146 lines
3.3 KiB
Bash
146 lines
3.3 KiB
Bash
#!/bin/bash
|
|
|
|
# xPatch_SparseChannel Classification Training Script for Multiple Datasets
|
|
export CUDA_VISIBLE_DEVICES=0
|
|
|
|
model_name=xPatch_SparseChannel
|
|
|
|
# Create results directory if it doesn't exist
|
|
mkdir -p ./results
|
|
|
|
|
|
|
|
# UWaveGestureLibrary dataset (seq_len=315, enc_in=3, k_graph=3)
|
|
python -u run.py \
|
|
--task_name classification \
|
|
--is_training 1 \
|
|
--root_path ./dataset/UWaveGestureLibrary/ \
|
|
--model_id UWaveGestureLibrary \
|
|
--model $model_name \
|
|
--data UEA \
|
|
--e_layers 2 \
|
|
--batch_size 64 \
|
|
--seq_len 315 \
|
|
--enc_in 3 \
|
|
--d_model 128 \
|
|
--d_ff 256 \
|
|
--n_heads 16 \
|
|
--patch_len 16 \
|
|
--stride 8 \
|
|
--dropout 0.1 \
|
|
--des 'xPatch_SparseChannel_UWaveGestureLibrary' \
|
|
--itr 1 \
|
|
--learning_rate 0.001 \
|
|
--train_epochs 100 \
|
|
--patience 30 \
|
|
--revin 0 \
|
|
--k_graph 3 | tee ./results/xPatch_SparseChannel_UWaveGestureLibrary.log
|
|
|
|
|
|
|
|
# EthanolConcentration dataset (seq_len=1751, enc_in=3, k_graph=3)
|
|
python -u run.py \
|
|
--task_name classification \
|
|
--is_training 1 \
|
|
--root_path ./dataset/EthanolConcentration/ \
|
|
--model_id EthanolConcentration \
|
|
--model $model_name \
|
|
--data UEA \
|
|
--e_layers 2 \
|
|
--batch_size 64 \
|
|
--seq_len 1751 \
|
|
--enc_in 3 \
|
|
--d_model 128 \
|
|
--d_ff 256 \
|
|
--n_heads 16 \
|
|
--patch_len 16 \
|
|
--stride 8 \
|
|
--dropout 0.1 \
|
|
--des 'xPatch_SparseChannel_EthanolConcentration' \
|
|
--itr 1 \
|
|
--learning_rate 0.0005 \
|
|
--train_epochs 100 \
|
|
--patience 30 \
|
|
--revin 0 \
|
|
--k_graph 3 | tee ./results/xPatch_SparseChannel_EthanolConcentration.log
|
|
|
|
# Handwriting dataset (seq_len=152, enc_in=3, k_graph=3)
|
|
python -u run.py \
|
|
--task_name classification \
|
|
--is_training 1 \
|
|
--root_path ./dataset/Handwriting/ \
|
|
--model_id Handwriting \
|
|
--model $model_name \
|
|
--data UEA \
|
|
--e_layers 2 \
|
|
--batch_size 64 \
|
|
--seq_len 152 \
|
|
--enc_in 3 \
|
|
--d_model 128 \
|
|
--d_ff 256 \
|
|
--n_heads 16 \
|
|
--patch_len 16 \
|
|
--stride 8 \
|
|
--dropout 0.1 \
|
|
--des 'xPatch_SparseChannel_Handwriting' \
|
|
--itr 1 \
|
|
--learning_rate 0.001 \
|
|
--train_epochs 100 \
|
|
--patience 30 \
|
|
--revin 0 \
|
|
--k_graph 3 | tee ./results/xPatch_SparseChannel_Handwriting.log
|
|
|
|
# JapaneseVowels dataset (seq_len=29, enc_in=12, k_graph=8)
|
|
python -u run.py \
|
|
--task_name classification \
|
|
--is_training 1 \
|
|
--root_path ./dataset/JapaneseVowels/ \
|
|
--model_id JapaneseVowels \
|
|
--model $model_name \
|
|
--data UEA \
|
|
--e_layers 2 \
|
|
--batch_size 64 \
|
|
--seq_len 29 \
|
|
--enc_in 12 \
|
|
--d_model 128 \
|
|
--d_ff 256 \
|
|
--n_heads 16 \
|
|
--patch_len 16 \
|
|
--stride 8 \
|
|
--dropout 0.1 \
|
|
--des 'xPatch_SparseChannel_JapaneseVowels' \
|
|
--itr 1 \
|
|
--learning_rate 0.0005 \
|
|
--train_epochs 100 \
|
|
--patience 30 \
|
|
--revin 0 \
|
|
--k_graph 8 | tee ./results/xPatch_SparseChannel_JapaneseVowels.log
|
|
|
|
|
|
|
|
# PEMS-SF dataset (seq_len=144, enc_in=963, k_graph=8)
|
|
python -u run.py \
|
|
--task_name classification \
|
|
--is_training 1 \
|
|
--root_path ./dataset/PEMS-SF/ \
|
|
--model_id PEMS-SF \
|
|
--model $model_name \
|
|
--data UEA \
|
|
--e_layers 2 \
|
|
--batch_size 16 \
|
|
--seq_len 144 \
|
|
--enc_in 963 \
|
|
--d_model 128 \
|
|
--d_ff 256 \
|
|
--n_heads 16 \
|
|
--patch_len 16 \
|
|
--stride 8 \
|
|
--dropout 0.1 \
|
|
--des 'xPatch_SparseChannel_PEMS-SF' \
|
|
--itr 1 \
|
|
--learning_rate 0.0005 \
|
|
--train_epochs 100 \
|
|
--patience 30 \
|
|
--revin 0 \
|
|
--k_graph 8 | tee ./results/xPatch_SparseChannel_PEMS-SF.log
|