFBD>
+//input_matrix_length <EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><EFBFBD><EFBFBD>ı߳<EFBFBD><EFBFBD><EFBFBD>102
+//c_rl <EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><EFBFBD><EFBFBD>ı߳<EFBFBD><EFBFBD><EFBFBD>100
+//<EFBFBD><EFBFBD><EFBFBD>ؾ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ľ<EFBFBD><EFBFBD><EFBFBD>
 float* convolution(Model model_w, Model model_b, const float* input_matrix, int input_matrix_length){
-    // 初始化卷积层参数
+    // <EFBFBD><EFBFBD>ʼ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
     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_temp; // <EFBFBD><EFBFBD>ʱ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ڴ洢<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>м<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
 
-    //用于合并前的数组，具有32*64*50*50(第二层)的大小
+    //<EFBFBD><EFBFBD><EFBFBD>ںϲ<EFBFBD>ǰ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>飬<EFBFBD><EFBFBD><EFBFBD><EFBFBD>32*64*50*50(<EFBFBD>ڶ<EFBFBD><EFBFBD><EFBFBD>)<29>Ĵ<EFBFBD>С
     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));
-    //子图合并后的数组
+    //<EFBFBD><EFBFBD>ͼ<EFBFBD>ϲ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
     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）
+    // <EFBFBD><EFBFBD><EFBFBD><EFBFBD>30<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ˣ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>30<EFBFBD><EFBFBD>ͨ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>
     for(int c=0; c < model_w.channel; c++){
         for(int k=0; k < model_w.num_kernels; k++){
             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
+                    conv_temp = 0; // ÿ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>س<EFBFBD>ʼ<EFBFBD><EFBFBD>Ϊ0
+                    // <EFBFBD><EFBFBD><EFBFBD><EFBFBD>3x3<EFBFBD>ľ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
                     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
+                            // <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><EFBFBD><EFBFBD>Ķ<EFBFBD>Ӧ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ȩ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ˣ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ۼӵ<EFBFBD>conv_temp
                             conv_temp += input_matrix[(c*im_l*im_l) + (row*(im_l)+col) + (x*(im_l)+y)]
                                     * model_w.array[((c+k*model_w.channel)*3*3) + (x*3+y)];
                         }
@@ -67,7 +67,7 @@ float* convolution(Model model_w, Model model_b, const float* input_matrix, int
                 }
            }
         }
-        //合并子图
+        //<EFBFBD>ϲ<EFBFBD><EFBFBD><EFBFBD>ͼ
         for(int k=0; k < model_w.num_kernels; k++) {
             for (int row = 0; row < cr_l; row++) {
                 for (int col = 0; col < cr_l; col++) {
@@ -80,13 +80,13 @@ float* convolution(Model model_w, Model model_b, const float* input_matrix, int
         for (int row = 0; row < cr_l; row++) {
             for (int col = 0; col < cr_l; col++) {
                 conv_temp = conv_rlst[(k * cr_l * cr_l) + (row * cr_l + col)];
-                // 加上对应卷积核的偏置
+                // <EFBFBD><EFBFBD><EFBFBD>϶<EFBFBD>Ӧ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>˵<EFBFBD>ƫ<EFBFBD><EFBFBD>
                 conv_temp += model_b.array[k];
-                // 激活函数：ReLU（将小于0的值设为0）
+                // <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ReLU<EFBFBD><EFBFBD><EFBFBD><EFBFBD>С<EFBFBD><EFBFBD>0<EFBFBD><EFBFBD>ֵ<EFBFBD><EFBFBD>Ϊ0<EFBFBD><EFBFBD>
                 if (conv_temp > 0)
-                    conv_rlst[(k * (cr_l * cr_l)) + (row * cr_l) + (col)] = conv_temp; // 如果卷积结果大于0，存入结果数组
+                    conv_rlst[(k * (cr_l * cr_l)) + (row * cr_l) + (col)] = conv_temp; // <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>0<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
                 else
-                    conv_rlst[(k * (cr_l * cr_l)) + (row * cr_l) + (col)] = 0; // 否则存入0
+                    conv_rlst[(k * (cr_l * cr_l)) + (row * cr_l) + (col)] = 0; // <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>0
             }
         }
     }
@@ -95,38 +95,38 @@ float* convolution(Model model_w, Model model_b, const float* input_matrix, int
     return conv_rlst;
 }
 
-//num_kernels 卷积核的个数：32
-//area 池化的面积：2*2
-//input_matrix 输入图像
-//input_matrix_length 输入图像的边长：100
-//输出图像的边长：50
-//返回池化的结果
-float* pooling(Model model_w, const float* input_matrix, u8 input_matrix_length){
-    u8 im_l = input_matrix_length;
-    float pool_temp = 0; // 临时变量，用于存储池化操作的最大值
+//num_kernels <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>˵ĸ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>32
+//area <EFBFBD>ػ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>2*2
+//input_matrix <EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><EFBFBD>
+//input_matrix_length <EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><EFBFBD><EFBFBD>ı߳<EFBFBD><EFBFBD><EFBFBD>100
+//<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><EFBFBD><EFBFBD>ı߳<EFBFBD><EFBFBD><EFBFBD>50
+//<EFBFBD><EFBFBD><EFBFBD>سػ<EFBFBD><EFBFBD>Ľ<EFBFBD><EFBFBD><EFBFBD>
+float* pooling(Model model_w, const float* input_matrix, uint8_t input_matrix_length){
+    uint8_t im_l = input_matrix_length;
+    float pool_temp = 0; // <EFBFBD><EFBFBD>ʱ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ڴ洢<EFBFBD>ػ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ֵ
     float* pool_rslt = (float *) malloc(sizeof (float)*model_w.num_kernels*im_l*im_l);
     memset(pool_rslt, 0, sizeof (float)*model_w.num_kernels*im_l*im_l);
-    // 遍历30个通道（与卷积核数量相同）
-    for(u8 n=0; n<model_w.num_kernels; n++)
+    // <EFBFBD><EFBFBD><EFBFBD><EFBFBD>30<EFBFBD><EFBFBD>ͨ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͬ<EFBFBD><EFBFBD>
+    for(uint8_t n=0; n<model_w.num_kernels; n++)
     {
-        // 遍历输入图像的每一行，步长为2（2x2的池化窗口）
-        for(u8 row=0; row<im_l; row=row+2)
+        // <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ÿһ<EFBFBD>У<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ϊ2<EFBFBD><EFBFBD>2x2<EFBFBD>ijػ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ڣ<EFBFBD>
+        for(uint8_t row=0; row<im_l; row=row+2)
         {
-            // 遍历输入图像的每一列，步长为2
-            for(u8 col=0; col<im_l; col=col+2)
+            // <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ͼ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ÿһ<EFBFBD>У<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ϊ2
+            for(uint8_t col=0; col<im_l; col=col+2)
             {
-                pool_temp = 0; // 每个池化区域的最大值初始化为0
-                // 进行2x2的最大池化操作
-                for(u8 x=0; x<2; x++)
+                pool_temp = 0; // ÿ<EFBFBD><EFBFBD><EFBFBD>ػ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ֵ<EFBFBD><EFBFBD>ʼ<EFBFBD><EFBFBD>Ϊ0
+                // <EFBFBD><EFBFBD><EFBFBD><EFBFBD>2x2<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ػ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
+                for(uint8_t x=0; x<2; x++)
                 {
-                    for(u8 y=0; y<2; y++)
+                    for(uint8_t y=0; y<2; y++)
                     {
-                        // 更新当前池化区域的最大值
+                        // <EFBFBD><EFBFBD><EFBFBD>µ<EFBFBD>ǰ<EFBFBD>ػ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ֵ
                         if(pool_temp <= input_matrix[row*im_l+col+x*im_l+y+n*(im_l*im_l)])
                             pool_temp = input_matrix[row*im_l+col+x*im_l+y+n*(im_l*im_l)];
                     }
                 }
-                // 将最大值存入池化结果数组
+                // <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ֵ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ػ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
                 pool_rslt[(row/2)*(im_l/2)+col/2+n*((im_l/2)*(im_l/2))] = pool_temp;
             }
         }
@@ -135,29 +135,29 @@ float* pooling(Model model_w, const float* input_matrix, u8 input_matrix_length)
 }
 
 float* hidden(const float* input_matrix){
-    float affine1_temp; // 临时变量，用于存储全连接层的中间结果
+    float affine1_temp; // <EFBFBD><EFBFBD>ʱ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ڴ洢ȫ<EFBFBD><EFBFBD><EFBFBD>Ӳ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>м<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
     float *affine1_rslt = (float *) malloc(sizeof(float)*128);
     memset(affine1_rslt, 0, sizeof(float)*128);
 
-    // 遍历128个神经元（假设隐藏层有128个神经元）
-    for(u8 n=0; n<128; n++)
+    // <EFBFBD><EFBFBD><EFBFBD><EFBFBD>128<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ԫ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ز<EFBFBD><EFBFBD><EFBFBD>128<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ԫ<EFBFBD><EFBFBD>
+    for(uint8_t n=0; n<128; n++)
     {
-        affine1_temp = 0; // 每个神经元的输出初始化为0
+        affine1_temp = 0; // ÿ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ԫ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ʼ<EFBFBD><EFBFBD>Ϊ0
 
-        // 进行矩阵乘法，将池化层输出展平为一维向量后，与全连接层权重进行点积
+        // <EFBFBD><EFBFBD><EFBFBD>о<EFBFBD><EFBFBD><EFBFBD><EFBFBD>˷<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ػ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>չƽΪһά<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ȫ<EFBFBD><EFBFBD><EFBFBD>Ӳ<EFBFBD>Ȩ<EFBFBD>ؽ<EFBFBD><EFBFBD>е<EFBFBD><EFBFBD><EFBFBD>
         for(int i=0; i<(128*12*12); i++)
         {
             affine1_temp = affine1_temp + input_matrix[i] * fc1_weight.array[i+(128*12*12)*n];
         }
 
-        // 加上对应神经元的偏置
+        // <EFBFBD><EFBFBD><EFBFBD>϶<EFBFBD>Ӧ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ԫ<EFBFBD><EFBFBD>ƫ<EFBFBD><EFBFBD>
         affine1_temp = affine1_temp + fc1_bias.array[n];
 
-        // 激活函数：ReLU（将小于0的值设为0）
+        // <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ReLU<EFBFBD><EFBFBD><EFBFBD><EFBFBD>С<EFBFBD><EFBFBD>0<EFBFBD><EFBFBD>ֵ<EFBFBD><EFBFBD>Ϊ0<EFBFBD><EFBFBD>
         if(affine1_temp > 0)
-            affine1_rslt[n] = affine1_temp; // 如果结果大于0，存入结果数组
+            affine1_rslt[n] = affine1_temp; // <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>0<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
         else
-            affine1_rslt[n] = 0; // 否则存入0
+            affine1_rslt[n] = 0; // <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>0
     }
 
     //    print_rslt(affine1_rslt,1,128);
@@ -166,25 +166,25 @@ float* hidden(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
+    uint8_t num = model_w.num_kernels;
+    float affine2_temp; // <EFBFBD><EFBFBD>ʱ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ڴ洢<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>м<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
     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）
+// <EFBFBD><EFBFBD><EFBFBD><EFBFBD>10<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ԫ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>10<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
     for(int n=0; n<num; n++)
     {
-        affine2_temp = 0; // 当前神经元的输出初始化为0
+        affine2_temp = 0; // <EFBFBD><EFBFBD>ǰ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ԫ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ʼ<EFBFBD><EFBFBD>Ϊ0
 
-        // 进行矩阵乘法，将隐藏层的输出与输出层权重进行点积
+        // <EFBFBD><EFBFBD><EFBFBD>о<EFBFBD><EFBFBD><EFBFBD><EFBFBD>˷<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ز<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ȩ<EFBFBD>ؽ<EFBFBD><EFBFBD>е<EFBFBD><EFBFBD><EFBFBD>
         for(int i=0; i<128; i++)
         {
             affine2_temp = affine2_temp + fc2_weight.array[i+128*n] * input_matrix[i];
         }
 
-        // 加上对应神经元的偏置
+        // <EFBFBD><EFBFBD><EFBFBD>϶<EFBFBD>Ӧ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ԫ<EFBFBD><EFBFBD>ƫ<EFBFBD><EFBFBD>
         affine2_temp = affine2_temp + fc2_weight.array[n];
-        affine2_rslt[n] = affine2_temp; // 存储输出层的结果
+        affine2_rslt[n] = affine2_temp; // <EFBFBD>洢<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ľ<EFBFBD><EFBFBD><EFBFBD>
     }
 
     return affine2_rslt;
@@ -214,7 +214,7 @@ void calculate_statistics(Model model, float* value)
     value[2] = sqrtf(variance);
 }
 
-u8 check_threshold(Model model, const float* value)
+uint8_t check_threshold(Model model, const float* value)
 {
     const float threshold = 20;
 
@@ -232,7 +232,7 @@ float* generateMatrix(Model model, const float* value)
     float* CNN_data = (float*) malloc(sizeof(float)*100*100);
     memset(CNN_data, 0, sizeof(float)*100*100);
 
-    u16 x = model.maxlength / 100;
+    uint16_t x = model.maxlength / 100;
     float y = value[0] / 100;
 
     for (int i = 0; i < model.maxlength; i++) {
@@ -256,7 +256,7 @@ float* generateMatrix(Model model, const float* value)
 float calculate_probabilities(Model model_w, float *input_array)
 {
     float sum = 0;
-    u8 input_num = model_w.num_kernels;
+    uint8_t input_num = model_w.num_kernels;
     float *result = (float *) malloc(sizeof(float)*input_num);
     memset(result, 0, sizeof(float)*input_num);
 
@@ -294,16 +294,16 @@ float calculate_probabilities(Model model_w, float *input_array)
 }
 
 
-u8 calculate_layer(Model model_w, float *input_array){
-    u8 input_num = model_w.num_kernels;
-    u8 predict_num = 0;
+uint8_t calculate_layer(Model model_w, float *input_array){
+    uint8_t input_num = model_w.num_kernels;
+    uint8_t predict_num = 0;
     float max_temp = -100;
     for(int n=0; n<input_num; n++)
     {
         if(max_temp <= input_array[n])
         {
-            max_temp = input_array[n]; // 更新最大值
-            predict_num = n; // 记录最大值对应的类别索引
+            max_temp = input_array[n]; // <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ֵ
+            predict_num = n; // <EFBFBD><EFBFBD>¼<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ֵ<EFBFBD><EFBFBD>Ӧ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
         }
     }
     print_rslt(input_array,input_num,input_num);
@@ -312,13 +312,12 @@ u8 calculate_layer(Model model_w, float *input_array){
 
 
 void cnn_run(){
-#if is1300000
     float value[3] = {0};
     calculate_statistics(data,&value[0]);
     if (check_threshold(data,&value[0])){
         float* _data = generateMatrix(data,&value[0]);
 
-        //第一层：填充102 * 102
+        //<EFBFBD><EFBFBD>һ<EFBFBD>㣺<EFBFBD><EFBFBD><EFBFBD><EFBFBD>102 * 102
         float* expand_matrix_1 = expand(_data, 100, 1);
         float* conv_rlst_1 = convolution(conv1_weight,conv1_bias,expand_matrix_1, 102);
         float* pool_rslt_1 = pooling(conv1_weight, conv_rlst_1, 100);
@@ -330,7 +329,7 @@ void cnn_run(){
         free(conv_rlst_1);
         conv_rlst_1 = NULL;
 
-        //第二层：填充32 * 52 * 52
+        //<EFBFBD>ڶ<EFBFBD><EFBFBD>㣺<EFBFBD><EFBFBD><EFBFBD><EFBFBD>32 * 52 * 52
         float* expand_matrix_2 = expand(pool_rslt_1, 50, 32);
         float* conv_rlst_2 = convolution(conv2_weight,conv2_bias,expand_matrix_2, 52);
         float* pool_rslt_2 = pooling(conv2_weight, conv_rlst_2, 50);
@@ -342,7 +341,7 @@ void cnn_run(){
         free(conv_rlst_2);
         conv_rlst_2 = NULL;
 
-        //第三层：填充 64 * 27 * 27
+        //<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>㣺<EFBFBD><EFBFBD><EFBFBD><EFBFBD> 64 * 27 * 27
         float* expand_matrix_3 = expand(pool_rslt_2, 25, 64);
         float* conv_rlst_3 = convolution(conv3_weight,conv3_bias,expand_matrix_3, 27);
         float* pool_rslt_3 = pooling(conv3_weight, conv_rlst_3, 25);
@@ -357,7 +356,7 @@ void cnn_run(){
         float* affine1_rslt = hidden(pool_rslt_3);
         float* affine2_rslt = output(fc2_weight, affine1_rslt);
 
-        printf("概率：%f\r\n",calculate_probabilities(fc2_weight, affine2_rslt));
+        printf("<EFBFBD><EFBFBD><EFBFBD>ʣ<EFBFBD>%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);
@@ -368,53 +367,6 @@ void cnn_run(){
         affine2_rslt = NULL;
 
     } else{
-        printf("未放电！最大值:%f 平均值:%f 标准差:%f\r\n",value[0],value[1],value[2]);
+        printf("δ<EFBFBD>ŵ磡<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ֵ:%f ƽ<><C6BD>ֵ:%f <20><>׼<EFBFBD><D7BC>:%f\r\n",value[0],value[1],value[2]);
     }
-#else
-        float* expand_matrix_1 = expand(data.array, 100, 1);
-        float* conv_rlst_1 = convolution(conv1_weight,conv1_bias,expand_matrix_1, 102);
-        float* pool_rslt_1 = pooling(conv1_weight, conv_rlst_1, 100);
-
-        free(expand_matrix_1);
-        expand_matrix_1 = NULL;
-        free(conv_rlst_1);
-        conv_rlst_1 = NULL;
-
-        //第二层：填充32 * 52 * 52
-        float* expand_matrix_2 = expand(pool_rslt_1, 50, 32);
-        float* conv_rlst_2 = convolution(conv2_weight,conv2_bias,expand_matrix_2, 52);
-        float* pool_rslt_2 = pooling(conv2_weight, conv_rlst_2, 50);
-
-        free(pool_rslt_1);
-        pool_rslt_1 = NULL;
-        free(expand_matrix_2);
-        expand_matrix_2 = NULL;
-        free(conv_rlst_2);
-        conv_rlst_2 = NULL;
-
-        //第三层：填充 64 * 27 * 27
-        float* expand_matrix_3 = expand(pool_rslt_2, 25, 64);
-        float* conv_rlst_3 = convolution(conv3_weight,conv3_bias,expand_matrix_3, 27);
-        float* pool_rslt_3 = pooling(conv3_weight, conv_rlst_3, 25);
-
-        free(pool_rslt_2);
-        pool_rslt_2 = NULL;
-        free(expand_matrix_3);
-        expand_matrix_3 = NULL;
-        free(conv_rlst_3);
-        conv_rlst_3 = NULL;
-
-        float* affine1_rslt = hidden(pool_rslt_3);
-        float* affine2_rslt = output(fc2_weight, affine1_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;
-        free(affine1_rslt);
-        affine1_rslt = NULL;
-        free(affine2_rslt);
-        affine2_rslt = NULL;
-#endif
 }

cnn.h

@@ -1,14 +1,7 @@
 #ifndef _CNN_H_
 #define _CNN_H_
 #include "cnn_model.h"
-#include <stdio.h>
-#include <math.h>
-
-
-
 
 void cnn_run(void);
 
-
-
 #endif

cnn_model.c

@@ -57,7 +57,7 @@ float* modelmym_init(char* model_name){
 
 
 
-u8 modelmym_free(char* model_name){
+uint8_t modelmym_free(char* model_name){
 	if(conv1_bias.array != NULL && strcmp(model_name, "conv1_bias") == 0){
 		free(conv1_bias.array);
 		conv1_bias.array = NULL;
@@ -143,7 +143,7 @@ u8 modelmym_free(char* model_name){
 
 
 
-u8 model_write(char* model_name)
+uint8_t model_write(char* model_name)
 {
 	if(strcmp(model_name, "all") == 0){
 		model_write("conv1_bias");
@@ -158,11 +158,10 @@ u8 model_write(char* model_name)
 		model_write("fc2_weight");
 
 		model_info("all");
-		SDRAM_USED();
 	}else{
-			u8 _times=0;
-			u32 _larr = 0;
-            u8 _len = strlen(model_name);
+			uint8_t _times=0;
+			uint32_t _larr = 0;
+            uint8_t _len = strlen(model_name);
 			char _path[_len+1+7+30];
             char _datapath[_len+1+7+30];
             char _fstr[READLENGTH+1] = {0};
@@ -173,7 +172,7 @@ u8 model_write(char* model_name)
                 sprintf(_path, "./dataset/_data/%s.txt", model_name);
                 FILE *file = fopen(_path, "r");
                 if(file == NULL){
-                    DEBUG_PRINTF("\r\n输入了一个无效的模型或Data数据集的名字\r\n");
+                    printf("\r\n<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>һ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ч<EFBFBD><EFBFBD>ģ<EFBFBD>ͻ<EFBFBD>Data<EFBFBD><EFBFBD><EFBFBD>ݼ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>\r\n");
                     return 199;
                 }else{
                     _model = model("data");
@@ -185,13 +184,13 @@ u8 model_write(char* model_name)
                 sprintf(_path, "./dataset/%s.txt", _model -> name);
                 FILE *file = fopen(_path, "r");
                 if(file == NULL){
-                    DEBUG_PRINTF("预设里没有这个模型：[%s]\r\n", _path);
+                    printf("Ԥ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>û<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ģ<EFBFBD>ͣ<EFBFBD>[%s]\r\n", _path);
                     return 4;
                 }
             }
             
             if(_model -> array == NULL && modelmym_init(_model -> name) == NULL){
-                DEBUG_PRINTF("无法创建模型参数[%s]的数组到SDRAM里\r\n", _model -> name);
+                printf("<EFBFBD>޷<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ģ<EFBFBD>Ͳ<EFBFBD><EFBFBD><EFBFBD>[%s]<5D><><EFBFBD><EFBFBD><EFBFBD>鵽SDRAM<EFBFBD><EFBFBD>\r\n", _model -> name);
                 return 200;
             }
 
@@ -199,40 +198,40 @@ u8 model_write(char* model_name)
             sprintf(_path, "./dataset/%s.txt", _model -> dname ? _datapath : _model -> name);
             FILE *file = fopen(_path, "r");
             if(file == NULL){
-                DEBUG_PRINTF("文件[%s]无法打开\r\n", _model -> dname ? _model -> dname : _model -> name);
+                printf("<EFBFBD>ļ<EFBFBD>[%s]<EFBFBD>޷<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>\r\n", _model -> dname ? _model -> dname : _model -> name);
                 return 199;
             }
 
-            DEBUG_PRINTF("写入的模型参数名字是：%s\r\n", _model -> name);
-            if(_model -> dname)DEBUG_PRINTF("写入的Data数据集是：%s\r\n", _model -> dname);
-            DEBUG_PRINTF("写入模型参数数组的最大长度为：%d\r\n", _model -> maxlength);
-            DEBUG_PRINTF("目前数组存活的元素数量为：%d", _model -> realength);
-			printf("\r\n正在写入模型参数'%s'，请稍后......\r\n",_model -> dname ? _model -> dname : _model -> name);
+            printf("д<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ģ<EFBFBD>Ͳ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ǣ<EFBFBD>%s\r\n", _model -> name);
+            if(_model -> dname)printf("д<EFBFBD><EFBFBD><EFBFBD><EFBFBD>Data<EFBFBD><EFBFBD><EFBFBD>ݼ<EFBFBD><EFBFBD>ǣ<EFBFBD>%s\r\n", _model -> dname);
+            printf("д<EFBFBD><EFBFBD>ģ<EFBFBD>Ͳ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>󳤶<EFBFBD>Ϊ<EFBFBD><EFBFBD>%d\r\n", _model -> maxlength);
+            printf("Ŀǰ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ԫ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ϊ<EFBFBD><EFBFBD>%d", _model -> realength);
+			printf("\r\n<EFBFBD><EFBFBD><EFBFBD><EFBFBD>д<EFBFBD><EFBFBD>ģ<EFBFBD>Ͳ<EFBFBD><EFBFBD><EFBFBD>'%s'<27><><EFBFBD><EFBFBD><EFBFBD>Ժ<EFBFBD>......\r\n",_model -> dname ? _model -> dname : _model -> name);
 
 			while(_larr < _model->maxlength && fgets(_fstr, sizeof(_fstr), file) != NULL){
                 char *endptr;
                 float value = strtof(_fstr, &endptr);
 
-                // 检查是否转换成功
+                // <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ƿ<EFBFBD>ת<EFBFBD><EFBFBD><EFBFBD>ɹ<EFBFBD>
                 if (endptr == _fstr) {
-                    fprintf(stderr, "第 %d 行不是有效的浮点数: %s", _larr + 1, _fstr);
-                    continue;  // 跳过无效行
+                    fprintf(stderr, "<EFBFBD><EFBFBD> %d <EFBFBD>в<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ч<EFBFBD>ĸ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>: %s", _larr + 1, _fstr);
+                    continue;  // <EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>Ч<EFBFBD><EFBFBD>
                 }
                 _model->array[_larr++] = value;
                 _model->realength++;
 
                 if(_model -> maxlength >= 73728 && (_larr >= (_model -> maxlength/10)*_times)){
 						progress = _larr >= _model -> maxlength ? 100 : _times++ == 0 ? 0 : progress + 10;
-						DEBUG_PRINTF("\r\n[");
-						for(u16 j=0; j<50;j++){
-							if(j < progress/2) DEBUG_PRINTF("=");
-							else DEBUG_PRINTF(" ");
+						printf("\r\n[");
+						for(uint16_t j=0; j<50;j++){
+							if(j < progress/2) printf("=");
+							else printf(" ");
 						}
-						DEBUG_PRINTF("] %d%%", progress);
+						printf("] %d%%", progress);
 					}
                 if(_larr >= _model -> maxlength)break;
 			}
-			DEBUG_PRINTF("\r\n模型参数[%s]已写入到内存中! 模型长度为 %d\r\n",_model -> dname ? _model -> dname : _model -> name,_model -> realength);
+			printf("\r\nģ<EFBFBD>Ͳ<EFBFBD><EFBFBD><EFBFBD>[%s]<5D><>д<EFBFBD>뵽<EFBFBD>ڴ<EFBFBD><DAB4><EFBFBD>! ģ<>ͳ<EFBFBD><CDB3><EFBFBD>Ϊ %d\r\n",_model -> dname ? _model -> dname : _model -> name,_model -> realength);
 			return 0;
 	}
 	return 1;
@@ -240,7 +239,7 @@ u8 model_write(char* model_name)
 
 
 
-u8 model_read(char* model_name, u32 start, u32 end, u32 gap){
+uint8_t model_read(char* model_name, uint32_t start, uint32_t end, uint32_t gap){
 	if(strcmp(model_name, "all") == 0){
 		model_read("conv1_bias", start, end, gap);
 		model_read("conv1_weight", start, end, gap);
@@ -257,7 +256,7 @@ u8 model_read(char* model_name, u32 start, u32 end, u32 gap){
 		Model *_model = model(model_name);
 		if(_model == NULL || end == 0 || start > _model -> realength || end > _model -> realength)return 0;
 		if(_model -> realength){
-			for (u32 i=0;i<((end > (_model -> realength) ? _model -> realength : (end-start))+(end > (_model -> realength) ? _model -> realength : (end-start)%gap ? 2 : 1));i+=gap)
+			for (uint32_t i=0;i<((end > (_model -> realength) ? _model -> realength : (end-start))+(end > (_model -> realength) ? _model -> realength : (end-start)%gap ? 2 : 1));i+=gap)
 				printf("\r\n%s_floatArray[%d]: %f",_model->name,(i+start)<_model->realength ? i+start : _model->realength-1,(i+start)<_model->realength ? _model->array[i+start] : _model->array[_model -> realength-1]);
 			printf("\r\n");
 			return 1;
@@ -268,30 +267,30 @@ u8 model_read(char* model_name, u32 start, u32 end, u32 gap){
 
 
 
-u8 model_switchdata(char* data_name){
-	u8 _len = strlen(data_name);
+uint8_t model_switchdata(char* data_name){
+	uint8_t _len = strlen(data_name);
 	char _path[_len+1+7];
 	if(data.array != NULL)modelmym_free("data");
     sprintf(_path, "./dataset/_data/%s.txt", data_name);
     FILE *file = fopen(_path, "r");
     if(file == NULL){
-		DEBUG_PRINTF("\r\nData数据集[%s]不存在\r\n",data_name);
+		printf("\r\nData<EFBFBD><EFBFBD><EFBFBD>ݼ<EFBFBD>[%s]<5D><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>\r\n",data_name);
 		return 0;
 	}else{
-		u8 _res = model_write(data_name);
+		uint8_t _res = model_write(data_name);
 		if (_res) {
-			DEBUG_PRINTF("Data数据集[%s]切换失败！！\r\n",data_name);
+			printf("Data<EFBFBD><EFBFBD><EFBFBD>ݼ<EFBFBD>[%s]<5D>л<EFBFBD>ʧ<EFBFBD>ܣ<EFBFBD><DCA3><EFBFBD>\r\n",data_name);
 			return 0;
 		}
-		else DEBUG_PRINTF("Data数据集[%s]切换成功！\r\n",data_name);
-		DEBUG_PRINTF("data_name的长度为：%d\r\n_path的长度为：%d\r\n_path为：%s\r\n",_len,sizeof(_path),_path);
+		else printf("Data<EFBFBD><EFBFBD><EFBFBD>ݼ<EFBFBD>[%s]<5D>л<EFBFBD><D0BB>ɹ<EFBFBD><C9B9><EFBFBD>\r\n",data_name);
+		printf("data_name<EFBFBD>ij<EFBFBD><EFBFBD><EFBFBD>Ϊ<EFBFBD><EFBFBD>%d\r\n_path<EFBFBD>ij<EFBFBD><EFBFBD><EFBFBD>Ϊ<EFBFBD><EFBFBD>%d\r\n_pathΪ<EFBFBD><EFBFBD>%s\r\n",_len,sizeof(_path),_path);
 		return 1;
 	}
 }
 
 
 
-u8 model_info(char* model_name){
+uint8_t model_info(char* model_name){
 	if(strcmp(model_name, "all") == 0){
 		model_info("conv1_bias");
 		model_info("conv1_weight");
@@ -311,7 +310,7 @@ u8 model_info(char* model_name){
 		printf("model.array.address is: 0X%X\r\n",_model -> array);
 		printf("model.maxlength is: %d\r\n",_model -> maxlength);
 		printf("model.realength is: %d\r\n",_model -> realength);
-		//if(strcmp(_model -> name, "data") == 0)printf("dataset is: %s\r\n",_model -> dname); 这函数有BUG，用model_dataset函数
+		//if(strcmp(_model -> name, "data") == 0)printf("dataset is: %s\r\n",_model -> dname); <EFBFBD>⺯<EFBFBD><EFBFBD><EFBFBD><EFBFBD>BUG<EFBFBD><EFBFBD><EFBFBD><EFBFBD>model_dataset<EFBFBD><EFBFBD><EFBFBD><EFBFBD>
 		return 1;
 	}
 	return 0;
@@ -346,7 +345,7 @@ void model_init(){
 	conv1_weight.array = modelmym_init(conv1_weight.name);
 	conv1_weight.maxlength = CONV1_WEIGHT_ARRSIZE;
     conv1_weight.channel = 1;
-    conv1_weight.num_kernels = 32;
+    conv1_weight.num_kernels = CONV1_BIAS_ARRSIZE;
 
 	conv2_bias.name = "conv2_bias";
 	conv2_bias.array = modelmym_init(conv2_bias.name);
@@ -356,7 +355,7 @@ void model_init(){
 	conv2_weight.array = modelmym_init(conv2_weight.name);
 	conv2_weight.maxlength = CONV2_WEIGHT_ARRSIZE;
     conv2_weight.channel = 32;
-    conv2_weight.num_kernels = 64;
+    conv2_weight.num_kernels = CONV2_BIAS_ARRSIZE;
 
 	conv3_bias.name = "conv3_bias";
 	conv3_bias.array = modelmym_init(conv3_bias.name);
@@ -366,7 +365,7 @@ void model_init(){
 	conv3_weight.array = modelmym_init(conv3_weight.name);
 	conv3_weight.maxlength = CONV3_WEIGHT_ARRSIZE;
     conv3_weight.channel = 64;
-    conv3_weight.num_kernels = 128;
+    conv3_weight.num_kernels = CONV3_BIAS_ARRSIZE;
 
 	fc1_bias.name = "fc1_bias";
 	fc1_bias.array = modelmym_init(fc1_bias.name);
@@ -383,7 +382,7 @@ 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;
+    fc2_weight.num_kernels = FC2_BIAS_ARRSIZE;
 
 	data.name = "data";
 	data.array = modelmym_init(data.name);

cnn_model.h

@@ -1,8 +1,9 @@
 #ifndef CNNMODEL_H_
 #define CNNMODEL_H_
 #include "malloc.h"
-#include "debug.h"
+#include <math.h>
 #include <stdio.h>
+#include <stdarg.h>
 #include <stdlib.h>
 #include <string.h>
 #include <stdint.h>
@@ -13,11 +14,11 @@ typedef struct {
 	char* 	name;
 	char* 	dname;
 	float* 	array;
-	u32 	maxlength;
-	u32		realength;
+	uint32_t 	maxlength;
+	uint32_t		realength;
 
-    u8      channel;
-    u8      num_kernels;
+    uint8_t      channel;
+    uint8_t      num_kernels;
 
 } Model;
 
@@ -36,16 +37,7 @@ typedef struct {
 #define FC2_BIAS_ARRSIZE (4)
 #define FC2_WEIGHT_ARRSIZE (4*128)			//896
 
-#define FC2_WEIGHT_KERNELS 4                //4个卷积核
-
-
-//1为跑原始数据   0为跑100矩阵
-#define is1300000 1
-#if is1300000
 #define DATA_ARRSIZE (1300000)
-#else
-    #define DATA_ARRSIZE (100 * 100)
-#endif
 
 
 
@@ -64,11 +56,11 @@ extern Model data;
 
 
 float* modelmym_init(char* model_name);
-u8 modelmym_free(char* model_name);
-u8 model_write(char* model_name);
-u8 model_read(char* model_name, u32 start, u32 end, u32 gap);
-u8 model_switchdata(char* model_name);
-u8 model_info(char* model_name);
+uint8_t modelmym_free(char* model_name);
+uint8_t model_write(char* model_name);
+uint8_t model_read(char* model_name, uint32_t start, uint32_t end, uint32_t gap);
+uint8_t model_switchdata(char* model_name);
+uint8_t model_info(char* model_name);
 void* model(char* model_name);
 void model_init(void);
 

dataset/conv1_bias.txt

@@ -0,0 +1,32 @@
+-0.004631
+-0.072553
+-0.001263
+-0.222981
+0.005574
+-0.430715
+-0.312284
+-0.161185
+-0.151565
+-0.060493
+0.231827
+-0.357113
+-0.438887
+0.198280
+0.193470
+-0.002525
+0.179678
+-0.308586
+-0.112759
+0.013523
+-0.020758
+-0.320550
+0.258272
+0.125773
+-0.437729
+0.151137
+-0.316827
+0.176797
+-0.178724
+0.223349
+-0.279238
+0.006178

dataset/conv1_weight.txt

@@ -0,0 +1,288 @@
+0.264396
+0.012110
+0.255190
+-0.113358
+0.184589
+0.057568
+0.191846
+0.117760
+-0.245086
+-0.005652
+0.013235
+0.293202
+-0.188579
+0.014698
+0.025587
+-0.051998
+0.127640
+0.287372
+-0.119986
+-0.208348
+0.278841
+-0.066475
+-0.081250
+0.092410
+-0.147814
+0.047256
+0.173974
+-0.329876
+-0.423296
+-0.584921
+-0.356580
+-0.229025
+-0.270002
+-0.255455
+0.316085
+0.004567
+0.028426
+-0.334845
+-0.003385
+0.140219
+0.062490
+-0.118646
+0.094126
+0.255222
+-0.107712
+0.002896
+-0.081413
+-0.043819
+0.240522
+0.022667
+0.231820
+-0.123995
+-0.079158
+-0.108327
+-0.027508
+-0.109963
+0.288606
+-0.335862
+-0.232113
+0.018983
+-0.193671
+-0.184815
+-0.126731
+-0.178352
+0.144650
+0.175241
+-0.073164
+0.199136
+-0.253890
+0.004484
+0.067553
+0.093465
+-0.234934
+-0.018499
+0.011184
+0.112343
+-0.244534
+0.285953
+0.256141
+0.238208
+-0.024591
+-0.110319
+-0.337829
+-0.268405
+-0.332071
+-0.051145
+0.120763
+-0.024749
+0.248383
+-0.101862
+-0.352574
+-0.188207
+-0.154518
+-0.308905
+-0.424403
+-0.342620
+0.047083
+-0.204670
+0.273949
+0.185371
+-0.183867
+0.222050
+-0.289864
+-0.377326
+-0.207679
+-0.126656
+0.228524
+-0.135648
+0.092356
+-0.157213
+0.134332
+0.084983
+-0.017297
+-0.296890
+-0.262142
+-0.014616
+0.111652
+-0.087568
+0.171141
+-0.299918
+0.290131
+-0.128769
+-0.010415
+0.076719
+0.221479
+-0.157894
+0.068890
+0.009761
+-0.258660
+-0.186225
+0.254293
+0.106144
+-0.124818
+-0.352556
+0.231044
+0.277191
+-0.140471
+-0.176610
+0.164834
+-0.223433
+0.117251
+-0.090913
+-0.154972
+-0.073200
+0.225833
+-0.121053
+-0.519649
+-0.030060
+0.188377
+-0.082648
+0.023897
+-0.181276
+-0.320161
+-0.050713
+-0.041318
+0.074467
+0.171705
+-0.126269
+0.033716
+-0.183754
+-0.362817
+-0.192327
+-0.358436
+0.258644
+0.221722
+0.208344
+-0.113581
+-0.216356
+-0.303875
+0.022764
+0.062494
+-0.168767
+-0.236584
+0.388117
+-0.067814
+0.008403
+0.050595
+-0.108519
+-0.262208
+0.319813
+-0.006849
+-0.024721
+0.007692
+0.137184
+0.061543
+-0.048481
+-0.166637
+-0.211058
+-0.306806
+-0.057978
+-0.096503
+0.288373
+0.151038
+-0.333258
+-0.186567
+0.161674
+0.262806
+0.237353
+-0.361782
+-0.131101
+-0.373160
+-0.334982
+-0.396515
+-0.323307
+-0.202191
+-0.373635
+0.046574
+0.177337
+-0.233079
+0.197044
+-0.324467
+0.273250
+-0.320781
+0.233871
+0.165834
+-0.151728
+-0.184531
+0.166097
+-0.040754
+0.147207
+0.131238
+0.264796
+0.291155
+-0.200281
+0.195431
+0.128389
+0.181558
+-0.252029
+0.181836
+0.083001
+-0.180665
+-0.252472
+-0.259574
+-0.150548
+-0.247819
+-0.253767
+-0.109555
+0.158506
+-0.197960
+0.196803
+0.252319
+-0.030197
+-0.055893
+0.254212
+0.229382
+-0.207656
+0.037787
+0.028231
+-0.347745
+-0.275953
+-0.166275
+-0.229568
+0.267331
+-0.257930
+0.010068
+0.209660
+0.150940
+-0.060589
+-0.319203
+0.035770
+0.092334
+-0.230210
+0.245727
+-0.068963
+0.086573
+-0.061331
+0.122903
+0.080436
+0.119416
+-0.024226
+-0.188355
+-0.234652
+0.152925
+0.201060
+0.063903
+0.123116
+0.042292
+-0.172743
+0.206093
+0.406446
+-0.190303
+-0.234680
+-0.100517
+-0.102583
+0.154145
+-0.134558
+0.108717
+0.010376

dataset/conv2_bias.txt

@@ -0,0 +1,64 @@
+-0.005641
+0.045337
+0.022688
+0.022603
+-0.003216
+-0.030439
+-0.141888
+0.079657
+-0.013354
+-0.038644
+-0.059302
+-0.031407
+-0.038632
+0.039465
+0.048158
+-0.046950
+-0.032638
+-0.062939
+-0.034895
+0.013741
+-0.037496
+-0.019306
+-0.007166
+-0.045438
+-0.017623
+-0.029193
+0.109553
+-0.093409
+0.000137
+-0.009409
+0.028830
+0.002159
+0.052856
+-0.064427
+-0.015227
+0.012225
+0.000874
+-0.005634
+0.036257
+-0.054466
+-0.003907
+-0.143422
+0.013692
+-0.030289
+-0.038533
+-0.112211
+0.034781
+-0.008203
+-0.054083
+-0.016784
+0.033634
+-0.088775
+0.017148
+0.021540
+0.015659
+0.011183
+-0.001851
+-0.031995
+-0.080671
+0.051828
+0.059321
+-0.025378
+-0.054386
+-0.016398

dataset/conv3_bias.txt

@@ -0,0 +1,128 @@
+0.080931
+-0.005371
+-0.036383
+-0.045386
+-0.054329
+0.108908
+-0.031541
+0.028897
+-0.060491
+-0.020571
+-0.018694
+-0.025861
+-0.030109
+-0.052287
+-0.096520
+-0.006112
+0.012281
+-0.040138
+-0.026996
+-0.028355
+0.023113
+-0.018347
+0.021012
+-0.024404
+-0.020810
+-0.033943
+-0.031695
+-0.002791
+-0.005490
+0.052321
+-0.037635
+-0.028975
+-0.072508
+-0.010565
+0.089627
+0.051853
+-0.049895
+-0.028532
+0.120244
+-0.065720
+-0.033159
+0.008072
+-0.022876
+0.002690
+0.028910
+0.019758
+-0.027900
+-0.018999
+-0.040230
+0.012506
+-0.044383
+-0.028107
+-0.035639
+-0.008712
+-0.042572
+-0.007128
+-0.026243
+-0.047105
+-0.071900
+0.004547
+-0.037700
+-0.033511
+-0.045946
+-0.062072
+-0.063012
+0.037648
+0.014312
+0.006599
+-0.023144
+-0.023018
+0.013904
+-0.052499
+0.017902
+-0.040357
+-0.098850
+0.011475
+0.070717
+-0.013301
+0.003174
+-0.030168
+-0.034066
+-0.013226
+-0.023046
+-0.030439
+-0.026275
+0.031229
+-0.053149
+-0.067014
+0.003809
+0.001963
+0.023235
+-0.000045
+-0.027270
+-0.007391
+-0.012155
+-0.003669
+-0.041546
+0.016943
+0.022902
+-0.045985
+-0.053092
+0.021105
+-0.032606
+-0.028661
+-0.048068
+-0.042106
+-0.017811
+-0.019530
+-0.028386
+-0.047340
+0.015859
+-0.040411
+-0.006513
+0.007619
+0.012492
+0.011398
+0.030425
+-0.049798
+-0.052375
+-0.017007
+-0.002935
+0.080796
+-0.064879
+0.012029
+0.002495
+-0.053315
+0.021447
+-0.037960

dataset/fc1_bias.txt

@@ -0,0 +1,128 @@
+-0.003303
+-0.017162
+-0.012434
+-0.042197
+-0.033317
+-0.043120
+0.000561
+-0.011677
+-0.000354
+-0.008174
+-0.032653
+-0.000064
+-0.007428
+0.006177
+-0.005123
+-0.042697
+-0.007818
+-0.025033
+0.017732
+-0.065127
+-0.036435
+-0.038140
+0.016812
+-0.012877
+-0.035449
+-0.035985
+0.000884
+-0.026337
+-0.003007
+0.001144
+-0.022054
+0.032519
+-0.008320
+-0.004585
+0.015589
+-0.028719
+-0.021946
+-0.029944
+-0.026863
+-0.012840
+-0.002333
+-0.078329
+-0.016042
+-0.001515
+-0.007396
+0.017858
+-0.014112
+-0.040561
+0.000165
+-0.032233
+0.014843
+-0.055889
+-0.032679
+-0.002448
+-0.023142
+-0.031932
+-0.061502
+-0.011828
+0.054338
+0.025108
+-0.036360
+-0.014974
+-0.005564
+0.006727
+-0.001447
+-0.011361
+-0.032685
+-0.042424
+0.028065
+-0.010904
+-0.018815
+0.019042
+-0.014854
+-0.016071
+-0.028355
+-0.010906
+-0.002750
+-0.004453
+-0.036549
+0.008669
+-0.031032
+-0.004035
+-0.004741
+0.009571
+-0.013948
+-0.024394
+-0.014121
+-0.000113
+0.030506
+-0.020288
+0.024610
+-0.008582
+0.029013
+-0.003862
+0.005664
+-0.034357
+0.015551
+-0.007669
+0.004395
+-0.022629
+-0.005918
+-0.037288
+0.000774
+-0.049784
+-0.010008
+0.006821
+-0.006700
+0.000237
+-0.005140
+-0.010332
+0.035143
+-0.009957
+-0.010226
+-0.004023
+-0.021022
+0.009519
+0.002547
+-0.019027
+-0.007719
+-0.005774
+-0.012114
+0.008235
+-0.014013
+-0.001604
+0.003618
+-0.007368
+0.010152
+-0.027849

dataset/fc2_bias.txt

@@ -0,0 +1,4 @@
+-0.039707
+-0.097658
+0.063971
+0.022999

dataset/fc2_weight.txt

@@ -0,0 +1,512 @@
+-0.058931
+0.082741
+0.021586
+-0.026039
+-0.056528
+0.057236
+-0.030001
+-0.048260
+-0.062991
+-0.012605
+-0.008995
+0.075796
+0.001260
+0.070238
+-0.038573
+-0.010643
+-0.061193
+-0.055042
+0.076177
+-0.079882
+0.052359
+0.034187
+0.046499
+0.045794
+-0.020017
+0.001001
+-0.040188
+-0.079907
+-0.022438
+0.077808
+-0.026346
+0.016867
+-0.062112
+0.064634
+-0.019558
+-0.020223
+0.074465
+-0.026598
+0.011714
+-0.064458
+-0.059716
+-0.069215
+-0.052678
+0.051622
+0.010130
+0.075898
+-0.060538
+-0.024788
+-0.023184
+-0.051868
+0.158896
+0.028924
+-0.009646
+-0.042931
+-0.054899
+-0.068251
+-0.013731
+-0.074485
+0.034882
+0.048279
+-0.028229
+-0.033060
+-0.042540
+0.069897
+-0.015747
+-0.062755
+0.050984
+-0.058438
+-0.000549
+0.037112
+-0.008702
+0.052537
+-0.032729
+0.085430
+0.005522
+0.036936
+-0.035021
+-0.047576
+0.021966
+0.049943
+0.004276
+-0.002239
+-0.025785
+0.082176
+0.004891
+-0.070603
+-0.061674
+0.076097
+0.054244
+-0.121986
+-0.098606
+0.020275
+0.089327
+-0.065931
+0.035219
+-0.030433
+0.016048
+-0.082111
+-0.005972
+-0.033918
+-0.045327
+-0.055175
+-0.021159
+-0.045618
+-0.045335
+0.080924
+-0.043546
+-0.011036
+0.033512
+0.030838
+-0.125617
+0.027708
+-0.081700
+0.065922
+0.051387
+0.084328
+0.047665
+-0.026571
+-0.035575
+-0.024575
+0.046240
+0.077493
+0.061416
+-0.060857
+0.001534
+-0.071932
+0.028963
+-0.075439
+-0.017644
+0.054737
+-0.003758
+0.052111
+0.011470
+0.055459
+-0.081584
+-0.002910
+-0.001614
+0.000437
+-0.010231
+-0.080488
+-0.063019
+0.034179
+-0.069775
+0.043572
+-0.049781
+0.072616
+0.047185
+0.030650
+0.082894
+0.046069
+-0.027637
+0.023121
+0.038936
+0.052421
+-0.069802
+0.077418
+-0.046277
+-0.008017
+0.001110
+0.032984
+-0.056455
+-0.036477
+-0.012413
+-0.005958
+-0.032892
+-0.021581
+0.007284
+0.061327
+-0.051783
+-0.007153
+-0.034280
+0.085166
+0.074945
+0.028743
+0.059292
+0.034775
+-0.081315
+0.087160
+-0.116786
+0.049679
+0.054562
+-0.052033
+0.037010
+0.039532
+0.042317
+-0.055750
+0.060417
+-0.009291
+0.044491
+0.046893
+-0.034242
+-0.014553
+-0.054940
+-0.081220
+0.085371
+-0.014649
+0.024209
+0.089537
+0.016395
+-0.023147
+0.058076
+0.070385
+0.006581
+-0.001097
+0.004920
+0.048413
+0.068944
+-0.047064
+-0.022705
+-0.037674
+-0.048809
+0.040081
+0.065193
+0.043707
+0.022925
+0.066769
+-0.012898
+-0.077912
+-0.052976
+-0.081205
+-0.023203
+-0.015376
+-0.003481
+-0.002040
+-0.040693
+-0.030009
+-0.038227
+0.033057
+-0.044845
+0.066074
+-0.025737
+0.093399
+0.059980
+-0.061411
+0.068019
+0.067066
+-0.037720
+0.035344
+-0.099108
+0.057824
+0.010723
+0.073095
+0.064890
+-0.069253
+-0.035479
+0.014207
+0.033675
+0.036457
+0.037335
+-0.056124
+0.070666
+-0.016997
+-0.042993
+0.019509
+0.010573
+-0.073225
+0.010903
+0.021510
+0.046624
+0.016918
+-0.034955
+-0.004001
+-0.071385
+0.052190
+0.054234
+0.028797
+-0.016552
+-0.049426
+-0.008711
+-0.003803
+0.060791
+-0.065849
+0.068911
+0.072407
+0.052717
+-0.037600
+-0.029691
+0.041340
+-0.003574
+0.028065
+-0.056100
+-0.039326
+0.015791
+0.011882
+0.045606
+-0.016313
+-0.063804
+-0.023912
+-0.007011
+0.080493
+-0.011968
+-0.048865
+0.068336
+0.063640
+-0.018984
+0.016588
+-0.080367
+-0.076863
+0.069094
+0.075082
+0.072882
+-0.018639
+-0.034967
+-0.034542
+-0.057999
+0.033039
+-0.045209
+0.023652
+-0.051687
+-0.077231
+0.046792
+0.048312
+0.084837
+0.036035
+0.003179
+-0.019603
+-0.036890
+-0.063492
+-0.045074
+-0.083299
+0.024020
+0.047446
+-0.078694
+-0.071320
+0.062962
+0.072505
+-0.048483
+0.005049
+0.013660
+-0.064950
+-0.032050
+-0.026682
+0.072182
+0.080960
+-0.044191
+-0.041733
+0.073146
+-0.064979
+0.078111
+-0.039892
+-0.002631
+0.024524
+0.063003
+0.063028
+0.022161
+0.036528
+0.005402
+0.017228
+0.012027
+-0.017038
+0.072777
+0.000782
+-0.042552
+0.014487
+0.000573
+0.032583
+0.048010
+0.043475
+-0.041002
+0.024025
+0.073455
+0.013298
+0.074902
+0.064317
+0.045918
+-0.005138
+0.003448
+0.078688
+0.048412
+0.047301
+0.061309
+-0.015053
+0.003955
+-0.067288
+0.076958
+0.033469
+-0.058862
+0.018029
+0.078768
+0.039408
+-0.033360
+-0.022925
+-0.119977
+-0.025087
+-0.068750
+0.035327
+0.073855
+0.047400
+-0.075196
+0.010550
+-0.046624
+0.029754
+-0.057842
+-0.052033
+0.017090
+0.019803
+0.015316
+-0.043967
+0.015191
+-0.061190
+-0.021009
+0.074461
+0.034960
+-0.051407
+-0.044240
+0.029745
+-0.033547
+-0.027608
+-0.006036
+0.030190
+-0.007352
+-0.065457
+0.040382
+0.001965
+-0.033753
+0.075904
+0.003190
+0.088717
+0.082109
+-0.039526
+-0.002976
+-0.028223
+-0.028789
+-0.069433
+0.030751
+-0.041434
+0.007254
+0.040745
+0.063343
+-0.102280
+-0.021423
+-0.042879
+0.065601
+0.064359
+-0.098568
+-0.014232
+-0.023211
+-0.014910
+0.090127
+-0.109398
+-0.166171
+-0.019305
+0.074800
+-0.038058
+0.012383
+0.078751
+0.045327
+-0.020477
+-0.010302
+-0.045507
+0.033407
+-0.035723
+0.075363
+-0.055598
+0.062439
+0.006332
+-0.011081
+-0.023165
+-0.097509
+0.051888
+0.005613
+0.042150
+0.026668
+-0.085304
+0.069341
+-0.081594
+-0.054077
+0.075259
+-0.074176
+-0.072364
+-0.071474
+-0.015352
+-0.084154
+0.034522
+0.073654
+-0.020917
+-0.002134
+-0.049188
+-0.054401
+-0.064645
+-0.093849
+0.072262
+0.071346
+-0.067045
+-0.009426
+-0.000583
+0.070907
+-0.070580
+0.029851
+0.061377
+-0.040588
+0.056680
+-0.075749
+0.076125
+0.009514
+0.015952
+0.092470
+-0.072424
+0.045863
+0.036735
+-0.078604
+0.061958
+-0.081167
+0.005184
+0.022731
+-0.023145
+-0.013089
+-0.000382
+-0.053056
+-0.019507
+0.018815
+-0.035313

debug.c

@@ -1,27 +0,0 @@
-#include "debug.h"
-
-
-u8 _DEBUG = 0;
-
-
-
-void DEBUG(){
-	_DEBUG = !_DEBUG;
-	printf("DEBUG IS: %s",(_DEBUG ? "ON" : "OFF"));
-}
-
-
-
-void DEBUG_PRINTF(const char *fmt, ...) {
-	if(_DEBUG){
-		va_list args;
-		va_start(args, fmt);
-		vprintf(fmt, args);
-		va_end(args);
-	}
-}
-
-
-
-void SDRAM_USED(){
-}

debug.h

@@ -1,19 +0,0 @@
-#ifndef DEBUG_H_
-#define DEBUG_H_
-
-#include <stdio.h>
-#include <stdarg.h>
-#include <stdlib.h>
-#include <string.h>
-#include <stdint.h>
-#include "malloc.h"
-
-typedef uint32_t u32;
-typedef uint16_t u16;
-typedef uint8_t u8;
-
-void DEBUG(void);
-void DEBUG_PRINTF(const char *fmt, ...);
-void SDRAM_USED(void);
-
-#endif

main.c

@@ -5,108 +5,10 @@
 #include "cnn_model.h"
 #include "cnn.h"
 
-
-
-void run_dataset(){
-    char* modelusearr_origin[] = {
-            "C1autosave00095_right_new_2",
-            "C1autosave00096_right_new_2",
-            "C1autosave00097_right_new_2",
-            "C1autosave00098_right_new_2",
-            "C1autosave00099_right_new_2",
-            "C1autosave00100_right_new_2",
-            "C1autosave00101_right_new_2",
-            "C1autosave00102_right_new_2",
-            "C1autosave00103_right_new_2",
-            "C1autosave00104_right_new_2",
-            "C1autosave00105_right_new_2",
-            "C1autosave00106_right_new_2",
-            "C1autosave00107_right_new_2",
-            "C1autosave00108_right_new_2",
-            "C1autosave00109_right_new_2",
-            "C1autosave00110_right_new_2",
-            "C1autosave00111_right_new_2",
-            "C1autosave00112_right_new_2",
-            "C1autosave00113_right_new_2",
-            "C1autosave00114_right_new_2",
-            "C1autosave00115_right_new_2",
-            "C1autosave00116_right_new_2",
-            "C1autosave00117_right_new_2",
-            "C1autosave00118_right_new_2",
-            "C1autosave00119_right_new_2",
-            "C1autosave00120_right_new_2",
-            "C1autosave00121_right_new_2",
-            "C1autosave00122_right_new_2",
-            "C1autosave00123_right_new_2",
-            "C1autosave00124_right_new_2",
-
-            "filtered_C1autosave00011_right_new",
-            "filtered_C1autosave00015_right_new",
-            "filtered_C1autosave00043_right_new",
-            "filtered_C1autosave00067_right_new",
-            "filtered_C1autosave00090_right_new",
-            "filtered_C1autosave00106_right_new",
-            "filtered_C1autosave00118_right_new",
-
-            "filtered_C1autosave00007_right_new",
-            "filtered_C1autosave00035_right_new",
-            "filtered_C1autosave00036_right_new",
-            "filtered_C1autosave00040_right_new",
-            "filtered_C1autosave00053_right_new",
-            "filtered_C1autosave00061_right_new",
-            "filtered_C1autosave00074_right_new",
-            "filtered_C1autosave00077_right_new",
-            "filtered_C1autosave00080_right_new",
-            "filtered_C1autosave00085_right_new",
-            "filtered_C1autosave00098_right_new",
-            "filtered_C1autosave00100_right_new",
-            "filtered_C1autosave00104_right_new",
-            "filtered_C1autosave00122_right_new",
-            "filtered_C1autosave00124_right_new",
-
-            "filtered_C1autosave00108_right_new",
-
-            "filtered_C1autosave00004_right_new",
-            "filtered_C1autosave00039_right_new",
-            "filtered_C1autosave00062_right_new",
-    };
-    char* modelusearr_output[] = {
-            "split_1",
-            "split_2",
-            "split_3",
-            "split_4",
-            "split_5",
-            "split_6",
-            "split_7",
-            "split_8",
-            "split_9",
-            "split_10",
-    };
-/* 可用数据集如下
- * modelusearr_origin
- * modelusearr_output
-*/
-    #define modelusearr modelusearr_output
-    for(int a=0;a<(sizeof(modelusearr) / sizeof(modelusearr[0]));a++){
-        SDRAM_USED();
-        model_switchdata(modelusearr[a]);
-        cnn_run();
-    }
-    printf("\r\n运行完成\r\n");
-}
-
-
-
 int main(){
     model_init();
     model_write("all");
-
-#if is1300000
-    run_dataset();
-#else
-    model_switchdata("EEC30B3F 2024-11-13 11-49-24");
+    model_switchdata("split_3");
     cnn_run();
-#endif
-
-    DEBUG_PRINTF("\r\nEnd结束");
+    printf("\r\n<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>\r\n");
 }