Improving the efficiency of c++ code calling Eigen to do the linear operation of the matrix
0
$begingroup$
I am a rookie of coding and I have written a c++ procedure which calls the Eigen library to do the linear operation of the matrix. Please help me to improve the efficiency of the loop.
#include <iostream>
#include <fstream>
#include <Eigen/Dense>
#include <time.h>
using namespace std;
int main()
{
// Weight coefficient matrix
Eigen::MatrixXd wi_1,wi_2,wi_3,wi_4;
wi_1.resize(100,2);
wi_2.resize(100,100);
wi_3.resize(100,100);
wi_4.resize(5,100);
wi_1.setOnes();
wi_2.setOnes();
wi_3.setOnes();
wi_4.setOnes();
// Bias vector
Eigen::VectorXd bias_1,bias_2,bias_3,bias_4,Y;
bias_1.resize(100);
bias_2.resize(100);
bias_3.resize(100);
bias_4.resize(5);
bias_1.setOnes();
bias_2.setOnes();
bias_3.setOnes();
bias_4.setOnes();
Eigen::Matrix<double,5,1> y_mean;
Eigen::Matrix<double,5,1> y_scale;
Eigen::Matrix<double,2,1> x_mean;
Eigen::Matrix<double,2,1> x_scale;
y_mean.setOnes();
y_scale.setOnes();
y_mean.setOnes();
x_scale.setOnes();
int n = 0;
int layer;
clock_t start,finish;
double totaltime;
start=clock();
while (n<10000)
{
Y.resize(2);
layer = 0;
Y << 0.185, 0.285;//inputx[1], x[0];
Y = (Y.array() - x_mean.array()) / x_scale.array();
//ANN forward
while (layer < 4)
{
layer++;
switch (layer) {
case 1:{
Y = wi_1 * Y + bias_1;
// Info << "ANN forward layer1" << endl;
break;
}
case 2:{
Y = wi_2 * Y + bias_2;
// Info << "ANN forward layer2" << endl;
break;
}
case 3:{
Y = wi_3 * Y + bias_3;
// Info << "ANN forward layer3" << endl;
break;
}
case 4:{
Y = wi_4 * Y + bias_4;
// Info << "ANN forward layer4" << endl;
break;
}
default:{
cout<<"error"<<endl;
break;
}
}
//Relu activation function
if (layer < 4)
{
for (int i = 0; i < Y.size(); i++)
{
Y(i) = ((Y(i) > 0) ? Y(i) : 0);
}
}
}
//inverse standardization
Y = Y.array() * y_scale.array() + y_mean.array();
n++;
}
finish=clock();
totaltime=(double)(finish-start)/CLOCKS_PER_SEC*1000;
cout<<"n Running time is "<<totaltime<<"ms！"<<endl;
}
c++ eigen
asked 2 mins ago
CharryzzzCharryzzz
1
New contributor
Charryzzz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
0
$begingroup$
I am a rookie of coding and I have written a c++ procedure which calls the Eigen library to do the linear operation of the matrix. Please help me to improve the efficiency of the loop.
#include <iostream>
#include <fstream>
#include <Eigen/Dense>
#include <time.h>
using namespace std;
int main()
{
// Weight coefficient matrix
Eigen::MatrixXd wi_1,wi_2,wi_3,wi_4;
wi_1.resize(100,2);
wi_2.resize(100,100);
wi_3.resize(100,100);
wi_4.resize(5,100);
wi_1.setOnes();
wi_2.setOnes();
wi_3.setOnes();
wi_4.setOnes();
// Bias vector
Eigen::VectorXd bias_1,bias_2,bias_3,bias_4,Y;
bias_1.resize(100);
bias_2.resize(100);
bias_3.resize(100);
bias_4.resize(5);
bias_1.setOnes();
bias_2.setOnes();
bias_3.setOnes();
bias_4.setOnes();
Eigen::Matrix<double,5,1> y_mean;
Eigen::Matrix<double,5,1> y_scale;
Eigen::Matrix<double,2,1> x_mean;
Eigen::Matrix<double,2,1> x_scale;
y_mean.setOnes();
y_scale.setOnes();
y_mean.setOnes();
x_scale.setOnes();
int n = 0;
int layer;
clock_t start,finish;
double totaltime;
start=clock();
while (n<10000)
{
Y.resize(2);
layer = 0;
Y << 0.185, 0.285;//inputx[1], x[0];
Y = (Y.array() - x_mean.array()) / x_scale.array();
//ANN forward
while (layer < 4)
{
layer++;
switch (layer) {
case 1:{
Y = wi_1 * Y + bias_1;
// Info << "ANN forward layer1" << endl;
break;
}
case 2:{
Y = wi_2 * Y + bias_2;
// Info << "ANN forward layer2" << endl;
break;
}
case 3:{
Y = wi_3 * Y + bias_3;
// Info << "ANN forward layer3" << endl;
break;
}
case 4:{
Y = wi_4 * Y + bias_4;
// Info << "ANN forward layer4" << endl;
break;
}
default:{
cout<<"error"<<endl;
break;
}
}
//Relu activation function
if (layer < 4)
{
for (int i = 0; i < Y.size(); i++)
{
Y(i) = ((Y(i) > 0) ? Y(i) : 0);
}
}
}
//inverse standardization
Y = Y.array() * y_scale.array() + y_mean.array();
n++;
}
finish=clock();
totaltime=(double)(finish-start)/CLOCKS_PER_SEC*1000;
cout<<"n Running time is "<<totaltime<<"ms！"<<endl;
}
c++ eigen
asked 2 mins ago
CharryzzzCharryzzz
1
New contributor
Charryzzz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
0
0
0
$begingroup$
I am a rookie of coding and I have written a c++ procedure which calls the Eigen library to do the linear operation of the matrix. Please help me to improve the efficiency of the loop.
#include <iostream>
#include <fstream>
#include <Eigen/Dense>
#include <time.h>
using namespace std;
int main()
{
// Weight coefficient matrix
Eigen::MatrixXd wi_1,wi_2,wi_3,wi_4;
wi_1.resize(100,2);
wi_2.resize(100,100);
wi_3.resize(100,100);
wi_4.resize(5,100);
wi_1.setOnes();
wi_2.setOnes();
wi_3.setOnes();
wi_4.setOnes();
// Bias vector
Eigen::VectorXd bias_1,bias_2,bias_3,bias_4,Y;
bias_1.resize(100);
bias_2.resize(100);
bias_3.resize(100);
bias_4.resize(5);
bias_1.setOnes();
bias_2.setOnes();
bias_3.setOnes();
bias_4.setOnes();
Eigen::Matrix<double,5,1> y_mean;
Eigen::Matrix<double,5,1> y_scale;
Eigen::Matrix<double,2,1> x_mean;
Eigen::Matrix<double,2,1> x_scale;
y_mean.setOnes();
y_scale.setOnes();
y_mean.setOnes();
x_scale.setOnes();
int n = 0;
int layer;
clock_t start,finish;
double totaltime;
start=clock();
while (n<10000)
{
Y.resize(2);
layer = 0;
Y << 0.185, 0.285;//inputx[1], x[0];
Y = (Y.array() - x_mean.array()) / x_scale.array();
//ANN forward
while (layer < 4)
{
layer++;
switch (layer) {
case 1:{
Y = wi_1 * Y + bias_1;
// Info << "ANN forward layer1" << endl;
break;
}
case 2:{
Y = wi_2 * Y + bias_2;
// Info << "ANN forward layer2" << endl;
break;
}
case 3:{
Y = wi_3 * Y + bias_3;
// Info << "ANN forward layer3" << endl;
break;
}
case 4:{
Y = wi_4 * Y + bias_4;
// Info << "ANN forward layer4" << endl;
break;
}
default:{
cout<<"error"<<endl;
break;
}
}
//Relu activation function
if (layer < 4)
{
for (int i = 0; i < Y.size(); i++)
{
Y(i) = ((Y(i) > 0) ? Y(i) : 0);
}
}
}
//inverse standardization
Y = Y.array() * y_scale.array() + y_mean.array();
n++;
}
finish=clock();
totaltime=(double)(finish-start)/CLOCKS_PER_SEC*1000;
cout<<"n Running time is "<<totaltime<<"ms！"<<endl;
}
c++ eigen
asked 2 mins ago
CharryzzzCharryzzz
1
New contributor
Charryzzz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am a rookie of coding and I have written a c++ procedure which calls the Eigen library to do the linear operation of the matrix. Please help me to improve the efficiency of the loop.
#include <iostream>
#include <fstream>
#include <Eigen/Dense>
#include <time.h>
using namespace std;
int main()
{
// Weight coefficient matrix
Eigen::MatrixXd wi_1,wi_2,wi_3,wi_4;
wi_1.resize(100,2);
wi_2.resize(100,100);
wi_3.resize(100,100);
wi_4.resize(5,100);
wi_1.setOnes();
wi_2.setOnes();
wi_3.setOnes();
wi_4.setOnes();
// Bias vector
Eigen::VectorXd bias_1,bias_2,bias_3,bias_4,Y;
bias_1.resize(100);
bias_2.resize(100);
bias_3.resize(100);
bias_4.resize(5);
bias_1.setOnes();
bias_2.setOnes();
bias_3.setOnes();
bias_4.setOnes();
Eigen::Matrix<double,5,1> y_mean;
Eigen::Matrix<double,5,1> y_scale;
Eigen::Matrix<double,2,1> x_mean;
Eigen::Matrix<double,2,1> x_scale;
y_mean.setOnes();
y_scale.setOnes();
y_mean.setOnes();
x_scale.setOnes();
int n = 0;
int layer;
clock_t start,finish;
double totaltime;
start=clock();
while (n<10000)
{
Y.resize(2);
layer = 0;
Y << 0.185, 0.285;//inputx[1], x[0];
Y = (Y.array() - x_mean.array()) / x_scale.array();
//ANN forward
while (layer < 4)
{
layer++;
switch (layer) {
case 1:{
Y = wi_1 * Y + bias_1;
// Info << "ANN forward layer1" << endl;
break;
}
case 2:{
Y = wi_2 * Y + bias_2;
// Info << "ANN forward layer2" << endl;
break;
}
case 3:{
Y = wi_3 * Y + bias_3;
// Info << "ANN forward layer3" << endl;
break;
}
case 4:{
Y = wi_4 * Y + bias_4;
// Info << "ANN forward layer4" << endl;
break;
}
default:{
cout<<"error"<<endl;
break;
}
}
//Relu activation function
if (layer < 4)
{
for (int i = 0; i < Y.size(); i++)
{
Y(i) = ((Y(i) > 0) ? Y(i) : 0);
}
}
}
//inverse standardization
Y = Y.array() * y_scale.array() + y_mean.array();
n++;
}
finish=clock();
totaltime=(double)(finish-start)/CLOCKS_PER_SEC*1000;
cout<<"n Running time is "<<totaltime<<"ms！"<<endl;
}
c++ eigen
c++ eigen
asked 2 mins ago
CharryzzzCharryzzz
1
New contributor
Charryzzz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
CharryzzzCharryzzz
1
New contributor
Charryzzz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
CharryzzzCharryzzz
1
New contributor
Charryzzz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
CharryzzzCharryzzz
1
asked 2 mins ago
CharryzzzCharryzzz
1
1
New contributor
Charryzzz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Charryzzz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Charryzzz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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