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src/nnet3/nnet-discriminative-training.h
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// nnet3/nnet-discriminative-training.h
// Copyright 2012-2015 Johns Hopkins University (author: Daniel Povey)
// 2014-2015 Vimal Manohar
// See ../../COPYING for clarification regarding multiple authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#ifndef KALDI_NNET3_NNET_DISCRIMINATIVE_TRAINING_H_
#define KALDI_NNET3_NNET_DISCRIMINATIVE_TRAINING_H_
#include "nnet3/nnet-example.h"
#include "nnet3/nnet-computation.h"
#include "nnet3/nnet-compute.h"
#include "nnet3/nnet-optimize.h"
#include "nnet3/nnet-discriminative-example.h"
#include "nnet3/nnet-training.h"
#include "nnet3/discriminative-training.h"
namespace kaldi {
namespace nnet3 {
struct NnetDiscriminativeOptions {
NnetTrainerOptions nnet_config;
discriminative::DiscriminativeOptions discriminative_config;
bool apply_deriv_weights;
NnetDiscriminativeOptions(): apply_deriv_weights(true) { }
void Register(OptionsItf *opts) {
nnet_config.Register(opts);
discriminative_config.Register(opts);
opts->Register("apply-deriv-weights", &apply_deriv_weights,
"If true, apply the per-frame derivative weights stored with "
"the example.");
}
};
// This struct is used in multiple nnet training classes for keeping
// track of objective function values.
// Also see struct AccuracyInfo, in nnet-diagnostics.h.
struct DiscriminativeObjectiveFunctionInfo {
int32 current_phase;
discriminative::DiscriminativeObjectiveInfo stats;
discriminative::DiscriminativeObjectiveInfo stats_this_phase;
DiscriminativeObjectiveFunctionInfo():
current_phase(0) { }
// This function updates the stats and, if the phase has just changed,
// prints a message indicating progress. The phase equals
// minibatch_counter / minibatches_per_phase. Its only function is to
// control how frequently we print logging messages.
void UpdateStats(const std::string &output_name,
const std::string &criterion,
int32 minibatches_per_phase,
int32 minibatch_counter,
discriminative::DiscriminativeObjectiveInfo stats);
// Prints stats for the current phase.
void PrintStatsForThisPhase(const std::string &output_name,
const std::string &criterion,
int32 minibatches_per_phase) const;
// Prints total stats, and returns true if total stats' weight was nonzero.
bool PrintTotalStats(const std::string &output_name,
const std::string &criterion) const;
};
/**
This class is for single-threaded discriminative training of neural nets
*/
class NnetDiscriminativeTrainer {
public:
NnetDiscriminativeTrainer(const NnetDiscriminativeOptions &config,
const TransitionModel &tmodel,
const VectorBase<BaseFloat> &priors,
Nnet *nnet);
// train on one minibatch.
void Train(const NnetDiscriminativeExample &eg);
// Prints out the final stats, and return true if there was a nonzero count.
bool PrintTotalStats() const;
~NnetDiscriminativeTrainer();
private:
void ProcessOutputs(const NnetDiscriminativeExample &eg,
NnetComputer *computer);
const NnetDiscriminativeOptions opts_;
const TransitionModel &tmodel_;
CuVector<BaseFloat> log_priors_;
Nnet *nnet_;
Nnet *delta_nnet_; // Only used if momentum != 0.0. nnet representing
// accumulated parameter-change (we'd call this
// gradient_nnet_, but due to natural-gradient update,
// it's better to consider it as a delta-parameter nnet.
CachingOptimizingCompiler compiler_;
int32 num_minibatches_processed_;
// This code supports multiple output layers, even though in the
// normal case there will be just one output layer named "output".
// So we store the objective functions per output layer.
unordered_map<std::string, DiscriminativeObjectiveFunctionInfo, StringHasher> objf_info_;
};
} // namespace nnet3
} // namespace kaldi
#endif // KALDI_NNET3_NNET_DISCRIMINATIVE_TRAINING_H_
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