# Edge-Engine ## Edge : 一个开源的科学计算引擎 [README for English_version](./README_EN.md) [ ](https://github.com/AllenZYJ/Edge-Computing-Engine/blob/add-license-1/LICENSE) > 项目开始日期 : 2019/10/01 > 目前项目总代码 : 810 行 > > 测试 : main.cpp | nerual_network.cpp | 新增全连接神经网络架构(新增全连接网络正向传播和反向传播的测试demo) > > 测试环境: > > MacBook Pro > > 编译器环境: > > Configured with: --prefix=/Applications/Xcode.app/Contents/Developer/usr --with-gxx-include-dir=/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.14.sdk/usr/include/c++/4.2.1 > Apple LLVM version 10.0.1 (clang-1001.0.46.4) > Target: x86_64-apple-darwin18.7.0 > Thread model: posix 这是什么?  ## 安装编译 git clone git@github.com:AllenZYJ/Edge-Computing-Engine.git cd to this dir ` 进入root目录: 执行 ``` make make install ``` 编译demo入口程序 ```shell ➜ edge-computing-engine git:(master) ✗ g++ main.cpp -o ma -lautodiff ``` 或者BP测试程序 ```shell ➜ edge-computing-engine git:(master) ✗ g++ nerual_network.cpp -o ma ``` 运行 ```shell ➜ edge-computing-engine git:(master) ✗ ./main ``` ## 新的demo程序实现5层全连接层,可自定义神经元和激活函数,损失函数 全连接层使用方法: 第一层的权重自定义,而后调用forward函数前向传播一层,自动求出激活以后的值,激活函数可自定义. 首先定义一个权重矩阵和偏置矩阵,第一个矩阵的维度大小使用数据列去定义: ```c Matrix bias1 = CreateRandMat(2,1); Matrix weight1 = CreateRandMat(2,data.col); ``` 之后可以输出第一层前向传播的值,同时可以定义下一层的bias的维度, row使用第一层的权重矩阵的行,第二层的权重矩阵的行使用了第一层的输出的行, 而列自行定义即可, 这一点体现了前向传播算法的维度相容. 也就是: ```c Matrix output1 = sequaltial.forward(get_T(get_row(data_mine,index)),weight1,bias1); ``` ```c Matrix weight2 = CreateRandMat(output1.row,2); Matrix bias2 = CreateRandMat(weight2.row,1); Matrix output2 = sequaltial.forward(output1,weight2,bias2); ``` 同时第二层的输出也可以求出来,以此类推 . 最终输出代码见nerual_test.cpp  代码: ```c Matrix data_mine = CreateRandMat(2,1); Matrix label = CreateMatrix(2,1); Matrix weight1 = CreateRandMat(2,2); Matrix weight2 = CreateRandMat(2,2); Matrix weight3 = CreateRandMat(2,2); Matrix weight4 = CreateRandMat(2,2); for(int epoch = 0;epoch<20;epoch++) { cout_mat(weight1); edge_network sequaltial(2,2); Matrix output1 = sequaltial.forward(data_mine,weight1); Matrix output2 = sequaltial.forward(output1,weight2); Matrix output3 = sequaltial.forward(output2,weight3); Matrix output4 = sequaltial.forward(output3,weight4); Matrix output_end = sequaltial.end_layer_backward(label,output4); //get the forward Matrix backward1 = sequaltial.backward(output_end,output3,weight4); Matrix grad_w1w2 = mul_simple(backward1,data_mine); Matrix backward2 = sequaltial.backward(backward1,output2,weight3); Matrix grad_w3w4 = mul_simple(backward2,data_mine); Matrix backward3 = sequaltial.backward(backward2,output1,weight2); Matrix grad_w5w6 = mul_simple(backward3,data_mine); Matrix backward4 = sequaltial.backward(backward3,output4,weight1); Matrix grad_w7w8 = mul_simple(backward4,data_mine); weight1 = subtract(weight1,times_mat(0.0001,padding(grad_w1w2,2,2))); weight2 = subtract(weight2,times_mat(0.0001,padding(grad_w3w4,2,2))); weight3 = subtract(weight3,times_mat(0.0001,padding(grad_w5w6,2,2))); weight4 = subtract(weight4,times_mat(0.0001,padding(grad_w7w8,2,2))); } ``` ```shell ---------epoch: 0------------ loss: 4.65667 loss: 3.28273 ---------epoch: 1------------ loss: 4.65655 loss: 3.28265 ---------epoch: 2------------ loss: 4.65643 loss: 3.28257 ---------epoch: 3------------ loss: 4.65631 loss: 3.28249 ---------epoch: 4------------ loss: 4.65619 loss: 3.2824 ---------epoch: 5------------ loss: 4.65607 loss: 3.28232 ---------epoch: 6------------ loss: 4.65596 loss: 3.28224 ---------epoch: 7------------ loss: 4.65584 loss: 3.28216 ---------epoch: 8------------ loss: 4.65572 loss: 3.28208 ---------epoch: 9------------ loss: 4.6556 loss: 3.282 ---------epoch: 10------------ loss: 4.65548 loss: 3.28192 ---------epoch: 11------------ loss: 4.65536 loss: 3.28184 ---------epoch: 12------------ loss: 4.65524 loss: 3.28176 ---------epoch: 13------------ loss: 4.65512 loss: 3.28168 ---------epoch: 14------------ loss: 4.65501 loss: 3.2816 ---------epoch: 15------------ loss: 4.65489 loss: 3.28152 ---------epoch: 16------------ loss: 4.65477 loss: 3.28144 ---------epoch: 17------------ loss: 4.65465 loss: 3.28136 ---------epoch: 18------------ loss: 4.65453 loss: 3.28128 ---------epoch: 19------------ loss: 4.65441 loss: 3.2812 ``` ## Bp反向传播的demo程序基于Pytorch官方代码模拟实现测试 迭代结果 : W1: 0.6944 1.52368 -1.46644 -0.154097 W2: 1.10079 0.462984 loss: 0.559269 epoch:100 , 可自行测试. 输出最终损失和参数迭代结果. -----------split-line----------- 2.79955 0.36431 -0.451694 epoch: 100 error: 6.05895 -----------split-line----------- 0.009167(sum of loss) ### 目前实现的程序接口 ### API: - [x] Matrix read_csv(string &file_path)读取格式化文件(csv),返回一个自动计算长度的矩阵. - [x] 实现格式化文件写入接口.比较pandas.to_csv. - [x] 矩阵广播机制,实现padding接口 - [x] 全连接层前向传播和反向传播接口,支持自动求导 - [x] 矩阵微分和自动求导接口封装 - [x] int save_txt(Matrix mid1,string path = "./",string delimiter = ",",string header="./") 设计文件流获取文件头部接口 , 写入格式化文件 , 已设计支持矩阵类型数据写入,支持自定义表头,写入文件路径 , 自定义分隔符,默认为" , ". - [x] Create a matrix : create(row,cols)开辟一个矩阵结构的内存,元素初值为0; - [x] Change the element for matrix void move_ele(int &ele1, int &ele2),修改某一个位置的元素的值. - [x] Matrix1+Matrix2 : Matrix add(Matrix mid1,Matrix mid2,int flag=1),矩阵加和操作接口,可选位运算加速. - [x] Flag is how to compete the ele ,default 1 ,bitwise operation(位运算加速). - [x] Matrix1-Matrix2 : Matrix subtract(Matrix mid1,Matrix mid2) - [x] Matrix1*Matrix2 : Matrix mul(Matrix mid1,Matrix mid2) - [x] Matrix1*n : Matrix times_mat(int times,Matrix mid1) - [x] Matrix1's Transposition : Matrix get_T(Matrix mid1)矩阵转置 - [x] Mul(matrix1,matrix2)矩阵乘积(完整数学定义). - [x] double* flatten(Matrix mid1) : Return a flattened array.矩阵展开 - [x] Matrix matrix_rs(Matrix mid1,int rs_row,int rs_col) 矩阵的结构压缩 - [x] double matrix_sum(Matrix mid1)矩阵求和 - [x] double matrix_mean(Matrix mid1)均值 - [x] Matrix appply(Matrix mid1,Matrix mid2,int axis = 0)矩阵拼接 - [x] Matrix iloc(Matrix mid1,int start_x=0,int end_x=0,int start_y=0,int end_y=0)矩阵切片 - [x] Matrix mul_simple(Matrix mid1,Matrix mid2)为了贴合机器学习的需要,实现了矩阵对应元素相乘,请与传统意义的矩阵乘法区分开. - [x] Relu激活函数矩阵接口 - [x] 均方误差矩阵接口 - [x] 创建随机权重矩阵接口 ### 即将着手开发: - [ ] 卷积神经网络定义(包括但不限于卷积核,池化层定义,自定义损失接口). - [ ] 随机森林算法封装. - [ ] 主流网络架构实现. ## 反向传播测试demo: ```c #include #include #include #include #include #include #include #include"./matrix/matrix_def.h" #include"./matrix/matrix_pro.h" #include"./welcome/score_wel.cpp" #include"./logistic/logistic_def.h" #include"./file_pro/data_read.h" using namespace std; clock_t start, stop; double duration; int main() { welcome(); string path = "./data/nerual_data.csv"; Matrix data = read_csv(path); Matrix bais = CreateMatrix(data.row,1); Matrix x = iloc(data,0,100,0,2); Matrix y = iloc(data,0,100,2,3); int N=100,in_Dim=2,H_num=2,out_Dim=2; double learning_rate = 0.0001; Matrix W1 = CreateRandMat(in_Dim,H_num); Matrix W2 = CreateRandMat(H_num,out_Dim); cout_mat(W1); cout_mat(W2); for(int epoch = 0;epoch<100;epoch++) { Matrix x_w1 = mul(x,W1); Matrix re = mat_relu(x_w1); Matrix out = mul(re,W2); Matrix mat_sq = mat_sq_loss(out,y); Matrix grad_y_pred = times_mat(2.0,subtract(out,y)); Matrix grad_w2 = mul(get_T(re),grad_y_pred); Matrix grad_h_relu = mul(grad_y_pred,get_T(W2)); Matrix grad_h_relu_copy = mat_relu(grad_h_relu); Matrix grad_w1 = mul(get_T(x),grad_h_relu_copy); Matrix dw1 = times_mat(learning_rate,mul(get_T(x),grad_h_relu_copy)); W1 = subtract(W1,dw1); W2 = subtract(W2,times_mat(learning_rate,grad_w2)); cout<<"W1: "; cout_mat(W1); cout<<"W2: "; cout_mat(W2); cout<<"loss"<<": "; cout< 参数 axis=0 : | 0 | 7 | 2 | | ---- | ---- | ---- | | 0 | 3 | 1 | | 0 | 0 | 0 | | 0 | 0 | 11 | | 0 | 7 | 2 | | 0 | 3 | 1 | | 0 | 0 | 0 | | 0 | 0 | 11 | ------ > axis = 1: | 0 | 7 | 2 | 0 | 7 | 2 | | ---- | ---- | ---- | ---- | ---- | ---- | | 0 | 3 | 1 | 0 | 3 | 1 | | 0 | 0 | 0 | 0 | 0 | 0 | | 0 | 0 | 11 | 0 | 0 | 11 | ------ ## 更新2019/11/18/00:12 - [x] read_csv 通过文件流读取逗号分隔符文件,返回一个自动计算长度的矩阵. 例如 CSV's head : | -0.017612 | 14.053064 | 0 | | --------- | --------- | ---- | | -1.395634 | 4.662541 | 1 | | -0.752157 | 6.53862 | 0 | | -1.322371 | 7.152853 | 0 | | 0.423363 | 11.054677 | 0 | | 0.406704 | 7.067335 | 1 | Get:  ## Logistic Regression demo base Edge: ```c #include #include #include #include #include #include #include"./matrix/matrix_def.h" #include"./matrix/matrix_pro.h" #include"./welcome/score_wel.cpp" #include"./logistic/logistic_def.h" #include"./file_pro/data_read.h" using namespace std; clock_t start, stop; double duration; int main() { welcome(); string path = "./new_data2.csv"; Matrix data = read_csv(path); Matrix bais = CreateMatrix(data.row,1); data = appply(data,bais,1); Matrix y = iloc(data,0,0,3,4); Matrix x_1 = iloc(data,0,0,0,3); Matrix x_2 = get_T(x_1); double alpha = 0.002; int max_epoch = 100; Matrix weight = CreateMatrix(3,1); change_va(weight,0,0,1); change_va(weight,1,0,1); change_va(weight,2,0,1); int epoch = 0; for(epoch = 0;epoch<=max_epoch;epoch++) { cout<<"-----------split-line-----------"< 1. 矩阵元素默认为1 > 2. 使用位运算加速防止填充过大的数值,但是会损失一定精度,慎用. > 3. 记得delete(matrix)在你使用完一个矩阵计算单元以后. > 4. api接口更多的接近于pandas和numpy的使用习惯. > 5. 更多的细节参见目前最新的代码 > 6. 欢迎star和关注. > 7. autodiff部分感谢国外博主Omar的思路提醒. > ------

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