Edge
4 years ago
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| @@ -1,21 +1,548 @@ | |||
| #### 从命令行创建一个新的仓库 | |||
| new line | |||
| ```bash | |||
| touch README.md | |||
| git init | |||
| git add README.md | |||
| git commit -m "first commit" | |||
| git remote add origin https://git.trustie.net/Edge/Edge-Computing-Engine.git | |||
| git push -u origin master | |||
| <div align=center><img src="./picture/01.svg"/></div> | |||
| # Edge-Engine | |||
| ## Edge : 一个开源的科学计算引擎 | |||
| [README for English_version](./README_EN.md) | |||
| 声明：本项目禁止闭源商用，如有需要请和作者取得联系 | |||
| email: zk@likedge.top | |||
| <br>[](https://raw.githubusercontent.com/tesseract-ocr/tesseract/master/LICENSE)</br> | |||
| ------ | |||
| > 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 | |||
| <img src="image-20200418210521131.png" alt="image-20200418210521131" /> | |||
| ------ | |||
| 这是什么? | |||
|  | |||
| ## 安装编译 | |||
| ``` | |||
| git clone git@github.com:AllenZYJ/Edge-Computing-Engine.git | |||
| cd to install_diff | |||
| ``` | |||
| 进入install_diff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 | |||
| ``` | |||
| 最新卷积实现： | |||
| ```c++ | |||
| double conv_test(Matrix mid1,int input_dim = 3,int output_channels = 3,int stride = 1,int kernel_size = 2,int mode = 0,int padding = 0) | |||
| ``` | |||
| 序贯模型api使用方法: | |||
| edge_network(int input, int num_neuron) | |||
| 作为序列模型api | |||
| edge_network作为一个类型存在,位于matrix_grad.h中结构体类型的数据 | |||
| 定义了前向传播函数,前向传播无激活版,反向传播,末层反向传播,四大最常用的函数主体. | |||
| 完整的序列模型: | |||
|  | |||
| ## 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); | |||
| ``` | |||
| #### 从命令行推送已经创建的仓库 | |||
| ```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  | |||
| ```bash | |||
| git remote add origin https://git.trustie.net/Edge/Edge-Computing-Engine.git | |||
| git push -u origin master | |||
| 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<iostream> | |||
| #include<ctime> | |||
| #include<string> | |||
| #include<time.h> | |||
| #include<math.h> | |||
| #include<fstream> | |||
| #include<stdlib.h> | |||
| #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<<matrix_sum(mat_sq)/100<<endl; | |||
| } | |||
| } | |||
| ``` | |||
| ## 演示:矩阵乘法 | |||
| Matrix **A**： | |||
| | 第1列 | 第2列 | 第3列 | 第4列 | 第5列 | | |||
| | ------- | ------- | ------- | ------- | ------- | | |||
| | 72.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 64.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 16.0000 | 8.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 56.0000 | 16.0000 | 32.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| MAtrix **B**： | |||
| | 第1列 | 第2列 | 第3列 | 第4列 | 第5列 | 第6列 | | |||
| | ------- | ------- | ------- | ------- | ------ | ------ | | |||
| | 72.0000 | 0.0000 | 16.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 64.0000 | 8.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 56.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 16.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 32.0000 | 0.0000 | 0.0000 | | |||
| To | |||
| | 第1列 | 第2列 | 第3列 | 第4列 | 第5列 | 第6列 | | |||
| | --------- | --------- | --------- | --------- | ------ | ------ | | |||
| | 5184.0000 | 0.0000 | 1152.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 4096.0000 | 512.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 1152.0000 | 512.0000 | 320.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 4416.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| ## 演示: 矩阵展开(flatten). | |||
| double* flatten(Matrix mid1) | |||
| | 1 | 2 | 3 | | |||
| | :--: | :--: | :--: | | |||
| | 2 | 4 | 6 | | |||
| | 7 | 8 | 9 | | |||
| To | |||
| | 1 | 2 | 3 | 2 | 4 | 6 | 7 | 8 | 9 | | |||
| | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | :----------------- | | |||
| | | | | | | | | | Like numpy.flatten | | |||
| function: | |||
| ## 演示: 邻接矩阵的参数定义: | |||
| Matrix appply(Matrix mid1,Matrix mid2,int axis = 0) | |||
| > 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<iostream> | |||
| #include<ctime> | |||
| #include<string> | |||
| #include <time.h> | |||
| #include <math.h> | |||
| #include <fstream> | |||
| #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-----------"<<endl; | |||
| Matrix temp_mul = mul(x_1,weight); | |||
| Matrix h =e_sigmoid(temp_mul); | |||
| Matrix error = subtract(y,h); | |||
| Matrix temp_update = mul(x_2,error); | |||
| Matrix updata = add(weight,times_mat(alpha,temp_update),0); | |||
| cout_mat(weight); | |||
| cout<<"epoch: "<<epoch<<" error: "<<matrix_sum(error)<<endl; | |||
| cout<<"-----------split-line-----------"<<endl; | |||
| } | |||
| stop = clock(); | |||
| printf("%f\n", (double)(stop - start) / CLOCKS_PER_SEC); | |||
| return 0; | |||
| } | |||
| ``` | |||
| Something : | |||
| > 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. | |||
| > | |||
| ------ | |||
| <div align = center><img src = './picture/星月.svg'></div> | |||
| 个人小站:[极度空间](http://likedge.top/) | |||
| 作者邮箱:zk@likedge.top | edge@ibooker.org.cn | |||
| QQ:2533524298 | |||
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| <div align=center><img src="./picture/01.svg"/></div> | |||
| # Edge-Engine | |||
| ## Edge : 一个开源的科学计算引擎 | |||
| [ ](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 : 709 行 | |||
| > | |||
| > 测试代码 : 810 行 | |||
| > | |||
| > 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 | |||
| > | |||
| > 目前实现的: | |||
| How to install and run the demo: | |||
| `git clone git@github.com:AllenZYJ/Edge-Computing-Engine.git` | |||
| `cd to this dir ` | |||
| `g++ main.cpp -o main` | |||
| `./main` | |||
| Matrix API: | |||
| - [x] Matrix read_csv(string &file_path) | |||
| - [x] Create a matrix : create(row,cols) | |||
| - [x] Change the element for matrix void move_ele(int &ele1, int &ele2) | |||
| - [x] Matrix1+Matrix2 : Matrix add(Matrix mid1,Matrix mid2,int flag=1) | |||
| - [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) | |||
| - [x] Mul(matrix1,matrix2) | |||
| - [x] double* flatten(Matrix mid1) : Return a flattened array. | |||
| - [x] Matrix matrix_rs(Matrix mid1,int rs_row,int rs_col) | |||
| - [x] double matrix_sum(Matrix mid1) | |||
| - [x] double matrix_mean(Matrix mid1) | |||
| - [x] Matrix appply(Matrix mid1,Matrix mid2,int axis = 0) | |||
| - [x] Matrix iloc(Matrix mid1,int start_x=0,int end_x=0,int start_y=0,int end_y=0) | |||
| ## Demo: mat*mat | |||
| Matrix **A**： | |||
| | 第1列 | 第2列 | 第3列 | 第4列 | 第5列 | | |||
| | ------- | ------- | ------- | ------- | ------- | | |||
| | 72.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 64.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 16.0000 | 8.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 56.0000 | 16.0000 | 32.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| MAtrix **B**： | |||
| | 第1列 | 第2列 | 第3列 | 第4列 | 第5列 | 第6列 | | |||
| | ------- | ------- | ------- | ------- | ------ | ------ | | |||
| | 72.0000 | 0.0000 | 16.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 64.0000 | 8.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 56.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 16.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 32.0000 | 0.0000 | 0.0000 | | |||
| To | |||
| | 第1列 | 第2列 | 第3列 | 第4列 | 第5列 | 第6列 | | |||
| | --------- | --------- | --------- | --------- | ------ | ------ | | |||
| | 5184.0000 | 0.0000 | 1152.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 4096.0000 | 512.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 1152.0000 | 512.0000 | 320.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 4416.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | |||
| ## Demo : mat.flatten | |||
| double* flatten(Matrix mid1) | |||
| | 1 | 2 | 3 | | |||
| | :--: | :--: | :--: | | |||
| | 2 | 4 | 6 | | |||
| | 7 | 8 | 9 | | |||
| To | |||
| | 1 | 2 | 3 | 2 | 4 | 6 | 7 | 8 | 9 | | |||
| | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | :----------------- | | |||
| | | | | | | | | | Like numpy.flatten | | |||
| ## Demo : apply nearly mat | |||
| Matrix appply(Matrix mid1,Matrix mid2,int axis = 0) | |||
| > if 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 | | |||
| - [x] read_csv | |||
| - [x] return a matrix | |||
| CSV 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: | |||
|  | |||
| ## Test demo: | |||
| ```c | |||
| #include<iostream> | |||
| #include<ctime> | |||
| #include<string> | |||
| #include <time.h> | |||
| #include <math.h> | |||
| #include <fstream> | |||
| #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-----------"<<endl; | |||
| Matrix temp_mul = mul(x_1,weight); | |||
| Matrix h =e_sigmoid(temp_mul); | |||
| Matrix error = subtract(y,h); | |||
| Matrix temp_update = mul(x_2,error); | |||
| Matrix updata = add(weight,times_mat(alpha,temp_update),0); | |||
| cout_mat(weight); | |||
| cout<<"epoch: "<<epoch<<" error: "<<matrix_sum(error)<<endl; | |||
| cout<<"-----------split-line-----------"<<endl; | |||
| } | |||
| stop = clock(); | |||
| printf("%f\n", (double)(stop - start) / CLOCKS_PER_SEC); | |||
| return 0; | |||
| } | |||
| ``` | |||
| Something : | |||
| > 1. Matrix'element is default 1 | |||
| > 2. Dynamically allocate memory to prevent matrix from being too large | |||
| > 3. To save memory and delete later, use pointer to open up array space temporarily | |||
| > 4. if free please delete(matrix); | |||
| > 5. Api design like numpy or pandas | |||
| > 6. Talking is cheap u can get the code | |||
| > 7. welcome 🏃watched and star. | |||
| > | |||
| ------ | |||
| <div align = center><img src = './picture/星月.svg'></div> | |||
| 个人小站:[极度空间](likedge.top) | |||
| 作者邮箱:zk@likedge.top | edge@ibooker.org.cn | |||
| QQ:2533524298 | |||
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| #ifndef DYADIC_OPERATION_RESULT | |||
| #define DYADIC_OPERATION_RESULT | |||
| struct DyadicOperationResult { | |||
| double value; | |||
| double left_grad; | |||
| double right_grad; | |||
| DyadicOperationResult(double value, double left_grad, double right_grad){ | |||
| this->value = value; | |||
| this->left_grad = left_grad; | |||
| this->right_grad = right_grad; | |||
| } | |||
| }; | |||
| #endif /* end of include guard: DYADIC_OPERATION_RESULT */ | |||
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| #ifndef GRAPH_H | |||
| #define GRAPH_H | |||
| #include <map> | |||
| #include <vector> | |||
| #include <utility> | |||
| class Graph { | |||
| private: | |||
| std::map<long int, std::vector<std::pair<double, long int> > > nodes; | |||
| static Graph* instance; | |||
| Graph(); | |||
| public: | |||
| static long int uid_counter; | |||
| static long int uid(); | |||
| static Graph* getInstance(); | |||
| void connect(const long int& uid, const std::pair<double, long int>& edge); | |||
| std::vector<std::pair<double, long int> > get(const long int& uid) const; | |||
| bool has(const long int& uid) const; | |||
| void new_recording(); | |||
| }; | |||
| #endif /* end of include guard: GRAPH_H */ | |||
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| #ifndef MONADIC_OPERATION_RESULT | |||
| #define MONADIC_OPERATION_RESULT | |||
| struct MonadicOperationResult { | |||
| double value; | |||
| double grad; | |||
| MonadicOperationResult(double value, double grad){ | |||
| this->value = value; | |||
| this->grad = grad; | |||
| } | |||
| }; | |||
| #endif /* end of include guard: MONADIC_OPERATION_RESULT */ | |||
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| #ifndef NODE_H | |||
| #define NODE_H | |||
| #include <cmath> | |||
| #include <iostream> | |||
| #include "graph.h" | |||
| #include "mor.h" | |||
| #include "dor.h" | |||
| #include "por.h" | |||
| class Node { | |||
| private: | |||
| double value; | |||
| long int uid; | |||
| double gradient_recursive(Graph* graph, const long int& current_uid, const long int& stop_uid) const; | |||
| public: | |||
| Node(const double& value=0); | |||
| Node(const Node& node); | |||
| static Node monadic_operation(const Node& n, MonadicOperationResult (*)(const double&)); | |||
| static Node dyadic_operation(const Node& l, const Node& r, DyadicOperationResult (*)(const double&, const double&)); | |||
| static Node polyadic_operation(const std::vector<Node>& nodes, PolyadicOperationResult (*)(const std::vector<double>&)); | |||
| double gradient(const Node& node) const; | |||
| std::vector<double> gradient(const std::vector<Node>& nodes) const; | |||
| std::vector<std::vector<double> > gradient(const std::vector<std::vector<Node> >& nodes) const; | |||
| friend Node operator+(const Node& l, const Node& r); | |||
| friend Node operator-(const Node& l, const Node& r); | |||
| friend Node operator*(const Node& l, const Node& r); | |||
| friend Node operator/(const Node& l, const Node& r); | |||
| Node& operator+=(const Node& r); | |||
| Node& operator-=(const Node& r); | |||
| Node& operator*=(const Node& r); | |||
| Node& operator/=(const Node& r); | |||
| friend bool operator==(const Node& l, const Node& r); | |||
| friend bool operator<(const Node& l, const Node& r); | |||
| friend bool operator>(const Node& l, const Node& r); | |||
| friend bool operator<=(const Node& l, const Node& r); | |||
| friend bool operator>=(const Node& l, const Node& r); | |||
| friend Node sin(const Node& x); | |||
| friend Node cos(const Node& x); | |||
| friend Node tan(const Node& x); | |||
| friend Node sinh(const Node& x); | |||
| friend Node cosh(const Node& x); | |||
| friend Node tanh(const Node& x); | |||
| friend Node asin(const Node& x); | |||
| friend Node acos(const Node& x); | |||
| friend Node atan(const Node& x); | |||
| friend Node log(const Node& x, const Node& base); | |||
| friend Node log10(const Node& x); | |||
| friend Node ln(const Node& x); | |||
| friend Node pow(const Node& x, const Node& p); | |||
| friend Node exp(const Node& x); | |||
| friend Node sqrt(const Node& x); | |||
| friend Node abs(const Node& x); | |||
| friend Node min(const Node& l, const Node& r); | |||
| friend Node max(const Node& l, const Node& r); | |||
| friend std::ostream& operator<<(std::ostream& os, const Node& node); | |||
| }; | |||
| #endif /* end of include guard: NODE_H */ | |||
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| #ifndef POLYADIC_OPERATION_RESULT | |||
| #define POLYADIC_OPERATION_RESULT | |||
| #include <vector> | |||
| struct PolyadicOperationResult { | |||
| double value; | |||
| std::vector<double> gradients; | |||
| PolyadicOperationResult(double value, const std::vector<double>& gradients){ | |||
| this->value = value; | |||
| this->gradients = gradients; | |||
| } | |||
| }; | |||
| #endif /* end of include guard: POLYADIC_OPERATION_RESULT */ | |||
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| #ifndef VECTMATH | |||
| #define VECTMATH | |||
| #include <vector> | |||
| #include <cassert> | |||
| #include <iostream> | |||
| #include <algorithm> | |||
| #include <functional> | |||
| // dot product | |||
| template <typename T> | |||
| std::vector<std::vector<T> > dot(const std::vector<std::vector<T> >& a, const std::vector<std::vector<T> >& b){ | |||
| assert(a[0].size()==b.size()); | |||
| T w=0; | |||
| std::vector<std::vector<T> > result(a.size(), std::vector<T>(b[0].size())); | |||
| for (int i=0 ; i<a.size() ; i++){ | |||
| for (int j=0 ; j<b[0].size() ; j++){ | |||
| for (int h=0 ; h<b.size() ; h++){ | |||
| w += a[i][h]*b[h][j]; | |||
| } | |||
| result[i][j] = w; | |||
| w=0; | |||
| } | |||
| } | |||
| return result; | |||
| } | |||
| // operators | |||
| template <typename U, typename V> | |||
| std::vector<U>& operator-=(std::vector<U>& u, const std::vector<V>& v){ | |||
| assert(u.size()==v.size()); | |||
| for(size_t i=0 ; i<u.size() ; i++){ | |||
| u[i] -= v[i]; | |||
| } | |||
| return u; | |||
| } | |||
| template <typename U> | |||
| std::vector<U> operator+(const std::vector<U>& u, const std::vector<U>& v){ | |||
| assert(u.size()==v.size()); | |||
| std::vector<U> w(u.size()); | |||
| for(size_t i=0 ; i<w.size() ; i++){ | |||
| w[i] = u[i]+v[i]; | |||
| } | |||
| return w; | |||
| } | |||
| template <typename U, typename S> | |||
| std::vector<U> operator*(const S& s, const std::vector<U>& u){ | |||
| std::vector<U> result(u.size()); | |||
| for(size_t i=0 ; i<u.size() ; i++){ | |||
| result[i] = s*u[i]; | |||
| } | |||
| return result; | |||
| } | |||
| template <typename U> | |||
| std::vector<U>& operator>>(U (*fun)(U&), std::vector<U>& u){ | |||
| std::transform(u.begin(), u.end(), u.begin(), fun); | |||
| return u; | |||
| } | |||
| template <typename U, typename S> | |||
| std::vector<U>& operator>>(S (*fun)(S&), std::vector<U>& u){ | |||
| for(auto& v : u){ | |||
| fun >> v; | |||
| } | |||
| return u; | |||
| } | |||
| template <typename U> | |||
| std::vector<U>& operator>>(U (*fun)(), std::vector<U>& u){ | |||
| for(auto& e : u){ | |||
| e = fun(); | |||
| } | |||
| return u; | |||
| } | |||
| template <typename U, typename S> | |||
| std::vector<U>& operator>>(S (*fun)(), std::vector<U>& u){ | |||
| for(auto& v : u){ | |||
| fun >> v; | |||
| } | |||
| return u; | |||
| } | |||
| template <typename U> | |||
| std::ostream& operator<<(std::ostream& os, const std::vector<U>& u){ | |||
| os << "["; | |||
| for(size_t i=0 ; i<u.size() ; i++){ | |||
| os << u[i] << (i<u.size()-1?", ":""); | |||
| } | |||
| os << "]"; | |||
| return os; | |||
| } | |||
| #endif | |||
+ 34
- 0
conv_.cpp
View File
| @@ -0,0 +1,34 @@ | |||
| /* | |||
| ███████╗██████╗ ██████╗ ███████╗ ███████╗███╗ ██╗ ██████╗ ██╗███╗ ██╗███████╗ | |||
| ██╔════╝██╔══██╗██╔════╝ ██╔════╝ ██╔════╝████╗ ██║██╔════╝ ██║████╗ ██║██╔════╝ | |||
| █████╗ ██║ ██║██║ ███╗█████╗ █████╗ ██╔██╗ ██║██║ ███╗██║██╔██╗ ██║█████╗ | |||
| ██╔══╝ ██║ ██║██║ ██║██╔══╝ ██╔══╝ ██║╚██╗██║██║ ██║██║██║╚██╗██║██╔══╝ | |||
| ███████╗██████╔╝╚██████╔╝███████╗ ███████╗██║ ╚████║╚██████╔╝██║██║ ╚████║███████╗ | |||
| ╚══════╝╚═════╝ ╚═════╝ ╚══════╝ ╚══════╝╚═╝ ╚═══╝ ╚═════╝ ╚═╝╚═╝ ╚═══╝╚══════╝ | |||
| Author：Edge | |||
| Web：likedge.top | |||
| Date：20200925 | |||
| */ | |||
| #include<iostream> | |||
| #include<ctime> | |||
| #include<string> | |||
| #include <time.h> | |||
| #include <math.h> | |||
| #include <fstream> | |||
| #include "./autodiff/node.h" | |||
| #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" | |||
| #include"./grad_edge/matrix_grad.h" | |||
| using namespace std; | |||
| clock_t start, stop; | |||
| double duration; | |||
| int main() | |||
| { | |||
| welcome(); | |||
| conv_test(CreateRandMat(5,6),3,4,1,2,0,0); | |||
| return 0; | |||
| } | |||
+ 1800
- 0
data/data.csv
File diff suppressed because it is too large
View File
+ 100
- 0
data/nerual_data.csv
View File
| @@ -0,0 +1,100 @@ | |||
| -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 | |||
| 0.667394,12.741452,0 | |||
| -2.46015,6.866805,1 | |||
| 0.569411,9.548755,0 | |||
| -0.026632,10.427743,0 | |||
| 0.850433,6.920334,1 | |||
| 1.347183,13.1755,0 | |||
| 1.176813,3.16702,1 | |||
| -1.781871,9.097953,0 | |||
| -0.566606,5.749003,1 | |||
| 0.931635,1.589505,1 | |||
| -0.024205,6.151823,1 | |||
| -0.036453,2.690988,1 | |||
| -0.196949,0.444165,1 | |||
| 1.014459,5.754399,1 | |||
| 1.985298,3.230619,1 | |||
| -1.693453,-0.55754,1 | |||
| -0.576525,11.778922,0 | |||
| -0.346811,-1.67873,1 | |||
| -2.124484,2.672471,1 | |||
| 1.217916,9.597015,0 | |||
| -0.733928,9.098687,0 | |||
| -3.642001,-1.618087,1 | |||
| 0.315985,3.523953,1 | |||
| 1.416614,9.619232,0 | |||
| -0.386323,3.989286,1 | |||
| 0.556921,8.294984,1 | |||
| 1.224863,11.58736,0 | |||
| -1.347803,-2.406051,1 | |||
| 1.196604,4.951851,1 | |||
| 0.275221,9.543647,0 | |||
| 0.470575,9.332488,0 | |||
| -1.889567,9.542662,0 | |||
| -1.527893,12.150579,0 | |||
| -1.185247,11.309318,0 | |||
| -0.445678,3.297303,1 | |||
| 1.042222,6.105155,1 | |||
| -0.618787,10.320986,0 | |||
| 1.152083,0.548467,1 | |||
| 0.828534,2.676045,1 | |||
| -1.237728,10.549033,0 | |||
| -0.683565,-2.166125,1 | |||
| 0.229456,5.921938,1 | |||
| -0.959885,11.555336,0 | |||
| 0.492911,10.993324,0 | |||
| 0.184992,8.721488,0 | |||
| -0.355715,10.325976,0 | |||
| -0.397822,8.058397,0 | |||
| 0.824839,13.730343,0 | |||
| 1.507278,5.027866,1 | |||
| 0.099671,6.835839,1 | |||
| -0.344008,10.717485,0 | |||
| 1.785928,7.718645,1 | |||
| -0.918801,11.560217,0 | |||
| -0.364009,4.7473,1 | |||
| -0.841722,4.119083,1 | |||
| 0.490426,1.960539,1 | |||
| -0.007194,9.075792,0 | |||
| 0.356107,12.447863,0 | |||
| 0.342578,12.281162,0 | |||
| -0.810823,-1.466018,1 | |||
| 2.530777,6.476801,1 | |||
| 1.296683,11.607559,0 | |||
| 0.475487,12.040035,0 | |||
| -0.783277,11.009725,0 | |||
| 0.074798,11.02365,0 | |||
| -1.337472,0.468339,1 | |||
| -0.102781,13.763651,0 | |||
| -0.147324,2.874846,1 | |||
| 0.518389,9.887035,0 | |||
| 1.015399,7.571882,0 | |||
| -1.658086,-0.027255,1 | |||
| 1.319944,2.171228,1 | |||
| 2.056216,5.019981,1 | |||
| -0.851633,4.375691,1 | |||
| -1.510047,6.061992,0 | |||
| -1.076637,-3.181888,1 | |||
| 1.821096,10.28399,0 | |||
| 3.01015,8.401766,1 | |||
| -1.099458,1.688274,1 | |||
| -0.834872,-1.733869,1 | |||
| -0.846637,3.849075,1 | |||
| 1.400102,12.628781,0 | |||
| 1.752842,5.468166,1 | |||
| 0.078557,0.059736,1 | |||
| 0.089392,-0.7153,1 | |||
| 1.825662,12.693808,0 | |||
| 0.197445,9.744638,0 | |||
| 0.126117,0.922311,1 | |||
| -0.679797,1.22053,1 | |||
| 0.677983,2.556666,1 | |||
| 0.761349,10.693862,0 | |||
| -2.168791,0.143632,1 | |||
| 1.38861,9.341997,0 | |||
| 0.317029,14.739025,0 | |||
+ 100
- 0
data/new_data2.csv
View File
| @@ -0,0 +1,100 @@ | |||
| 1,-0.017612,14.053064,0 | |||
| 1,-1.395634,4.662541,1 | |||
| 1,-0.752157,6.53862,0 | |||
| 1,-1.322371,7.152853,0 | |||
| 1,0.423363,11.054677,0 | |||
| 1,0.406704,7.067335,1 | |||
| 1,0.667394,12.741452,0 | |||
| 1,-2.46015,6.866805,1 | |||
| 1,0.569411,9.548755,0 | |||
| 1,-0.026632,10.427743,0 | |||
| 1,0.850433,6.920334,1 | |||
| 1,1.347183,13.1755,0 | |||
| 1,1.176813,3.16702,1 | |||
| 1,-1.781871,9.097953,0 | |||
| 1,-0.566606,5.749003,1 | |||
| 1,0.931635,1.589505,1 | |||
| 1,-0.024205,6.151823,1 | |||
| 1,-0.036453,2.690988,1 | |||
| 1,-0.196949,0.444165,1 | |||
| 1,1.014459,5.754399,1 | |||
| 1,1.985298,3.230619,1 | |||
| 1,-1.693453,-0.55754,1 | |||
| 1,-0.576525,11.778922,0 | |||
| 1,-0.346811,-1.67873,1 | |||
| 1,-2.124484,2.672471,1 | |||
| 1,1.217916,9.597015,0 | |||
| 1,-0.733928,9.098687,0 | |||
| 1,-3.642001,-1.618087,1 | |||
| 1,0.315985,3.523953,1 | |||
| 1,1.416614,9.619232,0 | |||
| 1,-0.386323,3.989286,1 | |||
| 1,0.556921,8.294984,1 | |||
| 1,1.224863,11.58736,0 | |||
| 1,-1.347803,-2.406051,1 | |||
| 1,1.196604,4.951851,1 | |||
| 1,0.275221,9.543647,0 | |||
| 1,0.470575,9.332488,0 | |||
| 1,-1.889567,9.542662,0 | |||
| 1,-1.527893,12.150579,0 | |||
| 1,-1.185247,11.309318,0 | |||
| 1,-0.445678,3.297303,1 | |||
| 1,1.042222,6.105155,1 | |||
| 1,-0.618787,10.320986,0 | |||
| 1,1.152083,0.548467,1 | |||
| 1,0.828534,2.676045,1 | |||
| 1,-1.237728,10.549033,0 | |||
| 1,-0.683565,-2.166125,1 | |||
| 1,0.229456,5.921938,1 | |||
| 1,-0.959885,11.555336,0 | |||
| 1,0.492911,10.993324,0 | |||
| 1,0.184992,8.721488,0 | |||
| 1,-0.355715,10.325976,0 | |||
| 1,-0.397822,8.058397,0 | |||
| 1,0.824839,13.730343,0 | |||
| 1,1.507278,5.027866,1 | |||
| 1,0.099671,6.835839,1 | |||
| 1,-0.344008,10.717485,0 | |||
| 1,1.785928,7.718645,1 | |||
| 1,-0.918801,11.560217,0 | |||
| 1,-0.364009,4.7473,1 | |||
| 1,-0.841722,4.119083,1 | |||
| 1,0.490426,1.960539,1 | |||
| 1,-0.007194,9.075792,0 | |||
| 1,0.356107,12.447863,0 | |||
| 1,0.342578,12.281162,0 | |||
| 1,-0.810823,-1.466018,1 | |||
| 1,2.530777,6.476801,1 | |||
| 1,1.296683,11.607559,0 | |||
| 1,0.475487,12.040035,0 | |||
| 1,-0.783277,11.009725,0 | |||
| 1,0.074798,11.02365,0 | |||
| 1,-1.337472,0.468339,1 | |||
| 1,-0.102781,13.763651,0 | |||
| 1,-0.147324,2.874846,1 | |||
| 1,0.518389,9.887035,0 | |||
| 1,1.015399,7.571882,0 | |||
| 1,-1.658086,-0.027255,1 | |||
| 1,1.319944,2.171228,1 | |||
| 1,2.056216,5.019981,1 | |||
| 1,-0.851633,4.375691,1 | |||
| 1,-1.510047,6.061992,0 | |||
| 1,-1.076637,-3.181888,1 | |||
| 1,1.821096,10.28399,0 | |||
| 1,3.01015,8.401766,1 | |||
| 1,-1.099458,1.688274,1 | |||
| 1,-0.834872,-1.733869,1 | |||
| 1,-0.846637,3.849075,1 | |||
| 1,1.400102,12.628781,0 | |||
| 1,1.752842,5.468166,1 | |||
| 1,0.078557,0.059736,1 | |||
| 1,0.089392,-0.7153,1 | |||
| 1,1.825662,12.693808,0 | |||
| 1,0.197445,9.744638,0 | |||
| 1,0.126117,0.922311,1 | |||
| 1,-0.679797,1.22053,1 | |||
| 1,0.677983,2.556666,1 | |||
| 1,0.761349,10.693862,0 | |||
| 1,-2.168791,0.143632,1 | |||
| 1,1.38861,9.341997,0 | |||
| 1,0.317029,14.739025,0 | |||
+ 107
- 0
data_struct/data_struct_pro.c
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| @@ -0,0 +1,107 @@ | |||
| /* | |||
| ███████╗██████╗ ██████╗ ███████╗ ███████╗███╗ ██╗ ██████╗ ██╗███╗ ██╗███████╗ | |||
| ██╔════╝██╔══██╗██╔════╝ ██╔════╝ ██╔════╝████╗ ██║██╔════╝ ██║████╗ ██║██╔════╝ | |||
| █████╗ ██║ ██║██║ ███╗█████╗ █████╗ ██╔██╗ ██║██║ ███╗██║██╔██╗ ██║█████╗ | |||
| ██╔══╝ ██║ ██║██║ ██║██╔══╝ ██╔══╝ ██║╚██╗██║██║ ██║██║██║╚██╗██║██╔══╝ | |||
| ███████╗██████╔╝╚██████╔╝███████╗ ███████╗██║ ╚████║╚██████╔╝██║██║ ╚████║███████╗ | |||
| ╚══════╝╚═════╝ ╚═════╝ ╚══════╝ ╚══════╝╚═╝ ╚═══╝ ╚═════╝ ╚═╝╚═╝ ╚═══╝╚══════╝ | |||
| Author：Edge | |||
| Web：likedge.top | |||
| Date：20201213 | |||
| */ | |||
| #include "data_struct_pro.h" | |||
| #define random(x) (rand()%x) | |||
| link_list::link_list() | |||
| { | |||
| cout<<"test"<<endl; | |||
| } | |||
| //init function on link_list | |||
| //n:len | |||
| link_list::link_list(int n) | |||
| { | |||
| this->len=n; | |||
| this->link_list_head =(lnode*)malloc(sizeof(lnode)); | |||
| lnode *node1=(lnode*)malloc(sizeof(lnode)); | |||
| lnode *p= this->link_list_head; | |||
| // this->link_list_head =new lnode(1,0); | |||
| for(int index_link_node=0;index_link_node<n;index_link_node++) | |||
| { | |||
| lnode *node1=(lnode*)malloc(sizeof(lnode)); | |||
| p->next_index=node1; | |||
| p->data=index_link_node; | |||
| cout<<p->data<<endl; | |||
| cout<<p->next_index<<endl; | |||
| p=p->next_index; | |||
| // cout<<"Node:"<<this->link_list_head->next_index<<endl; | |||
| } | |||
| } | |||
| //travel around the link_list | |||
| void list_ergodic(link_list link_list_ergodic) | |||
| { | |||
| cout<<"len:"<<link_list_ergodic.len<<endl; | |||
| lnode *ldir_head=link_list_ergodic.link_list_head; | |||
| for(int i = 0;i<=link_list_ergodic.len;i++){ | |||
| cout<<"list["<<i<<"]:"<<ldir_head->data<<endl; | |||
| ldir_head=ldir_head->next_index; | |||
| } | |||
| } | |||
| //insert element for what u input,in this index | |||
| void insert_element_ll(int index_toinsert,link_list ll_toinsert,int to_insertele) | |||
| { | |||
| lnode *insert_p=ll_toinsert.link_list_head; | |||
| for(int index_move = 0;index_move<ll_toinsert.len;index_move++){ | |||
| if(index_move==index_toinsert-1) | |||
| { | |||
| lnode *node1=(lnode*)malloc(sizeof(lnode)); | |||
| lnode *old_next_index=insert_p->next_index; | |||
| insert_p->next_index=node1; | |||
| insert_p->next_index->data=to_insertele; | |||
| node1->next_index=old_next_index; | |||
| } | |||
| insert_p=insert_p->next_index; | |||
| } | |||
| } | |||
| //delete the element from where you index | |||
| void dele_element_ll(int index_todele,link_list ll_todele) | |||
| { | |||
| lnode *dele_p=ll_todele.link_list_head; | |||
| for(int index_move=0;index_move<ll_todele.len;index_move++) | |||
| { | |||
| if(index_move==index_todele-1) | |||
| { | |||
| lnode *node1=(lnode*)malloc(sizeof(lnode)); | |||
| lnode *old_index_to_dele=dele_p->next_index; | |||
| dele_p->next_index=old_index_to_dele->next_index; | |||
| } | |||
| dele_p=dele_p->next_index; | |||
| } | |||
| } | |||
| void insert_end_ll(int to_insert_end,link_list ll_toinsert_end) | |||
| { | |||
| lnode *end_p=ll_toinsert_end.link_list_head; | |||
| for(int index_move_toend=0;index_move_toend<=ll_toinsert_end.len;index_move_toend++) | |||
| { | |||
| cout<<index_move_toend<<endl; | |||
| if(index_move_toend==ll_toinsert_end.len) | |||
| { | |||
| lnode *node_to_insert_end=(lnode*)malloc(sizeof(lnode)); | |||
| node_to_insert_end->data=to_insert_end; | |||
| cout<<index_move_toend<<endl; | |||
| end_p->next_index=node_to_insert_end; | |||
| } | |||
| end_p=end_p->next_index; | |||
| } | |||
| ll_toinsert_end.len+=1; | |||
| cout<<ll_toinsert_end.len<<endl; | |||
| //insert element to end of link_list | |||
| } | |||
+ 47
- 0
data_struct/data_struct_pro.h
View File
| @@ -0,0 +1,47 @@ | |||
| /* | |||
| ███████╗██████╗ ██████╗ ███████╗ ███████╗███╗ ██╗ ██████╗ ██╗███╗ ██╗███████╗ | |||
| ██╔════╝██╔══██╗██╔════╝ ██╔════╝ ██╔════╝████╗ ██║██╔════╝ ██║████╗ ██║██╔════╝ | |||
| █████╗ ██║ ██║██║ ███╗█████╗ █████╗ ██╔██╗ ██║██║ ███╗██║██╔██╗ ██║█████╗ | |||
| ██╔══╝ ██║ ██║██║ ██║██╔══╝ ██╔══╝ ██║╚██╗██║██║ ██║██║██║╚██╗██║██╔══╝ | |||
| ███████╗██████╔╝╚██████╔╝███████╗ ███████╗██║ ╚████║╚██████╔╝██║██║ ╚████║███████╗ | |||
| ╚══════╝╚═════╝ ╚═════╝ ╚══════╝ ╚══════╝╚═╝ ╚═══╝ ╚═════╝ ╚═╝╚═╝ ╚═══╝╚══════╝ | |||
| Author：Edge | |||
| Web：likedge.top | |||
| Date：20201213 | |||
| Data_struct_define | |||
| link_list: ll | |||
| graph | |||
| --------------------------------------------------------------- | |||
| if you have the better answer on it , it is nothing, just test~ | |||
| --------------------------------------------------------------- | |||
| */ | |||
| #ifndef DATA_STRUCT_PRO | |||
| #define DATA_STRUCT_PRO | |||
| // #pragma once | |||
| struct lnode{ | |||
| int data; | |||
| lnode *next_index; | |||
| lnode(int n,lnode next) // 构造函数 | |||
| { | |||
| next_index=&next; | |||
| data = n; | |||
| } | |||
| }; | |||
| class link_list{ | |||
| public: | |||
| int len; | |||
| lnode *NODE_link_list; | |||
| lnode *link_list_head; | |||
| link_list(); | |||
| link_list(int n); | |||
| void list_ergodic(link_list link_list_ergodic); | |||
| void insert_element_ll(int index_toinsert,link_list ll_toinsert); | |||
| void dele_element_ll(int index_todele,link_list ll_todele); | |||
| void insert_end_ll(int to_insert_end,link_list ll_toinsert_end); | |||
| }; | |||
| #endif | |||
+ 93
- 0
data_struct_test.cpp
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| @@ -0,0 +1,93 @@ | |||
| /* | |||
| ███████╗██████╗ ██████╗ ███████╗ ███████╗███╗ ██╗ ██████╗ ██╗███╗ ██╗███████╗ | |||
| ██╔════╝██╔══██╗██╔════╝ ██╔════╝ ██╔════╝████╗ ██║██╔════╝ ██║████╗ ██║██╔════╝ | |||
| █████╗ ██║ ██║██║ ███╗█████╗ █████╗ ██╔██╗ ██║██║ ███╗██║██╔██╗ ██║█████╗ | |||
| ██╔══╝ ██║ ██║██║ ██║██╔══╝ ██╔══╝ ██║╚██╗██║██║ ██║██║██║╚██╗██║██╔══╝ | |||
| ███████╗██████╔╝╚██████╔╝███████╗ ███████╗██║ ╚████║╚██████╔╝██║██║ ╚████║███████╗ | |||
| ╚══════╝╚═════╝ ╚═════╝ ╚══════╝ ╚══════╝╚═╝ ╚═══╝ ╚═════╝ ╚═╝╚═╝ ╚═══╝╚══════╝ | |||
| Author：Edge | |||
| Web：likedge.top | |||
| Date：20201213 | |||
| */ | |||
| #include<iostream> | |||
| #include<ctime> | |||
| #include<string> | |||
| #include <time.h> | |||
| #include <math.h> | |||
| #include <fstream> | |||
| #include"./autodiff/node.h" | |||
| #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" | |||
| #include"./grad_edge/matrix_grad.h" | |||
| #include"./data_struct/data_struct_pro.h" | |||
| #include"./data_struct/data_struct_pro.c" | |||
| using namespace std; | |||
| int a[101],n;//定义全局变量，这两个变量需要在子函数中使用 | |||
| int i,j,t; | |||
| //quick_sort | |||
| void quicksort(int left,int right) | |||
| { | |||
| int i,j,t,temp; | |||
| if(left>right) | |||
| return; | |||
| temp=a[left]; //temp中存的就是基准数 | |||
| i=left; | |||
| j=right; | |||
| while(i!=j) | |||
| { | |||
| //顺序很重要，要先从右边开始找 | |||
| while(a[j]>=temp && i<j) | |||
| j--; | |||
| //再找右边的 | |||
| while(a[i]<=temp && i<j) | |||
| i++; | |||
| //交换两个数在数组中的位置 | |||
| if(i<j) | |||
| { | |||
| t=a[i]; | |||
| a[i]=a[j]; | |||
| a[j]=t; | |||
| } | |||
| } | |||
| //最终将基准数归位 | |||
| a[left]=a[i]; | |||
| a[i]=temp; | |||
| quicksort(left,i-1);//继续处理左边的，这里是一个递归的过程 | |||
| quicksort(i+1,right);//继续处理右边的 ，这里是一个递归的过程 | |||
| } | |||
| int main() | |||
| { | |||
| // Node_link_list c(3); | |||
| int len=10; | |||
| link_list list1 = link_list(5); | |||
| //遍历 | |||
| list_ergodic(list1); | |||
| cout<<"----------insert element------"<<endl; | |||
| insert_element_ll(1,list1,5); | |||
| list_ergodic(list1); | |||
| cout<<"-------delement_ll---------"<<endl; | |||
| dele_element_ll(119,list1); | |||
| list_ergodic(list1); | |||
| cout<<"----------------end insert------------------"<<endl; | |||
| insert_end_ll(9,list1); | |||
| list_ergodic(list1); | |||
| // return 1; | |||
| //quicksort | |||
| //读入数据 | |||
| scanf("%d",&n); | |||
| for(i=1;i<=n;i++) | |||
| scanf("%d",&a[i]); | |||
| quicksort(1,n); //快速排序调用 | |||
| //输出排序后的结果 | |||
| for(i=1;i<=n;i++) | |||
| printf("%d ",a[i]); | |||
| getchar();getchar(); | |||
| return 0; | |||
| } | |||
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file_pro/data_read.h
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| #include"../matrix/matrix_pro.h" | |||
| #include"../matrix/matrix_def.h" | |||
| #include<iostream> | |||
| #include<fstream> | |||
| #include<string> | |||
| #include<typeinfo> | |||
| #include<cstring> | |||
| using namespace std; | |||
| string data; | |||
| double str2double(char *src) | |||
| { | |||
| double ret = 0,sign = 1; | |||
| char *p = src; | |||
| if(*p == '+'){sign = 1;p ++;} | |||
| else if(*p == '-'){sign = -1; p ++; } | |||
| while(*p && (*p != '.')) | |||
| { | |||
| ret*=10; | |||
| ret += (*p) - '0'; | |||
| p++; | |||
| } | |||
| if(*p == '.') | |||
| { | |||
| double step = 0.1; | |||
| p++; | |||
| while(*p) | |||
| { | |||
| ret+=step*((*p)-'0'); | |||
| step/=10; | |||
| p++; | |||
| } | |||
| } | |||
| return ret*sign; | |||
| } | |||
| str_Matrix read_file(string &file_path) | |||
| { | |||
| int count_rows = 0,count_col = 0; | |||
| ifstream infile; | |||
| infile.open(file_path); | |||
| while(!infile.eof()) | |||
| { | |||
| count_col = 0; | |||
| count_rows+=1; | |||
| infile >> data; | |||
| int iSize = data.size(); | |||
| int flag_if = 1; | |||
| for(int i = 0;i < iSize; i++) | |||
| { | |||
| if(data[i]==',') | |||
| { | |||
| count_col++; | |||
| } | |||
| } | |||
| } | |||
| cout<<count_rows<<endl; | |||
| cout<<count_col+1<<endl; | |||
| str_Matrix data_ma = CreateStr_Ma(1666,1666); | |||
| ifstream infile2; | |||
| infile2.open(file_path); | |||
| int next_flag = 0; | |||
| while(!infile2.eof()) | |||
| { | |||
| infile2 >> data; | |||
| string base = ""; | |||
| string added =""; | |||
| string added2 = ""; | |||
| string base2 = ""; | |||
| int iSize = data.size(); | |||
| int flag_if = 1; | |||
| int count_times = 0; | |||
| int count_times2 = 0; | |||
| for(int i = 0;i < iSize; i++) | |||
| { | |||
| //cout<<data[i]<<endl; | |||
| if(data[i]!=',') | |||
| { | |||
| added+=data[i]; | |||
| } | |||
| else if(data[i]==',') | |||
| { | |||
| count_times+=1; | |||
| base = added; | |||
| char *result = (char*)base.data(); | |||
| //cout<<result<<endl; | |||
| data_ma.str_matrix[next_flag][count_times-1] = result; | |||
| added =""; | |||
| base = ""; | |||
| } | |||
| if(count_times==count_col) | |||
| { | |||
| added2+=data[i+1]; | |||
| base2 = added2; | |||
| char *result2 = (char*)base2.data(); | |||
| //cout<<result2<<endl; | |||
| data_ma.str_matrix[next_flag][count_times] = result2; | |||
| } | |||
| } | |||
| next_flag+=1; | |||
| } | |||
| return data_ma; | |||
| } | |||
| Matrix read_csv(string &file_path) | |||
| { | |||
| int count_rows = 0,count_col = 0; | |||
| ifstream infile; | |||
| infile.open(file_path); | |||
| while(!infile.eof()) | |||
| { | |||
| count_col = 0; | |||
| count_rows+=1; | |||
| infile >> data; | |||
| int iSize = data.size(); | |||
| int flag_if = 1; | |||
| for(int i = 0;i < iSize; i++) | |||
| { | |||
| if(data[i]==',') | |||
| { | |||
| count_col++; | |||
| } | |||
| } | |||
| } | |||
| Matrix data_ma = CreateMatrix(count_rows,count_col+1); | |||
| ifstream infile2; | |||
| infile2.open(file_path); | |||
| int next_flag = 0; | |||
| while(!infile2.eof()) | |||
| { | |||
| infile2 >> data; | |||
| string base = ""; | |||
| string added =""; | |||
| string added2 = ""; | |||
| string base2 = ""; | |||
| int iSize = data.size(); | |||
| int flag_if = 1; | |||
| int count_times = 0; | |||
| int count_times2 = 0; | |||
| for(int i = 0;i < iSize; i++) | |||
| { | |||
| if(data[i]!=',') | |||
| { | |||
| added+=data[i]; | |||
| } | |||
| else if(data[i]==',') | |||
| { | |||
| count_times+=1; | |||
| base = added; | |||
| char *result = (char*)base.data(); | |||
| data_ma.matrix[next_flag][count_times-1] = str2double(result); | |||
| added =""; | |||
| base = ""; | |||
| } | |||
| if(count_times==count_col) | |||
| { | |||
| added2+=data[i+1]; | |||
| base2 = added2; | |||
| char *result2 = (char*)base2.data(); | |||
| data_ma.matrix[next_flag][count_times] = str2double(result2); | |||
| } | |||
| } | |||
| next_flag+=1; | |||
| } | |||
| return data_ma; | |||
| } | |||
| int save_txt(Matrix mid1,string path = "./",string delimiter = ",",string header="./") | |||
| { | |||
| int index_x,index_y; | |||
| ofstream fout(path,ios::app); | |||
| fout<<header<<endl; | |||
| for(index_x=0;index_x<mid1.row;index_x++) | |||
| { | |||
| for(index_y=0;index_y<mid1.col;index_y++) | |||
| { | |||
| fout<<mid1.matrix[index_x][index_y]<<delimiter; | |||
| } | |||
| fout<<endl; | |||
| } | |||
| fout.close(); | |||
| return 0; | |||
| } | |||
| /* | |||
| int read_file(string path = "./",string delimiter = ",",string header = "./") | |||
| { | |||
| ystring list_word[]=[]; | |||
| string *p = list_word; | |||
| for(int i = 0;i<5;i++) | |||
| { | |||
| *(list_word+i) = 1; | |||
| cout<<"*p("<<i<<"): "; | |||
| cout<<*(p+i)<<endl; | |||
| } | |||
| } | |||
| */ | |||
| //-----------split------------ | |||
| // | |||
| // | |||
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| #include<iostream> | |||
| #include<ctime> | |||
| #include<string> | |||
| #include <time.h> | |||
| #include <math.h> | |||
| #include <fstream> | |||
| #include"./autodiff/node.h" | |||
| #include"../matrix/matrix_def.h" | |||
| #include"../matrix/matrix_pro.h" | |||
| using namespace std; | |||
| float sigmoid(float x){ | |||
| return 1/(1+exp(x)); | |||
| } | |||
| Node sigmoid(Node z){ | |||
| Node sigmoid = 1/(1+(1/exp(z))); | |||
| return sigmoid; | |||
| } | |||
| struct edge_network | |||
| { | |||
| edge_network(int input, int num_neuron){ | |||
| } | |||
| Matrix forward(Matrix data,Matrix weights,Matrix bais) | |||
| { | |||
| // cout<<"data: "; | |||
| // cout_mat(data); | |||
| // cout<<"weights: "<<endl; | |||
| // cout_mat(weights); | |||
| Matrix output = mul(weights,data); | |||
| output = add(output,bais,0); | |||
| Matrix output1 = e_sigmoid(output); | |||
| // cout<<"----------forward data--------"<<endl; | |||
| // cout_mat(data); | |||
| // cout<<"---------weight1---------"<<endl; | |||
| // cout_mat(weights); | |||
| // cout<<"---------bais----------"<<endl; | |||
| // cout_mat(bais); | |||
| // cout<<"---------output--------"<<endl; | |||
| // cout_mat(output1); | |||
| return output1; | |||
| } | |||
| Matrix forward_without_act(Matrix data,Matrix weights,Matrix bais) | |||
| { | |||
| Matrix output = mul(weights,data); | |||
| output = add(output,bais,0); | |||
| // Matrix output1 = e_sigmoid(output); | |||
| // cout<<"----------forward data--------"<<endl; | |||
| // cout_mat(data); | |||
| // cout<<"---------weight1---------"<<endl; | |||
| // cout_mat(weights); | |||
| // cout<<"---------bais----------"<<endl; | |||
| // cout_mat(bais); | |||
| // cout<<"---------output--------"<<endl; | |||
| // cout_mat(output); | |||
| return output; | |||
| } | |||
| Matrix backward(Matrix grad_next, Matrix output_before,Matrix weights,Node p_(Node)) | |||
| { | |||
| for(int index = 0;index<output_before.row;index++) | |||
| { | |||
| Node z = output_before.matrix[index][0]; | |||
| Node anyone = p_(z); | |||
| change_va(output_before,index,0,anyone.gradient(z)); | |||
| } | |||
| return mul_simple(mul(weights,grad_next),output_before); | |||
| } | |||
| Matrix end_layer_backward(Matrix label,Matrix acti_val,Node loss_fun(Node,Node),Node act_fun(Node)) | |||
| { | |||
| Matrix loss_act = CreateMatrix(acti_val.row,acti_val.col); | |||
| Matrix act_output = CreateMatrix(acti_val.row,acti_val.col); | |||
| int index_x,index_y; | |||
| for(index_x=0;index_x<loss_act.row;index_x++) | |||
| { | |||
| Node t1 = label.matrix[index_x][0],z31 =acti_val.matrix[index_x][0]; | |||
| Node a13 = 1/(1+(1/exp(z31))); | |||
| Node loss = loss_fun(t1,a13); | |||
| cout<<"loss:"<<loss<<endl; | |||
| Node act = act_fun(z31); | |||
| act_output.matrix[index_x][0] = act.gradient(z31); | |||
| loss_act.matrix[index_x][0] = loss.gradient(a13); | |||
| } | |||
| Matrix mid_grad_end = mul_simple(loss_act,act_output); | |||
| cout_mat(mid_grad_end); | |||
| return mid_grad_end; | |||
| } | |||
| }; | |||
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| CXX = g++ | |||
| BIN = bin | |||
| LIB = lib | |||
| LIB_NAME = autodiff | |||
| OBJS_GRADIENT_DESCENT = root/obj/node.o root/obj/graph.o examples/obj/gradient_descent.o | |||
| OBJS_ANN = root/obj/node.o root/obj/graph.o examples/obj/ann.o | |||
| OBJS_GRADIENT = root/obj/node.o root/obj/graph.o examples/obj/gradient.o | |||
| OBJS_SPEED = root/obj/node.o root/obj/graph.o examples/obj/speed.o | |||
| OBJS_SIMPLE = root/obj/node.o root/obj/graph.o examples/obj/simple.o | |||
| all : gradient_descent ann gradient speed simple | |||
| gradient_descent : $(BIN) root/include/vectmath.h root/include/mor.h root/include/dor.h root/include/por.h | |||
| $(MAKE) -C examples obj obj/gradient_descent.o | |||
| $(MAKE) -C root obj obj/node.o obj/graph.o | |||
| $(CXX) -o $(BIN)/gradient_descent $(OBJS_GRADIENT_DESCENT) $(LIBS) | |||
| ann : $(BIN) root/include/vectmath.h root/include/mor.h root/include/dor.h root/include/por.h | |||
| $(MAKE) -C examples obj obj/ann.o | |||
| $(MAKE) -C root obj obj/node.o obj/graph.o | |||
| $(CXX) -o $(BIN)/ann $(OBJS_ANN) $(LIBS) | |||
| gradient : $(BIN) root/include/vectmath.h root/include/mor.h root/include/dor.h root/include/por.h | |||
| $(MAKE) -C examples obj obj/gradient.o | |||
| $(MAKE) -C root obj obj/node.o obj/graph.o | |||
| $(CXX) -o $(BIN)/gradient $(OBJS_GRADIENT) $(LIBS) | |||
| speed : $(BIN) root/include/vectmath.h root/include/mor.h root/include/dor.h root/include/por.h | |||
| $(MAKE) -C examples obj obj/speed.o | |||
| $(MAKE) -C root obj obj/node.o obj/graph.o | |||
| $(CXX) -o $(BIN)/speed $(OBJS_SPEED) $(LIBS) | |||
| simple : $(BIN) root/include/mor.h root/include/dor.h root/include/por.h | |||
| $(MAKE) -C examples obj obj/simple.o | |||
| $(MAKE) -C root obj obj/node.o obj/graph.o | |||
| $(CXX) -o $(BIN)/simple $(OBJS_SIMPLE) $(LIBS) | |||
| $(BIN) : | |||
| if [ ! -d $(BIN) ]; then mkdir $(BIN); fi | |||
| $(LIB) : | |||
| if [ ! -d $(LIB) ]; then mkdir $(LIB); fi | |||
| clean : | |||
| $(MAKE) -C root clean | |||
| $(MAKE) -C examples clean | |||
| if [ -d $(BIN) ]; then rm $(BIN) -r; fi | |||
| install : $(LIB) | |||
| ar rcs $(LIB)/lib$(LIB_NAME).a root/obj/graph.o root/obj/node.o | |||
| if [ ! -d /usr/local/include/$(LIB_NAME) ]; then sudo mkdir /usr/local/include/$(LIB_NAME); fi | |||
| sudo cp $(LIB)/lib$(LIB_NAME).a /usr/local/lib | |||
| sudo cp root/include/*.h /usr/local/include/$(LIB_NAME) | |||
| .PHONY : all | |||
| .PHONY : gradient_descent | |||
| .PHONY : ann | |||
| .PHONY : gradient | |||
| .PHONY : speed | |||
| .PHONY : simple | |||
| .PHONY : clean | |||
| .PHONY : install | |||
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| CXX = g++ | |||
| ODIR = obj | |||
| CXXFLAGS = -std=c++11 -O3 | |||
| OBJS = $(ODIR)/gradient_descent.o $(ODIR)/ann.o $(ODIR)/gradient.o $(ODIR)/speed.o $(ODIR)/simple.o | |||
| all : $(ODIR) $(OBJS) | |||
| $(ODIR)/gradient_descent.o : src/gradient_descent.cpp ../root/include/vectmath.h ../root/include/node.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR)/ann.o : src/ann.cpp ../root/include/vectmath.h ../root/include/node.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR)/gradient.o : src/gradient.cpp ../root/include/vectmath.h ../root/include/node.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR)/speed.o : src/speed.cpp ../root/include/vectmath.h ../root/include/node.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR)/simple.o : src/simple.cpp ../root/include/node.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR) : | |||
| if [ ! -d $(ODIR) ]; then mkdir $(ODIR); fi | |||
| clean : | |||
| if [ -d $(ODIR) ]; then rm $(ODIR) -r; fi | |||
| .PHONY : all | |||
| .PHONY : clean | |||
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install_diff/examples/src/ann.cpp
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| #include <iostream> | |||
| #include <ctime> | |||
| #include "../../root/include/vectmath.h" | |||
| #include "../../root/include/node.h" | |||
| typedef std::vector<Node> Vector; | |||
| typedef std::vector<Vector> Matrix; | |||
| Node random_number(){ | |||
| return rand()/(double)RAND_MAX; | |||
| } | |||
| Node tan_h(Node& x){ | |||
| return (1-exp(-2*x))/(1+exp(-2*x)); | |||
| } | |||
| Node mean_square_error(Vector& y_true, Vector& y_pred){ | |||
| Node loss; | |||
| for(size_t i=0 ; i<y_true.size() ; i++){ | |||
| loss += pow(y_true[i]-y_pred[i], 2); | |||
| } | |||
| return loss; | |||
| } | |||
| struct Layer { | |||
| Matrix weights; | |||
| Matrix bias; | |||
| Node (*activation)(Node&); | |||
| int input_shape; | |||
| int output_shape; | |||
| Layer(int input, int output, Node (*activation)(Node&)){ | |||
| this->activation = activation; | |||
| this->input_shape = input; | |||
| this->output_shape = output; | |||
| weights.resize(input, Vector(output)); | |||
| bias.resize(1, Vector(output)); | |||
| random_number >> weights; | |||
| random_number >> bias; | |||
| } | |||
| Matrix forward(Matrix& previous){ | |||
| Matrix output = dot(previous, weights) + bias; | |||
| output = activation >> output; | |||
| return output; | |||
| } | |||
| void backward(Node& loss, const float& learning_rate){ | |||
| weights -= learning_rate*loss.gradient(weights); | |||
| bias -= learning_rate*loss.gradient(bias); | |||
| } | |||
| }; | |||
| struct Network { | |||
| std::vector<Layer> layers; | |||
| int input_shape; | |||
| Graph* graph; | |||
| Network(){ | |||
| graph = Graph::getInstance(); | |||
| } | |||
| void input_layer(int input_shape){ | |||
| this->input_shape = input_shape; | |||
| } | |||
| void add(int output_shape, Node (*activation)(Node&)){ | |||
| int input = layers.empty()?input_shape:layers.back().output_shape; | |||
| layers.push_back(Layer(input, output_shape, activation)); | |||
| } | |||
| Matrix run(Matrix& input){ | |||
| Matrix output(input.size()); | |||
| for(size_t j=0 ; j<input.size() ; j++){ | |||
| Matrix out = {input[j]}; | |||
| for(auto& lay : layers){ | |||
| out = lay.forward(out); | |||
| } | |||
| output[j] = out[0]; | |||
| } | |||
| return output; | |||
| } | |||
| void fit(Matrix& input, Matrix& output, Node (*loss_function)(Vector&, Vector&), int epochs, float learning_rate){ | |||
| int p=0; | |||
| for(size_t i=0 ; i<epochs ; i++){ | |||
| std::cout << "\r" << i+1 << "/" << epochs; | |||
| for(size_t j=0 ; j<input.size() ; j++){ | |||
| // compute input | |||
| Matrix out = {input[j]}; | |||
| for(auto& lay : layers){ | |||
| out = lay.forward(out); | |||
| } | |||
| // compute loss | |||
| Node loss = loss_function(output[j], out[0]); | |||
| // update parameters | |||
| for(auto& lay : layers){ | |||
| lay.backward(loss, learning_rate); | |||
| } | |||
| graph->new_recording(); | |||
| } | |||
| } | |||
| std::cout << std::endl; | |||
| } | |||
| }; | |||
| int main(int argc, char const *argv[]) { | |||
| srand(time(NULL)); | |||
| Matrix input = {{0,0},{0,1},{1,0},{1,1}}; | |||
| Matrix output = {{0},{1},{1},{0}}; | |||
| Network network; | |||
| network.input_layer(2); | |||
| network.add(3, tan_h); | |||
| network.add(1, tan_h); | |||
| network.fit(input, output, mean_square_error, 500, 0.1); | |||
| Matrix pred = network.run(input); | |||
| std::cout << pred << std::endl; | |||
| return 0; | |||
| } | |||
+ 15
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install_diff/examples/src/gradient.cpp
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| @@ -0,0 +1,15 @@ | |||
| #include <iostream> | |||
| #include "../../root/include/vectmath.h" | |||
| #include "../../root/include/node.h" | |||
| Node function(std::vector<Node> x){ | |||
| return pow(x[0]-x[1], 2) + x[0]*x[1]*x[2]; // (x-y)^2 + x*y*z | |||
| } | |||
| int main(int argc, char const *argv[]) { | |||
| std::vector<Node> x = {5,6,7}; | |||
| Node f = function(x); | |||
| std::cout << "grad(f) = " << f.gradient(x) << std::endl; | |||
| return 0; | |||
| } | |||
+ 27
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install_diff/examples/src/gradient_descent.cpp
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| @@ -0,0 +1,27 @@ | |||
| #include <iostream> | |||
| #include "../../root/include/vectmath.h" | |||
| #include "../../root/include/node.h" | |||
| Node function(std::vector<Node>& x){ | |||
| return pow(x[0], 2) + pow(x[1], 2); // x^2 + y^2 | |||
| } | |||
| int main(int argc, char const *argv[]) { | |||
| Graph* graph = Graph::getInstance(); | |||
| std::vector<Node> x = {50, 50}; | |||
| Node f; | |||
| int epochs = 30; | |||
| float learning_rate = 0.1; | |||
| for(size_t i=0 ; i<epochs ; i++){ | |||
| f = function(x); | |||
| x -= learning_rate*f.gradient(x); | |||
| graph->new_recording(); | |||
| } | |||
| std::cout << "f = " << f << std::endl; | |||
| std::cout << "x = " << x << std::endl; | |||
| return 0; | |||
| } | |||
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install_diff/examples/src/ma
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install_diff/examples/src/simple.cpp
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| @@ -0,0 +1,13 @@ | |||
| #include <iostream> | |||
| #include "../../root/include/node.h" | |||
| int main(int argc, char const *argv[]) { | |||
| Node x=2, y=3; | |||
| Node f = x*y + sin(x); | |||
| std::cout << "f(x,y) = x*y + sin(x)" << std::endl; | |||
| std::cout << "f(" << x << "," << y << ") = " << f << std::endl; | |||
| std::cout << "∂f/∂x = " << f.gradient(x) << std::endl; | |||
| std::cout << "∂f/∂y = " << f.gradient(y) << std::endl; | |||
| return 0; | |||
| } | |||
+ 51
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install_diff/examples/src/speed.cpp
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| @@ -0,0 +1,51 @@ | |||
| #include <iostream> | |||
| #include <iomanip> | |||
| #include <cassert> | |||
| #include <ctime> | |||
| #include <chrono> | |||
| #include "../../root/include/vectmath.h" | |||
| #include "../../root/include/node.h" | |||
| template <class T> | |||
| std::vector<std::vector<T> > get_random_matrix(const int& height, const int& width, T t){ | |||
| std::vector<std::vector<T> > mat(height, std::vector<T>(width)); | |||
| for(auto& v : mat){ | |||
| for(auto& e : v){ | |||
| e = rand()/(double)RAND_MAX; | |||
| } | |||
| } | |||
| return mat; | |||
| } | |||
| int main(int argc, char const *argv[]) { | |||
| srand(time(0)); | |||
| int size = 30; | |||
| std::vector<std::vector<double> > a = get_random_matrix(size, size, double()); | |||
| std::vector<std::vector<double> > b = get_random_matrix(size, size, double()); | |||
| std::vector<std::vector<Node> > c = get_random_matrix(size, size, Node()); | |||
| std::vector<std::vector<Node> > d = get_random_matrix(size, size, Node()); | |||
| std::cout << std::fixed; | |||
| std::cout << std::setprecision(10); | |||
| std::cout << "Running with double...\t"; | |||
| std::cout.flush(); | |||
| auto start = std::chrono::high_resolution_clock::now(); | |||
| std::vector<std::vector<double> > ab = dot(a, b); | |||
| auto finish = std::chrono::high_resolution_clock::now(); | |||
| std::chrono::duration<double> elapsed = finish - start; | |||
| std::cout << "Elapsed time: " << elapsed.count() << " s" << std::endl; | |||
| std::cout << "Running with Node...\t"; | |||
| std::cout.flush(); | |||
| start = std::chrono::high_resolution_clock::now(); | |||
| std::vector<std::vector<Node> > cd = dot(c, d); | |||
| finish = std::chrono::high_resolution_clock::now(); | |||
| elapsed = finish - start; | |||
| std::cout << "Elapsed time: " << elapsed.count() << " s" << std::endl; | |||
| std::cout << "Yet to be improved..." << std::endl; | |||
| return 0; | |||
| } | |||
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install_diff/root/Makefile
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| @@ -0,0 +1,22 @@ | |||
| CXX = g++ | |||
| ODIR = obj | |||
| CXXFLAGS = -std=c++11 -O3 | |||
| OBJS = $(ODIR)/graph.o $(ODIR)/node.o | |||
| all : $(ODIR) $(OBJS) | |||
| $(ODIR)/graph.o : src/graph.cpp include/graph.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR)/node.o : src/node.cpp include/node.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR) : | |||
| if [ ! -d $(ODIR) ]; then mkdir $(ODIR); fi | |||
| clean : | |||
| if [ -d $(ODIR) ]; then rm $(ODIR) -r; fi | |||
| .PHONY : all | |||
| .PHONY : clean | |||
+ 16
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install_diff/root/include/dor.h
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| @@ -0,0 +1,16 @@ | |||
| #ifndef DYADIC_OPERATION_RESULT | |||
| #define DYADIC_OPERATION_RESULT | |||
| struct DyadicOperationResult { | |||
| double value; | |||
| double left_grad; | |||
| double right_grad; | |||
| DyadicOperationResult(double value, double left_grad, double right_grad){ | |||
| this->value = value; | |||
| this->left_grad = left_grad; | |||
| this->right_grad = right_grad; | |||
| } | |||
| }; | |||
| #endif /* end of include guard: DYADIC_OPERATION_RESULT */ | |||
+ 26
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install_diff/root/include/graph.h
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| @@ -0,0 +1,26 @@ | |||
| #ifndef GRAPH_H | |||
| #define GRAPH_H | |||
| #include <map> | |||
| #include <vector> | |||
| #include <utility> | |||
| class Graph { | |||
| private: | |||
| std::map<long int, std::vector<std::pair<double, long int> > > nodes; | |||
| static Graph* instance; | |||
| Graph(); | |||
| public: | |||
| static long int uid_counter; | |||
| static long int uid(); | |||
| static Graph* getInstance(); | |||
| void connect(const long int& uid, const std::pair<double, long int>& edge); | |||
| std::vector<std::pair<double, long int> > get(const long int& uid) const; | |||
| bool has(const long int& uid) const; | |||
| void new_recording(); | |||
| }; | |||
| #endif /* end of include guard: GRAPH_H */ | |||
+ 14
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install_diff/root/include/mor.h
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| @@ -0,0 +1,14 @@ | |||
| #ifndef MONADIC_OPERATION_RESULT | |||
| #define MONADIC_OPERATION_RESULT | |||
| struct MonadicOperationResult { | |||
| double value; | |||
| double grad; | |||
| MonadicOperationResult(double value, double grad){ | |||
| this->value = value; | |||
| this->grad = grad; | |||
| } | |||
| }; | |||
| #endif /* end of include guard: MONADIC_OPERATION_RESULT */ | |||
+ 72
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install_diff/root/include/node.h
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| @@ -0,0 +1,72 @@ | |||
| #ifndef NODE_H | |||
| #define NODE_H | |||
| #include <cmath> | |||
| #include <iostream> | |||
| #include "graph.h" | |||
| #include "mor.h" | |||
| #include "dor.h" | |||
| #include "por.h" | |||
| class Node { | |||
| private: | |||
| double value; | |||
| long int uid; | |||
| double gradient_recursive(Graph* graph, const long int& current_uid, const long int& stop_uid) const; | |||
| public: | |||
| Node(const double& value=0); | |||
| Node(const Node& node); | |||
| static Node monadic_operation(const Node& n, MonadicOperationResult (*)(const double&)); | |||
| static Node dyadic_operation(const Node& l, const Node& r, DyadicOperationResult (*)(const double&, const double&)); | |||
| static Node polyadic_operation(const std::vector<Node>& nodes, PolyadicOperationResult (*)(const std::vector<double>&)); | |||
| double gradient(const Node& node) const; | |||
| std::vector<double> gradient(const std::vector<Node>& nodes) const; | |||
| std::vector<std::vector<double> > gradient(const std::vector<std::vector<Node> >& nodes) const; | |||
| friend Node operator+(const Node& l, const Node& r); | |||
| friend Node operator-(const Node& l, const Node& r); | |||
| friend Node operator*(const Node& l, const Node& r); | |||
| friend Node operator/(const Node& l, const Node& r); | |||
| Node& operator+=(const Node& r); | |||
| Node& operator-=(const Node& r); | |||
| Node& operator*=(const Node& r); | |||
| Node& operator/=(const Node& r); | |||
| friend bool operator==(const Node& l, const Node& r); | |||
| friend bool operator<(const Node& l, const Node& r); | |||
| friend bool operator>(const Node& l, const Node& r); | |||
| friend bool operator<=(const Node& l, const Node& r); | |||
| friend bool operator>=(const Node& l, const Node& r); | |||
| friend Node sin(const Node& x); | |||
| friend Node cos(const Node& x); | |||
| friend Node tan(const Node& x); | |||
| friend Node sinh(const Node& x); | |||
| friend Node cosh(const Node& x); | |||
| friend Node tanh(const Node& x); | |||
| friend Node asin(const Node& x); | |||
| friend Node acos(const Node& x); | |||
| friend Node atan(const Node& x); | |||
| friend Node log(const Node& x, const Node& base); | |||
| friend Node log10(const Node& x); | |||
| friend Node ln(const Node& x); | |||
| friend Node pow(const Node& x, const Node& p); | |||
| friend Node exp(const Node& x); | |||
| friend Node sqrt(const Node& x); | |||
| friend Node abs(const Node& x); | |||
| friend Node min(const Node& l, const Node& r); | |||
| friend Node max(const Node& l, const Node& r); | |||
| friend std::ostream& operator<<(std::ostream& os, const Node& node); | |||
| }; | |||
| #endif /* end of include guard: NODE_H */ | |||
+ 16
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install_diff/root/include/por.h
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| @@ -0,0 +1,16 @@ | |||
| #ifndef POLYADIC_OPERATION_RESULT | |||
| #define POLYADIC_OPERATION_RESULT | |||
| #include <vector> | |||
| struct PolyadicOperationResult { | |||
| double value; | |||
| std::vector<double> gradients; | |||
| PolyadicOperationResult(double value, const std::vector<double>& gradients){ | |||
| this->value = value; | |||
| this->gradients = gradients; | |||
| } | |||
| }; | |||
| #endif /* end of include guard: POLYADIC_OPERATION_RESULT */ | |||
+ 99
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install_diff/root/include/vectmath.h
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| @@ -0,0 +1,99 @@ | |||
| #ifndef VECTMATH | |||
| #define VECTMATH | |||
| #include <vector> | |||
| #include <cassert> | |||
| #include <iostream> | |||
| #include <algorithm> | |||
| #include <functional> | |||
| // dot product | |||
| template <typename T> | |||
| std::vector<std::vector<T> > dot(const std::vector<std::vector<T> >& a, const std::vector<std::vector<T> >& b){ | |||
| assert(a[0].size()==b.size()); | |||
| T w=0; | |||
| std::vector<std::vector<T> > result(a.size(), std::vector<T>(b[0].size())); | |||
| for (int i=0 ; i<a.size() ; i++){ | |||
| for (int j=0 ; j<b[0].size() ; j++){ | |||
| for (int h=0 ; h<b.size() ; h++){ | |||
| w += a[i][h]*b[h][j]; | |||
| } | |||
| result[i][j] = w; | |||
| w=0; | |||
| } | |||
| } | |||
| return result; | |||
| } | |||
| // operators | |||
| template <typename U, typename V> | |||
| std::vector<U>& operator-=(std::vector<U>& u, const std::vector<V>& v){ | |||
| assert(u.size()==v.size()); | |||
| for(size_t i=0 ; i<u.size() ; i++){ | |||
| u[i] -= v[i]; | |||
| } | |||
| return u; | |||
| } | |||
| template <typename U> | |||
| std::vector<U> operator+(const std::vector<U>& u, const std::vector<U>& v){ | |||
| assert(u.size()==v.size()); | |||
| std::vector<U> w(u.size()); | |||
| for(size_t i=0 ; i<w.size() ; i++){ | |||
| w[i] = u[i]+v[i]; | |||
| } | |||
| return w; | |||
| } | |||
| template <typename U, typename S> | |||
| std::vector<U> operator*(const S& s, const std::vector<U>& u){ | |||
| std::vector<U> result(u.size()); | |||
| for(size_t i=0 ; i<u.size() ; i++){ | |||
| result[i] = s*u[i]; | |||
| } | |||
| return result; | |||
| } | |||
| template <typename U> | |||
| std::vector<U>& operator>>(U (*fun)(U&), std::vector<U>& u){ | |||
| std::transform(u.begin(), u.end(), u.begin(), fun); | |||
| return u; | |||
| } | |||
| template <typename U, typename S> | |||
| std::vector<U>& operator>>(S (*fun)(S&), std::vector<U>& u){ | |||
| for(auto& v : u){ | |||
| fun >> v; | |||
| } | |||
| return u; | |||
| } | |||
| template <typename U> | |||
| std::vector<U>& operator>>(U (*fun)(), std::vector<U>& u){ | |||
| for(auto& e : u){ | |||
| e = fun(); | |||
| } | |||
| return u; | |||
| } | |||
| template <typename U, typename S> | |||
| std::vector<U>& operator>>(S (*fun)(), std::vector<U>& u){ | |||
| for(auto& v : u){ | |||
| fun >> v; | |||
| } | |||
| return u; | |||
| } | |||
| template <typename U> | |||
| std::ostream& operator<<(std::ostream& os, const std::vector<U>& u){ | |||
| os << "["; | |||
| for(size_t i=0 ; i<u.size() ; i++){ | |||
| os << u[i] << (i<u.size()-1?", ":""); | |||
| } | |||
| os << "]"; | |||
| return os; | |||
| } | |||
| #endif | |||
BIN
install_diff/root/obj/graph.o
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BIN
install_diff/root/obj/node.o
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install_diff/root/src/graph.cpp
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| @@ -0,0 +1,33 @@ | |||
| #include "../include/graph.h" | |||
| Graph* Graph::instance = 0; | |||
| long int Graph::uid_counter = 1; | |||
| long int Graph::uid(){ | |||
| return uid_counter++; | |||
| } | |||
| Graph* Graph::getInstance(){ | |||
| if(instance==NULL){ | |||
| instance = new Graph(); | |||
| } | |||
| return instance; | |||
| } | |||
| Graph::Graph(){} | |||
| void Graph::connect(const long int& uid, const std::pair<double, long int>& edge){ | |||
| nodes[uid].push_back(edge); | |||
| } | |||
| std::vector<std::pair<double, long int> > Graph::get(const long int& uid) const{ | |||
| return nodes.at(uid); | |||
| } | |||
| bool Graph::has(const long int& uid) const{ | |||
| return nodes.find(uid)!=nodes.end(); | |||
| } | |||
| void Graph::new_recording(){ | |||
| nodes.clear(); | |||
| } | |||
+ 275
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install_diff/root/src/node.cpp
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| @@ -0,0 +1,275 @@ | |||
| #include "../include/node.h" | |||
| Node::Node(const double& value) { | |||
| this->value = value; | |||
| this->uid = Graph::uid(); | |||
| } | |||
| Node::Node(const Node& node){ | |||
| this->value = node.value; | |||
| this->uid = node.uid; | |||
| } | |||
| double Node::gradient_recursive(Graph* graph, const long int& current_uid, const long int& stop_uid) const{ | |||
| if(current_uid==stop_uid){ | |||
| return 1.0; | |||
| } | |||
| double sum=0.0; | |||
| if(graph->has(current_uid)){ | |||
| for(auto& pair : graph->get(current_uid)){ | |||
| sum += pair.first*gradient_recursive(graph, pair.second, stop_uid); | |||
| } | |||
| } | |||
| return sum; | |||
| } | |||
| double Node::gradient(const Node& node) const{ | |||
| Graph* graph = Graph::getInstance(); | |||
| return gradient_recursive(graph, this->uid, node.uid); | |||
| } | |||
| std::vector<double> Node::gradient(const std::vector<Node>& nodes) const{ | |||
| Graph* graph = Graph::getInstance(); | |||
| std::vector<double> grad(nodes.size()); | |||
| for(size_t i=0 ; i<nodes.size() ; i++){ | |||
| grad[i] = gradient_recursive(graph, this->uid, nodes[i].uid); | |||
| } | |||
| return grad; | |||
| } | |||
| std::vector<std::vector<double> > Node::gradient(const std::vector<std::vector<Node> >& nodes) const{ | |||
| Graph* graph = Graph::getInstance(); | |||
| std::vector<std::vector<double> > grad(nodes.size()); | |||
| for(size_t i=0 ; i<nodes.size() ; i++){ | |||
| grad[i].resize(nodes[i].size()); | |||
| for(size_t j=0 ; j<nodes[i].size() ; j++){ | |||
| grad[i][j] = gradient_recursive(graph, this->uid, nodes[i][j].uid); | |||
| } | |||
| } | |||
| return grad; | |||
| } | |||
| Node Node::monadic_operation(const Node& n, MonadicOperationResult (*fun)(const double&)){ | |||
| MonadicOperationResult res = fun(n.value); | |||
| Node result(res.value); | |||
| Graph* graph = Graph::getInstance(); | |||
| graph->connect(result.uid, std::make_pair(res.grad, n.uid)); | |||
| return result; | |||
| } | |||
| Node Node::dyadic_operation(const Node& left, const Node& right, DyadicOperationResult (*fun)(const double&, const double&)){ | |||
| DyadicOperationResult res = fun(left.value, right.value); | |||
| Node result(res.value); | |||
| Graph* graph = Graph::getInstance(); | |||
| graph->connect(result.uid, std::make_pair(res.left_grad, left.uid)); | |||
| graph->connect(result.uid, std::make_pair(res.right_grad, right.uid)); | |||
| return result; | |||
| } | |||
| Node Node::polyadic_operation(const std::vector<Node>& nodes, PolyadicOperationResult (*fun)(const std::vector<double>&)){ | |||
| std::vector<double> values(nodes.size()); | |||
| for(size_t i=0 ; i<nodes.size() ; i++){ | |||
| values[i] = nodes[i].value; | |||
| } | |||
| PolyadicOperationResult res = fun(values); | |||
| Node result(res.value); | |||
| Graph* graph = Graph::getInstance(); | |||
| for(size_t i=0 ; i<nodes.size() ; i++){ | |||
| graph->connect(result.uid, std::make_pair(res.gradients[i], nodes[i].uid)); | |||
| } | |||
| return result; | |||
| } | |||
| Node operator+(const Node& left, const Node& right){ | |||
| return Node::dyadic_operation(left, right, [](const double& l, const double& r){ | |||
| return DyadicOperationResult(l+r, 1.0, 1.0); | |||
| }); | |||
| } | |||
| Node operator-(const Node& left, const Node& right){ | |||
| return Node::dyadic_operation(left, right, [](const double& l, const double& r){ | |||
| return DyadicOperationResult(l-r, 1.0, -1.0); | |||
| }); | |||
| } | |||
| Node operator*(const Node& left, const Node& right){ | |||
| return Node::dyadic_operation(left, right, [](const double& l, const double& r){ | |||
| return DyadicOperationResult(l*r, r, l); | |||
| }); | |||
| } | |||
| Node operator/(const Node& left, const Node& right){ | |||
| return Node::dyadic_operation(left, right, [](const double& l, const double& r){ | |||
| return DyadicOperationResult(l/r, 1.0/r, -1.0*l/(r*r)); | |||
| }); | |||
| } | |||
| Node& Node::operator+=(const Node& r){ | |||
| *this = *this + r; | |||
| return *this; | |||
| } | |||
| Node& Node::operator-=(const Node& r){ | |||
| *this = *this - r; | |||
| return *this; | |||
| } | |||
| Node& Node::operator*=(const Node& r){ | |||
| *this = *this * r; | |||
| return *this; | |||
| } | |||
| Node& Node::operator/=(const Node& r){ | |||
| *this = *this / r; | |||
| return *this; | |||
| } | |||
| bool operator==(const Node& left, const Node& right){ | |||
| return left.value==right.value; | |||
| } | |||
| bool operator<(const Node& left, const Node& right){ | |||
| return left.value<right.value; | |||
| } | |||
| bool operator>(const Node& left, const Node& right){ | |||
| return left.value>right.value; | |||
| } | |||
| bool operator<=(const Node& left, const Node& right){ | |||
| return left.value<=right.value; | |||
| } | |||
| bool operator>=(const Node& left, const Node& right){ | |||
| return left.value>=right.value; | |||
| } | |||
| Node sin(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| return MonadicOperationResult(::sin(n), ::cos(n)); | |||
| }); | |||
| } | |||
| Node cos(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| return MonadicOperationResult(::cos(n), -1.0*::sin(n)); | |||
| }); | |||
| } | |||
| Node tan(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| return MonadicOperationResult(::tan(n), 1.0/::pow(::cos(n), 2)); | |||
| }); | |||
| } | |||
| Node sinh(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| return MonadicOperationResult(::sinh(n), ::cosh(n)); | |||
| }); | |||
| } | |||
| Node cosh(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| return MonadicOperationResult(::cosh(n), ::sinh(n)); | |||
| }); | |||
| } | |||
| Node asin(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| return MonadicOperationResult(::asin(n), 1.0/(::sqrt(1-n*n))); | |||
| }); | |||
| } | |||
| Node acos(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| return MonadicOperationResult(::acos(n), -1.0/(::sqrt(1-n*n))); | |||
| }); | |||
| } | |||
| Node atan(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| return MonadicOperationResult(::atan(n), 1.0/(1+n*n)); | |||
| }); | |||
| } | |||
| Node tanh(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| return MonadicOperationResult(::tanh(n), 1.0-::pow(::tanh(n), 2)); | |||
| }); | |||
| } | |||
| Node log(const Node& x, const Node& base){ | |||
| return Node::dyadic_operation(x, base, [](const double& a, const double& b){ | |||
| return DyadicOperationResult(::log(a)/::log(b), 1.0/(a*::log(b)), -1.0*::log(a)/(b*::log(b))); | |||
| }); | |||
| } | |||
| Node log10(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| return MonadicOperationResult(::log(n)/::log(10), 1.0/(n*::log(10))); | |||
| }); | |||
| } | |||
| Node ln(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| return MonadicOperationResult(::log(n), 1.0/::log(n)); | |||
| }); | |||
| } | |||
| Node pow(const Node& x, const Node& base){ | |||
| return Node::dyadic_operation(x, base, [](const double& a, const double& b){ | |||
| if(a<=0){ | |||
| return DyadicOperationResult(::pow(a,b), b*::pow(a,b-1), 0); | |||
| } | |||
| return DyadicOperationResult(::pow(a,b), b*::pow(a,b-1), ::log(a)*::pow(a,b)); | |||
| }); | |||
| } | |||
| Node exp(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| return MonadicOperationResult(::exp(n), ::exp(n)); | |||
| }); | |||
| } | |||
| Node sqrt(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| return MonadicOperationResult(::sqrt(n), 1.0/(2*::sqrt(n))); | |||
| }); | |||
| } | |||
| Node abs(const Node& x){ | |||
| return Node::monadic_operation(x, [](const double& n){ | |||
| int sign = n==0 ? 0 : n/::abs(n); | |||
| return MonadicOperationResult(::abs(n), sign); | |||
| }); | |||
| } | |||
| Node min(const Node& left, const Node& right){ | |||
| return Node::dyadic_operation(left, right, [](const double& a, const double& b){ | |||
| if(a<b){ | |||
| return DyadicOperationResult(a, 1, 0); | |||
| } | |||
| if(a>b){ | |||
| return DyadicOperationResult(b, 0, 1); | |||
| } | |||
| return DyadicOperationResult(a, 0, 0); | |||
| }); | |||
| } | |||
| Node max(const Node& left, const Node& right){ | |||
| return Node::dyadic_operation(left, right, [](const double& a, const double& b){ | |||
| if(a>b){ | |||
| return DyadicOperationResult(a, 1, 0); | |||
| } | |||
| if(a<b){ | |||
| return DyadicOperationResult(b, 0, 1); | |||
| } | |||
| return DyadicOperationResult(a, 0, 0); | |||
| }); | |||
| } | |||
| std::ostream& operator<<(std::ostream& os, const Node& node){ | |||
| os << node.value; | |||
| return os; | |||
| } | |||
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logistic/.logistic_def.h.swp
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logistic/logistic_def.cpp
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| @@ -0,0 +1,20 @@ | |||
| #include<iostream> | |||
| #include"../matrix/matrix_def.cpp" | |||
| #include"../welcome/score_wel.cpp" | |||
| #include"../matrix/matrix_pro.cpp" | |||
| #include<math.h> | |||
| /* | |||
| ███████╗██████╗ ██████╗ ███████╗ ███████╗███╗ ██╗ ██████╗ ██╗███╗ ██╗███████╗ | |||
| ██╔════╝██╔══██╗██╔════╝ ██╔════╝ ██╔════╝████╗ ██║██╔════╝ ██║████╗ ██║██╔════╝ | |||
| █████╗ ██║ ██║██║ ███╗█████╗ █████╗ ██╔██╗ ██║██║ ███╗██║██╔██╗ ██║█████╗ | |||
| ██╔══╝ ██║ ██║██║ ██║██╔══╝ ██╔══╝ ██║╚██╗██║██║ ██║██║██║╚██╗██║██╔══╝ | |||
| ███████╗██████╔╝╚██████╔╝███████╗ ███████╗██║ ╚████║╚██████╔╝██║██║ ╚████║███████╗ | |||
| ╚══════╝╚═════╝ ╚═════╝ ╚══════╝ ╚══════╝╚═╝ ╚═══╝ ╚═════╝ ╚═╝╚═╝ ╚═══╝╚══════╝ | |||
| Author：Edge | |||
| Web：likedge.top | |||
| Date：20200925 | |||
| */ | |||
| double sigmoid_def() | |||
| { | |||
| cout<<exp(2)<<endl; | |||
| } | |||
+ 35
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logistic/logistic_def.h
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| @@ -0,0 +1,35 @@ | |||
| #include"../matrix/matrix_def.h" | |||
| #include"../matrix/matrix_pro.h" | |||
| #include<math.h> | |||
| #include<iostream> | |||
| using namespace std; | |||
| /* | |||
| ███████╗██████╗ ██████╗ ███████╗ ███████╗███╗ ██╗ ██████╗ ██╗███╗ ██╗███████╗ | |||
| ██╔════╝██╔══██╗██╔════╝ ██╔════╝ ██╔════╝████╗ ██║██╔════╝ ██║████╗ ██║██╔════╝ | |||
| █████╗ ██║ ██║██║ ███╗█████╗ █████╗ ██╔██╗ ██║██║ ███╗██║██╔██╗ ██║█████╗ | |||
| ██╔══╝ ██║ ██║██║ ██║██╔══╝ ██╔══╝ ██║╚██╗██║██║ ██║██║██║╚██╗██║██╔══╝ | |||
| ███████╗██████╔╝╚██████╔╝███████╗ ███████╗██║ ╚████║╚██████╔╝██║██║ ╚████║███████╗ | |||
| ╚══════╝╚═════╝ ╚═════╝ ╚══════╝ ╚══════╝╚═╝ ╚═══╝ ╚═════╝ ╚═╝╚═╝ ╚═══╝╚══════╝ | |||
| Author：Edge | |||
| Web：likedge.top | |||
| Date：20200925 | |||
| */ | |||
| double get_sigmoid(double temp) | |||
| { | |||
| // cout<<"temp"<<temp<<endl; | |||
| return 1/1+exp(0-temp); | |||
| } | |||
| Matrix e_sigmoid(Matrix mid1) | |||
| { | |||
| Matrix result = CreateMatrix(mid1.row,mid1.col); | |||
| for(int index_x = 0;index_x<mid1.row;index_x++) | |||
| { | |||
| for(int index_y=0;index_y<mid1.col;index_y++) | |||
| { | |||
| // cout<<"ex:"<<get_sigmoid(mid1.matrix[index_x][index_y])<<endl; | |||
| result.matrix[index_x][index_y]= 1.0/(1+exp(-mid1.matrix[index_x][index_y])); | |||
| } | |||
| } | |||
| return result; | |||
| } | |||
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main.cpp
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| @@ -0,0 +1,96 @@ | |||
| /* | |||
| ███████╗██████╗ ██████╗ ███████╗ ███████╗███╗ ██╗ ██████╗ ██╗███╗ ██╗███████╗ | |||
| ██╔════╝██╔══██╗██╔════╝ ██╔════╝ ██╔════╝████╗ ██║██╔════╝ ██║████╗ ██║██╔════╝ | |||
| █████╗ ██║ ██║██║ ███╗█████╗ █████╗ ██╔██╗ ██║██║ ███╗██║██╔██╗ ██║█████╗ | |||
| ██╔══╝ ██║ ██║██║ ██║██╔══╝ ██╔══╝ ██║╚██╗██║██║ ██║██║██║╚██╗██║██╔══╝ | |||
| ███████╗██████╔╝╚██████╔╝███████╗ ███████╗██║ ╚████║╚██████╔╝██║██║ ╚████║███████╗ | |||
| ╚══════╝╚═════╝ ╚═════╝ ╚══════╝ ╚══════╝╚═╝ ╚═══╝ ╚═════╝ ╚═╝╚═╝ ╚═══╝╚══════╝ | |||
| Author：Edge | |||
| Web：likedge.top | |||
| Date：20200925 | |||
| */ | |||
| #include<iostream> | |||
| #include<ctime> | |||
| #include<string> | |||
| #include <time.h> | |||
| #include <math.h> | |||
| #include <fstream> | |||
| #include "./autodiff/node.h" | |||
| #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" | |||
| #include"./grad_edge/matrix_grad.h" | |||
| #include"./data_struct/data_struct_pro.h" | |||
| using namespace std; | |||
| clock_t start, stop; | |||
| double duration; | |||
| Node z=1; | |||
| Node t1 = 1,a13 = 1; | |||
| /*自定义的损失函数*/ | |||
| Node loss_act(Node t1,Node a13) | |||
| { | |||
| Node loss = 0.5*(pow((t1-a13),2)); | |||
| return loss; | |||
| } | |||
| Node sigmoid_act(Node z) | |||
| { | |||
| Node sigmoid_act = 1/(1+(1/exp(z))); | |||
| return sigmoid_act; | |||
| } | |||
| //loss | |||
| Node (*loss)(Node,Node) = loss_act; | |||
| Node (*act)(Node) = sigmoid_act; | |||
| int main() | |||
| { | |||
| Matrix data_1 = ones(1,3); | |||
| Matrix data_2 = ones(3,1); | |||
| cout_mat(mul(data_1,data_2)); | |||
| welcome(); | |||
| //begin | |||
| cout<<"auto build on gcc"<<endl; | |||
| cout<<"---------autodiff for neraul network-----------"<<endl; | |||
| Matrix data_mine = ones(3,3); | |||
| cout<<"data mine"<<endl; | |||
| cout_mat(data_mine); | |||
| cout<<"data mine"<<endl; | |||
| Matrix label = CreateRandMat(3,1); | |||
| cout_mat(label); | |||
| Matrix weight1 = CreateRandMat(3,3); | |||
| cout<<"weight"<<endl; | |||
| cout_mat(weight1); | |||
| Matrix bais1 = ones(3,1); | |||
| cout_mat(bais1); | |||
| Matrix weight2 = CreateRandMat(3,3); | |||
| Matrix bais2 = ones(3,1); | |||
| for(int epoch = 0;epoch<10;epoch++) | |||
| { | |||
| cout<<"---------epoch: "<<epoch<<"------------"<<endl; | |||
| cout_mat(weight1); | |||
| int input_dim = 3; | |||
| int output_dim = 3; | |||
| edge_network sequaltial(input_dim,output_dim); | |||
| // define the network | |||
| Matrix output1 = sequaltial.forward(data_mine,weight1,bais1); | |||
| Matrix output1_without_act = sequaltial.forward_without_act(data_mine,weight1,bais1); | |||
| // layer1 | |||
| Matrix output2 = sequaltial.forward(output1,weight2,bais2); | |||
| Matrix output2_without_act = sequaltial.forward_without_act(output1,weight2,bais2); | |||
| // layer2 | |||
| Matrix output_end = sequaltial.end_layer_backward(label,output2_without_act,*loss,*act); | |||
| // layer3 | |||
| Matrix backward3 = sequaltial.backward(output_end,output1_without_act,weight2,*act); | |||
| // bp | |||
| Matrix weight_2_grad = mul(output_end,get_T(output1)); | |||
| Matrix weight_1_grad = mul(backward3,get_T(data_mine)); | |||
| weight1 = subtract(weight1,times_mat(0.001,weight_1_grad)); | |||
| bais1 = subtract(bais1,times_mat(0.001,backward3)); | |||
| weight2 = subtract(weight2,times_mat(0.001,weight_2_grad)); | |||
| bais2 = subtract(bais2,times_mat(0.001,output_end)); | |||
| cout<<"neraul end;"<<endl; | |||
| } | |||
| return 0; | |||
| } | |||
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matrix/.matrix_pro.h.swp
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matrix/conv_test.cpp
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| @@ -0,0 +1,15 @@ | |||
| #include<iostream> | |||
| #include<stdlib.h> | |||
| #include<string> | |||
| /* | |||
| ███████╗██████╗ ██████╗ ███████╗ ███████╗███╗ ██╗ ██████╗ ██╗███╗ ██╗███████╗ | |||
| ██╔════╝██╔══██╗██╔════╝ ██╔════╝ ██╔════╝████╗ ██║██╔════╝ ██║████╗ ██║██╔════╝ | |||
| █████╗ ██║ ██║██║ ███╗█████╗ █████╗ ██╔██╗ ██║██║ ███╗██║██╔██╗ ██║█████╗ | |||
| ██╔══╝ ██║ ██║██║ ██║██╔══╝ ██╔══╝ ██║╚██╗██║██║ ██║██║██║╚██╗██║██╔══╝ | |||
| ███████╗██████╔╝╚██████╔╝███████╗ ███████╗██║ ╚████║╚██████╔╝██║██║ ╚████║███████╗ | |||
| ╚══════╝╚═════╝ ╚═════╝ ╚══════╝ ╚══════╝╚═╝ ╚═══╝ ╚═════╝ ╚═╝╚═╝ ╚═══╝╚══════╝ | |||
| Author：Edge | |||
| Web：likedge.top | |||
| Date：20200925 | |||
| */ | |||
| int main() | |||
+ 121
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matrix/matrix_def.h
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| @@ -0,0 +1,121 @@ | |||
| #ifndef MATRIX_DEF | |||
| #define MATRIX_DEF | |||
| #pragma once | |||
| #include<iostream> | |||
| #include<sys/malloc.h> | |||
| #include<stdio.h> | |||
| #include<string> | |||
| using namespace std; | |||
| /* | |||
| ███████╗██████╗ ██████╗ ███████╗ ███████╗███╗ ██╗ ██████╗ ██╗███╗ ██╗███████╗ | |||
| ██╔════╝██╔══██╗██╔════╝ ██╔════╝ ██╔════╝████╗ ██║██╔════╝ ██║████╗ ██║██╔════╝ | |||
| █████╗ ██║ ██║██║ ███╗█████╗ █████╗ ██╔██╗ ██║██║ ███╗██║██╔██╗ ██║█████╗ | |||
| ██╔══╝ ██║ ██║██║ ██║██╔══╝ ██╔══╝ ██║╚██╗██║██║ ██║██║██║╚██╗██║██╔══╝ | |||
| ███████╗██████╔╝╚██████╔╝███████╗ ███████╗██║ ╚████║╚██████╔╝██║██║ ╚████║███████╗ | |||
| ╚══════╝╚═════╝ ╚═════╝ ╚══════╝ ╚══════╝╚═╝ ╚═══╝ ╚═════╝ ╚═╝╚═╝ ╚═══╝╚══════╝ | |||
| Author：Edge | |||
| Web：likedge.top | |||
| Date：20200925 | |||
| */ | |||
| //CreateMatrix Create a Matrix for ones; | |||
| //ex: | |||
| //2*3 | |||
| //0 0 0 | |||
| //0 0 0 | |||
| //change the value in matrix | |||
| typedef struct | |||
| { | |||
| int row,col; | |||
| float **matrix; | |||
| }Matrix; | |||
| typedef struct | |||
| { | |||
| int row,col; | |||
| string **str_matrix; | |||
| }str_Matrix; | |||
| str_Matrix CreateStr_Ma(int ro, int co) | |||
| { | |||
| str_Matrix str_arr; | |||
| int row,col; | |||
| string **designma; | |||
| designma = (string**)malloc(ro*sizeof(string*)); | |||
| for(int i = 0;i<ro;i++) | |||
| { | |||
| designma[i] = (string*)malloc(co*sizeof(string)); | |||
| } | |||
| for(int i = 0;i <ro;i++) | |||
| { | |||
| for(int j =0;j<co;j++) | |||
| { | |||
| //cout<<"designma"<<designma[i][j]<<endl; | |||
| designma[i][j] = "edge"; | |||
| } | |||
| } | |||
| str_arr.row = ro; | |||
| str_arr.col = co; | |||
| str_arr.str_matrix = designma; | |||
| return str_arr; | |||
| } | |||
| Matrix CreateMatrix(int ro,int co) | |||
| { | |||
| Matrix m; | |||
| int row,col; | |||
| float **inputMatrix; | |||
| inputMatrix=(float**)malloc(ro*sizeof(float*)); | |||
| for(int i = 0;i<ro;i++) | |||
| { | |||
| inputMatrix[i]=(float*)malloc(co*sizeof(float)); | |||
| } | |||
| for(int i=0; i<ro; i++) | |||
| { | |||
| for(int j=0; j<co; j++) | |||
| { | |||
| inputMatrix[i][j] = 0; | |||
| } | |||
| } | |||
| m.col = co; | |||
| m.row = ro; | |||
| m.matrix = inputMatrix; | |||
| return m; | |||
| } | |||
| Matrix ones(int ro,int co) | |||
| { | |||
| Matrix m; | |||
| int row,col; | |||
| float **inputMatrix; | |||
| inputMatrix=(float**)malloc(ro*sizeof(float*)); | |||
| for(int i = 0;i<ro;i++) | |||
| { | |||
| inputMatrix[i]=(float*)malloc(co*sizeof(float)); | |||
| } | |||
| for(int i=0; i<ro; i++) | |||
| { | |||
| for(int j=0; j<co; j++) | |||
| { | |||
| inputMatrix[i][j] = 1; | |||
| } | |||
| } | |||
| m.col = co; | |||
| m.row = ro; | |||
| m.matrix = inputMatrix; | |||
| return m; | |||
| } | |||
| void cout_strmat(str_Matrix mid1) | |||
| { | |||
| for(int index_x = 0;index_x<mid1.row;index_x++) | |||
| { | |||
| for(int index_y=0;index_y<mid1.col;index_y++) | |||
| { | |||
| cout<<mid1.str_matrix[index_x][index_y]<<","; | |||
| } | |||
| cout<<endl; | |||
| } | |||
| } | |||
| int change_va(Matrix Matrix ,int index_x,int index_y,float value) | |||
| { | |||
| Matrix.matrix[index_x][index_y] = value; | |||
| return 0; | |||
| } | |||
| #endif | |||
+ 442
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matrix/matrix_pro.h
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| /* | |||
| ███████╗██████╗ ██████╗ ███████╗ ███████╗███╗ ██╗ ██████╗ ██╗███╗ ██╗███████╗ | |||
| ██╔════╝██╔══██╗██╔════╝ ██╔════╝ ██╔════╝████╗ ██║██╔════╝ ██║████╗ ██║██╔════╝ | |||
| █████╗ ██║ ██║██║ ███╗█████╗ █████╗ ██╔██╗ ██║██║ ███╗██║██╔██╗ ██║█████╗ | |||
| ██╔══╝ ██║ ██║██║ ██║██╔══╝ ██╔══╝ ██║╚██╗██║██║ ██║██║██║╚██╗██║██╔══╝ | |||
| ███████╗██████╔╝╚██████╔╝███████╗ ███████╗██║ ╚████║╚██████╔╝██║██║ ╚████║███████╗ | |||
| ╚══════╝╚═════╝ ╚═════╝ ╚══════╝ ╚══════╝╚═╝ ╚═══╝ ╚═════╝ ╚═╝╚═╝ ╚═══╝╚══════╝ | |||
| Author：Edge | |||
| Web：likedge.top | |||
| Date：20200925 | |||
| matrix add | |||
| flag stand use binary or not default 1 | |||
| move_ele change the x,y to y,x | |||
| get_T :get the matrix transpose | |||
| input:Matrix a; | |||
| output:Matrix a.transpose | |||
| and -- just test | |||
| --------------------------------------------------------------- | |||
| if you have the better answer on it , it is nothing, just test~ | |||
| --------------------------------------------------------------- | |||
| */ | |||
| #ifndef MATRIX_PRO | |||
| #define MATRIX_PRO | |||
| #include<iostream> | |||
| #include<stdlib.h> | |||
| #include<string> | |||
| #define random(x) (rand()%x) | |||
| void move_ele(int &ele1, int &ele2) | |||
| { | |||
| ele1 ^= ele2 ^= ele1 ^= ele2; | |||
| } | |||
| int add_by(int a,int b) | |||
| { | |||
| int ans; | |||
| while(b) | |||
| { | |||
| ans = a^b; | |||
| b = ((a&b)<<1); | |||
| a = ans; | |||
| } | |||
| return a; | |||
| } | |||
| //two matrix add | |||
| Matrix add(Matrix mid1,Matrix mid2,int flag=1) | |||
| { | |||
| if(mid1.row == mid2.row&&mid1.col == mid2.col) | |||
| { | |||
| for(int x = 0;x<mid1.row;x++) | |||
| { | |||
| for(int y = 0;y<mid1.col;y++) | |||
| { | |||
| if(flag==1) | |||
| mid1.matrix[x][y]=add_by(mid1.matrix[x][y],mid2.matrix[x][y]); | |||
| else | |||
| mid1.matrix[x][y]+=mid2.matrix[x][y]; | |||
| } | |||
| } | |||
| } | |||
| else{return mid1;} | |||
| return mid1; | |||
| } | |||
| //subtract use default | |||
| Matrix subtract(Matrix mid1,Matrix mid2) | |||
| { | |||
| Matrix result_subtract = CreateMatrix(mid1.row,mid2.col); | |||
| for(int i=0; i<mid1.row; i++) | |||
| { | |||
| for(int j=0; j<mid1.col; j++) | |||
| { | |||
| result_subtract.matrix[i][j] = mid1.matrix[i][j]-mid2.matrix[i][j]; | |||
| } | |||
| } | |||
| return result_subtract; | |||
| } | |||
| Matrix mul(Matrix mid1,Matrix mid2) | |||
| { | |||
| Matrix mid3 = CreateMatrix(mid1.row,mid2.col); | |||
| for(int i = 0;i<mid1.row;i++) | |||
| { | |||
| for(int j = 0;j<mid2.col;j++) | |||
| { | |||
| for(int k = 0;k<mid1.col;k++) | |||
| { | |||
| mid3.matrix[i][j]=mid3.matrix[i][j]+mid1.matrix[i][k]*mid2.matrix[k][j]; | |||
| } | |||
| } | |||
| } | |||
| return mid3; | |||
| } | |||
| Matrix times_mat(double times,Matrix mid1) | |||
| { | |||
| for(int index_x=0; index_x<mid1.row; index_x++) | |||
| { | |||
| for(int index_y=0; index_y<mid1.col; index_y++) | |||
| { | |||
| mid1.matrix[index_x][index_y] = mid1.matrix[index_x][index_y]*times; | |||
| } | |||
| } | |||
| return mid1; | |||
| } | |||
| Matrix get_T(Matrix mid1) | |||
| { | |||
| Matrix mid2 = CreateMatrix(mid1.col,mid1.row); | |||
| for(int index_x = 0;index_x<mid1.row;index_x++) | |||
| { | |||
| for(int index_y=0;index_y<mid1.col;index_y++) | |||
| { | |||
| mid2.matrix[index_y][index_x]=mid1.matrix[index_x][index_y]; | |||
| } | |||
| } | |||
| return mid2; | |||
| } | |||
| double* flatten(Matrix mid1) | |||
| { | |||
| int size = mid1.row*mid1.col; | |||
| double balance[size]; | |||
| double *p; | |||
| p = balance; | |||
| for ( int i = 0; i <size; i++ ) | |||
| { | |||
| int index_x = i/mid1.col; | |||
| int index_y = i%mid1.col; | |||
| *(p+i) = mid1.matrix[index_x][index_y]; | |||
| } | |||
| return balance; | |||
| } | |||
| Matrix matrix_rs(Matrix mid1,int rs_row,int rs_col) | |||
| { | |||
| int size = mid1.row*mid1.col; | |||
| double balance[size]; | |||
| double *p; | |||
| p = balance; | |||
| int index_x=0,index_y=0; | |||
| Matrix result = CreateMatrix(rs_row,rs_col); | |||
| for ( int i = 0; i <size; i++ ) | |||
| { | |||
| int index_x = i/mid1.col; | |||
| int index_y = i%mid1.col; | |||
| *(p+i) = mid1.matrix[index_x][index_y]; | |||
| } | |||
| int added = mid1.row*mid1.col; | |||
| for(int i = 0;i<size;i++) | |||
| { | |||
| if(index_y == rs_col) | |||
| { | |||
| index_y=0; | |||
| index_x+=1; | |||
| } | |||
| result.matrix[index_x][index_y]=*(p+i); | |||
| index_y++; | |||
| } | |||
| return result; | |||
| } | |||
| double matrix_sum(Matrix mid1) | |||
| { | |||
| double sum = 0; | |||
| for(int index_x = 0;index_x<mid1.row;++index_x) | |||
| { | |||
| for(int index_y=0;index_y<mid1.col;++index_y) | |||
| { | |||
| sum+=mid1.matrix[index_x][index_y]; | |||
| } | |||
| } | |||
| return sum; | |||
| } | |||
| double matrix_mean(Matrix mid1) | |||
| { | |||
| double ele = mid1.row*mid1.col; | |||
| return matrix_sum(mid1)/ele; | |||
| } | |||
| Matrix mat_apply(Matrix mid1,Matrix mid2,int axis = 0) | |||
| { | |||
| int new_row = mid1.row+mid2.row; | |||
| int new_col = mid1.col+mid2.col; | |||
| Matrix result; | |||
| if(axis ==1) | |||
| {result = CreateMatrix(mid1.row,new_col);} | |||
| else{result = CreateMatrix(new_row+1,mid2.col);} | |||
| for(int index_x = 0;index_x<mid1.row;++index_x) | |||
| { | |||
| for(int index_y=0;index_y<mid1.col;++index_y) | |||
| { | |||
| result.matrix[index_x][index_y] = mid1.matrix[index_x][index_y]; | |||
| } | |||
| } | |||
| if(axis == 1) | |||
| { | |||
| for(int index_row=0;index_row<mid2.row;++index_row) | |||
| { | |||
| for(int index_col = mid1.col;index_col<new_col;++index_col) | |||
| { | |||
| result.matrix[index_row][index_col]= mid2.matrix[index_row][mid2.col-new_col+index_col]; | |||
| } | |||
| } | |||
| } | |||
| else | |||
| { | |||
| for(int index_row=mid1.row;index_row<new_row;++index_row) | |||
| { | |||
| for(int index_col = 0;index_col<mid2.col;++index_col) | |||
| { | |||
| result.matrix[index_row][index_col]= mid2.matrix[index_row-mid2.row][index_col]; | |||
| } | |||
| } | |||
| } | |||
| return result; | |||
| } | |||
| Matrix head(Matrix mid1) | |||
| { | |||
| int col = mid1.col; | |||
| Matrix mid_return = CreateMatrix(6,mid1.col); | |||
| for(int index_x = 0;index_x<6;++index_x) | |||
| { | |||
| for(int index_y=0;index_y<col;++index_y) | |||
| { | |||
| mid_return.matrix[index_x][index_y] = mid1.matrix[index_x][index_y]; | |||
| } | |||
| } | |||
| return mid_return; | |||
| } | |||
| void cout_mat(Matrix mid1) | |||
| { | |||
| for(int index_x = 0;index_x<mid1.row;index_x++) | |||
| { | |||
| for(int index_y=0;index_y<mid1.col;index_y++) | |||
| { | |||
| cout<<mid1.matrix[index_x][index_y]<<","; | |||
| } | |||
| cout<<endl; | |||
| } | |||
| } | |||
| //iloc [x1,x2,y1,y2] | |||
| Matrix iloc(Matrix mid1,int start_x=0,int end_x=0,int start_y=0,int end_y=0) | |||
| { | |||
| if(end_y == 0) | |||
| { | |||
| end_y = mid1.col; | |||
| } | |||
| else if(end_x == 0) | |||
| { | |||
| end_x = mid1.row; | |||
| } | |||
| // cout<<mid1.row<<end_x<<" "<<start_x<<" "<<end_y<<" "<<start_y<<endl; | |||
| int new_row = end_x-start_x; | |||
| int new_col = end_y-start_y; | |||
| // cout<<"nc"<<new_col<<" nr"<<new_row<<endl; | |||
| Matrix mid_return = CreateMatrix(new_row,new_col); | |||
| for(int index_x = start_x;index_x<end_x;++index_x) | |||
| { | |||
| for(int index_y=start_y;index_y<end_y;++index_y) | |||
| { | |||
| mid_return.matrix[index_x-start_x][index_y-start_y] = mid1.matrix[index_x][index_y]; | |||
| } | |||
| } | |||
| return mid_return; | |||
| } | |||
| Matrix mul_simple(Matrix mid1,Matrix mid2) | |||
| { | |||
| if(mid1.row != mid2.row||mid1.col != mid2.col) | |||
| { | |||
| cout<<"Error: shape A&B"<<endl; | |||
| return mid1; | |||
| } | |||
| Matrix result = CreateMatrix(mid1.row,mid1.col); | |||
| for(int index_x = 0;index_x<mid1.row;index_x++) | |||
| { | |||
| for(int index_y=0;index_y<mid1.col;index_y++) | |||
| { | |||
| result.matrix[index_x][index_y] = mid1.matrix[index_x][index_y]*mid2.matrix[index_x][index_y]; | |||
| } | |||
| } | |||
| return result; | |||
| } | |||
| Matrix CreateRandMat(int x_dim,int y_dim) | |||
| { | |||
| int index_x,index_y; | |||
| Matrix result = CreateMatrix(x_dim,y_dim); | |||
| for(index_x = 0;index_x<x_dim;++index_x) | |||
| { | |||
| for(index_y=0;index_y<y_dim;++index_y) | |||
| { | |||
| double temp_val = (rand()%30000)*0.0001-1; | |||
| change_va(result,index_x,index_y,temp_val); | |||
| } | |||
| } | |||
| return result; | |||
| } | |||
| double edge_relu(double val_relu) | |||
| { | |||
| if(val_relu>0) | |||
| {return val_relu;} | |||
| else | |||
| {return 0;} | |||
| } | |||
| Matrix mat_relu(Matrix mid1) | |||
| { | |||
| Matrix relu_mat = CreateMatrix(mid1.row,mid1.col); | |||
| for(int index_x = 0;index_x<mid1.row;++index_x) | |||
| for(int index_y = 0;index_y<mid1.row;++index_y) | |||
| { | |||
| { | |||
| relu_mat.matrix[index_x][index_y] = edge_relu(mid1.matrix[index_x][index_y]); | |||
| } | |||
| } | |||
| return relu_mat; | |||
| } | |||
| Matrix mat_sq_loss(Matrix mid1,Matrix mid2) | |||
| { | |||
| int index_x,index_y; | |||
| Matrix mat_sq = CreateMatrix(mid1.row,1); | |||
| for(index_x = 0;index_x<mid1.row;++index_x) | |||
| { | |||
| mat_sq.matrix[index_x][0] = pow(mid1.matrix[index_x][0]-mid2.matrix[index_x][0],2); | |||
| } | |||
| return mat_sq; | |||
| } | |||
| Matrix padding(Matrix mid1,int shape1,int shape2) | |||
| { | |||
| Matrix result = CreateMatrix(shape1,shape2); | |||
| for(int index_x = 0;index_x<shape1;index_x++) | |||
| { | |||
| for(int index_y = 0;index_y<shape2;index_y++) | |||
| { | |||
| result.matrix[index_x][index_y] = mid1.matrix[index_x][0]; | |||
| } | |||
| } | |||
| return result; | |||
| } | |||
| Matrix get_row(Matrix mid1,int index) | |||
| { | |||
| int index_y = 0; | |||
| Matrix mid2 = CreateMatrix(1,mid1.col); | |||
| for(index_y=0;index_y<mid1.col;index_y++) | |||
| { | |||
| mid2.matrix[0][index_y] = mid1.matrix[index][index_y]; | |||
| } | |||
| return mid2; | |||
| } | |||
| Matrix conv_element(Matrix mid1,Matrix kernel,int kernel_size = 2,int stride = 1) | |||
| { | |||
| Matrix conv_result = CreateMatrix(((mid1.row-kernel_size)/stride)+1,((mid1.col-kernel_size)/stride)+1); | |||
| for(int x_ = 0;x_<=(mid1.row-kernel_size)/stride;x_+=stride) | |||
| { | |||
| for(int y_ = 0;y_<=(mid1.col-kernel_size)/stride;y_+=stride) | |||
| { | |||
| Matrix crop_pic = iloc(mid1,x_,x_+kernel.col,y_,y_+kernel.row); | |||
| change_va(conv_result,x_,y_,matrix_sum(mul_simple(crop_pic,kernel))); | |||
| } | |||
| } | |||
| // cout<<"row: "<<conv_result.row<<" , "<<"col: "<<conv_result.col<<endl; | |||
| // cout_mat(conv_result); | |||
| return conv_result; | |||
| } | |||
| /* | |||
| parameter: | |||
| Matrix mid1, | |||
| int input_dim = 3 | |||
| int output_channels = 3 | |||
| int stride = 1 | |||
| int kernel_size = 2 | |||
| int mode = 0 | |||
| int padding = 0 | |||
| */ | |||
| double conv_test(Matrix mid1,int input_dim = 3,int output_channels = 3,int stride = 1,int kernel_size = 2,int mode = 0,int padding = 0) | |||
| { | |||
| // cout_mat(mid1); | |||
| Matrix mid_rgb[input_dim]; | |||
| for(int rgb_idx = 0;rgb_idx<input_dim;rgb_idx++) | |||
| { | |||
| mid_rgb[rgb_idx] = CreateRandMat(mid1.row,mid1.col); | |||
| } | |||
| Matrix filters[output_channels][input_dim]; | |||
| for(int channel_index = 0;channel_index<input_dim;channel_index++) | |||
| { | |||
| for(int filter_index = 0;filter_index<output_channels;filter_index++) | |||
| { | |||
| Matrix kernel = ones(kernel_size,kernel_size); | |||
| filters[channel_index][filter_index] = kernel; | |||
| // cout<<"---------"<<endl; | |||
| // cout<<"channel: "<<channel_index<<", index: "<<filter_index<<endl; | |||
| // cout_mat(filters[channel_index][filter_index]); | |||
| } | |||
| } | |||
| if(mode == 0) | |||
| { | |||
| cout<<"input_img:"<<endl; | |||
| for(int i =0;i<input_dim;i++) | |||
| { | |||
| cout<<"---------rgb: "<<i<<"---------"<<endl; | |||
| cout_mat(mid_rgb[i]); | |||
| } | |||
| Matrix conv_result = CreateMatrix(((mid1.row-kernel_size)/stride)+1,((mid1.col-kernel_size)/stride)+1); | |||
| Matrix kernel = ones(kernel_size,kernel_size); | |||
| cout<<"--------- kernels: 3x3--------"<<endl; | |||
| cout_mat(kernel); | |||
| cout<<"--------- output: ---------"<<endl; | |||
| Matrix feature_maps[output_channels]; | |||
| for(int filter_idx = 0;filter_idx<output_channels;filter_idx++) | |||
| { | |||
| Matrix sum_rgb = CreateMatrix(((mid1.row-kernel_size)/stride)+1,((mid1.col-kernel_size)/stride)+1); | |||
| for(int channel_idx=0;channel_idx<input_dim;channel_idx++) | |||
| { | |||
| sum_rgb = add(sum_rgb,conv_element(mid_rgb[channel_idx],filters[channel_idx][filter_idx],kernel_size,stride),0); | |||
| cout<<"sum_rgb"<<"filters_index: "<<filter_idx<<" "<<endl; | |||
| cout_mat(sum_rgb); | |||
| } | |||
| feature_maps[filter_idx]=sum_rgb; | |||
| } | |||
| for(int i = 0;i < output_channels;i++) | |||
| { | |||
| cout<<"==========filter: "<<i<<"========="<<endl; | |||
| cout_mat(feature_maps[i]); | |||
| } | |||
| return 0.0; | |||
| } | |||
| } | |||
| #endif | |||
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mytest.csv
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mytest.txt
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neral/test.py
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| class Tensor: | |||
| def __init__(self, data, depend=[]): | |||
| """初始化""" | |||
| self.data = data | |||
| self.depend = depend | |||
| self.grad = 0 | |||
| def __mul__(self, data): | |||
| """乘法""" | |||
| def grad_fn1(grad): | |||
| return grad * data.data | |||
| def grad_fn2(grad): | |||
| return grad * self.data | |||
| depend = [(self, grad_fn1), (data, grad_fn2)] | |||
| new = Tensor(self.data * data.data, depend) | |||
| return new | |||
| def __rmul__(self, data): | |||
| def grad_fn1(grad): | |||
| return grad * data.data | |||
| def grad_fn2(grad): | |||
| return grad * self.data | |||
| depend = [(self, grad_fn1), (data, grad_fn2)] | |||
| new = Tensor(self.data * data.data, depend) | |||
| return new | |||
| def __add__(self, data): | |||
| """加法""" | |||
| def grad_fn(grad): | |||
| return grad | |||
| depend = [(self, grad_fn), (data, grad_fn)] | |||
| new = Tensor(self.data * data.data, depend) | |||
| return new | |||
| def __radd__(self, data): | |||
| def grad_fn(grad): | |||
| return grad | |||
| depend = [(self, grad_fn), (data, grad_fn)] | |||
| new = Tensor(self.data * data.data, depend) | |||
| return new | |||
| def __repr__(self): | |||
| return f"Tensor:{self.data}" | |||
| def backward(self, grad=None): | |||
| """ | |||
| 反向传播，需要递归计算 | |||
| """ | |||
| if grad == None: | |||
| self.grad = 1 | |||
| else: | |||
| # 这一步用于计算图中的分支 | |||
| self.grad += grad | |||
| # 这一步是递归计算 | |||
| for tensor, grad_fn in self.depend: | |||
| bw = grad_fn(self.grad) | |||
| tensor.backward(bw) | |||
| x = Tensor(4) | |||
| f = x * x | |||
| g = x * x | |||
| y = f + g | |||
| y.backward() | |||
| print(x) | |||
| print(y, g.grad, x.grad) | |||
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nerual_test.cpp
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| @@ -0,0 +1,91 @@ | |||
| #include<iostream> | |||
| #include<time.h> | |||
| #include<string> | |||
| #include<math.h> | |||
| #include<fstream> | |||
| #include"./autodiff/node.h" | |||
| #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" | |||
| #include"./grad_edge/matrix_grad.h" | |||
| using namespace std; | |||
| Node z = 1; | |||
| Node t1 = 1,a13 = 1; | |||
| Node loss_act(Node t1,Node a13) | |||
| { | |||
| Node loss = 0.5*(pow((t1-a13),2)); | |||
| return loss; | |||
| } | |||
| Node sigmoid_act(Node z) | |||
| { | |||
| Node sigmoid_act = 1/(1+(1/exp(z))); | |||
| return sigmoid_act; | |||
| } | |||
| Node (*loss)(Node,Node) = loss_act; | |||
| Node (*act)(Node) = sigmoid_act; | |||
| int main() | |||
| { | |||
| cout<<"begin to train"<<endl; | |||
| int input_dim = 2; | |||
| int output_dim = 2; | |||
| edge_network sequaltial(input_dim,output_dim); | |||
| Matrix data_mine = CreateMatrix(100,3); | |||
| Matrix label_mine = CreateRandMat(100,1); | |||
| Matrix data = iloc(data_mine,0,1,0,0); | |||
| cout<<"data: "; | |||
| cout_mat(data); | |||
| Matrix label = iloc(label_mine,0,3,0,0); | |||
| cout<<"label: "; | |||
| cout_mat(label); | |||
| Matrix bias1 = CreateRandMat(2,1); | |||
| Matrix weight1 = CreateRandMat(2,data.col); | |||
| cout<<"weight1: "; | |||
| cout_mat(weight1); | |||
| int index = 1; | |||
| Matrix backward2 = CreateMatrix(2,1); | |||
| Matrix before_backward2 = CreateMatrix(2,1); | |||
| change_va(before_backward2,1,0,1); | |||
| for(index = 0;index<99;index++){ | |||
| Matrix output1 = sequaltial.forward(get_T(get_row(data_mine,index)),weight1,bias1); | |||
| Matrix output1_without_act = sequaltial.forward_without_act(get_T(get_row(data_mine,index)),weight1,bias1); | |||
| cout<<"output1_without_act: "; | |||
| cout_mat(output1_without_act); | |||
| Matrix weight2 = CreateRandMat(2,output1.row); | |||
| cout<<"weight2: "<<endl; | |||
| cout_mat(weight2); | |||
| Matrix bias2 = CreateRandMat(weight2.row,1); | |||
| Matrix output2 = sequaltial.forward(output1,weight2,bias2); | |||
| Matrix output2_without_act = sequaltial.forward_without_act(output1,weight2,bias2); | |||
| cout<<"output2_without_act: "; | |||
| cout_mat(output2_without_act); | |||
| cout<<"output2: "; | |||
| cout_mat(output2); | |||
| cout<<"output1:"<<endl; | |||
| cout_mat(output1); | |||
| Matrix weight3 = CreateRandMat(3,output2.row); | |||
| Matrix bias3 = CreateRandMat(weight3.row,1); | |||
| Matrix output3 = sequaltial.forward(output2,weight3,bias3); | |||
| Matrix output3_without_act = sequaltial.forward_without_act(output2,weight3,bias3); | |||
| cout<<"row: "<<label.row<<"col: "<<label.col; | |||
| Matrix output_end = sequaltial.end_layer_backward(label,output3_without_act,*loss,*act); | |||
| cout<<"outputend: "; | |||
| cout_mat(output_end); | |||
| cout<<"output3_without_act: "; | |||
| cout_mat(output3_without_act); | |||
| cout<<"output3: "; | |||
| cout_mat(output3); | |||
| Matrix backward3 = sequaltial.backward(output_end,output2_without_act,get_T(weight3),*act); | |||
| cout<<"backward_before: "; | |||
| cout_mat(before_backward2); | |||
| before_backward2 = add(before_backward2,backward2); | |||
| backward2 = sequaltial.backward(backward3,output1_without_act,get_T(weight2),*act); | |||
| cout<<"backward2: "; | |||
| save_txt(backward2,"a.csv"); | |||
| cout_mat(before_backward2); | |||
| cout_mat(backward2); | |||
| //can change the before_backward2 values; | |||
| } | |||
| } | |||
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| .gmake | |||
| obj/ | |||
| bin/ | |||
| lib/ | |||
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requitement/Makefile
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| CXX = g++ | |||
| BIN = bin | |||
| LIB = lib | |||
| LIB_NAME = autodiff | |||
| OBJS_GRADIENT_DESCENT = root/obj/node.o root/obj/graph.o examples/obj/gradient_descent.o | |||
| OBJS_ANN = root/obj/node.o root/obj/graph.o examples/obj/ann.o | |||
| OBJS_GRADIENT = root/obj/node.o root/obj/graph.o examples/obj/gradient.o | |||
| OBJS_SPEED = root/obj/node.o root/obj/graph.o examples/obj/speed.o | |||
| OBJS_SIMPLE = root/obj/node.o root/obj/graph.o examples/obj/simple.o | |||
| all : gradient_descent ann gradient speed simple | |||
| gradient_descent : $(BIN) root/include/vectmath.h root/include/mor.h root/include/dor.h root/include/por.h | |||
| $(MAKE) -C examples obj obj/gradient_descent.o | |||
| $(MAKE) -C root obj obj/node.o obj/graph.o | |||
| $(CXX) -o $(BIN)/gradient_descent $(OBJS_GRADIENT_DESCENT) $(LIBS) | |||
| ann : $(BIN) root/include/vectmath.h root/include/mor.h root/include/dor.h root/include/por.h | |||
| $(MAKE) -C examples obj obj/ann.o | |||
| $(MAKE) -C root obj obj/node.o obj/graph.o | |||
| $(CXX) -o $(BIN)/ann $(OBJS_ANN) $(LIBS) | |||
| gradient : $(BIN) root/include/vectmath.h root/include/mor.h root/include/dor.h root/include/por.h | |||
| $(MAKE) -C examples obj obj/gradient.o | |||
| $(MAKE) -C root obj obj/node.o obj/graph.o | |||
| $(CXX) -o $(BIN)/gradient $(OBJS_GRADIENT) $(LIBS) | |||
| speed : $(BIN) root/include/vectmath.h root/include/mor.h root/include/dor.h root/include/por.h | |||
| $(MAKE) -C examples obj obj/speed.o | |||
| $(MAKE) -C root obj obj/node.o obj/graph.o | |||
| $(CXX) -o $(BIN)/speed $(OBJS_SPEED) $(LIBS) | |||
| simple : $(BIN) root/include/mor.h root/include/dor.h root/include/por.h | |||
| $(MAKE) -C examples obj obj/simple.o | |||
| $(MAKE) -C root obj obj/node.o obj/graph.o | |||
| $(CXX) -o $(BIN)/simple $(OBJS_SIMPLE) $(LIBS) | |||
| $(BIN) : | |||
| if [ ! -d $(BIN) ]; then mkdir $(BIN); fi | |||
| $(LIB) : | |||
| if [ ! -d $(LIB) ]; then mkdir $(LIB); fi | |||
| clean : | |||
| $(MAKE) -C root clean | |||
| $(MAKE) -C examples clean | |||
| if [ -d $(BIN) ]; then rm $(BIN) -r; fi | |||
| install : $(LIB) | |||
| ar rcs $(LIB)/lib$(LIB_NAME).a root/obj/graph.o root/obj/node.o | |||
| if [ ! -d /usr/local/include/$(LIB_NAME) ]; then sudo mkdir /usr/local/include/$(LIB_NAME); fi | |||
| sudo cp $(LIB)/lib$(LIB_NAME).a /usr/local/lib | |||
| sudo cp root/include/*.h /usr/local/include/$(LIB_NAME) | |||
| .PHONY : all | |||
| .PHONY : gradient_descent | |||
| .PHONY : ann | |||
| .PHONY : gradient | |||
| .PHONY : speed | |||
| .PHONY : simple | |||
| .PHONY : clean | |||
| .PHONY : install | |||
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requitement/examples/Makefile
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| CXX = g++ | |||
| ODIR = obj | |||
| CXXFLAGS = -std=c++11 -O3 | |||
| OBJS = $(ODIR)/gradient_descent.o $(ODIR)/ann.o $(ODIR)/gradient.o $(ODIR)/speed.o $(ODIR)/simple.o | |||
| all : $(ODIR) $(OBJS) | |||
| $(ODIR)/gradient_descent.o : src/gradient_descent.cpp ../root/include/vectmath.h ../root/include/node.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR)/ann.o : src/ann.cpp ../root/include/vectmath.h ../root/include/node.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR)/gradient.o : src/gradient.cpp ../root/include/vectmath.h ../root/include/node.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR)/speed.o : src/speed.cpp ../root/include/vectmath.h ../root/include/node.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR)/simple.o : src/simple.cpp ../root/include/node.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR) : | |||
| if [ ! -d $(ODIR) ]; then mkdir $(ODIR); fi | |||
| clean : | |||
| if [ -d $(ODIR) ]; then rm $(ODIR) -r; fi | |||
| .PHONY : all | |||
| .PHONY : clean | |||
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requitement/examples/src/ann.cpp
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| #include <iostream> | |||
| #include <ctime> | |||
| #include "../../root/include/vectmath.h" | |||
| #include "../../root/include/node.h" | |||
| typedef std::vector<Node> Vector; | |||
| typedef std::vector<Vector> Matrix; | |||
| Node random_number(){ | |||
| return rand()/(double)RAND_MAX; | |||
| } | |||
| Node tan_h(Node& x){ | |||
| return (1-exp(-2*x))/(1+exp(-2*x)); | |||
| } | |||
| Node mean_square_error(Vector& y_true, Vector& y_pred){ | |||
| Node loss; | |||
| for(size_t i=0 ; i<y_true.size() ; i++){ | |||
| loss += pow(y_true[i]-y_pred[i], 2); | |||
| } | |||
| return loss; | |||
| } | |||
| struct Layer { | |||
| Matrix weights; | |||
| Matrix bias; | |||
| Node (*activation)(Node&); | |||
| int input_shape; | |||
| int output_shape; | |||
| Layer(int input, int output, Node (*activation)(Node&)){ | |||
| this->activation = activation; | |||
| this->input_shape = input; | |||
| this->output_shape = output; | |||
| weights.resize(input, Vector(output)); | |||
| bias.resize(1, Vector(output)); | |||
| random_number >> weights; | |||
| random_number >> bias; | |||
| } | |||
| Matrix forward(Matrix& previous){ | |||
| Matrix output = dot(previous, weights) + bias; | |||
| output = activation >> output; | |||
| return output; | |||
| } | |||
| void backward(Node& loss, const float& learning_rate){ | |||
| weights -= learning_rate*loss.gradient(weights); | |||
| bias -= learning_rate*loss.gradient(bias); | |||
| } | |||
| }; | |||
| struct Network { | |||
| std::vector<Layer> layers; | |||
| int input_shape; | |||
| Graph* graph; | |||
| Network(){ | |||
| graph = Graph::getInstance(); | |||
| } | |||
| void input_layer(int input_shape){ | |||
| this->input_shape = input_shape; | |||
| } | |||
| void add(int output_shape, Node (*activation)(Node&)){ | |||
| int input = layers.empty()?input_shape:layers.back().output_shape; | |||
| layers.push_back(Layer(input, output_shape, activation)); | |||
| } | |||
| Matrix run(Matrix& input){ | |||
| Matrix output(input.size()); | |||
| for(size_t j=0 ; j<input.size() ; j++){ | |||
| Matrix out = {input[j]}; | |||
| for(auto& lay : layers){ | |||
| out = lay.forward(out); | |||
| } | |||
| output[j] = out[0]; | |||
| } | |||
| return output; | |||
| } | |||
| void fit(Matrix& input, Matrix& output, Node (*loss_function)(Vector&, Vector&), int epochs, float learning_rate){ | |||
| int p=0; | |||
| for(size_t i=0 ; i<epochs ; i++){ | |||
| std::cout << "\r" << i+1 << "/" << epochs; | |||
| for(size_t j=0 ; j<input.size() ; j++){ | |||
| // compute input | |||
| Matrix out = {input[j]}; | |||
| for(auto& lay : layers){ | |||
| out = lay.forward(out); | |||
| } | |||
| // compute loss | |||
| Node loss = loss_function(output[j], out[0]); | |||
| // update parameters | |||
| for(auto& lay : layers){ | |||
| lay.backward(loss, learning_rate); | |||
| } | |||
| graph->new_recording(); | |||
| } | |||
| } | |||
| std::cout << std::endl; | |||
| } | |||
| }; | |||
| int main(int argc, char const *argv[]) { | |||
| srand(time(NULL)); | |||
| Matrix input = {{0,0},{0,1},{1,0},{1,1}}; | |||
| Matrix output = {{0},{1},{1},{0}}; | |||
| Network network; | |||
| network.input_layer(2); | |||
| network.add(3, tan_h); | |||
| network.add(1, tan_h); | |||
| network.fit(input, output, mean_square_error, 500, 0.1); | |||
| Matrix pred = network.run(input); | |||
| std::cout << pred << std::endl; | |||
| return 0; | |||
| } | |||
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| #include <iostream> | |||
| #include "../../root/include/vectmath.h" | |||
| #include "../../root/include/node.h" | |||
| Node function(std::vector<Node> x){ | |||
| return pow(x[0]-x[1], 2) + x[0]*x[1]*x[2]; // (x-y)^2 + x*y*z | |||
| } | |||
| int main(int argc, char const *argv[]) { | |||
| std::vector<Node> x = {5,6,7}; | |||
| Node f = function(x); | |||
| std::cout << "grad(f) = " << f.gradient(x) << std::endl; | |||
| return 0; | |||
| } | |||
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requitement/examples/src/gradient_descent.cpp
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| #include <iostream> | |||
| #include "../../root/include/vectmath.h" | |||
| #include "../../root/include/node.h" | |||
| Node function(std::vector<Node>& x){ | |||
| return pow(x[0], 2) + pow(x[1], 2); // x^2 + y^2 | |||
| } | |||
| int main(int argc, char const *argv[]) { | |||
| Graph* graph = Graph::getInstance(); | |||
| std::vector<Node> x = {50, 50}; | |||
| Node f; | |||
| int epochs = 30; | |||
| float learning_rate = 0.1; | |||
| for(size_t i=0 ; i<epochs ; i++){ | |||
| f = function(x); | |||
| x -= learning_rate*f.gradient(x); | |||
| graph->new_recording(); | |||
| } | |||
| std::cout << "f = " << f << std::endl; | |||
| std::cout << "x = " << x << std::endl; | |||
| return 0; | |||
| } | |||
+ 13
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requitement/examples/src/simple.cpp
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| @@ -0,0 +1,13 @@ | |||
| #include <iostream> | |||
| #include "../../root/include/node.h" | |||
| int main(int argc, char const *argv[]) { | |||
| Node x=2, y=3; | |||
| Node f = x*y + sin(x); | |||
| std::cout << "f(x,y) = x*y + sin(x)" << std::endl; | |||
| std::cout << "f(" << x << "," << y << ") = " << f << std::endl; | |||
| std::cout << "∂f/∂x = " << f.gradient(x) << std::endl; | |||
| std::cout << "∂f/∂y = " << f.gradient(y) << std::endl; | |||
| return 0; | |||
| } | |||
+ 51
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requitement/examples/src/speed.cpp
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| @@ -0,0 +1,51 @@ | |||
| #include <iostream> | |||
| #include <iomanip> | |||
| #include <cassert> | |||
| #include <ctime> | |||
| #include <chrono> | |||
| #include "../../root/include/vectmath.h" | |||
| #include "../../root/include/node.h" | |||
| template <class T> | |||
| std::vector<std::vector<T> > get_random_matrix(const int& height, const int& width, T t){ | |||
| std::vector<std::vector<T> > mat(height, std::vector<T>(width)); | |||
| for(auto& v : mat){ | |||
| for(auto& e : v){ | |||
| e = rand()/(double)RAND_MAX; | |||
| } | |||
| } | |||
| return mat; | |||
| } | |||
| int main(int argc, char const *argv[]) { | |||
| srand(time(0)); | |||
| int size = 30; | |||
| std::vector<std::vector<double> > a = get_random_matrix(size, size, double()); | |||
| std::vector<std::vector<double> > b = get_random_matrix(size, size, double()); | |||
| std::vector<std::vector<Node> > c = get_random_matrix(size, size, Node()); | |||
| std::vector<std::vector<Node> > d = get_random_matrix(size, size, Node()); | |||
| std::cout << std::fixed; | |||
| std::cout << std::setprecision(10); | |||
| std::cout << "Running with double...\t"; | |||
| std::cout.flush(); | |||
| auto start = std::chrono::high_resolution_clock::now(); | |||
| std::vector<std::vector<double> > ab = dot(a, b); | |||
| auto finish = std::chrono::high_resolution_clock::now(); | |||
| std::chrono::duration<double> elapsed = finish - start; | |||
| std::cout << "Elapsed time: " << elapsed.count() << " s" << std::endl; | |||
| std::cout << "Running with Node...\t"; | |||
| std::cout.flush(); | |||
| start = std::chrono::high_resolution_clock::now(); | |||
| std::vector<std::vector<Node> > cd = dot(c, d); | |||
| finish = std::chrono::high_resolution_clock::now(); | |||
| elapsed = finish - start; | |||
| std::cout << "Elapsed time: " << elapsed.count() << " s" << std::endl; | |||
| std::cout << "Yet to be improved..." << std::endl; | |||
| return 0; | |||
| } | |||
+ 22
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requitement/root/Makefile
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| @@ -0,0 +1,22 @@ | |||
| CXX = g++ | |||
| ODIR = obj | |||
| CXXFLAGS = -std=c++11 -O3 | |||
| OBJS = $(ODIR)/graph.o $(ODIR)/node.o | |||
| all : $(ODIR) $(OBJS) | |||
| $(ODIR)/graph.o : src/graph.cpp include/graph.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR)/node.o : src/node.cpp include/node.h | |||
| $(CXX) -c $< -o $@ $(CXXFLAGS) | |||
| $(ODIR) : | |||
| if [ ! -d $(ODIR) ]; then mkdir $(ODIR); fi | |||
| clean : | |||
| if [ -d $(ODIR) ]; then rm $(ODIR) -r; fi | |||
| .PHONY : all | |||
| .PHONY : clean | |||
+ 16
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requitement/root/include/dor.h
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| @@ -0,0 +1,16 @@ | |||
| #ifndef DYADIC_OPERATION_RESULT | |||
| #define DYADIC_OPERATION_RESULT | |||
| struct DyadicOperationResult { | |||
| double value; | |||
| double left_grad; | |||
| double right_grad; | |||
| DyadicOperationResult(double value, double left_grad, double right_grad){ | |||
| this->value = value; | |||
| this->left_grad = left_grad; | |||
| this->right_grad = right_grad; | |||
| } | |||
| }; | |||
| #endif /* end of include guard: DYADIC_OPERATION_RESULT */ | |||
+ 26
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requitement/root/include/graph.h
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| @@ -0,0 +1,26 @@ | |||
| #ifndef GRAPH_H | |||
| #define GRAPH_H | |||
| #include <map> | |||
| #include <vector> | |||
| #include <utility> | |||
| class Graph { | |||
| private: | |||
| std::map<long int, std::vector<std::pair<double, long int> > > nodes; | |||
| static Graph* instance; | |||
| Graph(); | |||
| public: | |||
| static long int uid_counter; | |||
| static long int uid(); | |||
| static Graph* getInstance(); | |||
| void connect(const long int& uid, const std::pair<double, long int>& edge); | |||
| std::vector<std::pair<double, long int> > get(const long int& uid) const; | |||
| bool has(const long int& uid) const; | |||
| void new_recording(); | |||
| }; | |||
| #endif /* end of include guard: GRAPH_H */ | |||
+ 14
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requitement/root/include/mor.h
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| @@ -0,0 +1,14 @@ | |||
| #ifndef MONADIC_OPERATION_RESULT | |||
| #define MONADIC_OPERATION_RESULT | |||
| struct MonadicOperationResult { | |||
| double value; | |||
| double grad; | |||
| MonadicOperationResult(double value, double grad){ | |||
| this->value = value; | |||
| this->grad = grad; | |||
| } | |||
| }; | |||
| #endif /* end of include guard: MONADIC_OPERATION_RESULT */ | |||
+ 72
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requitement/root/include/node.h
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| @@ -0,0 +1,72 @@ | |||
| #ifndef NODE_H | |||
| #define NODE_H | |||
| #include <cmath> | |||
| #include <iostream> | |||
| #include "graph.h" | |||
| #include "mor.h" | |||
| #include "dor.h" | |||
| #include "por.h" | |||
| class Node { | |||
| private: | |||
| double value; | |||
| long int uid; | |||
| double gradient_recursive(Graph* graph, const long int& current_uid, const long int& stop_uid) const; | |||
| public: | |||
| Node(const double& value=0); | |||
| Node(const Node& node); | |||
| static Node monadic_operation(const Node& n, MonadicOperationResult (*)(const double&)); | |||
| static Node dyadic_operation(const Node& l, const Node& r, DyadicOperationResult (*)(const double&, const double&)); | |||
| static Node polyadic_operation(const std::vector<Node>& nodes, PolyadicOperationResult (*)(const std::vector<double>&)); | |||
| double gradient(const Node& node) const; | |||
| std::vector<double> gradient(const std::vector<Node>& nodes) const; | |||
| std::vector<std::vector<double> > gradient(const std::vector<std::vector<Node> >& nodes) const; | |||
| friend Node operator+(const Node& l, const Node& r); | |||
| friend Node operator-(const Node& l, const Node& r); | |||
| friend Node operator*(const Node& l, const Node& r); | |||
| friend Node operator/(const Node& l, const Node& r); | |||
| Node& operator+=(const Node& r); | |||
| Node& operator-=(const Node& r); | |||
| Node& operator*=(const Node& r); | |||
| Node& operator/=(const Node& r); | |||
| friend bool operator==(const Node& l, const Node& r); | |||
| friend bool operator<(const Node& l, const Node& r); | |||
| friend bool operator>(const Node& l, const Node& r); | |||
| friend bool operator<=(const Node& l, const Node& r); | |||
| friend bool operator>=(const Node& l, const Node& r); | |||
| friend Node sin(const Node& x); | |||
| friend Node cos(const Node& x); | |||
| friend Node tan(const Node& x); | |||
| friend Node sinh(const Node& x); | |||
| friend Node cosh(const Node& x); | |||
| friend Node tanh(const Node& x); | |||
| friend Node asin(const Node& x); | |||
| friend Node acos(const Node& x); | |||
| friend Node atan(const Node& x); | |||
| friend Node log(const Node& x, const Node& base); | |||
| friend Node log10(const Node& x); | |||
| friend Node ln(const Node& x); | |||
| friend Node pow(const Node& x, const Node& p); | |||
| friend Node exp(const Node& x); | |||
| friend Node sqrt(const Node& x); | |||
| friend Node abs(const Node& x); | |||
| friend Node min(const Node& l, const Node& r); | |||
| friend Node max(const Node& l, const Node& r); | |||
| friend std::ostream& operator<<(std::ostream& os, const Node& node); | |||
| }; | |||
| #endif /* end of include guard: NODE_H */ | |||
+ 16
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requitement/root/include/por.h
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| @@ -0,0 +1,16 @@ | |||
| #ifndef POLYADIC_OPERATION_RESULT | |||
| #define POLYADIC_OPERATION_RESULT | |||
| #include <vector> | |||
| struct PolyadicOperationResult { | |||
| double value; | |||
| std::vector<double> gradients; | |||
| PolyadicOperationResult(double value, const std::vector<double>& gradients){ | |||
| this->value = value; | |||
| this->gradients = gradients; | |||
| } | |||
| }; | |||
| #endif /* end of include guard: POLYADIC_OPERATION_RESULT */ | |||