train.sh

#!/bin/bash
conda activate agu      # or source activate agu

ROOT_DIR="./autognnuq/"
DATA_DIR="./autognnuq/data/"
BATCH_SIZE=128
LEARNING_RATE=0.001
EPOCH=30
MAX_EVAL=1000

for SEED in {0..7}; do
  for DATASET in "delaney" "freesolv" "lipo" "qm7" "qm9"; do
    if [ "$DATASET" == "qm9" ]; then
      SPLIT_TYPE="811"
    else
      SPLIT_TYPE="523"
    fi
    
    for SIMPLE in 0 1; do
      python train.py --ROOT_DIR "$ROOT_DIR" \
        --DATA_DIR "$DATA_DIR" \
        --SPLIT_TYPE "$SPLIT_TYPE" \
        --seed $SEED \
        --dataset "$DATASET" \
        --batch_size $BATCH_SIZE \
        --learning_rate $LEARNING_RATE \
        --epoch $EPOCH \
        --simple $SIMPLE \
        --max_eval $MAX_EVAL
    done
  done
done