50 lines
1.2 KiB
Bash
50 lines
1.2 KiB
Bash
|
|
export CUDA_VISIBLE_DEVICES=0
|
|
|
|
# Model name
|
|
model_name=WPMixer
|
|
|
|
# Datasets and prediction lengths
|
|
dataset=traffic
|
|
seq_lens=(1200 1200 1200 1200)
|
|
pred_lens=(96 192 336 720)
|
|
learning_rates=(0.0010385 0.000567053 0.001026715 0.001496217)
|
|
batches=(16 16 16 16)
|
|
epochs=(60 60 50 60)
|
|
dropouts=(0.05 0.05 0.0 0.05)
|
|
patch_lens=(16 16 16 16)
|
|
lradjs=(type3 type3 type3 type3)
|
|
d_models=(16 32 32 32)
|
|
patiences=(12 12 12 12)
|
|
|
|
# Model params below need to be set in WPMixer.py Line 15, instead of this script
|
|
wavelets=(db3 db3 bior3.1 db3)
|
|
levels=(1 1 1 1)
|
|
tfactors=(3 3 7 7)
|
|
dfactors=(5 5 7 3)
|
|
strides=(8 8 8 8)
|
|
|
|
# Loop over datasets and prediction lengths
|
|
for i in "${!pred_lens[@]}"; do
|
|
python -u run.py \
|
|
--is_training 1 \
|
|
--root_path ./data/traffic/ \
|
|
--data_path traffic.csv \
|
|
--model_id wpmixer \
|
|
--model $model_name \
|
|
--task_name long_term_forecast \
|
|
--data $dataset \
|
|
--seq_len ${seq_lens[$i]} \
|
|
--pred_len ${pred_lens[$i]} \
|
|
--label_len 0 \
|
|
--d_model ${d_models[$i]} \
|
|
--patch_len ${patch_lens[$i]} \
|
|
--batch_size ${batches[$i]} \
|
|
--learning_rate ${learning_rates[$i]} \
|
|
--lradj ${lradjs[$i]} \
|
|
--dropout ${dropouts[$i]} \
|
|
--patience ${patiences[$i]} \
|
|
--train_epochs ${epochs[$i]} \
|
|
--use_amp
|
|
done
|