File size: 1,724 Bytes
5523920 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | #!/bin/sh
#
# file: run.sh
#
# This is a simple driver script that runs training and then decoding
# on the training set, the dev test set and the eval set.
#
# To run this script, execute the following line:
#
# run.sh train.dat test.dat eval.dat
#
# The first argument ($1) is the training data. The last two arguments,
# test data ($2) and evaluation data ($3) are optional.
#
# An example of how to run this is as follows:
#
# nedc_000_[1]: echo $PWD
# /data/isip/exp/tuh_dpath/exp_0074/v1.0
# nedc_000_[1]: ./run.sh data/train_set.txt data/dev_set.txt data/eval_set.txt
#
# This script will take you through the sequence of steps required to
# train a simple MLP network and evaluate it on some data.
#
# The script will then take the trained models and do an evaluation
# on the data in "test.dat". It will output the results to output/results.txt.
#
# If an eval set is specified, it will do the same for the eval set.
#
# decode the number of command line arguments
#
NARGS=$#
if (test "$NARGS" -eq "0") then
echo "usage: run.sh train.dat [test.dat] [eval.dat]"
exit 1
fi
# define a base directory for the experiment
#
DL_EXP=`pwd`;
DL_SCRIPTS="$DL_EXP/scripts";
DL_OUT="$DL_EXP/output";
DL_DECODE_ODIR="$DL_OUT";
# define the output directories for training/decoding/scoring
#
#DL_TRAIN_ODIR="$DL_OUT/00_train";
DL_TRAIN_ODIR="$DL_EXP/model";
DL_MDL_PATH="$DL_TRAIN_ODIR/semantic_cnn_model.pth";
# evaluate each data set that was specified
#
echo "... starting evaluation of $1 ..."
$DL_SCRIPTS/decode_demo.py $DL_DECODE_ODIR $DL_MDL_PATH $1 | \
tee $DL_OUT/01_decode_train.log | grep "Average"
echo "... finished evaluation of $1 ..."
echo "======= end of results ======="
#
# exit gracefully
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