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Param.h
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Param.h
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/*
* Param.h
*
* Created on: Jul 25, 2016
* Author: mason
*/
#ifndef PARAM_H_
#define PARAM_H_
//#include "Eigen/Dense"
#include "BaseParam.h"
// Notice: aux is an auxiliary variable to help parameter updating
class Param : public BaseParam {
public:
LDG::Tensor aux_square;
LDG::Tensor aux_mean;
int iter;
//LDG::Tensor cpu_grad;
LDG::Tensor v_r;
LDG::Tensor grad_square;
LDG::Tensor aux_eps;
LDG::Tensor aux_sqrt;
LDG::Tensor grad_alpha;
LDG::Tensor grad_aux;
LDG::Tensor belta_aux_mean;
LDG::Tensor belta_grad;
LDG::Tensor belta_aux_square;
LDG::Tensor belta_grad_square;
LDG::Tensor aux_square_eps;
LDG::Tensor aux_square_eps_sqrt;
LDG::Tensor aux_mean_lrt;
LDG::Tensor val_delta;
// allow sparse and dense parameters have different parameter initialization methods
inline void initial(int outDim, int inDim) {
//val.init(outDim, inDim);
//grad.init(outDim, inDim);
//aux_square.init(outDim, inDim);
//aux_mean.init(outDim, inDim);
//DEV->malloc(val, Shape({outDim, inDim}));
DEV->init(grad, Shape({outDim, inDim}));
DEV->init(aux_square, Shape({outDim, inDim}));
DEV->init(aux_mean, Shape({outDim, inDim}));
dtype bound = sqrt(6.0 / (outDim + inDim + 1));
//val.random(bound);
DEV->random_uniform(val, Shape({outDim, inDim}), -bound, bound);
DEV->init(v_r, val.shape());
DEV->init(grad_square, grad.shape());
DEV->init(aux_eps, aux_square.shape());
DEV->init(aux_sqrt, aux_square.shape());
DEV->init(grad_alpha, grad.shape());
DEV->init(grad_aux, grad.shape());
DEV->init(belta_aux_mean, aux_mean.shape());
DEV->init(belta_grad, grad.shape());
DEV->init(belta_aux_square, aux_square.shape());
DEV->init(belta_grad_square, grad.shape());
DEV->init(aux_square_eps, aux_square.shape());
DEV->init(aux_square_eps_sqrt, aux_square.shape());
DEV->init(aux_mean_lrt, aux_mean.shape());
DEV->init(val_delta, val.shape());
iter = 0;
//cpu_grad.device_type = CPU;
//cpu_grad.shape_ = grad.shape();
//cpu_grad.v = new dtype[grad.shape().size()];
}
inline int outDim() {
//return val.row;
return val.shape().dims()[0];
}
inline int inDim() {
//return val.col;
return val.shape().dims()[1];
}
inline void clearGrad() {
//grad.zero();
DEV->zero(grad);
}
inline void updateAdagrad(dtype alpha, dtype reg, dtype eps) {
if (outDim() > 1 && inDim() > 1) {
DEV->Fmultiply_scalar(val, reg, v_r);
DEV->Fadd_inplace(grad, v_r);
//DEV->Fadd(grad, v_r, grad);
//grad.vec() = grad.vec() + val.vec() * reg;
}
DEV->Fsquare(grad, grad_square);
DEV->Fadd_inplace(aux_square, grad_square);
//DEV->Fadd(aux_square, grad_square, aux_square);
//aux_square.vec() = aux_square.vec() + grad.vec().square();
DEV->Fadd_scalar(aux_square, eps, aux_eps);
DEV->Fsqrt(aux_eps, aux_sqrt);
DEV->Fmultiply_scalar(grad, alpha, grad_alpha);
DEV->Fdivide(grad_alpha, aux_sqrt, grad_aux);
DEV->Fsubtract_inplace(val, grad_aux);
//DEV->Fsubtract(val, grad_aux, val);
//val.vec() = val.vec() - grad.vec() * alpha / (aux_square.vec() + eps).sqrt();
}
inline void updateAdam(dtype belta1, dtype belta2, dtype alpha, dtype reg, dtype eps) {
if (outDim() > 1 && inDim() > 1) {
DEV->Fmultiply_scalar(val, reg, v_r);
DEV->Fadd_inplace(grad, v_r);
//DEV->Fadd(grad, v_r, grad);
}
DEV->Fmultiply_scalar(aux_mean, belta1, belta_aux_mean);
DEV->Fmultiply_scalar(grad, 1 - belta1, belta_grad);
DEV->Fadd(belta_aux_mean, belta_grad, aux_mean);
DEV->Fmultiply_scalar(aux_square, belta2, belta_aux_square);
DEV->Fsquare(grad, grad_square);
DEV->Fmultiply_scalar(grad_square, (1 - belta2), belta_grad_square);
DEV->Fadd(belta_aux_square, belta_grad_square, aux_square);
dtype lr_t = alpha * sqrt(1 - pow(belta2, iter + 1)) / (1 - pow(belta1, iter + 1));
DEV->Fadd_scalar(aux_square, eps, aux_square_eps);
DEV->Fsqrt(aux_square_eps, aux_square_eps_sqrt);
DEV->Fmultiply_scalar(aux_mean, lr_t, aux_mean_lrt);
DEV->Fdivide(aux_mean_lrt, aux_square_eps_sqrt, val_delta);
DEV->Fsubtract_inplace(val, val_delta);
//DEV->Fsubtract(val, val_delta, val);
iter++;
/*
if (val.col > 1 && val.row > 1)grad.vec() = grad.vec() + val.vec() * reg;
aux_mean.vec() = belta1 * aux_mean.vec() + (1 - belta1) * grad.vec();
aux_square.vec() = belta2 * aux_square.vec() + (1 - belta2) * grad.vec().square();
dtype lr_t = alpha * sqrt(1 - pow(belta2, iter + 1)) / (1 - pow(belta1, iter + 1));
val.vec() = val.vec() - aux_mean.vec() * lr_t / (aux_square.vec() + eps).sqrt();
iter++;
*/
}
inline void randpoint(int& idx, int &idy) {
//select indexes randomly
std::vector<int> idRows, idCols;
idRows.clear();
idCols.clear();
int dim0 = val.shape().dims()[0];
int dim1 = val.shape().dims()[1];
for (int i = 0; i < dim0; i++)
idRows.push_back(i);
for (int i = 0; i < dim1; i++)
idCols.push_back(i);
random_shuffle(idRows.begin(), idRows.end());
random_shuffle(idCols.begin(), idCols.end());
idy = idRows[0];
idx = idCols[0];
}
inline dtype squareGradNorm() {
dtype sumNorm = 0.0;
//DEV->to_cpu(grad, cpu_grad);
vector<dtype> vec_grad = DEV->to_vector(grad);
int size = grad.shape().size();
for (int i = 0; i < size; i++) {
sumNorm += vec_grad[i] * vec_grad[i];
}
return sumNorm;
}
inline void rescaleGrad(dtype scale) {
//grad.vec() = grad.vec() * scale;
DEV->Fmultiply_scalar_inplace(grad, scale);
//DEV->Fmultiply_scalar(grad, scale, grad);
}
inline void save(std::ofstream &os)const {
/*
val.save(os);
aux_square.save(os);
aux_mean.save(os);
os << iter << endl;
*/
}
inline void load(std::ifstream &is) {
/*
val.load(is);
aux_square.load(is);
aux_mean.load(is);
is >> iter;
*/
}
};
#endif /* PARAM_H_ */