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src/nnet2bin/nnet-compute-prob.cc
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// nnet2bin/nnet-compute-prob.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/train-nnet.h"
#include "nnet2/am-nnet.h"
int main(int argc, char *argv[]) {
try {
using namespace kaldi;
using namespace kaldi::nnet2;
typedef kaldi::int32 int32;
typedef kaldi::int64 int64;
const char *usage =
"Computes and prints the average log-prob per frame of the given data with a
"
"neural net. The input of this is the output of e.g. nnet-get-egs
"
"Aside from the logging output, which goes to the standard error, this program
"
"prints the average log-prob per frame to the standard output.
"
"Also see nnet-logprob, which produces a matrix of log-probs for each utterance.
"
"
"
"Usage: nnet-compute-prob [options] <model-in> <training-examples-in>
"
"e.g.: nnet-compute-prob 1.nnet ark:valid.egs
";
ParseOptions po(usage);
po.Read(argc, argv);
if (po.NumArgs() != 2) {
po.PrintUsage();
exit(1);
}
std::string nnet_rxfilename = po.GetArg(1),
examples_rspecifier = po.GetArg(2);
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);
}
std::vector<NnetExample> examples;
double tot_weight = 0.0, tot_like = 0.0, tot_accuracy = 0.0;
int64 num_examples = 0;
SequentialNnetExampleReader example_reader(examples_rspecifier);
for (; !example_reader.Done(); example_reader.Next(), num_examples++) {
if (examples.size() == 1000) {
double accuracy;
tot_like += ComputeNnetObjf(am_nnet.GetNnet(), examples, &accuracy);
tot_accuracy += accuracy;
tot_weight += TotalNnetTrainingWeight(examples);
examples.clear();
}
examples.push_back(example_reader.Value());
if (num_examples % 5000 == 0 && num_examples > 0)
KALDI_LOG << "Saw " << num_examples << " examples, average "
<< "probability is " << (tot_like / num_examples) << " with "
<< "total weight " << num_examples;
}
if (!examples.empty()) {
double accuracy;
tot_like += ComputeNnetObjf(am_nnet.GetNnet(), examples, &accuracy);
tot_accuracy += accuracy;
tot_weight += TotalNnetTrainingWeight(examples);
}
KALDI_LOG << "Saw " << num_examples << " examples, average "
<< "probability is " << (tot_like / tot_weight)
<< " and accuracy is " << (tot_accuracy / tot_weight) << " with "
<< "total weight " << tot_weight;
std::cout << (tot_like / tot_weight) << "
";
return (num_examples == 0 ? 1 : 0);
} catch(const std::exception &e) {
std::cerr << e.what() << '
';
return -1;
}
}
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