diff --git a/cnn.c b/cnn.c index b7c445d..caf704a 100644 --- a/cnn.c +++ b/cnn.c @@ -19,6 +19,8 @@ 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 @@ -81,6 +83,7 @@ float* convolution(Model model_w, Model model_b, const float* input_matrix, int } + //num_kernels 卷积核的个数：32 //area 池化的面积：2*2 //input_matrix 输入图像 @@ -121,6 +124,7 @@ float* pooling(Model model_w, const float* input_matrix, u8 input_matrix_length) } + void print_rslt(float* rslt, u8 input_matrix_length, u32 length){ int _tmp = 0; printf("[0:0]"); @@ -130,119 +134,88 @@ void print_rslt(float* rslt, u8 input_matrix_length, u32 length){ printf("\n[%d:%d]",++_tmp,i+1); } } - printf("\r\n\r\n"); + printf("\r\n"); } - - - -int main(){ - model_init(); - model_write("all"); - model_switchdata("data"); +void cnn_run(){ //第一层：填充102 * 102 float* expand_matrix_1 = expand(data.array, 100, 1); -// print_rslt(expand_matrix_1, 102, (1*10*102)); float* conv_rlst_1 = convolution(conv1_weight,conv1_bias,expand_matrix_1, 102); -// 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, (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*10*52)); float* conv_rlst_2 = convolution(conv2_weight,conv2_bias,expand_matrix_2, 52); -// print_rslt(conv_rlst_2, 50, (64*50*50)); float* pool_rslt_2 = pooling(conv2_weight, conv_rlst_2, 50); -// print_rslt(pool_rslt_2, 25, (1*25*25)); - //第三层：填充 64 * 27 * 27 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)); float* pool_rslt_3 = pooling(conv3_weight, conv_rlst_3, 25); -// print_rslt(pool_rslt_3, 12, (1*12*12)); - - - - - - - - - - - - - - -// 隐藏层参数地址 - float *affine1_rslt = (float *) malloc(sizeof(float)*128); - memset(affine1_rslt, 0, sizeof(float)*128); - float affine1_temp; // 临时变量，用于存储全连接层的中间结果 + { + // 隐藏层参数地址 + float *affine1_rslt = (float *) malloc(sizeof(float)*128); + memset(affine1_rslt, 0, sizeof(float)*128); + 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(u8 n=0; n<128; n++) - { - affine1_temp = 0; // 每个神经元的输出初始化为0 - - // 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 - for(int i=0; i<(128*12*12); i++) + for(u8 n=0; n<128; n++) { - affine1_temp = affine1_temp + pool_rslt_3[i] * fc1_weight.array[i+(128*12*12)*n]; + affine1_temp = 0; // 每个神经元的输出初始化为0 + + // 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 + for(int i=0; i<(128*12*12); i++) + { + affine1_temp = affine1_temp + pool_rslt_3[i] * fc1_weight.array[i+(128*12*12)*n]; + } + + // 加上对应神经元的偏置 + affine1_temp = affine1_temp + fc1_bias.array[n]; + + // 激活函数：ReLU（将小于0的值设为0） + if(affine1_temp > 0) + affine1_rslt[n] = affine1_temp; // 如果结果大于0，存入结果数组 + else + affine1_rslt[n] = 0; // 否则存入0 } - // 加上对应神经元的偏置 - affine1_temp = affine1_temp + fc1_bias.array[n]; - - // 激活函数：ReLU（将小于0的值设为0） - if(affine1_temp > 0) - affine1_rslt[n] = affine1_temp; // 如果结果大于0，存入结果数组 - else - 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_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） // 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） + 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） // 遍历10个输出神经元（假设有10个类别） - for(int 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(int n=0; n<7; n++) { - affine2_temp = affine2_temp + fc2_weight.array[i+128*n] * affine1_rslt[i]; + 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++) + { + affine2_temp = affine2_temp + fc2_weight.array[i+128*n] * affine1_rslt[i]; + } + + // 加上对应神经元的偏置 + affine2_temp = affine2_temp + fc2_weight.array[n]; + affine2_rslt[n] = affine2_temp; // 存储输出层的结果 + + // 寻找最大值 + if(temp <= affine2_rslt[n]) + { + temp = affine2_rslt[n]; // 更新最大值 + predict_num = n; // 记录最大值对应的类别索引 + } } - // 加上对应神经元的偏置 - affine2_temp = affine2_temp + fc2_weight.array[n]; - affine2_rslt[n] = affine2_temp; // 存储输出层的结果 + print_rslt(affine2_rslt,7,7); - // 寻找最大值 - if(temp <= affine2_rslt[n]) - { - temp = affine2_rslt[n]; // 更新最大值 - predict_num = n; // 记录最大值对应的类别索引 - } + printf("Label is:%d\r\n",predict_num+1); } - - print_rslt(affine2_rslt,1,7); - - printf("Label is:%d",predict_num+1); - - return 0; } diff --git a/cnn.h b/cnn.h index 5d72551..5b754f7 100644 --- a/cnn.h +++ b/cnn.h @@ -6,13 +6,7 @@ - - - - - - - +void cnn_run(void); diff --git a/cnn_model.c b/cnn_model.c index e866b5c..e6969ce 100644 --- a/cnn_model.c +++ b/cnn_model.c @@ -194,8 +194,12 @@ u8 model_write(char* model_name) return 200; } - sprintf(_path, "./dataset/%s.txt", _model -> name); + sprintf(_path, "./dataset/%s.txt", _model -> dname ? _model -> dname : _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); + 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); @@ -227,9 +231,9 @@ u8 model_write(char* model_name) 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); - return 1; + return 0; } - return 0; + return 1; } @@ -273,7 +277,7 @@ u8 model_switchdata(char* data_name){ return 0; }else{ u8 _res = model_write(data_name); - if (_res == 0) { + if (_res) { DEBUG_PRINTF("Data数据集[%s]切换失败！！\r\n",data_name); return 0; } diff --git a/debug.c b/debug.c index f7bbac0..c25bea5 100644 --- a/debug.c +++ b/debug.c @@ -1,7 +1,7 @@ #include "debug.h" -u8 _DEBUG = 1; +u8 _DEBUG = 0; diff --git a/main.c b/main.c index 2161761..6787288 100644 --- a/main.c +++ b/main.c @@ -1,20 +1,88 @@ -//// -//// Created by Qi on 2024/11/9. -//// // -//#include "cnn_model.h" +// Created by Qi on 2024/11/9. // -//void main(){ -// u8 res; -// model_init(); -// model_write("all"); -// model_switchdata("C1autosave00095_right_new_2"); -// -// -// -// -// -// -// model_info("all"); -// DEBUG_PRINTF("\r\nEnd结束"); -//} \ No newline at end of file + +#include "cnn_model.h" +#include "cnn.h" + + +void run_dataset(){ + char* modelusearr[] = { + "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", + }; + 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"); + + run_dataset(); + + DEBUG_PRINTF("\r\nEnd结束"); +} \ No newline at end of file