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#!/bin/bash
# Copyright 2012 Karel Vesely (Brno University of Technology)
# Apache 2.0
# Initialize neural network
# Begin configuration.
model_size=8000000 # nr. of parameteres in MLP
hid_layers=4 # nr. of hidden layers (prior to sotfmax or bottleneck)
bn_dim= # set value to get a bottleneck network
hid_dim= # set value to override the $model_size
seed=777 # seed for the initialization
init_opts="--gauss --negbias"
# End configuration.
echo "$0 $@" # Print the command line for logging
[ -f path.sh ] && . ./path.sh;
. parse_options.sh || exit 1;
if [ $# != 3 ]; then
echo "Usage: $0 <in-dim> <out-dim> <nnet-init>"
echo " e.g.: $0 400 3000 nnet.init"
echo "main options (for others, see top of script file)"
echo " --config <config-file> # config containing options"
echo " --model-size <N> # number of weights in the nnet"
echo " --hid-layers <N> # number of hidden layers"
echo " --bn-dim <N> # dim of linear bottleneck"
echo " --hid-dim <N> # dim of hidden layers (overrides --model-size)"
exit 1;
fi
in_dim=$1
out_dim=$2
nnet_out=$3
dir=$(dirname $nnet_out)
###
### What is the topology? Straight or bottleneck?
###
if [ -z "$bn_dim" ]; then #MLP w/o bottleneck
case "$hid_layers" in
0) #just logistic regresion
mlp_init=$dir/nnet_${in_dim}_${out_dim}.init
echo "Initializing MLP : $mlp_init"
utils/nnet/gen_mlp_init.py --dim=${in_dim}:${out_dim} \
${init_opts} --seed=$seed > $mlp_init || exit 1;
;;
1) #MLP with one hidden layer
if [ -z "$hid_dim" ]; then
hid_dim=$((model_size/(in_dim+out_dim)))
fi
mlp_init=$dir/nnet_${in_dim}_${hid_dim}_${out_dim}.init
echo "Initializing MLP : $mlp_init"
utils/nnet/gen_mlp_init.py --dim=${in_dim}:${hid_dim}:${out_dim} \
${init_opts} --seed=$seed > $mlp_init || exit 1;
;;
2|3|4|5|6|7|8|9|10) #MLP with more than 1 hidden layer
if [ -z "$hid_dim" ]; then
a=$((hid_layers-1))
b=$((in_dim+out_dim))
c=$((-model_size))
hid_dim=$(awk "BEGIN{ hid_dim= -$b/(2*$a) + sqrt($b^2 -4*$a*$c)/(2*$a); print int(hid_dim) }")
fi
#build the mlp name mlp_init and dim argument dim_arg
mlp_init=
dim_arg=
{
mlp_init=$dir/nnet_${in_dim}
dim_arg=${in_dim}
for i in $(seq $hid_layers); do
mlp_init=${mlp_init}_$hid_dim
dim_arg=${dim_arg}:${hid_dim}
done
mlp_init=${mlp_init}_${out_dim}.init
dim_arg=${dim_arg}:${out_dim}
}
echo "Initializing MLP : $mlp_init"
utils/nnet/gen_mlp_init.py --dim=${dim_arg} ${init_opts} \
--seed=$seed > $mlp_init || exit 1;
;;
*)
echo "Unsupported number of hidden layers $hid_layers"
exit 1;
esac
else #MLP with bottleneck
bn_dim=$bn_dim
case "$hid_layers" in # ie. number of layers in front of bottleneck
1) #1-hidden layer in front of the bottleneck
if [ -z "$hid_dim" ]; then
hid_dim=$((model_size/(in_dim+out_dim+(2*bn_dim))))
fi
mlp_init=$dir/nnet_${in_dim}_${hid_dim}_${bn_dim}_${hid_dim}_${out_dim}.init
echo "Initializing MLP : $mlp_init"
utils/nnet/gen_mlp_init.py --dim=${in_dim}:${hid_dim}:${bn_dim}:${hid_dim}:${out_dim} \
${init_opts} --seed=$seed --linBNdim=$bn_dim > $mlp_init || exit 1;
;;
2|3|4|5|6|7|8|9|10) #more than 1 hidden layer in front of bottleneck
if [ -z "$hid_dim" ]; then
a=$((hid_layers-1))
b=$((in_dim+2*bn_dim+out_dim))
c=$((-model_size))
hid_dim=$(awk "BEGIN{ hid_dim= -$b/(2*$a) + sqrt($b^2 -4*$a*$c)/(2*$a); print int(hid_dim) }")
fi
#build the nnet name mlp_init and dim agument dim_arg
mlp_init=
dim_arg=
{
mlp_init=$dir/nnet_${in_dim}
dim_arg=${in_dim}
for i in $(seq $hid_layers); do
mlp_init=${mlp_init}_$hid_dim
dim_arg=${dim_arg}:${hid_dim}
done
mlp_init=${mlp_init}_${bn_dim}lin_${hid_dim}_${out_dim}.init
dim_arg=${dim_arg}:${bn_dim}:${hid_dim}:${out_dim}
}
echo "Initializing MLP : $mlp_init"
utils/nnet/gen_mlp_init.py --dim=${dim_arg} ${init_opts} \
--seed=$seed --linBNdim=$bn_dim > $mlp_init || exit 1;
;;
*)
echo "Unsupported number of hidden layers $hid_layers"
exit 1;
esac
fi
#The output name same as the mlp name, we are done..
[ $nnet_out == $mlp_init ] && "Successfuly created '$nnet_out'" && exit 0;
#Or we need to link the destination file
#(we want to keep the name showing the topology)
([ -f $nnet_out ] && unlink $nnet_out; cd $dir; ln -s $(basename $mlp_init) $(basename $nnet_out))
echo "Successfuly created linked '$nnet_out'"
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