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train_p.sh
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train_p.sh
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#!/bin/bash
set -o nounset
set -o errexit
# code from http://stackoverflow.com/a/1116890
function readlink()
{
TARGET_FILE=$2
cd `dirname $TARGET_FILE`
TARGET_FILE=`basename $TARGET_FILE`
# Iterate down a (possible) chain of symlinks
while [ -L "$TARGET_FILE" ]
do
TARGET_FILE=`readlink $TARGET_FILE`
cd `dirname $TARGET_FILE`
TARGET_FILE=`basename $TARGET_FILE`
done
# Compute the canonicalized name by finding the physical path
# for the directory we're in and appending the target file.
PHYS_DIR=`pwd -P`
RESULT=$PHYS_DIR/$TARGET_FILE
echo $RESULT
}
export -f readlink
VERBOSE_MODE=0
function error_handler()
{
local STATUS=${1:-1}
[ ${VERBOSE_MODE} == 0 ] && exit ${STATUS}
echo "Exits abnormally at line "`caller 0`
exit ${STATUS}
}
trap "error_handler" ERR
PROGNAME=`basename ${BASH_SOURCE}`
function print_usage_and_exit()
{
set +x
local STATUS=$1
echo "Usage: ${PROGNAME} [-v] [-v] [-h] [--help]"
echo ""
echo " Options -"
echo " -v enables verbose mode 1"
echo " -v -v enables verbose mode 2"
echo " -h, --help shows this help message"
exit ${STATUS:-0}
}
function debug()
{
if [ "$VERBOSE_MODE" != 0 ]; then
echo $@
fi
}
GETOPT=`getopt vh $*`
if [ $? != 0 ] ; then print_usage_and_exit 1; fi
eval set -- "${GETOPT}"
while true
do case "$1" in
-v) let VERBOSE_MODE+=1; shift;;
-h|--help) print_usage_and_exit 0;;
--) shift; break;;
*) echo "Internal error!"; exit 1;;
esac
done
if (( VERBOSE_MODE > 1 )); then
set -x
fi
# template area is ended.
# -----------------------------------------------------------------------------
if [ ${#} != 0 ]; then print_usage_and_exit 1; fi
# current dir of this script
CDIR=$(readlink -f $(dirname $(readlink -f ${BASH_SOURCE[0]})))
PDIR=$(readlink -f $(dirname $(readlink -f ${BASH_SOURCE[0]}))/..)
# -----------------------------------------------------------------------------
# functions
function make_calmness()
{
exec 3>&2 # save 2 to 3
exec 2> /dev/null
}
function revert_calmness()
{
exec 2>&3 # restore 2 from previous saved 3(originally 2)
}
function close_fd()
{
exec 3>&-
}
function jumpto
{
label=$1
cmd=$(sed -n "/$label:/{:a;n;p;ba};" $0 | grep -v ':$')
eval "$cmd"
exit
}
# end functions
# -----------------------------------------------------------------------------
# -----------------------------------------------------------------------------
# main
make_calmness
if (( VERBOSE_MODE > 1 )); then
revert_calmness
fi
cd ${PDIR}
python=/usr/bin/python
SYNTAXNET_HOME=${PDIR}
BINDIR=$SYNTAXNET_HOME/bazel-bin/syntaxnet
CORPUS_DIR=${CDIR}/UD_English
CONTEXT=${CORPUS_DIR}/context.pbtxt_p
TMP_DIR=${CORPUS_DIR}/tmp_p/syntaxnet-output
mkdir -p ${TMP_DIR}
cat ${CONTEXT} | sed "s=OUTPATH=${TMP_DIR}=" > ${TMP_DIR}/context
MODEL_DIR=${CDIR}/models
PARSER_HIDDEN_LAYER_SIZES=512,512
PARSER_HIDDEN_LAYER_PARAMS='512x512'
BATCH_SIZE=256
BEAM_SIZE=16
function convert_corpus {
corpus_dir=$1
for corpus in $(ls ${corpus_dir}/*.conllu); do
${python} ${CDIR}/convert.py < ${corpus} > ${corpus}.conv
done
}
LP_PARAMS=${PARSER_HIDDEN_LAYER_PARAMS}-0.08-4400-0.85-4
function pretrain_parser {
${BINDIR}/parser_trainer \
--arg_prefix=brain_parser \
--batch_size=${BATCH_SIZE} \
--compute_lexicon \
--decay_steps=4400 \
--graph_builder=greedy \
--hidden_layer_sizes=${PARSER_HIDDEN_LAYER_SIZES} \
--learning_rate=0.08 \
--momentum=0.85 \
--output_path=${TMP_DIR} \
--task_context=${TMP_DIR}/context \
--seed=4 \
--projectivize_training_set \
--training_corpus=tagged-training-corpus \
--tuning_corpus=tagged-tuning-corpus \
--params=${LP_PARAMS} \
--num_epochs=12 \
--beam_size=1 \
--report_every=100 \
--checkpoint_every=1000 \
--logtostderr
}
function evaluate_pretrained_parser {
for SET in training tuning test; do
${BINDIR}/parser_eval \
--task_context=${TMP_DIR}/brain_parser/greedy/${LP_PARAMS}/context \
--hidden_layer_sizes=${PARSER_HIDDEN_LAYER_SIZES} \
--beam_size=1 \
--batch_size=${BATCH_SIZE} \
--input=tagged-$SET-corpus \
--output=parsed-$SET-corpus \
--arg_prefix=brain_parser \
--graph_builder=greedy \
--model_path=${TMP_DIR}/brain_parser/greedy/${LP_PARAMS}/model
done
}
GP_PARAMS=${PARSER_HIDDEN_LAYER_PARAMS}-0.02-100-0.9-0
function train_parser {
${BINDIR}/parser_trainer \
--arg_prefix=brain_parser \
--batch_size=${BATCH_SIZE} \
--compute_lexicon \
--decay_steps=100 \
--graph_builder=structured \
--hidden_layer_sizes=${PARSER_HIDDEN_LAYER_SIZES} \
--learning_rate=0.02 \
--momentum=0.9 \
--beam_size=${BEAM_SIZE} \
--output_path=${TMP_DIR} \
--task_context=${TMP_DIR}/brain_parser/greedy/${LP_PARAMS}/context \
--seed=0 \
--training_corpus=projectivized-training-corpus \
--tuning_corpus=tagged-tuning-corpus \
--params=${GP_PARAMS} \
--pretrained_params=${TMP_DIR}/brain_parser/greedy/${LP_PARAMS}/model \
--pretrained_params_names=embedding_matrix_0,embedding_matrix_1,embedding_matrix_2,bias_0,weights_0,bias_1,weights_1 \
--num_epochs=20 \
--report_every=25 \
--checkpoint_every=200 \
--logtostderr
}
function evaluate_parser {
for SET in training tuning test; do
${BINDIR}/parser_eval \
--task_context=${TMP_DIR}/brain_parser/structured/${GP_PARAMS}/context \
--hidden_layer_sizes=${PARSER_HIDDEN_LAYER_SIZES} \
--beam_size=${BEAM_SIZE} \
--batch_size=${BATCH_SIZE} \
--input=tagged-$SET-corpus \
--output=beam-parsed-$SET-corpus \
--arg_prefix=brain_parser \
--graph_builder=structured \
--model_path=${TMP_DIR}/brain_parser/structured/${GP_PARAMS}/model
done
}
function xcopy_model {
cp -rf ${TMP_DIR}/brain_parser/structured/${GP_PARAMS}/model ${MODEL_DIR}/parser-params
cp -rf ${TMP_DIR}/*-map ${MODEL_DIR}/
cp -rf ${TMP_DIR}/*-table ${MODEL_DIR}/
cp -rf ${TMP_DIR}/tag-to-category ${MODEL_DIR}/
}
function copy_model {
mkdir -p ${MODEL_DIR}/parser-params
cp -rf ${TMP_DIR}/brain_parser/structured/${GP_PARAMS}/model.* ${MODEL_DIR}/parser-params
cp -rf ${TMP_DIR}/*-map ${MODEL_DIR}/
cp -rf ${TMP_DIR}/*-table ${MODEL_DIR}/
cp -rf ${TMP_DIR}/tag-to-category ${MODEL_DIR}/
}
convert_corpus ${CORPUS_DIR}
pretrain_parser
evaluate_pretrained_parser
train_parser
evaluate_parser
copy_model
close_fd
# end main
# -----------------------------------------------------------------------------