mm script

script.sh

E_VERS="15"

EXP_ARGS=(
10,32,0.01,0,"Fixed"
8,32,0.01,0,"Fixed"
6,32,0.01,0,"Fixed"
4,32,0.01,1,"Fixed"
2,32,0.01,1,"Fixed"
1,32,0.01,1,"Fixed"
# 10,32,0.01,0,"Adaptive"
# 10,16,0.01,0,"Adaptive"
# 10,8,0.01,0,"Adaptive"
# 10,4,0.01,0,"Adaptive"
# 10,2,0.01,0,"Adaptive"
)

for i in "${EXP_ARGS[@]}"
do
    IFS=","; set -- $i
    EX_LENGTH=$1
    MSG_DIM=$2
    ENT_Z=$3
    DEVICE=$4
    MODEL=$5
    export CUDA_VISIBLE_DEVICES="${DEVICE}"
    nohup python advanced_setup.py \
    -visdom \
    -rec_w_dim $MSG_DIM \
    -sender_out_dim $MSG_DIM \
    -img_h_dim 512 \
    -batch_size 64 \
    -wv_dim 100 \
    -glove_path ~/data/glove/glove.6B.100d.txt \
    -learning_rate 1e-4 \
    -cuda \
    -experiment_name ${MODEL}-msg_${MSG_DIM}-ent_z_${ENT_Z}-ex_${EX_LENGTH}-eid_v04_$E_VERS \
    -max_epoch 500 \
    -top_k_dev 5 \
    -top_k_train 5 \
    -exchange_samples 5 \
    -use_binary \
    -entropy_s 0.08 \
    -entropy_sen $ENT_Z \
    -entropy_rec $ENT_Z \
    -s_bias 0 \
    -max_exchange 10 \
    -model_type $MODEL \
    -descr_train ../utils/mammals_v04_train.csv -descr_dev ../utils/mammals_v04_train.csv \
    -train_file ../train_v04.hdf5 \
    -dev_file ../dev_v04.hdf5 \
    &

done