shrink-nnet.h
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// nnet2/shrink-nnet.h
// Copyright 2012 Johns Hopkins University (author: Daniel Povey)
// 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_NNET2_SHRINK_NNET_H_
#define KALDI_NNET2_SHRINK_NNET_H_
#include "nnet2/nnet-update.h"
#include "nnet2/nnet-compute.h"
#include "itf/options-itf.h"
namespace kaldi {
namespace nnet2 {
/** Configuration class that controls neural net "shrinkage" which is actually a
scaling on the parameters of each of the updatable layers.
*/
struct NnetShrinkConfig {
int32 num_bfgs_iters; // The dimension is small (e.g. 3 to 5) so we do
// BFGS. We actually implement this as L-BFGS but setting the number of
// vectors to be the same as the dimension of the space. Note: this
// num-iters is in reality the number of function evaluations.
BaseFloat initial_step;
NnetShrinkConfig(): num_bfgs_iters(10), initial_step(0.1) { }
void Register(OptionsItf *opts) {
opts->Register("num-bfgs-iters", &num_bfgs_iters, "Number of iterations of "
"BFGS to use when optimizing shrinkage parameters");
opts->Register("initial-step", &initial_step, "Parameter in the optimization, "
"used to set the initial step length");
}
};
void ShrinkNnet(const NnetShrinkConfig &shrink_config,
const std::vector<NnetExample> &validation_set,
Nnet *nnet);
} // namespace nnet2
} // namespace kaldi
#endif