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src/rnnlmbin/rnnlm-get-word-embedding.cc
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// rnnlmbin/rnnlm-get-word-embedding.cc
// Copyright 2015-2017 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 "rnnlm/rnnlm-training.h"
#include "rnnlm/rnnlm-example-utils.h"
#include "nnet3/nnet-utils.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::rnnlm;
typedef kaldi::int32 int32;
typedef kaldi::int64 int64;
const char *usage =
"This very simple program multiplies a sparse matrix by a
"
"dense matrix compute the word embedding (which is also a dense matrix).
"
"The sparse matrix is in a text format specific to the RNNLM tools.
"
"Usage:
"
" rnnlm-get-word-embedding [options] <sparse-word-features-rxfilename> \\
"
" <feature-embedding-rxfilename> <word-embedding-wxfilename>
"
" e.g.:
"
" rnnlm-get-word-embedding word_features.txt feat_embedding.mat word_embedding.mat
"
"See also: rnnlm-get-egs, rnnlm-train
";
ParseOptions po(usage);
bool binary = true;
po.Register("binary", &binary, "If true, write output in binary format");
po.Read(argc, argv);
if (po.NumArgs() != 3) {
po.PrintUsage();
exit(1);
}
std::string word_features_rxfilename = po.GetArg(1),
feature_embedding_rxfilename = po.GetArg(2),
word_embedding_wxfilename = po.GetArg(3);
Matrix<BaseFloat> feature_embedding_mat;
ReadKaldiObject(feature_embedding_rxfilename,
&feature_embedding_mat);
SparseMatrix<BaseFloat> word_feature_mat;
{
Input input(word_features_rxfilename);
int32 feature_dim = feature_embedding_mat.NumRows();
ReadSparseWordFeatures(input.Stream(), feature_dim,
&word_feature_mat);
}
Matrix<BaseFloat> word_embedding_mat(word_feature_mat.NumRows(),
feature_embedding_mat.NumCols());
word_embedding_mat.AddSmatMat(1.0, word_feature_mat, kNoTrans,
feature_embedding_mat, 0.0);
WriteKaldiObject(word_embedding_mat, word_embedding_wxfilename, binary);
return 0;
} catch(const std::exception &e) {
std::cerr << e.what() << '
';
return -1;
}
}
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