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run.sh
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run.sh
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# === Internet Traffic ===
## training
#python3 run_ip_model.py \
# --train ./data/caida/equinix-chicago.dirA.20160121-130000.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130100.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130200.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130300.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130400.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130500.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130600.ports.npy \
# --valid ./data/caida/equinix-chicago.dirA.20160121-130700.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130800.ports.npy \
# --test ./data/caida/equinix-chicago.dirA.20160121-130900.ports.npy \
# --save exp20_ip_rnn_10min_r1-p2-h2_rmin65_ru64_bs512 --rnn_hiddens 64 --port_hiddens 16 8 --hiddens 32 32 --batch_size 512 --n_epoch 2000 --lr 0.0001 --regress_min 65
## resume training from an earlier model checkpoint
#python3 run_ip_model.py \
# --train ./data/caida/equinix-chicago.dirA.20160121-130000.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130100.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130200.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130300.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130400.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130500.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130600.ports.npy \
# --valid ./data/caida/equinix-chicago.dirA.20160121-130700.ports.npy \
# ./data/caida/equinix-chicago.dirA.20160121-130800.ports.npy \
# --test ./data/caida/equinix-chicago.dirA.20160121-130900.ports.npy \
# --save exp20_ip_rnn_10min_r1-p2-h2_rmin65_ru64_bs512_ep100 --rnn_hiddens 64 --port_hiddens 16 8 --hiddens 32 32 --batch_size 512 --n_epoch 2000 --lr 0.0001 --regress_min 65 \
# --resume model/exp20_ip_rnn_10min_r1-p2-h2_rmin65_ru64_bs512_20180505-214726_ep100.69 --start_epoch 101 --eval_n 10
## inference
#python3 run_ip_model.py \
# --train ./data/caida/equinix-chicago.dirA.20160121-130000.ports.npy \
# --valid ./data/caida/equinix-chicago.dirA.20160121-130700.ports.npy \
# --test ./data/caida/equinix-chicago.dirA.20160121-130900.ports.npy \
# --save pred_exp20_ip_rnn_10min_r1-p2-h2_rmin65_ru64_bs512_ep350_1309 --rnn_hiddens 64 --port_hiddens 16 8 --hiddens 32 32 --batch_size 512 --n_epoch 2000 --lr 0.0001 --regress_min 65 \
# --resume model/exp20_ip_rnn_10min_r1-p2-h2_rmin65_ru64_bs512_ep100_20180506-230316_ep350.69 --evaluate
# === Search Query ===
## training
#python3 ./run_aol_model.py \
# --train ./data/aol/char_embed/aol_0000_len60.npz \
# ./data/aol/char_embed/aol_0001_len60.npz \
# ./data/aol/char_embed/aol_0002_len60.npz \
# ./data/aol/char_embed/aol_0003_len60.npz \
# ./data/aol/char_embed/aol_0004_len60.npz \
# --valid ./data/aol/char_embed/aol_0005_len60.npz \
# --test ./data/aol/char_embed/aol_0006_len60.npz \
# --save exp22_aol_5d_r1-h1_u256-32_eb64_bs128_ra --embed_size 64 --rnn_hidden 256 --hiddens 32 --batch_size 128 --n_epoch 2000 --lr 0.0001 --word_max_len 60 --regress_actual --eval_n 10
## evaluation
#tday=06
#python3 ./run_aol_model.py \
# --train ./data/aol/char_embed/aol_0000_len60.npz \
# --valid ./data/aol/char_embed/aol_0005_len60.npz \
# --test ./data/aol/char_embed/aol_00${tday}_len60.npz \
# --save aol_inf_all_v05_t${tday}_exp22_aol_5d_r1-h1_u256-32_eb64_bs128_ra_20180514-160509_ep190 --embed_size 64 --rnn_hidden 256 --hiddens 32 --batch_size 128 \
# --n_epoch 2000 --lr 0.0001 --word_max_len 60 --regress_actual \
# --resume models_aol/exp22_aol_5d_r1-h1_u256-32_eb64_bs128_ra_20180514-160509_ep190.69 --evaluate