run_cnn_aug_1b.sh
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#!/bin/bash
# run_cnn_aug_1b is the same as run_cnn_1e but with data augmentation.
# accuracy is 0.857, vs. 0.83 for the un-augmented baseline.
# exp/cnn_aug_1b_cifar10: num-iters=60 nj=1..2 num-params=2.2M dim=96->10 combine=-0.40->-0.38 loglike:train/valid[39,59,final]=(-0.35,-0.26,-0.26/-0.47,-0.42,-0.42) accuracy:train/valid[39,59,final]=(0.88,0.91,0.91/0.84,0.86,0.86)
# grep Overall exp/cnn_aug_1b_cifar10/log/compute_prob_valid.final.log | grep acc
# LOG (nnet3-compute-prob[5.1]:PrintTotalStats():nnet-diagnostics.cc:165) Overall accuracy for 'output' is 0.8567 per frame, over 10000 frames.#
# Set -e here so that we catch if any executable fails immediately
set -euo pipefail
# training options
stage=0
train_stage=-10
dataset=cifar10
srand=0
reporting_email=
affix=_aug_1b
# End configuration section.
echo "$0 $@" # Print the command line for logging
. ./cmd.sh
. ./path.sh
. ./utils/parse_options.sh
if ! cuda-compiled; then
cat <<EOF && exit 1
This script is intended to be used with GPUs but you have not compiled Kaldi with CUDA
If you want to use GPUs (and have them), go to src/, and configure and make on a machine
where "nvcc" is installed.
EOF
fi
dir=exp/cnn${affix}_${dataset}
egs=exp/${dataset}_egs
if [ ! -d $egs ]; then
echo "$0: expected directory $egs to exist. Run the get_egs.sh commands in the"
echo " run.sh before this script."
exit 1
fi
# check that the expected files are in the egs directory.
for f in $egs/egs.1.ark $egs/train_diagnostic.egs $egs/valid_diagnostic.egs $egs/combine.egs \
$egs/info/feat_dim $egs/info/left_context $egs/info/right_context \
$egs/info/output_dim; do
if [ ! -e $f ]; then
echo "$0: expected file $f to exist."
exit 1;
fi
done
mkdir -p $dir/log
if [ $stage -le 1 ]; then
mkdir -p $dir
echo "$0: creating neural net configs using the xconfig parser";
num_targets=$(cat $egs/info/output_dim)
# Note: we hardcode in the CNN config that we are dealing with 32x3x color
# images.
common1="required-time-offsets=0 height-offsets=-1,0,1 num-filters-out=32"
common2="required-time-offsets=0 height-offsets=-1,0,1 num-filters-out=64"
mkdir -p $dir/configs
cat <<EOF > $dir/configs/network.xconfig
input dim=96 name=input
conv-relu-batchnorm-layer name=cnn1 height-in=32 height-out=32 time-offsets=-1,0,1 $common1
conv-relu-batchnorm-dropout-layer name=cnn2 height-in=32 height-out=16 time-offsets=-1,0,1 dropout-proportion=0.25 $common1 height-subsample-out=2
conv-relu-batchnorm-layer name=cnn3 height-in=16 height-out=16 time-offsets=-2,0,2 $common2
conv-relu-batchnorm-dropout-layer name=cnn4 height-in=16 height-out=8 time-offsets=-2,0,2 dropout-proportion=0.25 $common2 height-subsample-out=2
conv-relu-batchnorm-layer name=cnn5 height-in=8 height-out=8 time-offsets=-4,0,4 $common2
relu-dropout-layer name=fully_connected1 input=Append(2,6,10,14,18,22,26,30) dropout-proportion=0.5 dim=512
output-layer name=output dim=$num_targets
EOF
steps/nnet3/xconfig_to_configs.py --xconfig-file $dir/configs/network.xconfig --config-dir $dir/configs/
fi
if [ $stage -le 2 ]; then
steps/nnet3/train_raw_dnn.py --stage=$train_stage \
--cmd="$train_cmd" \
--image.augmentation-opts="--horizontal-flip-prob=0.5 --horizontal-shift=0.1 --vertical-shift=0.1 --num-channels=3" \
--trainer.srand=$srand \
--trainer.max-param-change=2.0 \
--trainer.num-epochs=30 \
--egs.frames-per-eg=1 \
--trainer.optimization.num-jobs-initial=1 \
--trainer.optimization.num-jobs-final=2 \
--trainer.optimization.initial-effective-lrate=0.003 \
--trainer.optimization.final-effective-lrate=0.0003 \
--trainer.optimization.minibatch-size=256,128,64 \
--trainer.shuffle-buffer-size=2000 \
--egs.dir="$egs" \
--use-gpu=true \
--reporting.email="$reporting_email" \
--dir=$dir || exit 1;
fi
exit 0;