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TriLayerLSTM.h
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TriLayerLSTM.h
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/*
* TriLayerLSTM.h
*
* Created on: Mar 18, 2015
* Author: mszhang
*/
#ifndef SRC_TriLayerLSTM_H_
#define SRC_TriLayerLSTM_H_
#include "tensor.h"
#include "MyLib.h"
#include "Utiltensor.h"
using namespace mshadow;
using namespace mshadow::expr;
using namespace mshadow::utils;
template<typename xpu>
class TriLayerLSTM {
public:
Tensor<xpu, 2, dtype> _W1;
Tensor<xpu, 2, dtype> _W2;
Tensor<xpu, 2, dtype> _W3;
Tensor<xpu, 2, dtype> _b;
Tensor<xpu, 2, dtype> _gradW1;
Tensor<xpu, 2, dtype> _gradW2;
Tensor<xpu, 2, dtype> _gradW3;
Tensor<xpu, 2, dtype> _gradb;
Tensor<xpu, 2, dtype> _eg2W1;
Tensor<xpu, 2, dtype> _eg2W2;
Tensor<xpu, 2, dtype> _eg2W3;
Tensor<xpu, 2, dtype> _eg2b;
bool _bUseB;
int _funcType; // 0: tanh, 1: sigmod, 2: f(x)=x, 3: exp
public:
TriLayerLSTM() {
}
inline void initial(int nOSize, int nISize1, int nISize2, bool bUseB = true, int seed = 0, int funcType = 0) {
dtype bound = sqrt(6.0 / (nOSize + nISize1 + nISize2 + 1));
//dtype bound = 0.01;
_W1 = NewTensor<xpu>(Shape2(nOSize, nISize1), d_zero);
_gradW1 = NewTensor<xpu>(Shape2(nOSize, nISize1), d_zero);
_eg2W1 = NewTensor<xpu>(Shape2(nOSize, nISize1), d_zero);
_W2 = NewTensor<xpu>(Shape2(nOSize, nISize2), d_zero);
_gradW2 = NewTensor<xpu>(Shape2(nOSize, nISize2), d_zero);
_eg2W2 = NewTensor<xpu>(Shape2(nOSize, nISize2), d_zero);
_W3 = NewTensor<xpu>(Shape2(1, nOSize), d_zero);
_gradW3 = NewTensor<xpu>(Shape2(1, nOSize), d_zero);
_eg2W3 = NewTensor<xpu>(Shape2(1, nOSize), d_zero);
_b = NewTensor<xpu>(Shape2(1, nOSize), d_zero);
_gradb = NewTensor<xpu>(Shape2(1, nOSize), d_zero);
_eg2b = NewTensor<xpu>(Shape2(1, nOSize), d_zero);
random(_W1, -1.0 * bound, 1.0 * bound, seed);
random(_W2, -1.0 * bound, 1.0 * bound, seed+1);
random(_W3, -1.0 * bound, 1.0 * bound, seed+2);
random(_b, -1.0 * bound, 1.0 * bound, seed+3);
_bUseB = bUseB;
_funcType = funcType;
}
inline void initial(Tensor<xpu, 2, dtype> W1, Tensor<xpu, 2, dtype> W2, Tensor<xpu, 2, dtype> W3, Tensor<xpu, 2, dtype> b, bool bUseB = true,
int funcType = 0) {
static int nOSize, nISize1, nISize2;
nOSize = W1.size(0);
nISize1 = W1.size(1);
nISize2 = W2.size(1);
_W1 = NewTensor<xpu>(Shape2(nOSize, nISize1), d_zero);
_gradW1 = NewTensor<xpu>(Shape2(nOSize, nISize1), d_zero);
_eg2W1 = NewTensor<xpu>(Shape2(nOSize, nISize1), d_zero);
Copy(_W1, W1);
_W2 = NewTensor<xpu>(Shape2(nOSize, nISize2), d_zero);
_gradW2 = NewTensor<xpu>(Shape2(nOSize, nISize2), d_zero);
_eg2W2 = NewTensor<xpu>(Shape2(nOSize, nISize2), d_zero);
Copy(_W2, W2);
_W3 = NewTensor<xpu>(Shape2(1, nOSize), d_zero);
_gradW3 = NewTensor<xpu>(Shape2(1, nOSize), d_zero);
_eg2W3 = NewTensor<xpu>(Shape2(1, nOSize), d_zero);
Copy(_W3, W3);
_b = NewTensor<xpu>(Shape2(1, nOSize), d_zero);
_gradb = NewTensor<xpu>(Shape2(1, nOSize), d_zero);
_eg2b = NewTensor<xpu>(Shape2(1, nOSize), d_zero);
if (bUseB)
Copy(_b, b);
_bUseB = bUseB;
_funcType = funcType;
}
inline void release() {
FreeSpace(&_W1);
FreeSpace(&_gradW1);
FreeSpace(&_eg2W1);
FreeSpace(&_W2);
FreeSpace(&_gradW2);
FreeSpace(&_eg2W2);
FreeSpace(&_W3);
FreeSpace(&_gradW3);
FreeSpace(&_eg2W3);
FreeSpace(&_b);
FreeSpace(&_gradb);
FreeSpace(&_eg2b);
}
virtual ~TriLayerLSTM() {
// TODO Auto-generated destructor stub
}
inline dtype squarenormAll() {
dtype result = squarenorm(_gradW1);
result += squarenorm(_gradW2);
result += squarenorm(_gradW3);
if (_bUseB) {
result += squarenorm(_gradb);
}
return result;
}
inline void scaleGrad(dtype scale) {
_gradW1 = _gradW1 * scale;
_gradW2 = _gradW2 * scale;
_gradW3 = _gradW3 * scale;
if (_bUseB) {
_gradb = _gradb * scale;
}
}
public:
inline void ComputeForwardScore(Tensor<xpu, 2, dtype> x1, Tensor<xpu, 2, dtype> x2, Tensor<xpu, 2, dtype> x3, Tensor<xpu, 2, dtype> y) {
y = dot(x1, _W1.T());
y += dot(x2, _W2.T());
y += x3 * _W3;
if (_bUseB)
y = y + _b;
if (_funcType == 0)
y = F<nl_tanh>(y);
else if (_funcType == 1)
y = F<nl_sigmoid>(y);
else if (_funcType == 3)
y = F<nl_exp>(y);
}
inline void ComputeForwardScore(Tensor<xpu, 3, dtype> x1, Tensor<xpu, 3, dtype> x2, Tensor<xpu, 3, dtype> x3, Tensor<xpu, 3, dtype> y) {
int seq_size = y.size(0);
for (int id = 0; id < seq_size; id++) {
y[id] = dot(x1[id], _W1.T());
y[id] += dot(x2[id], _W2.T());
y[id] += x3[id] * _W3;
if (_bUseB)
y[id] = y[id] + _b;
if (_funcType == 0)
y[id] = F<nl_tanh>(y[id]);
else if (_funcType == 1)
y[id] = F<nl_sigmoid>(y[id]);
else if (_funcType == 3)
y[id] = F<nl_exp>(y[id]);
}
}
inline void ComputeForwardScore(const std::vector<Tensor<xpu, 2, dtype> > &x1, const std::vector<Tensor<xpu, 2, dtype> > &x2,
const std::vector<Tensor<xpu, 2, dtype> > &x3, std::vector<Tensor<xpu, 2, dtype> > &y) {
int seq_size = y.size();
for (int id = 0; id < seq_size; id++) {
y[id] = dot(x1[id], _W1.T());
y[id] += dot(x2[id], _W2.T());
y[id] += x3[id] * _W3;
if (_bUseB)
y[id] = y[id] + _b;
if (_funcType == 0)
y[id] = F<nl_tanh>(y[id]);
else if (_funcType == 1)
y[id] = F<nl_sigmoid>(y[id]);
else if (_funcType == 3)
y[id] = F<nl_exp>(y[id]);
}
}
//please allocate the memory outside here
inline void ComputeBackwardLoss(Tensor<xpu, 2, dtype> x1, Tensor<xpu, 2, dtype> x2, Tensor<xpu, 2, dtype> x3, Tensor<xpu, 2, dtype> y,
Tensor<xpu, 2, dtype> ly, Tensor<xpu, 2, dtype> lx1, Tensor<xpu, 2, dtype> lx2, Tensor<xpu, 2, dtype> lx3, bool bclear = false) {
//_gradW
Tensor<xpu, 2, dtype> deri_yx(Shape2(y.size(0), y.size(1))), cly(Shape2(y.size(0), y.size(1)));
AllocSpace(&deri_yx);
AllocSpace(&cly);
if(bclear) {
lx1 = 0.0;
lx2 = 0.0;
lx3 = 0.0;
}
if (_funcType == 0) {
deri_yx = F<nl_dtanh>(y);
cly = ly * deri_yx;
} else if (_funcType == 1) {
deri_yx = F<nl_dsigmoid>(y);
cly = ly * deri_yx;
} else if (_funcType == 3) {
cly = ly * y;
} else {
//cly = ly;
Copy(cly, ly);
}
//_gradW
_gradW1 += dot(cly.T(), x1);
_gradW2 += dot(cly.T(), x2);
_gradW3 += cly * x3;
//_gradb
if (_bUseB)
_gradb += cly;
//lx
lx1 += dot(cly, _W1);
lx2 += dot(cly, _W2);
lx3 += cly * _W3;
FreeSpace(&deri_yx);
FreeSpace(&cly);
}
//please allocate the memory outside here
inline void ComputeBackwardLoss(Tensor<xpu, 3, dtype> x1, Tensor<xpu, 3, dtype> x2, Tensor<xpu, 3, dtype> x3, Tensor<xpu, 3, dtype> y,
Tensor<xpu, 3, dtype> ly, Tensor<xpu, 3, dtype> lx1, Tensor<xpu, 3, dtype> lx2, Tensor<xpu, 3, dtype> lx3, bool bclear = false) {
int seq_size = y.size(0);
int y_dim1 = y.size(1), y_dim2 = y.size(2);
//_gradW
Tensor<xpu, 2, dtype> deri_yx(Shape2(y_dim1, y_dim2)), cly(Shape2(y_dim1, y_dim2));
AllocSpace(&deri_yx);
AllocSpace(&cly);
if(bclear) {
lx1 = 0.0;
lx2 = 0.0;
lx3 = 0.0;
}
for (int id = 0; id < seq_size; id++) {
if (_funcType == 0) {
deri_yx = F<nl_dtanh>(y[id]);
cly = ly[id] * deri_yx;
} else if (_funcType == 1) {
deri_yx = F<nl_dsigmoid>(y[id]);
cly = ly[id] * deri_yx;
} else if (_funcType == 3) {
cly = ly[id] * y[id];
} else {
//cly = ly;
Copy(cly, ly[id]);
}
//_gradW
_gradW1 += dot(cly.T(), x1[id]);
_gradW2 += dot(cly.T(), x2[id]);
_gradW3 += cly * x3[id];
//_gradb
if (_bUseB)
_gradb += cly;
//lx
lx1[id] += dot(cly, _W1);
lx2[id] += dot(cly, _W2);
lx3[id] += cly * _W3;
}
FreeSpace(&deri_yx);
FreeSpace(&cly);
}
//please allocate the memory outside here
inline void ComputeBackwardLoss(const std::vector<Tensor<xpu, 2, dtype> > &x1, const std::vector<Tensor<xpu, 2, dtype> > &x2,
const std::vector<Tensor<xpu, 2, dtype> > &x3, const std::vector<Tensor<xpu, 2, dtype> > &y,
const std::vector<Tensor<xpu, 2, dtype> > &ly, std::vector<Tensor<xpu, 2, dtype> > &lx1,
std::vector<Tensor<xpu, 2, dtype> > &lx2, std::vector<Tensor<xpu, 2, dtype> > &lx3, bool bclear = false) {
int seq_size = y.size();
assert(seq_size > 0);
int y_dim1 = y[0].size(0), y_dim2 = y[0].size(1);
//_gradW
Tensor<xpu, 2, dtype> deri_yx(Shape2(y_dim1, y_dim2)), cly(Shape2(y_dim1, y_dim2));
AllocSpace(&deri_yx);
AllocSpace(&cly);
if(bclear) {
for (int id = 0; id < seq_size; id++) {
lx1[id] = 0.0;
lx2[id] = 0.0;
lx3[id] = 0.0;
}
}
for (int id = 0; id < seq_size; id++) {
if (_funcType == 0) {
deri_yx = F<nl_dtanh>(y[id]);
cly = ly[id] * deri_yx;
} else if (_funcType == 1) {
deri_yx = F<nl_dsigmoid>(y[id]);
cly = ly[id] * deri_yx;
} else if (_funcType == 3) {
cly = ly[id] * y[id];
} else {
//cly = ly;
Copy(cly, ly[id]);
}
//_gradW
_gradW1 += dot(cly.T(), x1[id]);
_gradW2 += dot(cly.T(), x2[id]);
_gradW3 += cly * x3[id];
//_gradb
if (_bUseB)
_gradb += cly;
//lx
lx1[id] += dot(cly, _W1);
lx2[id] += dot(cly, _W2);
lx3[id] += cly * _W3;
}
FreeSpace(&deri_yx);
FreeSpace(&cly);
}
inline void randomprint(int num) {
static int nOSize, nISize1, nISize2, nISize3;
nOSize = _W1.size(0);
nISize1 = _W1.size(1);
nISize2 = _W2.size(1);
nISize3 = _W3.size(1);
int count = 0;
while (count < num) {
int idx1 = rand() % nOSize;
int idy1 = rand() % nISize1;
int idx2 = rand() % nOSize;
int idy2 = rand() % nISize2;
int idx3 = rand() % nOSize;
int idy3 = rand() % nISize3;
std::cout << "_W1[" << idx1 << "," << idy1 << "]=" << _W1[idx1][idy1] << " ";
std::cout << "_W2[" << idx2 << "," << idy2 << "]=" << _W2[idx2][idy2] << " ";
std::cout << "_W3[" << idx3 << "," << idy3 << "]=" << _W3[idx3][idy3] << " ";
if (_bUseB) {
int idz = rand() % nOSize;
std::cout << "_b[0][" << idz << "]=" << _b[0][idz] << " ";
}
count++;
}
std::cout << std::endl;
}
inline void updateAdaGrad(dtype regularizationWeight, dtype adaAlpha, dtype adaEps) {
_gradW1 = _gradW1 + _W1 * regularizationWeight;
_eg2W1 = _eg2W1 + _gradW1 * _gradW1;
_W1 = _W1 - _gradW1 * adaAlpha / F<nl_sqrt>(_eg2W1 + adaEps);
_gradW2 = _gradW2 + _W2 * regularizationWeight;
_eg2W2 = _eg2W2 + _gradW2 * _gradW2;
_W2 = _W2 - _gradW2 * adaAlpha / F<nl_sqrt>(_eg2W2 + adaEps);
_gradW3 = _gradW3 + _W3 * regularizationWeight;
_eg2W3 = _eg2W3 + _gradW3 * _gradW3;
_W3 = _W3 - _gradW3 * adaAlpha / F<nl_sqrt>(_eg2W3 + adaEps);
if (_bUseB) {
_gradb = _gradb + _b * regularizationWeight;
_eg2b = _eg2b + _gradb * _gradb;
_b = _b - _gradb * adaAlpha / F<nl_sqrt>(_eg2b + adaEps);
}
clearGrad();
}
inline void clearGrad() {
_gradW1 = 0;
_gradW2 = 0;
_gradW3 = 0;
if (_bUseB)
_gradb = 0;
}
void writeModel(LStream &outf) {
SaveBinary(outf, _W1);
SaveBinary(outf, _W2);
SaveBinary(outf, _W3);
SaveBinary(outf, _b);
SaveBinary(outf, _gradW1);
SaveBinary(outf, _gradW2);
SaveBinary(outf, _gradW3);
SaveBinary(outf, _gradb);
SaveBinary(outf, _eg2W1);
SaveBinary(outf, _eg2W2);
SaveBinary(outf, _eg2W3);
SaveBinary(outf, _eg2b);
WriteBinary(outf, _bUseB);
WriteBinary(outf, _funcType);
// cout << "TrilayerLSTM " << _bUseB << _funcType << endl;
// cout << "TrilayerLSTM value: " << _W3.size(0) << " and " << _W3.size(1) << " value " << _W3[0][1] << endl;
}
void loadModel(LStream &inf) {
LoadBinary(inf, &_W1, false);
LoadBinary(inf, &_W2, false);
LoadBinary(inf, &_W3, false);
LoadBinary(inf, &_b, false);
LoadBinary(inf, &_gradW1, false);
LoadBinary(inf, &_gradW2, false);
LoadBinary(inf, &_gradW3, false);
LoadBinary(inf, &_gradb, false);
LoadBinary(inf, &_eg2W1, false);
LoadBinary(inf, &_eg2W2, false);
LoadBinary(inf, &_eg2W3, false);
LoadBinary(inf, &_eg2b, false);
ReadBinary(inf, _bUseB);
ReadBinary(inf, _funcType);
// cout << "TrilayerLSTM " << _bUseB << _funcType << endl;
// cout << "TrilayerLSTM value: " << _W3.size(0) << " and " << _W3.size(1) << " value " << _W3[0][1] << endl;
}
};
#endif /* SRC_TriLayerLSTM_H_ */