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AttRecursiveGatedNN.h
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/*
* AttRecursiveGatedNN.h
* Gated Recursive Neural network structure with attention technique.
* Created on: Nov 5, 2015
* Author: mszhang
*/
#ifndef SRC_AttRecursiveGatedNN_H_
#define SRC_AttRecursiveGatedNN_H_
#include "tensor.h"
#include "BiLayer.h"
#include "MyLib.h"
#include "Utiltensor.h"
using namespace mshadow;
using namespace mshadow::expr;
using namespace mshadow::utils;
template<typename xpu>
class AttRecursiveGatedNN {
public:
BiLayer<xpu> _reset_left;
BiLayer<xpu> _reset_right;
BiLayer<xpu> _update_left;
BiLayer<xpu> _update_right;
BiLayer<xpu> _update_tilde;
BiLayer<xpu> _recursive_tilde;
Tensor<xpu, 2, dtype> nxl;
Tensor<xpu, 2, dtype> nxr;
Tensor<xpu, 2, dtype> sum;
Tensor<xpu, 2, dtype> pxl;
Tensor<xpu, 2, dtype> pxr;
Tensor<xpu, 2, dtype> pmy;
Tensor<xpu, 2, dtype> lrxl;
Tensor<xpu, 2, dtype> lrxr;
Tensor<xpu, 2, dtype> lmy;
Tensor<xpu, 2, dtype> luxl;
Tensor<xpu, 2, dtype> luxr;
Tensor<xpu, 2, dtype> lumy;
Tensor<xpu, 2, dtype> lnxl;
Tensor<xpu, 2, dtype> lnxr;
Tensor<xpu, 2, dtype> lsum;
Tensor<xpu, 2, dtype> lpxl;
Tensor<xpu, 2, dtype> lpxr;
Tensor<xpu, 2, dtype> lpmy;
public:
AttRecursiveGatedNN() {
}
inline void initial(int dimension, int attDim, int seed = 0) {
_reset_left.initial(dimension, dimension, attDim, false, seed, 1);
_reset_right.initial(dimension, dimension, attDim, false, seed + 10, 1);
_update_left.initial(dimension, dimension, attDim, false, seed + 20, 3);
_update_right.initial(dimension, dimension, attDim, false, seed + 30, 3);
_update_tilde.initial(dimension, dimension, attDim, false, seed + 40, 3);
_recursive_tilde.initial(dimension, dimension, dimension, false, seed + 50, 0);
nxl = NewTensor<xpu>(Shape2(1, dimension), d_zero);
nxr = NewTensor<xpu>(Shape2(1, dimension), d_zero);
sum = NewTensor<xpu>(Shape2(1, dimension), d_zero);
pxl = NewTensor<xpu>(Shape2(1, dimension), d_zero);
pxr = NewTensor<xpu>(Shape2(1, dimension), d_zero);
pmy = NewTensor<xpu>(Shape2(1, dimension), d_zero);
lrxl = NewTensor<xpu>(Shape2(1, dimension), d_zero);
lrxr = NewTensor<xpu>(Shape2(1, dimension), d_zero);
lmy = NewTensor<xpu>(Shape2(1, dimension), d_zero);
luxl = NewTensor<xpu>(Shape2(1, dimension), d_zero);
luxr = NewTensor<xpu>(Shape2(1, dimension), d_zero);
lumy = NewTensor<xpu>(Shape2(1, dimension), d_zero);
lnxl = NewTensor<xpu>(Shape2(1, dimension), d_zero);
lnxr = NewTensor<xpu>(Shape2(1, dimension), d_zero);
lsum = NewTensor<xpu>(Shape2(1, dimension), d_zero);
lpxl = NewTensor<xpu>(Shape2(1, dimension), d_zero);
lpxr = NewTensor<xpu>(Shape2(1, dimension), d_zero);
lpmy = NewTensor<xpu>(Shape2(1, dimension), d_zero);
}
inline void initial(Tensor<xpu, 2, dtype> rW1, Tensor<xpu, 2, dtype> rU1,
Tensor<xpu, 2, dtype> rW2, Tensor<xpu, 2, dtype> rU2,
Tensor<xpu, 2, dtype> uW1, Tensor<xpu, 2, dtype> uU1,
Tensor<xpu, 2, dtype> uW2, Tensor<xpu, 2, dtype> uU2,
Tensor<xpu, 2, dtype> uW3, Tensor<xpu, 2, dtype> uU3,
Tensor<xpu, 2, dtype> W1, Tensor<xpu, 2, dtype> W2, Tensor<xpu, 2, dtype> W3,Tensor<xpu, 2, dtype> b) {
_reset_left.initial(rW1, rU1, 1);
_reset_right.initial(rW2, rU2, 1);
_update_left.initial(uW1, uU1, 3);
_update_right.initial(uW2, uU2, 3);
_update_tilde.initial(uW3, uU3, 3);
_recursive_tilde.initial(W1, W2, W3, b, 0);
}
inline void release() {
_reset_left.release();
_reset_right.release();
_update_left.release();
_update_right.release();
_update_tilde.release();
_recursive_tilde.release();
FreeSpace(&nxl);
FreeSpace(&nxr);
FreeSpace(&sum);
FreeSpace(&pxl);
FreeSpace(&pxr);
FreeSpace(&pmy);
FreeSpace(&lnxl);
FreeSpace(&lnxr);
FreeSpace(&lsum);
FreeSpace(&lpxl);
FreeSpace(&lpxr);
FreeSpace(&lpmy);
FreeSpace(&lrxl);
FreeSpace(&lrxr);
FreeSpace(&lmy);
FreeSpace(&luxl);
FreeSpace(&luxr);
FreeSpace(&lumy);
}
virtual ~AttRecursiveGatedNN() {
// TODO Auto-generated destructor stub
}
inline dtype squarenormAll() {
dtype norm = _reset_left.squarenormAll();
norm += _reset_right.squarenormAll();
norm += _update_left.squarenormAll();
norm += _update_right.squarenormAll();
norm += _update_tilde.squarenormAll();
norm += _recursive_tilde.squarenormAll();
return norm;
}
inline void scaleGrad(dtype scale) {
_reset_left.scaleGrad(scale);
_reset_right.scaleGrad(scale);
_update_left.scaleGrad(scale);
_update_right.scaleGrad(scale);
_update_tilde.scaleGrad(scale);
_recursive_tilde.scaleGrad(scale);
}
public:
inline void ComputeForwardScore(Tensor<xpu, 2, dtype> xl, Tensor<xpu, 2, dtype> xr, Tensor<xpu, 2, dtype> a,
Tensor<xpu, 2, dtype> rxl, Tensor<xpu, 2, dtype> rxr, Tensor<xpu, 2, dtype> my,
Tensor<xpu, 2, dtype> uxl, Tensor<xpu, 2, dtype> uxr, Tensor<xpu, 2, dtype> umy,
Tensor<xpu, 2, dtype> y) {
nxl = 0.0;
nxr = 0.0;
sum = 0.0;
pxl = 0.0;
pxr = 0.0;
pmy = 0.0;
_reset_left.ComputeForwardScore(xl, a, rxl);
_reset_right.ComputeForwardScore(xr, a, rxr);
nxl = rxl * xl;
nxr = rxr * xr;
_recursive_tilde.ComputeForwardScore(nxl, nxr, my);
_update_left.ComputeForwardScore(xl, a, uxl);
_update_right.ComputeForwardScore(xr, a, uxr);
_update_tilde.ComputeForwardScore(my, a, umy);
sum = uxl + uxr + umy;
pxl = uxl / sum;
pxr = uxr / sum;
pmy = umy / sum;
y = pxl * xl + pxr * xr + pmy * my;
}
//please allocate the memory outside here
inline void ComputeBackwardLoss(Tensor<xpu, 2, dtype> xl, Tensor<xpu, 2, dtype> xr, Tensor<xpu, 2, dtype> a,
Tensor<xpu, 2, dtype> rxl, Tensor<xpu, 2, dtype> rxr, Tensor<xpu, 2, dtype> my,
Tensor<xpu, 2, dtype> uxl, Tensor<xpu, 2, dtype> uxr, Tensor<xpu, 2, dtype> umy,
Tensor<xpu, 2, dtype> y, Tensor<xpu, 2, dtype> ly,
Tensor<xpu, 2, dtype> lxl, Tensor<xpu, 2, dtype> lxr, Tensor<xpu, 2, dtype> la,
bool bclear = false) {
if (bclear){
lxl = 0.0; lxr = 0.0; la = 0.0;
}
nxl = 0.0;
nxr = 0.0;
sum = 0.0;
pxl = 0.0;
pxr = 0.0;
pmy = 0.0;
lrxl = 0.0;
lrxr = 0.0;
lmy = 0.0;
luxl = 0.0;
luxr = 0.0;
lumy = 0.0;
lnxl = 0.0;
lnxr = 0.0;
lsum = 0.0;
lpxl = 0.0;
lpxr = 0.0;
lpmy = 0.0;
nxl = rxl * xl;
nxr = rxr * xr;
sum = uxl + uxr + umy;
pxl = uxl / sum;
pxr = uxr / sum;
pmy = umy / sum;
lpxl += ly * xl;
lxl += ly * pxl;
lpxr += ly * xr;
lxr += ly * pxr;
lpmy += ly * my;
lmy += ly * pmy;
luxl += lpxl / sum;
luxr += lpxr / sum;
lumy += lpmy / sum;
lsum -= lpxl * pxl / sum;
lsum -= lpxr * pxr / sum;
lsum -= lpmy * pmy / sum;
luxl += lsum;
luxr += lsum;
lumy += lsum;
_update_left.ComputeBackwardLoss(xl, a, uxl, luxl, lxl, la);
_update_right.ComputeBackwardLoss(xr, a, uxr, luxr, lxr, la);
_update_tilde.ComputeBackwardLoss(my, a, umy, lumy, lmy, la);
_recursive_tilde.ComputeBackwardLoss(nxl, nxr, my, lmy, lnxl, lnxr);
lrxl += lnxl * xl;
lxl += lnxl * rxl;
lrxr += lnxr * xr;
lxr += lnxr * rxr;
_reset_left.ComputeBackwardLoss(xl, a, rxl, lrxl, lxl, la);
_reset_right.ComputeBackwardLoss(xr, a, rxr, lrxr, lxr, la);
}
inline void randomprint(int num) {
_reset_left.randomprint(num);
_reset_right.randomprint(num);
_update_left.randomprint(num);
_update_right.randomprint(num);
_update_tilde.randomprint(num);
_recursive_tilde.randomprint(num);
}
inline void updateAdaGrad(dtype regularizationWeight, dtype adaAlpha, dtype adaEps) {
_reset_left.updateAdaGrad(regularizationWeight, adaAlpha, adaEps);
_reset_right.updateAdaGrad(regularizationWeight, adaAlpha, adaEps);
_update_left.updateAdaGrad(regularizationWeight, adaAlpha, adaEps);
_update_right.updateAdaGrad(regularizationWeight, adaAlpha, adaEps);
_update_tilde.updateAdaGrad(regularizationWeight, adaAlpha, adaEps);
_recursive_tilde.updateAdaGrad(regularizationWeight, adaAlpha, adaEps);
}
void writeModel(LStream &outf) {
_reset_left.writeModel(outf);
_reset_right.writeModel(outf);
_update_left.writeModel(outf);
_update_right.writeModel(outf);
_update_tilde.writeModel(outf);
_recursive_tilde.writeModel(outf);
SaveBinary(outf, nxl);
SaveBinary(outf, nxr);
SaveBinary(outf, sum);
SaveBinary(outf, pxl);
SaveBinary(outf, pxr);
SaveBinary(outf, pmy);
SaveBinary(outf, lrxl);
SaveBinary(outf, lrxr);
SaveBinary(outf, lmy);
SaveBinary(outf, luxl);
SaveBinary(outf, luxr);
SaveBinary(outf, lumy);
SaveBinary(outf, lnxl);
SaveBinary(outf, lnxr);
SaveBinary(outf, lsum);
SaveBinary(outf, lpxl);
SaveBinary(outf, lpxr);
SaveBinary(outf, lpmy);
}
void loadModel(LStream &inf) {
_reset_left.loadModel(inf);
_reset_right.loadModel(inf);
_update_left.loadModel(inf);
_update_right.loadModel(inf);
_update_tilde.loadModel(inf);
_recursive_tilde.loadModel(inf);
LoadBinary(inf, &nxl, false);
LoadBinary(inf, &nxr, false);
LoadBinary(inf, &sum, false);
LoadBinary(inf, &pxl, false);
LoadBinary(inf, &pxr, false);
LoadBinary(inf, &pmy, false);
LoadBinary(inf, &lrxl, false);
LoadBinary(inf, &lrxr, false);
LoadBinary(inf, &lmy, false);
LoadBinary(inf, &luxl, false);
LoadBinary(inf, &luxr, false);
LoadBinary(inf, &lumy, false);
LoadBinary(inf, &lnxl, false);
LoadBinary(inf, &lnxr, false);
LoadBinary(inf, &lsum, false);
LoadBinary(inf, &lpxl, false);
LoadBinary(inf, &lpxr, false);
LoadBinary(inf, &lpmy, false);
}
};
#endif /* SRC_AttRecursiveGatedNN_H_ */