nnet-set-learnrate.cc
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// nnetbin/nnet-set-learnrate.cc
// Copyright 2016, Brno University of Technology
// (author: Katerina Zmolikova, Karel Vesely)
// 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.
#include "util/common-utils.h"
#include "nnet/nnet-nnet.h"
#include "nnet/nnet-component.h"
#include "nnet/nnet-affine-transform.h"
#include "nnet/nnet-activation.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet1;
typedef kaldi::int32 int32;
const char *usage =
"Sets learning rate coefficient inside of 'nnet1' model\n"
"Usage: nnet-set-learnrate --components=<csl> --coef=<float> <nnet-in> <nnet-out>\n"
"e.g.: nnet-set-learnrate --components=1:3:5 --coef=0.5 --bias-coef=0.1 nnet-in nnet-out\n";
ParseOptions po(usage);
bool binary = true;
po.Register("binary", &binary, "Write output in binary mode");
std::string components_str = "";
po.Register("components", &components_str,
"Select components by 'csl' of 1..N values. Layout is the same as in "
"'nnet-info' output, (example 1:3:5)");
float coef = 1.0,
weight_coef = 1.0,
bias_coef = 1.0;
po.Register("coef", &coef,
"Learn-rate coefficient for both weight matrices and biases.");
po.Register("weight-coef", &weight_coef,
"Learn-rate coefficient for weight matrices "
"(used as: coef * weight_coef).");
po.Register("bias-coef", &bias_coef,
"Learn-rate coefficient for bias (used as: coef * bias_coef).");
po.Read(argc, argv);
if (po.NumArgs() != 2) {
po.PrintUsage();
exit(1);
}
std::string nnet_in_filename = po.GetArg(1),
nnet_out_filename = po.GetArg(2);
Nnet nnet;
nnet.Read(nnet_in_filename);
// A vector which contains indices of components,
// where we will set the 'learn-rate coefficients',
std::vector<int32> components;
if (components_str != "") {
// components were selected by the option,
kaldi::SplitStringToIntegers(components_str, ":", false, &components);
} else {
// otherwise select all the components (1..Ncomp),
for (int32 i = 1; i <= nnet.NumComponents(); i++) {
components.push_back(i);
}
}
// Setting the learning rate coefficients,
for (int32 i = 0; i < components.size(); i++) {
if (nnet.GetComponent(components[i]-1).IsUpdatable()) {
UpdatableComponent& comp =
dynamic_cast<UpdatableComponent&>(nnet.GetComponent(components[i]-1));
comp.SetLearnRateCoef(coef * weight_coef); // weight matrices, etc.,
comp.SetBiasLearnRateCoef(coef * bias_coef); // biases,
}
}
// Write the 'nnet1' network,
nnet.Write(nnet_out_filename, binary);
return 0;
} catch(const std::exception &e) {
std::cerr << e.what();
return -1;
}
}