<<<<<<< HEAD
[](https://raw.githubusercontent.com/tesseract-ocr/tesseract/master/LICENSE)
------
> 项目开始日期 : 2019/10/01
>
> 目前项目总代码 : 810 行
>
> 测试 : main.cpp | nerual_network.cpp | 新增全连接神经网络架构(新增全连接网络正向传播和反向传播的测试demo)
>
> 测试环境:
>
> MacBook Pro
>
> 编译器环境:
>
> Configured with: --prefix=/Applications/Xcode.app/Contents/Developer/usr --with-gxx-include-dir=/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.14.sdk/usr/include/c++/4.2.1
> Apple LLVM version 10.0.1 (clang-1001.0.46.4)
> Target: x86_64-apple-darwin18.7.0
> Thread model: posix
------
这是什么?
## 安装编译
```
git clone git@github.com:AllenZYJ/Edge-Computing-Engine.git
cd to 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);
```
```c
Matrix weight2 = CreateRandMat(output1.row,2);
Matrix bias2 = CreateRandMat(weight2.row,1);
Matrix output2 = sequaltial.forward(output1,weight2,bias2);
```
同时第二层的输出也可以求出来,以此类推 .
最终输出代码见nerual_test.cpp 
代码:
```c
Matrix data_mine = CreateRandMat(2,1);
Matrix label = CreateMatrix(2,1);
Matrix weight1 = CreateRandMat(2,2);
Matrix weight2 = CreateRandMat(2,2);
Matrix weight3 = CreateRandMat(2,2);
Matrix weight4 = CreateRandMat(2,2);
for(int epoch = 0;epoch<20;epoch++)
{
cout_mat(weight1);
edge_network sequaltial(2,2);
Matrix output1 = sequaltial.forward(data_mine,weight1);
Matrix output2 = sequaltial.forward(output1,weight2);
Matrix output3 = sequaltial.forward(output2,weight3);
Matrix output4 = sequaltial.forward(output3,weight4);
Matrix output_end = sequaltial.end_layer_backward(label,output4);
//get the forward
Matrix backward1 = sequaltial.backward(output_end,output3,weight4);
Matrix grad_w1w2 = mul_simple(backward1,data_mine);
Matrix backward2 = sequaltial.backward(backward1,output2,weight3);
Matrix grad_w3w4 = mul_simple(backward2,data_mine);
Matrix backward3 = sequaltial.backward(backward2,output1,weight2);
Matrix grad_w5w6 = mul_simple(backward3,data_mine);
Matrix backward4 = sequaltial.backward(backward3,output4,weight1);
Matrix grad_w7w8 = mul_simple(backward4,data_mine);
weight1 = subtract(weight1,times_mat(0.0001,padding(grad_w1w2,2,2)));
weight2 = subtract(weight2,times_mat(0.0001,padding(grad_w3w4,2,2)));
weight3 = subtract(weight3,times_mat(0.0001,padding(grad_w5w6,2,2)));
weight4 = subtract(weight4,times_mat(0.0001,padding(grad_w7w8,2,2)));
}
```
```shell
---------epoch: 0------------
loss: 4.65667
loss: 3.28273
---------epoch: 1------------
loss: 4.65655
loss: 3.28265
---------epoch: 2------------
loss: 4.65643
loss: 3.28257
---------epoch: 3------------
loss: 4.65631
loss: 3.28249
---------epoch: 4------------
loss: 4.65619
loss: 3.2824
---------epoch: 5------------
loss: 4.65607
loss: 3.28232
---------epoch: 6------------
loss: 4.65596
loss: 3.28224
---------epoch: 7------------
loss: 4.65584
loss: 3.28216
---------epoch: 8------------
loss: 4.65572
loss: 3.28208
---------epoch: 9------------
loss: 4.6556
loss: 3.282
---------epoch: 10------------
loss: 4.65548
loss: 3.28192
---------epoch: 11------------
loss: 4.65536
loss: 3.28184
---------epoch: 12------------
loss: 4.65524
loss: 3.28176
---------epoch: 13------------
loss: 4.65512
loss: 3.28168
---------epoch: 14------------
loss: 4.65501
loss: 3.2816
---------epoch: 15------------
loss: 4.65489
loss: 3.28152
---------epoch: 16------------
loss: 4.65477
loss: 3.28144
---------epoch: 17------------
loss: 4.65465
loss: 3.28136
---------epoch: 18------------
loss: 4.65453
loss: 3.28128
---------epoch: 19------------
loss: 4.65441
loss: 3.2812
```
## Bp反向传播的demo程序基于Pytorch官方代码模拟实现测试
迭代结果 :
W1: 0.6944 1.52368
-1.46644 -0.154097
W2: 1.10079
0.462984
loss: 0.559269
epoch:100 , 可自行测试.
输出最终损失和参数迭代结果.
-----------split-line-----------
2.79955
0.36431
-0.451694
epoch: 100 error: 6.05895
-----------split-line-----------
0.009167(sum of loss)
### 目前实现的程序接口
### API:
- [x] Matrix read_csv(string &file_path)读取格式化文件(csv),返回一个自动计算长度的矩阵.
- [x] 实现格式化文件写入接口.比较pandas.to_csv.
- [x] 矩阵广播机制,实现padding接口
- [x] 全连接层前向传播和反向传播接口,支持自动求导
- [x] 矩阵微分和自动求导接口封装
- [x] int save_txt(Matrix mid1,string path = "./",string delimiter = ",",string header="./") 设计文件流获取文件头部接口 , 写入格式化文件 , 已设计支持矩阵类型数据写入,支持自定义表头,写入文件路径 , 自定义分隔符,默认为" , ".
- [x] Create a matrix : create(row,cols)开辟一个矩阵结构的内存,元素初值为0;
- [x] Change the element for matrix void move_ele(int &ele1, int &ele2),修改某一个位置的元素的值.
- [x] Matrix1+Matrix2 : Matrix add(Matrix mid1,Matrix mid2,int flag=1),矩阵加和操作接口,可选位运算加速.
- [x] Flag is how to compete the ele ,default 1 ,bitwise operation(位运算加速).
- [x] Matrix1-Matrix2 : Matrix subtract(Matrix mid1,Matrix mid2)
- [x] Matrix1*Matrix2 : Matrix mul(Matrix mid1,Matrix mid2)
- [x] Matrix1*n : Matrix times_mat(int times,Matrix mid1)
- [x] Matrix1's Transposition : Matrix get_T(Matrix mid1)矩阵转置
- [x] Mul(matrix1,matrix2)矩阵乘积(完整数学定义).
- [x] double* flatten(Matrix mid1) : Return a flattened array.矩阵展开
- [x] Matrix matrix_rs(Matrix mid1,int rs_row,int rs_col) 矩阵的结构压缩
- [x] double matrix_sum(Matrix mid1)矩阵求和
- [x] double matrix_mean(Matrix mid1)均值
- [x] Matrix appply(Matrix mid1,Matrix mid2,int axis = 0)矩阵拼接
- [x] Matrix iloc(Matrix mid1,int start_x=0,int end_x=0,int start_y=0,int end_y=0)矩阵切片
- [x] Matrix mul_simple(Matrix mid1,Matrix mid2)为了贴合机器学习的需要,实现了矩阵对应元素相乘,请与传统意义的矩阵乘法区分开.
- [x] Relu激活函数矩阵接口
- [x] 均方误差矩阵接口
- [x] 创建随机权重矩阵接口
### 即将着手开发:
- [ ] 卷积神经网络定义(包括但不限于卷积核,池化层定义,自定义损失接口).
- [ ] 随机森林算法封装.
- [ ] 主流网络架构实现.
## 反向传播测试demo:
```c
#include
#include
#include
#include
#include
#include
#include
#include"./matrix/matrix_def.h"
#include"./matrix/matrix_pro.h"
#include"./welcome/score_wel.cpp"
#include"./logistic/logistic_def.h"
#include"./file_pro/data_read.h"
using namespace std;
clock_t start, stop;
double duration;
int main()
{
welcome();
string path = "./data/nerual_data.csv";
Matrix data = read_csv(path);
Matrix bais = CreateMatrix(data.row,1);
Matrix x = iloc(data,0,100,0,2);
Matrix y = iloc(data,0,100,2,3);
int N=100,in_Dim=2,H_num=2,out_Dim=2;
double learning_rate = 0.0001;
Matrix W1 = CreateRandMat(in_Dim,H_num);
Matrix W2 = CreateRandMat(H_num,out_Dim);
cout_mat(W1);
cout_mat(W2);
for(int epoch = 0;epoch<100;epoch++)
{
Matrix x_w1 = mul(x,W1);
Matrix re = mat_relu(x_w1);
Matrix out = mul(re,W2);
Matrix mat_sq = mat_sq_loss(out,y);
Matrix grad_y_pred = times_mat(2.0,subtract(out,y));
Matrix grad_w2 = mul(get_T(re),grad_y_pred);
Matrix grad_h_relu = mul(grad_y_pred,get_T(W2));
Matrix grad_h_relu_copy = mat_relu(grad_h_relu);
Matrix grad_w1 = mul(get_T(x),grad_h_relu_copy);
Matrix dw1 = times_mat(learning_rate,mul(get_T(x),grad_h_relu_copy));
W1 = subtract(W1,dw1);
W2 = subtract(W2,times_mat(learning_rate,grad_w2));
cout<<"W1: ";
cout_mat(W1);
cout<<"W2: ";
cout_mat(W2);
cout<<"loss"<<": ";
cout< 参数 axis=0 :
| 0 | 7 | 2 |
| ---- | ---- | ---- |
| 0 | 3 | 1 |
| 0 | 0 | 0 |
| 0 | 0 | 11 |
| 0 | 7 | 2 |
| 0 | 3 | 1 |
| 0 | 0 | 0 |
| 0 | 0 | 11 |
------
> axis = 1:
| 0 | 7 | 2 | 0 | 7 | 2 |
| ---- | ---- | ---- | ---- | ---- | ---- |
| 0 | 3 | 1 | 0 | 3 | 1 |
| 0 | 0 | 0 | 0 | 0 | 0 |
| 0 | 0 | 11 | 0 | 0 | 11 |
------
## 更新2019/11/18/00:12
- [x] read_csv 通过文件流读取逗号分隔符文件,返回一个自动计算长度的矩阵.
例如 CSV's head :
| -0.017612 | 14.053064 | 0 |
| --------- | --------- | ---- |
| -1.395634 | 4.662541 | 1 |
| -0.752157 | 6.53862 | 0 |
| -1.322371 | 7.152853 | 0 |
| 0.423363 | 11.054677 | 0 |
| 0.406704 | 7.067335 | 1 |
Get:
## Logistic Regression demo base Edge:
```c
#include
#include
#include
#include
#include
#include
#include"./matrix/matrix_def.h"
#include"./matrix/matrix_pro.h"
#include"./welcome/score_wel.cpp"
#include"./logistic/logistic_def.h"
#include"./file_pro/data_read.h"
using namespace std;
clock_t start, stop;
double duration;
int main()
{
welcome();
string path = "./new_data2.csv";
Matrix data = read_csv(path);
Matrix bais = CreateMatrix(data.row,1);
data = appply(data,bais,1);
Matrix y = iloc(data,0,0,3,4);
Matrix x_1 = iloc(data,0,0,0,3);
Matrix x_2 = get_T(x_1);
double alpha = 0.002;
int max_epoch = 100;
Matrix weight = CreateMatrix(3,1);
change_va(weight,0,0,1);
change_va(weight,1,0,1);
change_va(weight,2,0,1);
int epoch = 0;
for(epoch = 0;epoch<=max_epoch;epoch++)
{
cout<<"-----------split-line-----------"< 1. 矩阵元素默认为1
> 2. 使用位运算加速防止填充过大的数值,但是会损失一定精度,慎用.
> 3. 记得delete(matrix)在你使用完一个矩阵计算单元以后.
> 4. api接口更多的接近于pandas和numpy的使用习惯.
> 5. 更多的细节参见目前最新的代码
> 6. 欢迎star和关注.
> 7. autodiff部分感谢国外博主Omar的思路提醒.
------
个人小站:[极度空间](http://likedge.top/)
作者邮箱:zk@likedge.top | edge@ibooker.org.cn
QQ:2533524298
