rescale-nnet.h
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// nnet2/rescale-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_RESCALE_NNET_H_
#define KALDI_NNET2_RESCALE_NNET_H_
#include "nnet2/nnet-update.h"
#include "nnet2/nnet-compute.h"
#include "itf/options-itf.h"
// Neural net rescaling is a rescaling of the parameters of the various layers
// of a neural net, done so as to match certain specified statistics on the
// average derivative of the sigmoid, measured on sample data. This relates to
// how "saturated" the sigmoid is.
namespace kaldi {
namespace nnet2 {
struct NnetRescaleConfig {
BaseFloat target_avg_deriv;
BaseFloat target_first_layer_avg_deriv;
BaseFloat target_last_layer_avg_deriv;
// These are relatively unimportant; for now they have no
// command line options.
BaseFloat num_iters;
BaseFloat delta;
BaseFloat max_change; // maximum change on any one iteration (to
// ensure stability).
BaseFloat min_change; // minimum change on any one iteration (controls
// termination
NnetRescaleConfig(): target_avg_deriv(0.2),
target_first_layer_avg_deriv(0.3),
target_last_layer_avg_deriv(0.1),
num_iters(10),
delta(0.01),
max_change(0.2), min_change(1.0e-05) { }
void Register(OptionsItf *opts) {
opts->Register("target-avg-deriv", &target_avg_deriv, "Target average derivative "
"for hidden layers that are the not the first or last hidden layer "
"(as fraction of maximum derivative of the nonlinearity)");
opts->Register("target-first-layer-avg-deriv", &target_first_layer_avg_deriv,
"Target average derivative for the first hidden layer"
"(as fraction of maximum derivative of the nonlinearity)");
opts->Register("target-last-layer-avg-deriv", &target_last_layer_avg_deriv,
"Target average derivative for the last hidden layer, if "
"#hid-layers > 1"
"(as fraction of maximum derivative of the nonlinearity)");
}
};
void RescaleNnet(const NnetRescaleConfig &rescale_config,
const std::vector<NnetExample> &examples,
Nnet *nnet);
} // namespace nnet2
} // namespace kaldi
#endif