#!/bin/bash
  
  # THIS SCRIPT IS DEPRECATED, see ../train_raw_dnn.py
  
  # note, TDNN is the same as what we used to call multisplice.
  # THIS SCRIPT IS DEPRECATED, see ../train_raw_dnn.py
  
  # Copyright 2012-2015  Johns Hopkins University (Author: Daniel Povey).
  #           2013  Xiaohui Zhang
  #           2013  Guoguo Chen
  #           2014-2016  Vimal Manohar
  #           2014  Vijayaditya Peddinti
  # Apache 2.0.
  
  
  # Begin configuration section.
  cmd=run.pl
  num_epochs=15      # Number of epochs of training;
                     # the number of iterations is worked out from this.
  initial_effective_lrate=0.01
  final_effective_lrate=0.001
  rand_prune=4.0 # Relates to a speedup we do for LDA.
  minibatch_size=512  # This default is suitable for GPU-based training.
                      # Set it to 128 for multi-threaded CPU-based training.
  max_param_change=2.0  # max param change per minibatch
  samples_per_iter=400000 # each iteration of training, see this many samples
                          # per job.  This option is passed to get_egs.sh
  num_jobs_initial=1  # Number of neural net jobs to run in parallel at the start of training
  num_jobs_final=8   # Number of neural net jobs to run in parallel at the end of training
  prior_subset_size=20000 # 20k samples per job, for computing priors.
  num_jobs_compute_prior=10 # these are single-threaded, run on CPU.
  get_egs_stage=0    # can be used for rerunning after partial
  online_ivector_dir=
  remove_egs=true  # set to false to disable removing egs after training is done.
  
  max_models_combine=20 # The "max_models_combine" is the maximum number of models we give
    # to the final 'combine' stage, but these models will themselves be averages of
    # iteration-number ranges.
  
  shuffle_buffer_size=5000 # This "buffer_size" variable controls randomization of the samples
                  # on each iter.  You could set it to 0 or to a large value for complete
                  # randomization, but this would both consume memory and cause spikes in
                  # disk I/O.  Smaller is easier on disk and memory but less random.  It's
                  # not a huge deal though, as samples are anyway randomized right at the start.
                  # (the point of this is to get data in different minibatches on different iterations,
                  # since in the preconditioning method, 2 samples in the same minibatch can
                  # affect each others' gradients.
  
  add_layers_period=2 # by default, add new layers every 2 iterations.
  stage=-6
  exit_stage=-100 # you can set this to terminate the training early.  Exits before running this stage
  
  chunk_training=false  # if true training is done with chunk randomization, rather than frame randomization
  
  randprune=4.0 # speeds up LDA.
  use_gpu=true    # if true, we run on GPU.
  cleanup=true
  egs_dir=
  configs_dir=
  max_lda_jobs=10  # use no more than 10 jobs for the LDA accumulation.
  lda_opts=
  egs_opts=
  transform_dir=     # If supplied, this dir used instead of alidir to find transforms.
  cmvn_opts=  # will be passed to get_lda.sh and get_egs.sh, if supplied.
  frames_per_eg=8 # to be passed on to get_egs.sh
  
  # Raw nnet training options i.e. without transition model
  nj=4
  dense_targets=true        # Use dense targets instead of sparse targets
  
  # End configuration section.
  
  trap 'for pid in $(jobs -pr); do kill -KILL $pid; done' INT QUIT TERM
  
  echo "$0: THIS SCRIPT IS DEPRECATED"
  echo "$0 $@"  # Print the command line for logging
  
  if [ -f path.sh ]; then . ./path.sh; fi
  . parse_options.sh || exit 1;
  
  if [ $# != 3 ]; then
    echo "$0: THIS SCRIPT IS DEPRECATED, see ../train_raw_dnn.py"
    echo "Usage: $0 [opts] <data> <targets-scp> <exp-dir>"
    echo " e.g.: $0 data/train scp:snr_targets/targets.scp exp/nnet3_snr_predictor"
    echo ""
    echo "Main options (for others, see top of script file)"
    echo "  --config <config-file>                           # config file containing options"
    echo "  --cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs."
    echo "  --num-epochs <#epochs|15>                        # Number of epochs of training"
    echo "  --initial-effective-lrate <lrate|0.02> # effective learning rate at start of training."
    echo "  --final-effective-lrate <lrate|0.004>   # effective learning rate at end of training."
    echo "                                                   # data, 0.00025 for large data"
    echo "  --num-hidden-layers <#hidden-layers|2>           # Number of hidden layers, e.g. 2 for 3 hours of data, 4 for 100hrs"
    echo "  --add-layers-period <#iters|2>                   # Number of iterations between adding hidden layers"
    echo "  --num-jobs-initial <num-jobs|1>                  # Number of parallel jobs to use for neural net training, at the start."
    echo "  --num-jobs-final <num-jobs|8>                    # Number of parallel jobs to use for neural net training, at the end"
    echo "  --num-threads <num-threads|16>                   # Number of parallel threads per job, for CPU-based training (will affect"
    echo "                                                   # results as well as speed; may interact with batch size; if you increase"
    echo "                                                   # this, you may want to decrease the batch size."
    echo "  --parallel-opts <opts|\"--num-threads 16 --mem 1G\">      # extra options to pass to e.g. queue.pl for processes that"
    echo "                                                   # use multiple threads... note, you might have to reduce --mem"
    echo "                                                   # versus your defaults, because it gets multiplied by the --num-threads argument."
    echo "  --minibatch-size <minibatch-size|128>            # Size of minibatch to process (note: product with --num-threads"
    echo "                                                   # should not get too large, e.g. >2k)."
    echo "  --samples-per-iter <#samples|400000>             # Number of samples of data to process per iteration, per"
    echo "                                                   # process."
    echo "  --splice-indexes <string|layer0/-4:-3:-2:-1:0:1:2:3:4> "
    echo "                                                   # Frame indices used for each splice layer."
    echo "                                                   # Format : layer<hidden_layer_index>/<frame_indices>....layer<hidden_layer>/<frame_indices> "
    echo "                                                   # (note: we splice processed, typically 40-dimensional frames"
    echo "  --lda-dim <dim|''>                               # Dimension to reduce spliced features to with LDA"
    echo "  --stage <stage|-4>                               # Used to run a partially-completed training process from somewhere in"
    echo "                                                   # the middle."
  
  
    exit 1;
  fi
  
  data=$1
  targets_scp=$2
  dir=$3
  
  # Check some files.
  for f in $data/feats.scp $targets_scp; do
    [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
  done
  
  # in this dir we'll have just one job.
  sdata=$data/split$nj
  utils/split_data.sh $data $nj
  
  mkdir -p $dir/log
  echo $nj > $dir/num_jobs
  
  
  # First work out the feature and iVector dimension, needed for tdnn config creation.
  feat_dim=$(feat-to-dim --print-args=false scp:$data/feats.scp -) || \
        { echo "$0: Error getting feature dim"; exit 1; }
  
  if [ -z "$online_ivector_dir" ]; then
    ivector_dim=0
  else
    ivector_dim=$(feat-to-dim scp:$online_ivector_dir/ivector_online.scp -) || exit 1;
    steps/nnet2/get_ivector_id.sh $online_ivector_dir > $dir/final.ie.id || exit 1
  fi
  
  if [ ! -z "$configs_dir" ]; then
    cp -rT $configs_dir $dir/configs || exit 1
  fi
  
  if [ $stage -le -5 ]; then
    # Initialize as "raw" nnet, prior to training the LDA-like preconditioning
    # matrix.  This first config just does any initial splicing that we do;
    # we do this as it's a convenient way to get the stats for the 'lda-like'
    # transform.
    $cmd $dir/log/nnet_init.log \
      nnet3-init --srand=-2 $dir/configs/init.config $dir/init.raw || exit 1;
  fi
  
  # sourcing the "vars" below sets
  # model_left_context=(something)
  # model_right_context=(something)
  # num_hidden_layers=(something)
  # num_targets=(something)
  # add_lda=(true|false)
  # include_log_softmax=(true|false)
  # objective_type=(something)
  . $dir/configs/vars || exit 1;
  left_context=$model_left_context
  right_context=$model_right_context
  
  [ -z "$num_targets" ] && echo "\$num_targets is not defined. Needs to be defined in $dir/configs/vars." && exit 1
  [ -z "$add_lda" ] && echo "\$add_lda is not defined. Needs to be defined in $dir/configs/vars." && exit 1
  [ -z "$include_log_softmax" ] && echo "\$include_log_softmax is not defined. Needs to be defined in $dir/configs/vars." && exit 1
  [ -z "$objective_type" ] && echo "\$objective_type is not defined. Needs to be defined in $dir/configs/vars." && exit 1
  
  context_opts="--left-context=$left_context --right-context=$right_context"
  
  ! [ "$num_hidden_layers" -gt 0 ] && echo \
   "$0: Expected num_hidden_layers to be defined" && exit 1;
  
  if $dense_targets; then
    tmp_num_targets=`feat-to-dim scp:$targets_scp - 2>/dev/null` || exit 1
  
    if [ $tmp_num_targets -ne $num_targets ]; then
      echo "Mismatch between num-targets provided to script vs configs"
      exit 1
    fi
  fi
  
  if [ $stage -le -4 ] && [ -z "$egs_dir" ]; then
    extra_opts=()
    [ ! -z "$cmvn_opts" ] && extra_opts+=(--cmvn-opts "$cmvn_opts")
    [ ! -z "$online_ivector_dir" ] && extra_opts+=(--online-ivector-dir $online_ivector_dir)
    extra_opts+=(--transform-dir "$transform_dir")
    extra_opts+=(--left-context $left_context)
    extra_opts+=(--right-context $right_context)
    echo "$0: calling get_egs.sh"
  
    if $dense_targets; then
      target_type=dense
    else
      target_type=sparse
    fi
  
    steps/nnet3/get_egs_targets.sh $egs_opts "${extra_opts[@]}" \
      --samples-per-iter $samples_per_iter --stage $get_egs_stage \
      --cmd "$cmd" --nj $nj \
      --frames-per-eg $frames_per_eg \
      --target-type $target_type --num-targets $num_targets \
      $data $targets_scp $dir/egs || exit 1;
  fi
  
  [ -z $egs_dir ] && egs_dir=$dir/egs
  
  if [ ! -z "$online_ivector_dir" ] ; then
    steps/nnet2/check_ivectors_compatible.sh $online_ivector_dir $egs_dir/info || exit 1
  fi
  
  
  if [ "$feat_dim" != "$(cat $egs_dir/info/feat_dim)" ]; then
    echo "$0: feature dimension mismatch with egs, $feat_dim vs $(cat $egs_dir/info/feat_dim)";
    exit 1;
  fi
  if [ "$ivector_dim" != "$(cat $egs_dir/info/ivector_dim)" ]; then
    echo "$0: ivector dimension mismatch with egs, $ivector_dim vs $(cat $egs_dir/info/ivector_dim)";
    exit 1;
  fi
  
  # copy any of the following that exist, to $dir.
  cp $egs_dir/{cmvn_opts,splice_opts,final.mat} $dir 2>/dev/null
  
  # confirm that the egs_dir has the necessary context (especially important if
  # the --egs-dir option was used on the command line).
  egs_left_context=$(cat $egs_dir/info/left_context) || exit -1
  egs_right_context=$(cat $egs_dir/info/right_context) || exit -1
   ( [ $egs_left_context -lt $left_context ] || \
     [ $egs_right_context -lt $right_context ] ) && \
     echo "$0: egs in $egs_dir have too little context" && exit -1;
  
  frames_per_eg=$(cat $egs_dir/info/frames_per_eg) || { echo "error: no such file $egs_dir/info/frames_per_eg"; exit 1; }
  num_archives=$(cat $egs_dir/info/num_archives) || { echo "error: no such file $egs_dir/info/frames_per_eg"; exit 1; }
  
  # num_archives_expanded considers each separate label-position from
  # 0..frames_per_eg-1 to be a separate archive.
  if [ "$chunk_training" == "true" ]; then
    num_archives_expanded=$num_archives
  else
    num_archives_expanded=$[$num_archives*$frames_per_eg]
  fi
  
  [ $num_jobs_initial -gt $num_jobs_final ] && \
    echo "$0: --initial-num-jobs cannot exceed --final-num-jobs" && exit 1;
  
  [ $num_jobs_final -gt $num_archives_expanded ] && \
    echo "$0: --final-num-jobs cannot exceed #archives $num_archives_expanded." && exit 1;
  
  
  if $add_lda && [ $stage -le -3 ]; then
    echo "$0: getting preconditioning matrix for input features."
    num_lda_jobs=$num_archives
    [ $num_lda_jobs -gt $max_lda_jobs ] && num_lda_jobs=$max_lda_jobs
  
    # Write stats with the same format as stats for LDA.
    $cmd JOB=1:$num_lda_jobs $dir/log/get_lda_stats.JOB.log \
        nnet3-acc-lda-stats --rand-prune=$rand_prune \
          $dir/init.raw "ark:$egs_dir/egs.JOB.ark" $dir/JOB.lda_stats || exit 1;
  
    all_lda_accs=$(for n in $(seq $num_lda_jobs); do echo $dir/$n.lda_stats; done)
    $cmd $dir/log/sum_transform_stats.log \
      sum-lda-accs $dir/lda_stats $all_lda_accs || exit 1;
  
    rm $all_lda_accs || exit 1;
  
    # this computes a fixed affine transform computed in the way we described in
    # Appendix C.6 of http://arxiv.org/pdf/1410.7455v6.pdf; it's a scaled variant
    # of an LDA transform but without dimensionality reduction.
    $cmd $dir/log/get_transform.log \
       nnet-get-feature-transform $lda_opts $dir/lda.mat $dir/lda_stats || exit 1;
  
    ln -sf ../lda.mat $dir/configs/lda.mat
  fi
  
  
  if [ $stage -le -1 ]; then
    # Add the first layer; this will add in the lda.mat
    $cmd $dir/log/add_first_layer.log \
         nnet3-init --srand=-3 $dir/init.raw $dir/configs/layer1.config $dir/0.raw || exit 1;
  
  fi
  
  
  # set num_iters so that as close as possible, we process the data $num_epochs
  # times, i.e. $num_iters*$avg_num_jobs) == $num_epochs*$num_archives_expanded,
  # where avg_num_jobs=(num_jobs_initial+num_jobs_final)/2.
  
  num_archives_to_process=$[$num_epochs*$num_archives_expanded]
  num_archives_processed=0
  num_iters=$[($num_archives_to_process*2)/($num_jobs_initial+$num_jobs_final)]
  
  finish_add_layers_iter=$[$num_hidden_layers * $add_layers_period]
  
  ! [ $num_iters -gt $[$finish_add_layers_iter+2] ] \
    && echo "$0: Insufficient epochs" && exit 1
  
  echo "$0: Will train for $num_epochs epochs = $num_iters iterations"
  
  if $use_gpu; then
    parallel_suffix=""
    train_queue_opt="--gpu 1"
    combine_queue_opt="--gpu 1"
    prior_gpu_opt="--use-gpu=yes"
    prior_queue_opt="--gpu 1"
    parallel_train_opts=
    if ! cuda-compiled; then
      echo "$0: WARNING: you are running with one thread but you have not compiled"
      echo "   for CUDA.  You may be running a setup optimized for GPUs.  If you have"
      echo "   GPUs and have nvcc installed, go to src/ and do ./configure; make"
      exit 1
    fi
  else
    echo "$0: without using a GPU this will be very slow.  nnet3 does not yet support multiple threads."
    parallel_train_opts="--use-gpu=no"
    combine_queue_opt=""  # the combine stage will be quite slow if not using
                          # GPU, as we didn't enable that program to use
                          # multiple threads.
    prior_gpu_opt="--use-gpu=no"
    prior_queue_opt=""
  fi
  
  
  approx_iters_per_epoch_final=$[$num_archives_expanded/$num_jobs_final]
  # First work out how many iterations we want to combine over in the final
  # nnet3-combine-fast invocation.  (We may end up subsampling from these if the
  # number exceeds max_model_combine).  The number we use is:
  # min(max(max_models_combine, approx_iters_per_epoch_final),
  #     1/2 * iters_after_last_layer_added)
  num_iters_combine=$max_models_combine
  if [ $num_iters_combine -lt $approx_iters_per_epoch_final ]; then
     num_iters_combine=$approx_iters_per_epoch_final
  fi
  half_iters_after_add_layers=$[($num_iters-$finish_add_layers_iter)/2]
  if [ $num_iters_combine -gt $half_iters_after_add_layers ]; then
    num_iters_combine=$half_iters_after_add_layers
  fi
  first_model_combine=$[$num_iters-$num_iters_combine+1]
  
  x=0
  
  
  compute_accuracy=false
  if [ "$objective_type" == "linear" ]; then
    compute_accuracy=true
  fi
  
  while [ $x -lt $num_iters ]; do
    [ $x -eq $exit_stage ] && echo "$0: Exiting early due to --exit-stage $exit_stage" && exit 0;
  
    this_num_jobs=$(perl -e "print int(0.5+$num_jobs_initial+($num_jobs_final-$num_jobs_initial)*$x/$num_iters);")
  
    ilr=$initial_effective_lrate; flr=$final_effective_lrate; np=$num_archives_processed; nt=$num_archives_to_process;
    this_learning_rate=$(perl -e "print (($x + 1 >= $num_iters ? $flr : $ilr*exp($np*log($flr/$ilr)/$nt))*$this_num_jobs);");
  
    echo "On iteration $x, learning rate is $this_learning_rate."
  
    if [ $x -ge 0 ] && [ $stage -le $x ]; then
  
      # Set off jobs doing some diagnostics, in the background.
      # Use the egs dir from the previous iteration for the diagnostics
      $cmd $dir/log/compute_prob_valid.$x.log \
        nnet3-compute-prob --compute-accuracy=$compute_accuracy $dir/$x.raw \
        "ark,bg:nnet3-merge-egs ark:$egs_dir/valid_diagnostic.egs ark:- |" &
      $cmd $dir/log/compute_prob_train.$x.log \
        nnet3-compute-prob --compute-accuracy=$compute_accuracy $dir/$x.raw \
        "ark,bg:nnet3-merge-egs ark:$egs_dir/train_diagnostic.egs ark:- |" &
  
      if [ $x -gt 0 ]; then
        $cmd $dir/log/progress.$x.log \
          nnet3-show-progress --use-gpu=no $dir/$[x-1].raw $dir/$x.raw \
          "ark,bg:nnet3-merge-egs ark:$egs_dir/train_diagnostic.egs ark:-|" '&&' \
          nnet3-info $dir/$x.raw &
      fi
  
      echo "Training neural net (pass $x)"
  
      if [ $x -gt 0 ] && \
        [ $x -le $[($num_hidden_layers-1)*$add_layers_period] ] && \
        [ $[$x%$add_layers_period] -eq 0 ]; then
        do_average=false # if we've just mixed up, don't do averaging but take the
                         # best.
        cur_num_hidden_layers=$[1+$x/$add_layers_period]
        config=$dir/configs/layer$cur_num_hidden_layers.config
        raw="nnet3-copy --learning-rate=$this_learning_rate $dir/$x.raw - | nnet3-init --srand=$x - $config - |"
      else
        do_average=true
        if [ $x -eq 0 ]; then do_average=false; fi # on iteration 0, pick the best, don't average.
        raw="nnet3-copy --learning-rate=$this_learning_rate $dir/$x.raw -|"
      fi
      if $do_average; then
        this_minibatch_size=$minibatch_size
      else
        # on iteration zero or when we just added a layer, use a smaller minibatch
        # size (and we will later choose the output of just one of the jobs): the
        # model-averaging isn't always helpful when the model is changing too fast
        # (i.e. it can worsen the objective function), and the smaller minibatch
        # size will help to keep the update stable.
        this_minibatch_size=$[$minibatch_size/2];
      fi
  
      rm $dir/.error 2>/dev/null
  
  
      ( # this sub-shell is so that when we "wait" below,
        # we only wait for the training jobs that we just spawned,
        # not the diagnostic jobs that we spawned above.
  
        # We can't easily use a single parallel SGE job to do the main training,
        # because the computation of which archive and which --frame option
        # to use for each job is a little complex, so we spawn each one separately.
        for n in $(seq $this_num_jobs); do
          k=$[$num_archives_processed + $n - 1]; # k is a zero-based index that we'll derive
                                                 # the other indexes from.
          archive=$[($k%$num_archives)+1]; # work out the 1-based archive index.
          frame=$[(($k/$num_archives)%$frames_per_eg)]; # work out the 0-based frame
          # index; this increases more slowly than the archive index because the
          # same archive with different frame indexes will give similar gradients,
          # so we want to separate them in time.
  
          $cmd $train_queue_opt $dir/log/train.$x.$n.log \
            nnet3-train $parallel_train_opts \
            --max-param-change=$max_param_change "$raw" \
            "ark,bg:nnet3-copy-egs --frame=$frame $context_opts ark:$egs_dir/egs.$archive.ark ark:- | nnet3-shuffle-egs --buffer-size=$shuffle_buffer_size --srand=$x ark:- ark:-| nnet3-merge-egs --minibatch-size=$this_minibatch_size --discard-partial-minibatches=true ark:- ark:- |" \
            $dir/$[$x+1].$n.raw || touch $dir/.error &
        done
        wait
      )
      # the error message below is not that informative, but $cmd will
      # have printed a more specific one.
      [ -f $dir/.error ] && echo "$0: error on iteration $x of training" && exit 1;
  
      nnets_list=
      for n in `seq 1 $this_num_jobs`; do
        nnets_list="$nnets_list $dir/$[$x+1].$n.raw"
      done
  
      if $do_average; then
        # average the output of the different jobs.
        $cmd $dir/log/average.$x.log \
          nnet3-average $nnets_list $dir/$[x+1].raw || exit 1;
      else
        # choose the best from the different jobs.
        n=$(perl -e '($nj,$pat)=@ARGV; $best_n=1; $best_logprob=-1.0e+10; for ($n=1;$n<=$nj;$n++) {
            $fn = sprintf($pat,$n); open(F, "<$fn") || die "Error opening log file $fn";
            undef $logprob; while (<F>) { if (m/log-prob-per-frame=(\S+)/) { $logprob=$1; } }
            close(F); if (defined $logprob && $logprob > $best_logprob) { $best_logprob=$logprob;
            $best_n=$n; } } print "$best_n
  "; ' $this_num_jobs $dir/log/train.$x.%d.log) || exit 1;
        [ -z "$n" ] && echo "Error getting best model" && exit 1;
        $cmd $dir/log/select.$x.log \
          nnet3-copy $dir/$[$x+1].$n.raw $dir/$[$x+1].raw || exit 1;
      fi
  
      rm $nnets_list
      [ ! -f $dir/$[$x+1].raw ] && exit 1;
      if [ -f $dir/$[$x-1].raw ] && $cleanup && \
         [ $[($x-1)%100] -ne 0  ] && [ $[$x-1] -lt $first_model_combine ]; then
        rm $dir/$[$x-1].raw
      fi
    fi
    x=$[$x+1]
    num_archives_processed=$[$num_archives_processed+$this_num_jobs]
  done
  
  if [ $stage -le $num_iters ]; then
    echo "Doing final combination to produce final.raw"
  
    # Now do combination.  In the nnet3 setup, the logic
    # for doing averaging of subsets of the models in the case where
    # there are too many models to reliably esetimate interpolation
    # factors (max_models_combine) is moved into the nnet3-combine
    nnets_list=()
    for n in $(seq 0 $[num_iters_combine-1]); do
      iter=$[$first_model_combine+$n]
      nnet=$dir/$iter.raw
      [ ! -f $nnet ] && echo "Expected $nnet to exist" && exit 1;
      nnets_list[$n]=$nnet
    done
  
    # Below, we use --use-gpu=no to disable nnet3-combine-fast from using a GPU,
    # as if there are many models it can give out-of-memory error; and we set
    # num-threads to 8 to speed it up (this isn't ideal...)
  
    $cmd $combine_queue_opt $dir/log/combine.log \
      nnet3-combine --num-iters=40 \
      --enforce-sum-to-one=true --enforce-positive-weights=true \
      --verbose=3 "${nnets_list[@]}" "ark,bg:nnet3-merge-egs --minibatch-size=1024 ark:$egs_dir/combine.egs ark:-|" \
      $dir/final.raw || exit 1;
  
    # Compute the probability of the final, combined model with
    # the same subset we used for the previous compute_probs, as the
    # different subsets will lead to different probs.
    $cmd $dir/log/compute_prob_valid.final.log \
      nnet3-compute-prob --compute-accuracy=$compute_accuracy $dir/final.raw \
      "ark,bg:nnet3-merge-egs ark:$egs_dir/valid_diagnostic.egs ark:- |" &
    $cmd $dir/log/compute_prob_train.final.log \
      nnet3-compute-prob --compute-accuracy=$compute_accuracy $dir/final.raw \
      "ark,bg:nnet3-merge-egs ark:$egs_dir/train_diagnostic.egs ark:- |" &
  fi
  
  if $include_log_softmax && [ $stage -le $[$num_iters+1] ]; then
    echo "Getting average posterior for purpose of using as prior to convert posteriors to likelihoods."
    # Note: this just uses CPUs, using a smallish subset of data.
    if [ $num_jobs_compute_prior -gt $num_archives ]; then egs_part=1;
    else egs_part=JOB; fi
    rm $dir/post.$x.*.vec 2>/dev/null
    $cmd JOB=1:$num_jobs_compute_prior $prior_queue_opt $dir/log/get_post.$x.JOB.log \
      nnet3-copy-egs --frame=random $context_opts --srand=JOB ark:$egs_dir/egs.$egs_part.ark ark:- \| \
      nnet3-subset-egs --srand=JOB --n=$prior_subset_size ark:- ark:- \| \
      nnet3-merge-egs ark:- ark:- \| \
      nnet3-compute-from-egs $prior_gpu_opt --apply-exp=true \
      $dir/final.raw ark:- ark:- \| \
      matrix-sum-rows ark:- ark:- \| vector-sum ark:- $dir/post.$x.JOB.vec || exit 1;
  
    sleep 3;  # make sure there is time for $dir/post.$x.*.vec to appear.
  
    $cmd $dir/log/vector_sum.$x.log \
     vector-sum $dir/post.$x.*.vec $dir/post.$x.vec || exit 1;
  
    rm -f $dir/post.$x.*.vec;
  
  fi
  
  
  if [ ! -f $dir/final.raw ]; then
    echo "$0: $dir/final.raw does not exist."
    # we don't want to clean up if the training didn't succeed.
    exit 1;
  fi
  
  sleep 2
  
  echo Done
  
  if $cleanup; then
    echo Cleaning up data
    if $remove_egs && [[ $egs_dir =~ $dir/egs* ]]; then
      steps/nnet2/remove_egs.sh $egs_dir
    fi
  
    echo Removing most of the models
    for x in `seq 0 $num_iters`; do
      if [ $[$x%100] -ne 0 ] && [ $x -ne $num_iters ] && [ -f $dir/$x.mdl ]; then
         # delete all but every 100th model; don't delete the ones which combine to form the final model.
        rm $dir/$x.raw
      fi
    done
  fi