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#include "cuBiasedLinearity.h"
namespace TNet
{
void
CuBiasedLinearity::
PropagateFnc(const CuMatrix<BaseFloat>& X, CuMatrix<BaseFloat>& Y)
{
//Y.SetConst(0.0);
Y.AddScaledRow(1.0,mBias,0.0);
Y.Gemm('N','N', 1.0, X, mLinearity, 1.0);
}
void
CuBiasedLinearity::
BackpropagateFnc(const CuMatrix<BaseFloat>& X, CuMatrix<BaseFloat>& Y)
{
//Y.SetConst(0.0);
std::cout<<mLinearity.Rows()<<"\t"<<mLinearity.Cols()<<std::endl;
Y.Gemm('N', 'T', 1.0, X, mLinearity, 0.0);
}
void
CuBiasedLinearity::
Update()
{
#if 0
//former implementation
BaseFloat N = static_cast<BaseFloat>(GetInput().Rows());
mLinearityCorrection.Gemm('T','N',-mLearningRate/N,GetInput(),GetErrorInput(),mMomentum);
mBiasCorrection.AddColSum(-mLearningRate/N,GetErrorInput(),mMomentum);
//regularization weight decay
mLinearityCorrection.AddScaled(-mLearningRate*mWeightcost,mLinearity,1.0);
mLinearity.AddScaled(1.0,mLinearityCorrection,1.0);
mBias.AddScaled(1.0,mBiasCorrection,1.0);
#endif
#if 1
//new implementation
BaseFloat N = 1;
if(mGradDivFrm) {
N = static_cast<BaseFloat>(GetInput().Rows());
}
BaseFloat mmt_gain = static_cast<BaseFloat>(1.0/(1.0-mMomentum));
N *= mmt_gain;
mLinearityCorrection.Gemm('T','N',1.0,GetInput(),GetErrorInput(),mMomentum);
mBiasCorrection.AddColSum(1.0,GetErrorInput(),mMomentum);
mLinearity.AddScaled(-mLearningRate/N,mLinearityCorrection,1.0);
mBias.AddScaled(-mLearningRate/N,mBiasCorrection,1.0);
//regularization weight decay (from actual weights only)
BaseFloat L2_decay = -mLearningRate*mWeightcost*(mGradDivFrm?1.0:GetInput().Rows());
mLinearity.AddScaled(L2_decay, mLinearity,1.0);
#endif
}
void
CuBiasedLinearity::
ReadFromStream(std::istream& rIn)
{
//matrix is stored transposed as SNet does
BfMatrix transpose;
rIn >> transpose;
mLinearity.CopyFrom(BfMatrix(transpose, TRANS));
//biases stored normally
BfVector bias;
rIn >> bias;
mBias.CopyFrom(bias);
if(transpose.Cols()*transpose.Rows() == 0) {
Error("Missing linearity matrix in network file");
}
if(bias.Dim() == 0) {
Error("Missing bias vector in network file");
}
if(mLinearity.Cols() != GetNOutputs() ||
mLinearity.Rows() != GetNInputs() ||
mBias.Dim() != GetNOutputs()
){
std::ostringstream os;
os << "Wrong dimensionalities of matrix/vector in network file\n"
<< "Inputs:" << GetNInputs()
<< "Outputs:" << GetNOutputs()
<< "\n"
<< "linearityCols:" << mLinearity.Cols()
<< "linearityRows:" << mLinearity.Rows()
<< "biasDims:" << mBias.Dim()
<< "\n";
Error(os.str());
}
}
void
CuBiasedLinearity::
WriteToStream(std::ostream& rOut)
{
//matrix is stored transposed as SNet does
BfMatrix tmp;
mLinearity.CopyTo(tmp);
BfMatrix transpose(tmp, TRANS);
rOut << transpose;
//biases stored normally
BfVector vec;
mBias.CopyTo(vec);
rOut << vec;
rOut << std::endl;
}
} //namespace
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