nnet-compute-prob.cc File Reference

#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"

Include dependency graph for nnet-compute-prob.cc:

Go to the source code of this file.

Functions
int	main (int argc, char *argv[])

Function Documentation

main()

int main	(	int	argc,
		char *	argv[]
	)

Definition at line 27 of file nnet-compute-prob.cc.

References kaldi::nnet2::ComputeNnetObjf(), SequentialTableReader< Holder >::Done(), ParseOptions::GetArg(), AmNnet::GetNnet(), KALDI_LOG, SequentialTableReader< Holder >::Next(), ParseOptions::NumArgs(), ParseOptions::PrintUsage(), AmNnet::Read(), ParseOptions::Read(), TransitionModel::Read(), Input::Stream(), kaldi::nnet2::TotalNnetTrainingWeight(), and SequentialTableReader< Holder >::Value().

                                 {
  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\n"
        "neural net.  The input of this is the output of e.g. nnet-get-egs\n"
        "Aside from the logging output, which goes to the standard error, this program\n"
        "prints the average log-prob per frame to the standard output.\n"
        "Also see nnet-logprob, which produces a matrix of log-probs for each utterance.\n"
        "\n"
        "Usage:  nnet-compute-prob [options] <model-in> <training-examples-in>\n"
        "e.g.: nnet-compute-prob 1.nnet ark:valid.egs\n";
    
    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) << "\n";
    return (num_examples == 0 ? 1 : 0);
  } catch(const std::exception &e) {
    std::cerr << e.what() << '\n';
    return -1;
  }
}