// nnet3bin/nnet3-discriminative-compute-objf.cc
  
  // Copyright 2012-2015  Johns Hopkins University (author: Daniel Povey)
  //           2014-2015  Vimal Manohar
  
  // 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 "nnet3/nnet-discriminative-diagnostics.h"
  #include "nnet3/am-nnet-simple.h"
  #include "nnet3/nnet-utils.h"
  
  int main(int argc, char *argv[]) {
    try {
      using namespace kaldi;
      using namespace kaldi::nnet3;
      typedef kaldi::int32 int32;
      typedef kaldi::int64 int64;
  
      const char *usage =
          "Computes and prints to in logging messages the objective function per frame of
  "
          "the given data with an nnet3 neural net.  The input of this is the output of
  "
          "e.g. nnet3-discriminative-get-egs | nnet3-discriminative-merge-egs.
  "
          "
  "
          "Usage:  nnet3-discrminative-compute-objf [options] <nnet3-model-in> <training-examples-in>
  "
          "e.g.: nnet3-discriminative-compute-objf 0.mdl ark:valid.degs
  ";
  
      bool batchnorm_test_mode = true, dropout_test_mode = true;
  
      // This program doesn't support using a GPU, because these probabilities are
      // used for diagnostics, and you can just compute them with a small enough
      // amount of data that a CPU can do it within reasonable time.
      // It wouldn't be hard to make it support GPU, though.
  
      NnetComputeProbOptions nnet_opts;
      discriminative::DiscriminativeOptions discriminative_opts;
  
      ParseOptions po(usage);
  
      po.Register("batchnorm-test-mode", &batchnorm_test_mode,
                  "If true, set test-mode to true on any BatchNormComponents.");
      po.Register("dropout-test-mode", &dropout_test_mode,
                  "If true, set test-mode to true on any DropoutComponents and "
                  "DropoutMaskComponents.");
  
      nnet_opts.Register(&po);
      discriminative_opts.Register(&po);
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 2) {
        po.PrintUsage();
        exit(1);
      }
  
      std::string model_rxfilename = po.GetArg(1),
          examples_rspecifier = po.GetArg(2);
  
      TransitionModel tmodel;
      AmNnetSimple am_nnet;
  
      {
        bool binary;
        Input ki(model_rxfilename, &binary);
        tmodel.Read(ki.Stream(), binary);
        am_nnet.Read(ki.Stream(), binary);
      }
      
      Nnet* nnet = &(am_nnet.GetNnet());
  
      if (batchnorm_test_mode)
        SetBatchnormTestMode(true, nnet);
  
      if (dropout_test_mode)
        SetDropoutTestMode(true, nnet);
  
      NnetDiscriminativeComputeObjf discriminative_objf_computer(nnet_opts, 
                                                discriminative_opts, 
                                                tmodel, am_nnet.Priors(), 
                                                *nnet);
  
      SequentialNnetDiscriminativeExampleReader example_reader(examples_rspecifier);
  
      for (; !example_reader.Done(); example_reader.Next())
        discriminative_objf_computer.Compute(example_reader.Value());
  
      bool ok = discriminative_objf_computer.PrintTotalStats();
  
      return (ok ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what() << '
  ';
      return -1;
    }
  }