#include <discriminative-training.h>

Collaboration diagram for DiscriminativeOptions:

Public Member Functions
	DiscriminativeOptions ()

void	Register (OptionsItf *opts)

Public Attributes
std::string	criterion

BaseFloat	acoustic_scale

bool	drop_frames

bool	one_silence_class

BaseFloat	boost

std::string	silence_phones_str

BaseFloat	xent_regularize

BaseFloat	l2_regularize

bool	accumulate_gradients

bool	accumulate_output

int32	num_pdfs

Detailed Description

Definition at line 48 of file discriminative-training.h.

Constructor & Destructor Documentation

◆ DiscriminativeOptions()

DiscriminativeOptions ( )

inline

Definition at line 83 of file discriminative-training.h.

                          : criterion("smbr"), 
                            acoustic_scale(0.1),
                            drop_frames(false),
                            one_silence_class(false),
                            boost(0.0), 
                            xent_regularize(0.0), 
                            l2_regularize(0.0),
                            accumulate_gradients(false), 
                            accumulate_output(false),
                            num_pdfs(0) { }

Member Function Documentation

◆ Register()

void Register ( OptionsItf * opts )

inline

Definition at line 94 of file discriminative-training.h.

References OptionsItf::Register().

Referenced by main(), and NnetDiscriminativeOptions::Register().

                                   {
     opts->Register("criterion", &criterion, "Criterion, 'mmi'|'mpfe'|'smbr', "
                    "determines the objective function to use.  Should match "
                    "option used when we created the examples.");
     opts->Register("acoustic-scale", &acoustic_scale, "Weighting factor to "
                    "apply to acoustic likelihoods.");
     opts->Register("drop-frames", &drop_frames, "For MMI, if true we drop frames "
                    "with no overlap of num and den pdf-ids");
     opts->Register("boost", &boost, "Boosting factor for boosted MMI (e.g. 0.1)");
     opts->Register("one-silence-class", &one_silence_class, "If true, newer "
                    "behavior which will tend to reduce insertions "
                    "when using MPFE or SMBR objective");
     opts->Register("silence-phones", &silence_phones_str,
                    "For MPFE or SMBR objectives, colon-separated list of "
                    "integer ids of silence phones, e.g. 1:2:3");
     opts->Register("l2-regularize", &l2_regularize, "l2 regularization "
                    "constant for 'chain' output "
                    "of the neural net.");
     opts->Register("xent-regularize", &xent_regularize, "Cross-entropy "
                    "regularization constant for sequence training.  If "
                    "nonzero, the network is expected to have an output "
                    "named 'output-xent', which should have a softmax as "
                    "its final nonlinearity.");
     opts->Register("accumulate-gradients", &accumulate_gradients,
                    "Accumulate gradients wrt nnet output "
                    "for debugging discriminative training");
     opts->Register("accumulate-output", &accumulate_output,
                    "Accumulate nnet output "
                    "for debugging discriminative training");
     opts->Register("num-pdfs", &num_pdfs,
                    "Number of pdfs; "
                    "applicable when accumulate-output or accumulate-gradients "
                    "is true for discriminative training");
   }