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#include "BiasedLinearity.h"
namespace TNet {
void
BiasedLinearity::
PropagateFnc(const Matrix<BaseFloat>& X, Matrix<BaseFloat>& Y)
{
//y = b + x.A
//precopy bias
size_t rows = X.Rows();
for(size_t i=0; i<rows; i++) {
Y[i].Copy(*mpBias);
}
//multiply matrix by matrix with mLinearity
Y.BlasGemm(1.0f, X, NO_TRANS, *mpLinearity, NO_TRANS, 1.0f);
}
void
BiasedLinearity::
BackpropagateFnc(const Matrix<BaseFloat>& X, Matrix<BaseFloat>& Y)
{
// e' = e.A^T
Y.Zero();
Y.BlasGemm(1.0f, X, NO_TRANS, *mpLinearity, TRANS, 0.0f);
}
void
BiasedLinearity::
ReadFromStream(std::istream& rIn)
{
//matrix is stored transposed as SNet does
Matrix<BaseFloat> transpose;
rIn >> transpose;
mLinearity = Matrix<BaseFloat>(transpose, TRANS);
//biases stored normally
rIn >> mBias;
}
void
BiasedLinearity::
WriteToStream(std::ostream& rOut)
{
//matrix is stored transposed as SNet does
Matrix<BaseFloat> transpose(mLinearity, TRANS);
rOut << transpose;
//biases stored normally
rOut << mBias;
rOut << std::endl;
}
void
BiasedLinearity::
Gradient()
{
//calculate gradient of weight matrix
mLinearityCorrection.Zero();
mLinearityCorrection.BlasGemm(1.0f, GetInput(), TRANS,
GetErrorInput(), NO_TRANS,
0.0f);
//calculate gradient of bias
mBiasCorrection.Set(0.0f);
size_t rows = GetInput().Rows();
for(size_t i=0; i<rows; i++) {
mBiasCorrection.Add(GetErrorInput()[i]);
}
/*
//perform update
mLinearity.AddScaled(-mLearningRate, mLinearityCorrection);
mBias.AddScaled(-mLearningRate, mBiasCorrection);
*/
}
void
BiasedLinearity::
AccuGradient(const UpdatableComponent& src, int thr, int thrN) {
//cast the argument
const BiasedLinearity& src_comp = dynamic_cast<const BiasedLinearity&>(src);
//allocate accumulators when needed
if(mLinearityCorrectionAccu.MSize() == 0) {
mLinearityCorrectionAccu.Init(mLinearity.Rows(),mLinearity.Cols());
}
if(mBiasCorrectionAccu.MSize() == 0) {
mBiasCorrectionAccu.Init(mBias.Dim());
}
//need to find out which rows to sum...
int div = mLinearityCorrection.Rows() / thrN;
int mod = mLinearityCorrection.Rows() % thrN;
int origin = thr * div + ((mod > thr)? thr : mod);
int rows = div + ((mod > thr)? 1 : 0);
//create the matrix windows
const SubMatrix<BaseFloat> src_mat (
src_comp.mLinearityCorrection,
origin, rows,
0, mLinearityCorrection.Cols()
);
SubMatrix<double> tgt_mat (
mLinearityCorrectionAccu,
origin, rows,
0, mLinearityCorrection.Cols()
);
//sum the rows
Add(tgt_mat,src_mat);
//first thread will always sum the bias correction
if(thr == 0) {
Add(mBiasCorrectionAccu,src_comp.mBiasCorrection);
}
}
void
BiasedLinearity::
Update(int thr, int thrN)
{
//need to find out which rows to sum...
int div = mLinearity.Rows() / thrN;
int mod = mLinearity.Rows() % thrN;
int origin = thr * div + ((mod > thr)? thr : mod);
int rows = div + ((mod > thr)? 1 : 0);
//std::cout << "[P" << thr << "," << origin << "," << rows << "]" << std::flush;
//get the matrix windows
SubMatrix<double> src_mat (
mLinearityCorrectionAccu,
origin, rows,
0, mLinearityCorrection.Cols()
);
SubMatrix<BaseFloat> tgt_mat (
mLinearity,
origin, rows,
0, mLinearityCorrection.Cols()
);
//update weights
AddScaled(tgt_mat, src_mat, -mLearningRate);
//perform L2 regularization (weight decay)
BaseFloat L2_decay = -mLearningRate * mWeightcost * mBunchsize;
if(L2_decay != 0.0) {
tgt_mat.AddScaled(L2_decay, tgt_mat);
}
//first thread always update bias
if(thr == 0) {
//std::cout << "[" << thr << "BP]" << std::flush;
AddScaled(mBias, mBiasCorrectionAccu, -mLearningRate);
}
//reset the accumulators
src_mat.Zero();
if(thr == 0) {
mBiasCorrectionAccu.Zero();
}
}
} //namespace
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