#include <nnet-training.h>

Collaboration diagram for NnetTrainerOptions:

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Public Member Functions
	NnetTrainerOptions ()

void	Register (OptionsItf *opts)

Detailed Description

Definition at line 34 of file nnet-training.h.

Constructor & Destructor Documentation

◆ NnetTrainerOptions()

NnetTrainerOptions ( )

inline

Definition at line 51 of file nnet-training.h.

                       :
       zero_component_stats(true),
       store_component_stats(true),
       print_interval(100),
       debug_computation(false),
       momentum(0.0),
       l2_regularize_factor(1.0),
       backstitch_training_scale(0.0),
       backstitch_training_interval(1),
       batchnorm_stats_scale(0.8),
       binary_write_cache(true),
       max_param_change(2.0) { }

Member Function Documentation

◆ Register()

void Register ( OptionsItf * opts )

inline

Definition at line 63 of file nnet-training.h.

References OptionsItf::Register(), NnetComputeOptions::Register(), NnetOptimizeOptions::Register(), and CachingOptimizingCompilerOptions::Register().

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

                                   {
     opts->Register("store-component-stats", &store_component_stats,
                    "If true, store activations and derivatives for nonlinear "
                    "components during training.");
     opts->Register("zero-component-stats", &zero_component_stats,
                    "If both this and --store-component-stats are true, then "
                    "the component stats are zeroed before training.");
     opts->Register("print-interval", &print_interval, "Interval (measured in "
                    "minibatches) after which we print out objective function "
                    "during training\n");
     opts->Register("max-param-change", &max_param_change, "The maximum change in "
                    "parameters allowed per minibatch, measured in Euclidean norm "
                    "over the entire model (change will be clipped to this value)");
     opts->Register("momentum", &momentum, "Momentum constant to apply during "
                    "training (help stabilize update).  e.g. 0.9.  Note: we "
                    "automatically multiply the learning rate by (1-momenum) "
                    "so that the 'effective' learning rate is the same as "
                    "before (because momentum would normally increase the "
                    "effective learning rate by 1/(1-momentum))");
     opts->Register("l2-regularize-factor", &l2_regularize_factor, "Factor that "
                    "affects the strength of l2 regularization on model "
                    "parameters.  The primary way to specify this type of "
                    "l2 regularization is via the 'l2-regularize'"
                    "configuration value at the config-file level. "
                    " --l2-regularize-factor will be multiplied by the component-level "
                    "l2-regularize values and can be used to correct for effects "
                    "related to parallelization by model averaging.");
     opts->Register("batchnorm-stats-scale", &batchnorm_stats_scale,
                    "Factor by which we scale down the accumulated stats of batchnorm "
                    "layers after processing each minibatch.  Ensure that the final "
                    "model we write out has batchnorm stats that are fairly fresh.");
     opts->Register("backstitch-training-scale", &backstitch_training_scale,
                    "backstitch training factor. "
                    "if 0 then in the normal training mode. It is referred as "
                    "'\\alpha' in our publications.");
     opts->Register("backstitch-training-interval",
                    &backstitch_training_interval,
                    "do backstitch training with the specified interval of "
                    "minibatches. It is referred as 'n' in our publications.");
     opts->Register("read-cache", &read_cache, "The location from which to read "
                    "the cached computation.");
     opts->Register("write-cache", &write_cache, "The location to which to write "
                    "the cached computation.");
     opts->Register("binary-write-cache", &binary_write_cache, "Write "
                    "computation cache in binary mode");
 
     // register the optimization options with the prefix "optimization".
     ParseOptions optimization_opts("optimization", opts);
     optimize_config.Register(&optimization_opts);
     ParseOptions compiler_opts("compiler", opts);
     compiler_config.Register(&compiler_opts);
     // register the compute options with the prefix "computation".
     ParseOptions compute_opts("computation", opts);
     compute_config.Register(&compute_opts);
   }

Member Data Documentation

◆ backstitch_training_interval

int32 backstitch_training_interval

Definition at line 42 of file nnet-training.h.

Referenced by NnetChainTrainer::NnetChainTrainer(), NnetTrainer::NnetTrainer(), NnetChainTrainer::Train(), and NnetTrainer::Train().

◆ backstitch_training_scale

BaseFloat backstitch_training_scale

Definition at line 41 of file nnet-training.h.

Referenced by NnetChainTrainer::Train(), NnetTrainer::Train(), NnetChainTrainer::TrainInternalBackstitch(), and NnetTrainer::TrainInternalBackstitch().

◆ batchnorm_stats_scale

BaseFloat batchnorm_stats_scale

Definition at line 43 of file nnet-training.h.

Referenced by NnetChainTrainer::TrainInternal(), NnetTrainer::TrainInternal(), NnetChainTrainer::TrainInternalBackstitch(), and NnetTrainer::TrainInternalBackstitch().

◆ binary_write_cache

bool binary_write_cache

Definition at line 46 of file nnet-training.h.

Referenced by NnetChainTrainer::~NnetChainTrainer(), NnetDiscriminativeTrainer::~NnetDiscriminativeTrainer(), and NnetTrainer::~NnetTrainer().

◆ compiler_config

CachingOptimizingCompilerOptions compiler_config

Definition at line 50 of file nnet-training.h.

◆ compute_config

NnetComputeOptions compute_config

Definition at line 49 of file nnet-training.h.

Referenced by NnetDiscriminativeTrainer::Train(), NnetChainTrainer::TrainInternal(), NnetTrainer::TrainInternal(), NnetChainTrainer::TrainInternalBackstitch(), and NnetTrainer::TrainInternalBackstitch().

◆ debug_computation

bool debug_computation

Definition at line 38 of file nnet-training.h.

◆ l2_regularize_factor

BaseFloat l2_regularize_factor

Definition at line 40 of file nnet-training.h.

Referenced by NnetChainTrainer::TrainInternal(), NnetTrainer::TrainInternal(), NnetChainTrainer::TrainInternalBackstitch(), and NnetTrainer::TrainInternalBackstitch().

◆ max_param_change

BaseFloat max_param_change

Definition at line 47 of file nnet-training.h.

Referenced by NnetChainTrainer::NnetChainTrainer(), NnetDiscriminativeTrainer::NnetDiscriminativeTrainer(), NnetTrainer::NnetTrainer(), NnetDiscriminativeTrainer::Train(), NnetChainTrainer::TrainInternal(), NnetTrainer::TrainInternal(), NnetChainTrainer::TrainInternalBackstitch(), and NnetTrainer::TrainInternalBackstitch().

◆ momentum

BaseFloat momentum

Definition at line 39 of file nnet-training.h.

Referenced by NnetChainTrainer::NnetChainTrainer(), NnetDiscriminativeTrainer::NnetDiscriminativeTrainer(), NnetTrainer::NnetTrainer(), NnetChainTrainer::Train(), NnetDiscriminativeTrainer::Train(), NnetTrainer::Train(), NnetChainTrainer::TrainInternal(), and NnetTrainer::TrainInternal().

◆ optimize_config

NnetOptimizeOptions optimize_config

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

◆ print_interval

int32 print_interval

Definition at line 37 of file nnet-training.h.

Referenced by NnetChainTrainer::ProcessOutputs(), NnetDiscriminativeTrainer::ProcessOutputs(), and NnetTrainer::ProcessOutputs().

◆ read_cache

std::string read_cache

Definition at line 44 of file nnet-training.h.

Referenced by NnetChainTrainer::NnetChainTrainer(), NnetDiscriminativeTrainer::NnetDiscriminativeTrainer(), and NnetTrainer::NnetTrainer().

◆ store_component_stats

bool store_component_stats

Definition at line 36 of file nnet-training.h.

Referenced by NnetChainTrainer::Train(), NnetDiscriminativeTrainer::Train(), and NnetTrainer::Train().

◆ write_cache

std::string write_cache

Definition at line 45 of file nnet-training.h.