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// nnet2bin/nnet-am-info.cc
// 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.
#include "base/kaldi-common.h"
#include "util/common-utils.h"
#include "hmm/transition-model.h"
#include "nnet2/am-nnet.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet2;
typedef kaldi::int32 int32;
const char *usage =
"Print human-readable information about the neural network
"
"acoustic model to the standard output
"
"Usage: nnet-am-info [options] <nnet-in>
"
"e.g.:
"
" nnet-am-info 1.nnet
";
ParseOptions po(usage);
bool print_learning_rates = false;
po.Register("print-learning-rates", &print_learning_rates,
"If true, instead of printing the normal info, print a "
"colon-separated list of the learning rates for each updatable "
"layer, suitable to give to nnet-am-copy as the argument to"
"--learning-rates.");
po.Read(argc, argv);
if (po.NumArgs() != 1) {
po.PrintUsage();
exit(1);
}
std::string nnet_rxfilename = po.GetArg(1);
TransitionModel trans_model;
AmNnet am_nnet;
{
bool binary_read;
Input ki(nnet_rxfilename, &binary_read);
trans_model.Read(ki.Stream(), binary_read);
am_nnet.Read(ki.Stream(), binary_read);
}
if (print_learning_rates) {
Vector<BaseFloat> learning_rates(am_nnet.GetNnet().NumUpdatableComponents());
am_nnet.GetNnet().GetLearningRates(&learning_rates);
int32 nc = learning_rates.Dim();
for (int32 i = 0; i < nc; i++)
std::cout << learning_rates(i) << (i < nc - 1 ? ":" : "");
std::cout << std::endl;
KALDI_LOG << "Printed learning-rate info for " << nnet_rxfilename;
} else {
std::cout << am_nnet.Info();
KALDI_LOG << "Printed info about " << nnet_rxfilename;
}
} catch(const std::exception &e) {
std::cerr << e.what() << '
';
return -1;
}
}
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