#include <discriminative-training.h>

Collaboration diagram for DiscriminativeObjectiveInfo:

Public Member Functions
void	Print (const std::string &criterion, bool print_avg_gradients=false, bool print_avg_output=false) const

void	PrintAll (const std::string &criterion) const

void	PrintAvgGradientForPdf (int32 pdf_id) const

void	Add (const DiscriminativeObjectiveInfo &other)

double	TotalObjf (const std::string &criterion) const

bool	AccumulateGradients () const

bool	AccumulateOutput () const

	DiscriminativeObjectiveInfo ()

	DiscriminativeObjectiveInfo (int32 num_pdfs)

	DiscriminativeObjectiveInfo (const DiscriminativeOptions &opts)

void	Reset ()

void	Configure (const DiscriminativeOptions &opts)

Public Attributes
double	tot_t

double	tot_t_weighted

double	tot_objf

double	tot_num_count

double	tot_den_count

double	tot_num_objf

double	tot_l2_term

bool	accumulate_gradients

bool	accumulate_output

int32	num_pdfs

CuVector< double >	gradients

CuVector< double >	output

Detailed Description

Definition at line 130 of file discriminative-training.h.

Constructor & Destructor Documentation

◆ DiscriminativeObjectiveInfo() [1/3]

DiscriminativeObjectiveInfo ( )

Definition at line 28 of file discriminative-training.cc.

                                                          {
   std::memset((void *)this, 0, sizeof(*this));
 }

◆ DiscriminativeObjectiveInfo() [2/3]

DiscriminativeObjectiveInfo ( int32 num_pdfs )

Definition at line 32 of file discriminative-training.cc.

References DiscriminativeObjectiveInfo::gradients, DiscriminativeObjectiveInfo::output, DiscriminativeObjectiveInfo::Reset(), and CuVector< Real >::Resize().

                                                                        :
   accumulate_gradients(false),
   accumulate_output(false),
   num_pdfs(num_pdfs) {
   gradients.Resize(num_pdfs);
   output.Resize(num_pdfs);
   Reset();
 }

◆ DiscriminativeObjectiveInfo() [3/3]

DiscriminativeObjectiveInfo ( const DiscriminativeOptions & opts )

Definition at line 42 of file discriminative-training.cc.

References DiscriminativeObjectiveInfo::gradients, DiscriminativeOptions::num_pdfs, DiscriminativeObjectiveInfo::output, DiscriminativeObjectiveInfo::Reset(), and CuVector< Real >::Resize().

                                        :
   accumulate_gradients(opts.accumulate_gradients),
   accumulate_output(opts.accumulate_output),
   num_pdfs(opts.num_pdfs) {
   gradients.Resize(opts.num_pdfs);
   output.Resize(opts.num_pdfs);
   Reset();
 }

Member Function Documentation

◆ AccumulateGradients()

bool AccumulateGradients ( ) const

inline

Definition at line 188 of file discriminative-training.h.

References CuVectorBase< Real >::Dim().

Referenced by DiscriminativeComputation::Compute().

                                           {
     return accumulate_gradients && gradients.Dim() > 0;
   }

◆ AccumulateOutput()

bool AccumulateOutput ( ) const

inline

Definition at line 195 of file discriminative-training.h.

References kaldi::discriminative::ComputeDiscriminativeObjfAndDeriv(), and CuVectorBase< Real >::Dim().

Referenced by DiscriminativeComputation::Compute().

                                        {
     return accumulate_output && output.Dim() > 0;
   }

◆ Add()

void Add ( const DiscriminativeObjectiveInfo & other )

Definition at line 560 of file discriminative-training.cc.

References DiscriminativeObjectiveInfo::gradients, DiscriminativeObjectiveInfo::output, DiscriminativeObjectiveInfo::tot_den_count, DiscriminativeObjectiveInfo::tot_l2_term, DiscriminativeObjectiveInfo::tot_num_count, DiscriminativeObjectiveInfo::tot_num_objf, DiscriminativeObjectiveInfo::tot_objf, DiscriminativeObjectiveInfo::tot_t, and DiscriminativeObjectiveInfo::tot_t_weighted.

Referenced by DiscriminativeComputation::Compute().

                                                                               {
   tot_t += other.tot_t;
   tot_t_weighted += other.tot_t_weighted;
   tot_objf += other.tot_objf;             // Actually tot_den_objf for mmi
   tot_num_count += other.tot_num_count;
   tot_den_count += other.tot_den_count;
   tot_num_objf += other.tot_num_objf;     // Only for mmi
   tot_l2_term += other.tot_l2_term;
 
   if (AccumulateGradients()) {
     gradients.AddVec(1.0, other.gradients);
   }
   if (AccumulateOutput()) {
     output.AddVec(1.0, other.output);
   }
 }

◆ Configure()

void Configure ( const DiscriminativeOptions & opts )

Definition at line 66 of file discriminative-training.cc.

References DiscriminativeOptions::accumulate_gradients, DiscriminativeObjectiveInfo::accumulate_gradients, DiscriminativeOptions::accumulate_output, DiscriminativeObjectiveInfo::accumulate_output, DiscriminativeObjectiveInfo::gradients, DiscriminativeOptions::num_pdfs, DiscriminativeObjectiveInfo::num_pdfs, DiscriminativeObjectiveInfo::output, and CuVector< Real >::Resize().

                                                                              {
   accumulate_gradients = opts.accumulate_gradients;
   accumulate_output = opts.accumulate_output;
   num_pdfs = opts.num_pdfs;
   gradients.Resize(opts.num_pdfs);
   output.Resize(opts.num_pdfs);
 }

◆ Print()

void Print	(	const std::string &	criterion,
		bool	print_avg_gradients = `false`,
		bool	print_avg_output = `false`
	)		const

Definition at line 577 of file discriminative-training.cc.

References KALDI_LOG, KALDI_VLOG, and VectorBase< Real >::Scale().

Referenced by DiscriminativeComputation::Compute().

                                                                      {
   if (criterion == "mmi") {
     double num_objf = tot_num_objf / tot_t_weighted,
            den_objf = tot_objf / tot_t_weighted;
     double objf = num_objf - den_objf;
 
     double avg_post_per_frame = tot_num_count / tot_t_weighted;
 
     KALDI_LOG << "Number of frames is " << tot_t
               << " (weighted: " << tot_t_weighted
               << "), average (num or den) posterior per frame is "
               << avg_post_per_frame;
     KALDI_LOG << "MMI objective function is " << num_objf << " - "
               << den_objf << " = " << objf << " per frame, over "
               << tot_t_weighted << " frames.";
   } else if (criterion == "mpfe") {
     double avg_gradients = (tot_num_count + tot_den_count) / tot_t_weighted;
     double objf = tot_objf / tot_t_weighted;
     KALDI_LOG << "Average num+den count of MPFE stats is " << avg_gradients
               << " per frame, over "
               << tot_t_weighted << " frames";
     KALDI_LOG << "MPFE objective function is " << objf
               << " per frame, over " << tot_t_weighted << " frames.";
   } else if (criterion == "smbr") {
     double avg_gradients = (tot_num_count + tot_den_count) / tot_t_weighted;
     double objf = tot_objf / tot_t_weighted;
     KALDI_LOG << "Average num+den count of SMBR stats is " << avg_gradients
               << " per frame, over "
               << tot_t_weighted << " frames";
     KALDI_LOG << "SMBR objective function is " << objf
               << " per frame, over " << tot_t_weighted << " frames.";
   }
 
   if (AccumulateGradients()) {
     Vector<double> temp(gradients);
     temp.Scale(1.0/tot_t_weighted);
     if (print_avg_gradients) {
       KALDI_LOG << "Vector of average gradients wrt output activations is: \n" << temp;
     } else {
       KALDI_VLOG(4) << "Vector of average gradients wrt output activations is: \n" << temp;
     }
   }
   if (AccumulateOutput()) {
     Vector<double> temp(output);
     temp.Scale(1.0/tot_t_weighted);
     if (print_avg_output) {
       KALDI_LOG << "Average DNN output is: \n" << temp;
     } else {
       KALDI_VLOG(4) << "Average DNN output is: \n" << temp;
     }
   }
 }

◆ PrintAll()

void PrintAll ( const std::string & criterion ) const

inline

Definition at line 169 of file discriminative-training.h.

References fst::Print().

Referenced by DiscriminativeComputation::Compute(), and NnetDiscriminativeComputeObjf::PrintTotalStats().

                                                   {
     Print(criterion, true, true);
   }

◆ PrintAvgGradientForPdf()

void PrintAvgGradientForPdf ( int32 pdf_id ) const

Definition at line 632 of file discriminative-training.cc.

References KALDI_LOG.

                                                                            {
   if (pdf_id < gradients.Dim() && pdf_id >= 0) {
     KALDI_LOG << "Average gradient wrt output activations of pdf " << pdf_id
               << " is " << gradients(pdf_id) / tot_t_weighted
               << " per frame, over "
               << tot_t_weighted << " frames";
   }
 }

◆ Reset()

void Reset ( )

Definition at line 53 of file discriminative-training.cc.

References DiscriminativeObjectiveInfo::gradients, DiscriminativeObjectiveInfo::output, CuVectorBase< Real >::SetZero(), DiscriminativeObjectiveInfo::tot_den_count, DiscriminativeObjectiveInfo::tot_l2_term, DiscriminativeObjectiveInfo::tot_num_count, DiscriminativeObjectiveInfo::tot_num_objf, DiscriminativeObjectiveInfo::tot_objf, DiscriminativeObjectiveInfo::tot_t, and DiscriminativeObjectiveInfo::tot_t_weighted.

Referenced by DiscriminativeComputation::Compute(), and DiscriminativeObjectiveInfo::DiscriminativeObjectiveInfo().

                                         {
   gradients.SetZero();
   output.SetZero();
 
   tot_t = 0.0;
   tot_t_weighted = 0.0;
   tot_objf = 0.0;
   tot_num_count = 0.0;
   tot_den_count = 0.0;
   tot_num_objf = 0.0;
   tot_l2_term = 0.0;
 }

◆ TotalObjf()

double TotalObjf ( const std::string & criterion ) const

inline

Definition at line 180 of file discriminative-training.h.

Referenced by DiscriminativeComputation::Compute(), and NnetDiscriminativeComputeObjf::PrintTotalStats().

                                                             {
     if (criterion == "mmi") return (tot_num_objf - tot_objf);
     return tot_objf;
   }