// nnet3bin/nnet3-discriminative-train.cc
  
  // Copyright 2015  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 "nnet3/nnet-discriminative-training.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 =
          "Train nnet3 neural network parameters with discriminative sequence objective 
  "
          "gradient descent.  Minibatches are to be created by nnet3-discriminative-merge-egs in
  "
          "the input pipeline.  This training program is single-threaded (best to
  "
          "use it with a GPU).
  "
          "
  "
          "Usage:  nnet3-discriminative-train [options] <nnet-in> <discriminative-training-examples-in> <raw-nnet-out>
  "
          "
  "
          "nnet3-discriminative-train 1.mdl 'ark:nnet3-merge-egs 1.degs ark:-|' 2.raw
  ";
  
      bool binary_write = true;
      std::string use_gpu = "yes";
      bool dropout_test_mode = true;
      
      NnetDiscriminativeOptions opts;
  
      ParseOptions po(usage);
      po.Register("binary", &binary_write, "Write output in binary mode");
      po.Register("use-gpu", &use_gpu,
                  "yes|no|optional|wait, only has effect if compiled with CUDA");
      po.Register("dropout-test-mode", &dropout_test_mode,
                  "If true, set test-mode to true on any DropoutComponents and "
                  "DropoutMaskComponents.");
  
      opts.Register(&po);
  
      po.Read(argc, argv);
  
      if (po.NumArgs() != 3) {
        po.PrintUsage();
        exit(1);
      }
      
  #if HAVE_CUDA==1
      CuDevice::Instantiate().SelectGpuId(use_gpu);
  #endif
  
      std::string model_rxfilename = po.GetArg(1),
          examples_rspecifier = po.GetArg(2),
          model_wxfilename = po.GetArg(3);
  
      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 (dropout_test_mode)
        SetDropoutTestMode(true, &nnet);
      
      const VectorBase<BaseFloat> &priors = am_nnet.Priors();
  
      NnetDiscriminativeTrainer trainer(opts, tmodel, priors, &nnet);
  
      SequentialNnetDiscriminativeExampleReader example_reader(examples_rspecifier);
  
      for (; !example_reader.Done(); example_reader.Next())
        trainer.Train(example_reader.Value());
  
      bool ok = trainer.PrintTotalStats();
  
  #if HAVE_CUDA==1
      CuDevice::Instantiate().PrintProfile();
  #endif
      Output ko(model_wxfilename, binary_write);
      nnet.Write(ko.Stream(), binary_write);
      
      KALDI_LOG << "Wrote raw nnet model to " << model_wxfilename;
      return (ok ? 0 : 1);
    } catch(const std::exception &e) {
      std::cerr << e.what() << '
  ';
      return -1;
    }
  }