提交 15173bdb 编写于 作者: T tostq

modified

上级 b188f85d

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#v4.0:v100:false
Debug|Win32|E:\Code\VS2010 code\CNN\|
#v4.0:v100:false
Debug|Win32|E:\Code\VS2010 code\CNN\|
生成启动时间为 2016/6/30 21:07:01。
1>项目“E:\Code\VS2010 code\CNN\CNN\CNN.vcxproj”在节点 2 上(build 个目标)。
1>InitializeBuildStatus:
正在创建“Debug\CNN.unsuccessfulbuild”,因为已指定“AlwaysCreate”。
ClCompile:
d:\Microsoft Visual Studio 10.0\VC\bin\CL.exe /c /ZI /nologo /W3 /WX- /Od /Oy- /D _MBCS /Gm /EHsc /RTC1 /MDd /GS /fp:precise /Zc:wchar_t /Zc:forScope /Fo"Debug\\" /Fd"Debug\vc100.pdb" /Gd /TP /analyze- /errorReport:prompt cnn.cpp
cnn.cpp
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(58): warning C4244: “初始化”: 从“double”转换到“float”,可能丢失数据
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(151): warning C4244: “初始化”: 从“double”转换到“float”,可能丢失数据
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(193): warning C4996: 'fopen': This function or variable may be unsafe. Consider using fopen_s instead. To disable deprecation, use _CRT_SECURE_NO_WARNINGS. See online help for details.
d:\microsoft visual studio 10.0\vc\include\stdio.h(234) : 参见“fopen”的声明
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(225): warning C4996: 'fopen': This function or variable may be unsafe. Consider using fopen_s instead. To disable deprecation, use _CRT_SECURE_NO_WARNINGS. See online help for details.
d:\microsoft visual studio 10.0\vc\include\stdio.h(234) : 参见“fopen”的声明
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(291): warning C4244: “=”: 从“double”转换到“float”,可能丢失数据
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(537): warning C4244: “参数”: 从“double”转换到“float”,可能丢失数据
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(620): warning C4996: 'fopen': This function or variable may be unsafe. Consider using fopen_s instead. To disable deprecation, use _CRT_SECURE_NO_WARNINGS. See online help for details.
d:\microsoft visual studio 10.0\vc\include\stdio.h(234) : 参见“fopen”的声明
Link:
d:\Microsoft Visual Studio 10.0\VC\bin\link.exe /ERRORREPORT:PROMPT /OUT:"E:\Code\VS2010 code\CNN\Debug\CNN.exe" /NOLOGO libCbc.lib libCgl.lib libClp.lib libCoinUtils.lib libOsi.lib libOsiClp.lib opencv_calib3d2410d.lib opencv_contrib2410d.lib opencv_core2410d.lib opencv_features2d2410d.lib opencv_flann2410d.lib opencv_gpu2410d.lib opencv_highgui2410d.lib opencv_imgproc2410d.lib opencv_legacy2410d.lib opencv_ml2410d.lib opencv_objdetect2410d.lib opencv_ts2410d.lib opencv_video2410d.lib opencv_nonfree2410d.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib /MANIFEST /ManifestFile:"Debug\CNN.exe.intermediate.manifest" /MANIFESTUAC:"level='asInvoker' uiAccess='false'" /DEBUG /PDB:"E:\Code\VS2010 code\CNN\Debug\CNN.pdb" /HEAP:"2000" /TLBID:1 /DYNAMICBASE /NXCOMPAT /IMPLIB:"E:\Code\VS2010 code\CNN\Debug\CNN.lib" /MACHINE:X86 Debug\cnn.obj
Debug\main.obj
Debug\mat.obj
Debug\minst.obj
LINK : 没有找到 E:\Code\VS2010 code\CNN\Debug\CNN.exe 或上一个增量链接没有生成它;正在执行完全链接
CNN.vcxproj -> E:\Code\VS2010 code\CNN\Debug\CNN.exe
Manifest:
c:\Program Files (x86)\Microsoft SDKs\Windows\v7.0A\bin\mt.exe /nologo /verbose /outputresource:"E:\Code\VS2010 code\CNN\Debug\CNN.exe;#1" /manifest Debug\CNN.exe.intermediate.manifest
FinalizeBuildStatus:
正在删除文件“Debug\CNN.unsuccessfulbuild”。
正在对“Debug\CNN.lastbuildstate”执行 Touch 任务。
1>已完成生成项目“E:\Code\VS2010 code\CNN\CNN\CNN.vcxproj”(build 个目标)的操作。
生成成功。
已用时间 00:00:01.36
生成启动时间为 2016/6/30 21:07:01。
1>项目“E:\Code\VS2010 code\CNN\CNN\CNN.vcxproj”在节点 2 上(build 个目标)。
1>InitializeBuildStatus:
正在创建“Debug\CNN.unsuccessfulbuild”,因为已指定“AlwaysCreate”。
ClCompile:
d:\Microsoft Visual Studio 10.0\VC\bin\CL.exe /c /ZI /nologo /W3 /WX- /Od /Oy- /D _MBCS /Gm /EHsc /RTC1 /MDd /GS /fp:precise /Zc:wchar_t /Zc:forScope /Fo"Debug\\" /Fd"Debug\vc100.pdb" /Gd /TP /analyze- /errorReport:prompt cnn.cpp
cnn.cpp
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(58): warning C4244: “初始化”: 从“double”转换到“float”,可能丢失数据
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(151): warning C4244: “初始化”: 从“double”转换到“float”,可能丢失数据
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(193): warning C4996: 'fopen': This function or variable may be unsafe. Consider using fopen_s instead. To disable deprecation, use _CRT_SECURE_NO_WARNINGS. See online help for details.
d:\microsoft visual studio 10.0\vc\include\stdio.h(234) : 参见“fopen”的声明
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(225): warning C4996: 'fopen': This function or variable may be unsafe. Consider using fopen_s instead. To disable deprecation, use _CRT_SECURE_NO_WARNINGS. See online help for details.
d:\microsoft visual studio 10.0\vc\include\stdio.h(234) : 参见“fopen”的声明
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(291): warning C4244: “=”: 从“double”转换到“float”,可能丢失数据
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(537): warning C4244: “参数”: 从“double”转换到“float”,可能丢失数据
1>e:\code\vs2010 code\cnn\cnn\cnn.cpp(620): warning C4996: 'fopen': This function or variable may be unsafe. Consider using fopen_s instead. To disable deprecation, use _CRT_SECURE_NO_WARNINGS. See online help for details.
d:\microsoft visual studio 10.0\vc\include\stdio.h(234) : 参见“fopen”的声明
Link:
d:\Microsoft Visual Studio 10.0\VC\bin\link.exe /ERRORREPORT:PROMPT /OUT:"E:\Code\VS2010 code\CNN\Debug\CNN.exe" /NOLOGO libCbc.lib libCgl.lib libClp.lib libCoinUtils.lib libOsi.lib libOsiClp.lib opencv_calib3d2410d.lib opencv_contrib2410d.lib opencv_core2410d.lib opencv_features2d2410d.lib opencv_flann2410d.lib opencv_gpu2410d.lib opencv_highgui2410d.lib opencv_imgproc2410d.lib opencv_legacy2410d.lib opencv_ml2410d.lib opencv_objdetect2410d.lib opencv_ts2410d.lib opencv_video2410d.lib opencv_nonfree2410d.lib kernel32.lib user32.lib gdi32.lib winspool.lib comdlg32.lib advapi32.lib shell32.lib ole32.lib oleaut32.lib uuid.lib odbc32.lib odbccp32.lib /MANIFEST /ManifestFile:"Debug\CNN.exe.intermediate.manifest" /MANIFESTUAC:"level='asInvoker' uiAccess='false'" /DEBUG /PDB:"E:\Code\VS2010 code\CNN\Debug\CNN.pdb" /HEAP:"2000" /TLBID:1 /DYNAMICBASE /NXCOMPAT /IMPLIB:"E:\Code\VS2010 code\CNN\Debug\CNN.lib" /MACHINE:X86 Debug\cnn.obj
Debug\main.obj
Debug\mat.obj
Debug\minst.obj
LINK : 没有找到 E:\Code\VS2010 code\CNN\Debug\CNN.exe 或上一个增量链接没有生成它;正在执行完全链接
CNN.vcxproj -> E:\Code\VS2010 code\CNN\Debug\CNN.exe
Manifest:
c:\Program Files (x86)\Microsoft SDKs\Windows\v7.0A\bin\mt.exe /nologo /verbose /outputresource:"E:\Code\VS2010 code\CNN\Debug\CNN.exe;#1" /manifest Debug\CNN.exe.intermediate.manifest
FinalizeBuildStatus:
正在删除文件“Debug\CNN.unsuccessfulbuild”。
正在对“Debug\CNN.lastbuildstate”执行 Touch 任务。
1>已完成生成项目“E:\Code\VS2010 code\CNN\CNN\CNN.vcxproj”(build 个目标)的操作。
生成成功。
已用时间 00:00:01.36
此差异已折叠。
#ifndef __CNN_
#define __CNN_
#include <stdlib.h>
#include <string.h>
#include <stdio.h>
#include <math.h>
#include <random>
#include <time.h>
#include "minst.h"
#define AvePool 0
#define MaxPool 1
#define MinPool 2
// 卷积层
typedef struct convolutional_layer{
int inputWidth; //输入图像的宽
int inputHeight; //输入图像的长
int mapSize; //特征模板的大小
int mapNum; //特征模板的数目
int inChannels; //输入图像的数目
int outChannels; //输出图像的数目
int DataSize;
float* mapData; //存放特征模块的数据
float* basicData; //偏置
bool isFullConnect; //是否为全连接
bool* connectModel; //连接模式(默认为全连接)
// 下面三者的大小同输出的维度相同
float* v; // 进入激活函数的输入值
float* y; // 激活函数后神经元的输出
float* d; // 网络的局部梯度,δ值
}CovLayer;
// 采样层 pooling
typedef struct pooling_layer{
int inputWidth; //输入图像的宽
int inputHeight; //输入图像的长
int mapSize; //特征模板的大小
int inChannels; //输入图像的数目
int outChannels; //输出图像的数目
int poolType; //Pooling的方法
float* basicData; //偏置
bool isFullConnect; //是否为全连接
bool* connectModel; //连接模式(默认为全连接)
float* y; // 采样函数后神经元的输出,无激活函数
float* d; // 网络的局部梯度,δ值
}PoolLayer;
// 输出层 全连接的神经网络
typedef struct nn_layer{
int inputNum; //输入数据的数目
int outputNum; //输出数据的数目
float* wData; // 权重数据,为一个inputNum*outputNum大小
float* basicData; //偏置,大小为outputNum大小
// 下面三者的大小同输出的维度相同
float* v; // 进入激活函数的输入值
float* y; // 激活函数后神经元的输出
float* d; // 网络的局部梯度,δ值
bool isFullConnect; //是否为全连接
}OutLayer;
typedef struct cnn_network{
int layerNum;
CovLayer* C1;
PoolLayer* S2;
CovLayer* C3;
PoolLayer* S4;
OutLayer* O5;
float* e; // 训练误差
}CNN;
typedef struct ImgSize{
int w;
int h;
}nSize;
typedef struct train_opts{
int numepochs; // 训练的迭代次数
float alpha; // 学习速率
}CNNOpts;
void cnnsetup(CNN* cnn,nSize inputSize,int outputSize);
/*
CNN网络的训练函数
inputData,outputData分别存入训练数据
dataNum表明数据数目
*/
void cnntrain(CNN* cnn,nSize inputSize,int outputSize,
float* inputData,float* outputData,int dataNum,CNNOpts opts);
// 初始化卷积层
CovLayer* initCovLayer(int inputWidth,int inputHeight,int mapSize,int mapNum,int inChannels,int outChannels);
void CovLayerConnect(CovLayer* covL,bool* connectModel);
// 初始化采样层
PoolLayer* initPoolLayer(int inputWidth,int inputHeigh,int mapSize,int inChannels,int outChannels,int poolType);
void PoolLayerConnect(PoolLayer* poolL,bool* connectModel);
// 初始化输出层
OutLayer* initOutLayer(int inputNum,int outputNum);
// 激活函数 input是数据,inputNum说明数据数目,bas表明偏置
float* activation_Sigma(float* input,int inputNum,float bas); // sigma激活函数
void cnnff(CNN* cnn,float* inputData); // 网络的前向传播
void cnnbp(CNN* cnn,float* outputData); // 网络的后向传播
void cnnapplygrads(CNN* cnn,CNNOpts opts);
float* cov(float* map,nSize mapSize,float* inputData,nSize inSize); // 卷积操作
/*
Pooling Function
input 输入数据
inputNum 输入数据数目
mapSize 求平均的模块区域
*/
float* avgPooling(float* input,nSize inputSize,int mapSize); // 求平均值
/*
单层全连接神经网络的处理
nnSize是网络的大小
*/
float* nnff(float* input,float* wdata,float* bas,nSize nnSize); // 单层全连接神经网络的前向传播
#endif
#ifndef __CNN_
#define __CNN_
#include <stdlib.h>
#include <string.h>
#include <stdio.h>
#include <math.h>
#include <random>
#include <time.h>
#include "minst.h"
#define AvePool 0
#define MaxPool 1
#define MinPool 2
// 卷积层
typedef struct convolutional_layer{
int inputWidth; //输入图像的宽
int inputHeight; //输入图像的长
int mapSize; //特征模板的大小
int mapNum; //特征模板的数目
int inChannels; //输入图像的数目
int outChannels; //输出图像的数目
int DataSize;
float* mapData; //存放特征模块的数据
float* basicData; //偏置
bool isFullConnect; //是否为全连接
bool* connectModel; //连接模式(默认为全连接)
// 下面三者的大小同输出的维度相同
float* v; // 进入激活函数的输入值
float* y; // 激活函数后神经元的输出
float* d; // 网络的局部梯度,δ值
}CovLayer;
// 采样层 pooling
typedef struct pooling_layer{
int inputWidth; //输入图像的宽
int inputHeight; //输入图像的长
int mapSize; //特征模板的大小
int inChannels; //输入图像的数目
int outChannels; //输出图像的数目
int poolType; //Pooling的方法
float* basicData; //偏置
bool isFullConnect; //是否为全连接
bool* connectModel; //连接模式(默认为全连接)
float* y; // 采样函数后神经元的输出,无激活函数
float* d; // 网络的局部梯度,δ值
}PoolLayer;
// 输出层 全连接的神经网络
typedef struct nn_layer{
int inputNum; //输入数据的数目
int outputNum; //输出数据的数目
float* wData; // 权重数据,为一个inputNum*outputNum大小
float* basicData; //偏置,大小为outputNum大小
// 下面三者的大小同输出的维度相同
float* v; // 进入激活函数的输入值
float* y; // 激活函数后神经元的输出
float* d; // 网络的局部梯度,δ值
bool isFullConnect; //是否为全连接
}OutLayer;
typedef struct cnn_network{
int layerNum;
CovLayer* C1;
PoolLayer* S2;
CovLayer* C3;
PoolLayer* S4;
OutLayer* O5;
float* e; // 训练误差
}CNN;