#!/bin/bash
  
  # Copyright 2012  Johns Hopkins University (Author: Daniel Povey).
  #           2013  Xiaohui Zhang
  #           2013  Guoguo Chen
  #           2014  Vimal Manohar
  # Apache 2.0.
  
  
  # This script trains neural network with pnorm nonlinearities.
  # The difference with train_tanh.sh is that, instead of setting
  # hidden_layer_size, you should set pnorm_input_dim and pnorm_output_dim.
  # Also the P value (the order of the p-norm) should be set.
  #
  # [Vimal Manohar - Oct 2014]
  # The script now supports realignment during training, which can be done by
  # specifying realign_epochs.
  
  # Begin configuration section.
  cmd=run.pl
  num_epochs=15      # Number of epochs during which we reduce
                     # the learning rate; number of iteration is worked out from this.
  num_epochs_extra=5 # Number of epochs after we stop reducing
                     # the learning rate.
  num_iters_final=20 # Maximum number of final iterations to give to the
                     # optimization over the validation set.
  initial_learning_rate=0.04
  final_learning_rate=0.004
  bias_stddev=0.5
  softmax_learning_rate_factor=1.0 # In the default setting keep the same learning rate.
  
  combine_regularizer=1.0e-14 # Small regularizer so that parameters won't go crazy.
  pnorm_input_dim=3000
  pnorm_output_dim=300
  p=2
  minibatch_size=128 # by default use a smallish minibatch size for neural net
                     # training; this controls instability which would otherwise
                     # be a problem with multi-threaded update.  Note: it also
                     # interacts with the "preconditioned" update which generally
                     # works better with larger minibatch size, so it's not
                     # completely cost free.
  
  samples_per_iter=200000 # each iteration of training, see this many samples
                          # per job.  This option is passed to get_egs.sh
  num_jobs_nnet=16   # Number of neural net jobs to run in parallel.  This option
                     # is passed to get_egs.sh.
  get_egs_stage=0
  
  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.
  
  add_layers_period=2 # by default, add new layers every 2 iterations.
  num_hidden_layers=3
  stage=-5
  
  io_opts="--max-jobs-run 5" # for jobs with a lot of I/O, limits the number running at one time.
  splice_width=4 # meaning +- 4 frames on each side for second LDA
  randprune=4.0 # speeds up LDA.
  alpha=4.0
  max_change=10.0
  mix_up=0 # Number of components to mix up to (should be > #tree leaves, if
          # specified.)
  num_threads=16
  parallel_opts="--num-threads 16 --mem 1G" # by default we use 16 threads; this lets the queue know.
    # note: parallel_opts doesn't automatically get adjusted if you adjust num-threads.
  cleanup=true
  egs_dir=
  lda_opts=
  lda_dim=
  egs_opts=
  transform_dir=     # If supplied, overrides alidir
  cmvn_opts=  # will be passed to get_lda.sh and get_egs.sh, if supplied.
              # only relevant for "raw" features, not lda.
  feat_type=  # Can be used to force "raw" features.
  prior_subset_size=10000 # 10k samples per job, for computing priors.  Should be
                          # more than enough.
  align_cmd=              # The cmd that is passed to steps/nnet2/align.sh
  align_use_gpu=          # Passed to use_gpu in steps/nnet2/align.sh [yes/no]
  realign_epochs=         # List of epochs, the beginning of which realignment is done
  num_jobs_align=30       # Number of jobs for realignment
  
  # End configuration section.
  
  echo "$0 $@"  # Print the command line for logging
  
  if [ -f path.sh ]; then . ./path.sh; fi
  . parse_options.sh || exit 1;
  
  if [ $# != 4 ]; then
    echo "Usage: $0 [opts] <data> <lang> <ali-dir> <exp-dir>"
    echo " e.g.: $0 data/train data/lang exp/tri3_ali exp/tri4_nnet"
    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 main training"
    echo "                                                   # while reducing learning rate (determines #iterations, together"
    echo "                                                   # with --samples-per-iter and --num-jobs-nnet)"
    echo "  --num-epochs-extra <#epochs-extra|5>             # Number of extra epochs of training"
    echo "                                                   # after learning rate fully reduced"
    echo "  --initial-learning-rate <initial-learning-rate|0.02> # Learning rate at start of training, e.g. 0.02 for small"
    echo "                                                       # data, 0.01 for large data"
    echo "  --final-learning-rate  <final-learning-rate|0.004>   # Learning rate at end of training, e.g. 0.004 for small"
    echo "                                                   # data, 0.001 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 "  --mix-up <#pseudo-gaussians|0>                   # Can be used to have multiple targets in final output layer,"
    echo "                                                   # per context-dependent state.  Try a number several times #states."
    echo "  --num-jobs-nnet <num-jobs|8>                     # Number of parallel jobs to use for main neural net"
    echo "                                                   # training (will affect results as well as speed; try 8, 16)"
    echo "                                                   # Note: if you increase this, you may want to also increase"
    echo "                                                   # the learning rate."
    echo "  --num-threads <num-threads|16>                   # Number of parallel threads per job (will affect results"
    echo "                                                   # 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... "
    echo "  --io-opts <opts|\"--max-jobs-run 10\">                      # Options given to e.g. queue.pl for jobs that do a lot of I/O."
    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-width <width|4>                         # Number of frames on each side to append for feature input"
    echo "                                                   # (note: we splice processed, typically 40-dimensional frames"
    echo "  --lda-dim <dim|250>                              # Dimension to reduce spliced features to with LDA"
    echo "  --num-iters-final <#iters|20>                    # Number of final iterations to give to nnet-combine-fast to "
    echo "                                                   # interpolate parameters (the weights are learned with a validation set)"
    echo "  --egs-opts <opts>                                # Extra options to pass to get_egs.sh"
    echo "  --lda-opts <opts>                                # Extra options to pass to get_lda.sh"
    echo "  --realign-epochs <list-of-epochs|\"\">           # A list of space-separated epoch indices the beginning of which"
    echo "                                                   # realignment is to be done"
    echo "  --align-cmd (utils/run.pl|utils/queue.pl <queue opts>) # passed to align.sh"
    echo "  --align-use-gpu (yes/no)                         # specify is gpu is to be used for realignment"
    echo "  --num-jobs-align <#njobs|30>                     # Number of jobs to perform realignment"
    echo "  --stage <stage|-9>                               # Used to run a partially-completed training process from somewhere in"
    echo "                                                   # the middle."
  
    exit 1;
  fi
  
  data=$1
  lang=$2
  alidir=$3
  dir=$4
  
  if [ ! -z "$realign_epochs" ]; then
    [ -z "$align_cmd" ] && echo "$0: realign_epochs specified but align_cmd not specified" && exit 1
    [ -z "$align_use_gpu" ] && echo "$0: realign_epochs specified but align_use_gpu not specified" && exit 1
  fi
  
  # Check some files.
  for f in $data/feats.scp $lang/L.fst $alidir/ali.1.gz $alidir/final.mdl $alidir/tree; do
    [ ! -f $f ] && echo "$0: no such file $f" && exit 1;
  done
  
  
  # Set some variables.
  num_leaves=`tree-info $alidir/tree 2>/dev/null | grep num-pdfs | awk '{print $2}'` || exit 1
  [ -z $num_leaves ] && echo "\$num_leaves is unset" && exit 1
  [ "$num_leaves" -eq "0" ] && echo "\$num_leaves is 0" && exit 1
  
  nj=`cat $alidir/num_jobs` || exit 1;  # number of jobs in alignment dir...
  # in this dir we'll have just one job.
  sdata=$data/split$nj
  utils/split_data.sh $data $nj
  
  mkdir -p $dir/log
  echo $nj > $dir/num_jobs
  cp $alidir/tree $dir
  
  utils/lang/check_phones_compatible.sh $lang/phones.txt $alidir/phones.txt || exit 1;
  cp $lang/phones.txt $dir || exit 1;
  
  extra_opts=()
  [ ! -z "$cmvn_opts" ] && extra_opts+=(--cmvn-opts "$cmvn_opts")
  [ ! -z "$feat_type" ] && extra_opts+=(--feat-type $feat_type)
  [ ! -z "$online_ivector_dir" ] && extra_opts+=(--online-ivector-dir $online_ivector_dir)
  [ -z "$transform_dir" ] && transform_dir=$alidir
  extra_opts+=(--transform-dir $transform_dir)
  extra_opts+=(--splice-width $splice_width)
  
  if [ $stage -le -4 ]; then
    echo "$0: calling get_lda.sh"
    steps/nnet2/get_lda.sh $lda_opts "${extra_opts[@]}" --cmd "$cmd" $data $lang $alidir $dir || exit 1;
  fi
  
  # these files will have been written by get_lda.sh
  feat_dim=`cat $dir/feat_dim` || exit 1;
  lda_dim=`cat $dir/lda_dim` || exit 1;
  
  if [ $stage -le -3 ] && [ -z "$egs_dir" ]; then
    echo "$0: calling get_egs.sh"
    steps/nnet2/get_egs.sh $egs_opts "${extra_opts[@]}" \
        --samples-per-iter $samples_per_iter \
        --num-jobs-nnet $num_jobs_nnet --stage $get_egs_stage \
        --cmd "$cmd" $egs_opts --io-opts "$io_opts" \
        $data $lang $alidir $dir || exit 1;
  fi
  if [ -z $egs_dir ]; then
    egs_dir=$dir/egs
  fi
  
  iters_per_epoch=`cat $egs_dir/iters_per_epoch`  || exit 1;
  ! [ $num_jobs_nnet -eq `cat $egs_dir/num_jobs_nnet` ] && \
    echo "$0: Warning: using --num-jobs-nnet=`cat $egs_dir/num_jobs_nnet` from $egs_dir"
  num_jobs_nnet=`cat $egs_dir/num_jobs_nnet` || exit 1;
  
  
  if ! [ $num_hidden_layers -ge 1 ]; then
    echo "Invalid num-hidden-layers $num_hidden_layers"
    exit 1
  fi
  
  if [ $stage -le -2 ]; then
    echo "$0: initializing neural net";
  
    lda_mat=$dir/lda.mat
    ext_lda_dim=$lda_dim
    ext_feat_dim=$feat_dim
  
    stddev=`perl -e "print 1.0/sqrt($pnorm_input_dim);"`
    cat >$dir/nnet.config <<EOF
  SpliceComponent input-dim=$ext_feat_dim left-context=$splice_width right-context=$splice_width
  FixedAffineComponent matrix=$lda_mat
  AffineComponentPreconditioned input-dim=$ext_lda_dim output-dim=$pnorm_input_dim alpha=$alpha max-change=$max_change learning-rate=$initial_learning_rate param-stddev=$stddev bias-stddev=$bias_stddev
  PnormComponent input-dim=$pnorm_input_dim output-dim=$pnorm_output_dim p=$p
  NormalizeComponent dim=$pnorm_output_dim
  AffineComponentPreconditioned input-dim=$pnorm_output_dim output-dim=$num_leaves alpha=$alpha max-change=$max_change learning-rate=$initial_learning_rate param-stddev=0 bias-stddev=0
  SoftmaxComponent dim=$num_leaves
  EOF
  
    # to hidden.config it will write the part of the config corresponding to a
    # single hidden layer; we need this to add new layers.
    cat >$dir/hidden.config <<EOF
  AffineComponentPreconditioned input-dim=$pnorm_output_dim output-dim=$pnorm_input_dim alpha=$alpha max-change=$max_change learning-rate=$initial_learning_rate param-stddev=$stddev bias-stddev=$bias_stddev
  PnormComponent input-dim=$pnorm_input_dim output-dim=$pnorm_output_dim p=$p
  NormalizeComponent dim=$pnorm_output_dim
  EOF
    $cmd $dir/log/nnet_init.log \
      nnet-am-init $alidir/tree $lang/topo "nnet-init $dir/nnet.config -|" \
      $dir/0.mdl || exit 1;
  fi
  
  if [ $stage -le -1 ]; then
    echo "Training transition probabilities and setting priors"
    $cmd $dir/log/train_trans.log \
      nnet-train-transitions $dir/0.mdl "ark:gunzip -c $alidir/ali.*.gz|" $dir/0.mdl \
      || exit 1;
  fi
  
  num_iters_reduce=$[$num_epochs * $iters_per_epoch];
  num_iters_extra=$[$num_epochs_extra * $iters_per_epoch];
  num_iters=$[$num_iters_reduce+$num_iters_extra]
  
  echo "$0: Will train for $num_epochs + $num_epochs_extra epochs, equalling "
  echo "$0: $num_iters_reduce + $num_iters_extra = $num_iters iterations, "
  echo "$0: (while reducing learning rate) + (with constant learning rate)."
  
  # This is when we decide to mix up from: halfway between when we've finished
  # adding the hidden layers and the end of training.
  finish_add_layers_iter=$[$num_hidden_layers * $add_layers_period]
  mix_up_iter=$[($num_iters + $finish_add_layers_iter)/2]
  
  if [ $num_threads -eq 1 ]; then
    train_suffix="-simple" # this enables us to use GPU code if
                           # we have just one thread.
    if ! cuda-compiled; then
      echo "$0: WARNING: you are running with one thread but you have not compiled"
      echo "   for CUDA.  You may be running a setup optimized for GPUs.  If you have"
      echo "   GPUs and have nvcc installed, go to src/ and do ./configure; make"
    fi
  else
    train_suffix="-parallel --num-threads=$num_threads"
  fi
  
  x=0
  
  for realign_epoch in $realign_epochs; do
    realign_iter=`perl -e 'print int($ARGV[0] * $ARGV[1]);' $realign_epoch $iters_per_epoch`
    realign_this_iter[$realign_iter]=$realign_epoch
  done
  
  cur_egs_dir=$egs_dir
  
  while [ $x -lt $num_iters ]; do
  
    if [ ! -z "${realign_this_iter[$x]}" ]; then
      prev_egs_dir=$cur_egs_dir
      cur_egs_dir=$dir/egs_${realign_this_iter[$x]}
    fi
  
    if [ $x -ge 0 ] && [ $stage -le $x ]; then
      if [ ! -z "${realign_this_iter[$x]}" ]; then
        epoch=${realign_this_iter[$x]}
  
        echo "Getting average posterior for purposes of adjusting the priors."
        # Note: this just uses CPUs, using a smallish subset of data.
        rm $dir/post.*.vec 2>/dev/null
        $cmd JOB=1:$num_jobs_nnet $dir/log/get_post.JOB.log \
          nnet-subset-egs --n=$prior_subset_size ark:$prev_egs_dir/egs.JOB.0.ark ark:- \| \
          nnet-compute-from-egs "nnet-to-raw-nnet $dir/$x.mdl -|" ark:- ark:- \| \
          matrix-sum-rows ark:- ark:- \| vector-sum ark:- $dir/post.JOB.vec || exit 1;
  
        sleep 3;  # make sure there is time for $dir/post.*.vec to appear.
  
        $cmd $dir/log/vector_sum.log \
          vector-sum $dir/post.*.vec $dir/post.vec || exit 1;
  
        rm $dir/post.*.vec;
  
        echo "Re-adjusting priors based on computed posteriors"
        $cmd $dir/log/adjust_priors.$x.log \
          nnet-adjust-priors $dir/$x.mdl $dir/post.vec $dir/$x.mdl || exit 1;
  
        sleep 2
  
        steps/nnet2/align.sh --nj $num_jobs_align --cmd "$align_cmd" --use-gpu $align_use_gpu \
          --transform-dir "$transform_dir" \
          --iter $x $data $lang $dir $dir/ali_$epoch || exit 1
  
        steps/nnet2/relabel_egs.sh --cmd "$cmd" --iter $x $dir/ali_$epoch \
          $prev_egs_dir $cur_egs_dir || exit 1
  
        if $cleanup && [[ $prev_egs_dir =~ $dir/egs* ]]; then
          steps/nnet2/remove_egs.sh $prev_egs_dir
        fi
      fi
  
      # 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 \
        nnet-compute-prob $dir/$x.mdl ark:$cur_egs_dir/valid_diagnostic.egs &
      $cmd $dir/log/compute_prob_train.$x.log \
        nnet-compute-prob $dir/$x.mdl ark:$cur_egs_dir/train_diagnostic.egs &
      if [ $x -gt 0 ] && [ ! -f $dir/log/mix_up.$[$x-1].log ]; then
        $cmd $dir/log/progress.$x.log \
          nnet-show-progress --use-gpu=no $dir/$[$x-1].mdl $dir/$x.mdl \
          ark:$cur_egs_dir/train_diagnostic.egs '&&' \
          nnet-am-info $dir/$x.mdl &
      fi
  
      echo "Training neural net (pass $x)"
      if [ $x -gt 0 ] && \
        [ $x -le $[($num_hidden_layers-1)*$add_layers_period] ] && \
        [ $[($x-1) % $add_layers_period] -eq 0 ]; then
        mdl="nnet-init --srand=$x $dir/hidden.config - | nnet-insert $dir/$x.mdl - - |"
      else
        mdl=$dir/$x.mdl
      fi
  
  
      $cmd $parallel_opts JOB=1:$num_jobs_nnet $dir/log/train.$x.JOB.log \
        nnet-shuffle-egs --buffer-size=$shuffle_buffer_size --srand=$x \
        ark:$cur_egs_dir/egs.JOB.$[$x%$iters_per_epoch].ark ark:- \| \
        nnet-train$train_suffix \
           --minibatch-size=$minibatch_size --srand=$x "$mdl" \
          ark:- $dir/$[$x+1].JOB.mdl \
        || exit 1;
  
      nnets_list=
      for n in `seq 1 $num_jobs_nnet`; do
        nnets_list="$nnets_list $dir/$[$x+1].$n.mdl"
      done
  
      learning_rate=`perl -e '($x,$n,$i,$f)=@ARGV; print ($x >= $n ? $f : $i*exp($x*log($f/$i)/$n));' $[$x+1] $num_iters_reduce $initial_learning_rate $final_learning_rate`;
      softmax_learning_rate=`perl -e "print $learning_rate * $softmax_learning_rate_factor;"`;
      nnet-am-info $dir/$[$x+1].1.mdl > $dir/foo  2>/dev/null || exit 1
      nu=`cat $dir/foo | grep num-updatable-components | awk '{print $2}'`
      na=`cat $dir/foo | grep -v Fixed | grep AffineComponent | wc -l`
      # na is number of last updatable AffineComponent layer [one-based, counting only
      # updatable components.]
      lr_string="$learning_rate"
      for n in `seq 2 $nu`; do
        if [ $n -eq $na ] || [ $n -eq $[$na-1] ]; then lr=$softmax_learning_rate;
        else lr=$learning_rate; fi
        lr_string="$lr_string:$lr"
      done
  
      $cmd $dir/log/average.$x.log \
        nnet-am-average $nnets_list - \| \
        nnet-am-copy --learning-rates=$lr_string - $dir/$[$x+1].mdl || exit 1;
  
      if [ "$mix_up" -gt 0 ] && [ $x -eq $mix_up_iter ]; then
        # mix up.
        echo Mixing up from $num_leaves to $mix_up components
        $cmd $dir/log/mix_up.$x.log \
          nnet-am-mixup --min-count=10 --num-mixtures=$mix_up \
          $dir/$[$x+1].mdl $dir/$[$x+1].mdl || exit 1;
      fi
      rm $nnets_list
    fi
    x=$[$x+1]
  done
  
  # Now do combination.
  # At the end, final.mdl will be a combination of the last e.g. 10 models.
  nnets_list=()
  if [ $num_iters_final -gt $num_iters_extra ]; then
    echo "Setting num_iters_final=$num_iters_extra"
  fi
  start=$[$num_iters-$num_iters_final+1]
  for x in `seq $start $num_iters`; do
    idx=$[$x-$start]
    if [ $x -gt $mix_up_iter ]; then
      nnets_list[$idx]=$dir/$x.mdl # "nnet-am-copy --remove-dropout=true $dir/$x.mdl - |"
    fi
  done
  
  if [ $stage -le $num_iters ]; then
    echo "Doing final combination to produce final.mdl"
    # Below, use --use-gpu=no to disable nnet-combine-fast from using a GPU, as
    # if there are many models it can give out-of-memory error; set num-threads to 8
    # to speed it up (this isn't ideal...)
    this_num_threads=$num_threads
    [ $this_num_threads -lt 8 ] && this_num_threads=8
    num_egs=`nnet-copy-egs ark:$cur_egs_dir/combine.egs ark:/dev/null 2>&1 | tail -n 1 | awk '{print $NF}'`
    mb=$[($num_egs+$this_num_threads-1)/$this_num_threads]
    [ $mb -gt 512 ] && mb=512
    # Setting --initial-model to a large value makes it initialize the combination
    # with the average of all the models.  It's important not to start with a
    # single model, or, due to the invariance to scaling that these nonlinearities
    # give us, we get zero diagonal entries in the fisher matrix that
    # nnet-combine-fast uses for scaling, which after flooring and inversion, has
    # the effect that the initial model chosen gets much higher learning rates
    # than the others.  This prevents the optimization from working well.
    $cmd $parallel_opts $dir/log/combine.log \
      nnet-combine-fast --initial-model=100000 --num-lbfgs-iters=40 --use-gpu=no \
        --num-threads=$this_num_threads --regularizer=$combine_regularizer \
        --verbose=3 --minibatch-size=$mb "${nnets_list[@]}" ark:$cur_egs_dir/combine.egs \
        $dir/final.mdl || exit 1;
  
    # Normalize stddev for affine or block affine layers that are followed by a
    # pnorm layer and then a normalize layer.
    $cmd $parallel_opts $dir/log/normalize.log \
      nnet-normalize-stddev $dir/final.mdl $dir/final.mdl || 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 \
      nnet-compute-prob $dir/final.mdl ark:$cur_egs_dir/valid_diagnostic.egs &
    $cmd $dir/log/compute_prob_train.final.log \
      nnet-compute-prob $dir/final.mdl ark:$cur_egs_dir/train_diagnostic.egs &
  fi
  
  if [ $stage -le $[$num_iters+1] ]; then
    echo "Getting average posterior for purposes of adjusting the priors."
    # Note: this just uses CPUs, using a smallish subset of data.
    rm $dir/post.*.vec 2>/dev/null
    $cmd JOB=1:$num_jobs_nnet $dir/log/get_post.JOB.log \
      nnet-subset-egs --n=$prior_subset_size ark:$cur_egs_dir/egs.JOB.0.ark ark:- \| \
      nnet-compute-from-egs "nnet-to-raw-nnet $dir/final.mdl -|" ark:- ark:- \| \
      matrix-sum-rows ark:- ark:- \| vector-sum ark:- $dir/post.JOB.vec || exit 1;
  
    sleep 3;  # make sure there is time for $dir/post.*.vec to appear.
  
    $cmd $dir/log/vector_sum.log \
     vector-sum $dir/post.*.vec $dir/post.vec || exit 1;
  
    rm $dir/post.*.vec;
  
    echo "Re-adjusting priors based on computed posteriors"
    $cmd $dir/log/adjust_priors.final.log \
      nnet-adjust-priors $dir/final.mdl $dir/post.vec $dir/final.mdl || exit 1;
  fi
  
  
  sleep 2
  
  echo Done
  
  if $cleanup; then
    echo Cleaning up data
    if [[ $cur_egs_dir =~ $dir/egs* ]]; then
      steps/nnet2/remove_egs.sh $cur_egs_dir
    fi
    echo Removing most of the models
    for x in `seq 0 $num_iters`; do
      if [ $[$x%100] -ne 0 ] && [ $x -lt $[$num_iters-$num_iters_final+1] ]; then
         # delete all but every 100th model; don't delete the ones which combine to form the final model.
        rm $dir/$x.mdl
      fi
    done
  fi