config_finetune.yaml

name: 'new_BT_7'
patience: 150
batch_size: 32                  # batch size
epochs: 800                     # total number of epochs
eval_every_n_epochs: 1          # validation frequency
fine_tune_from: BT8   # sub directory of pre-trained model in ./ckpt
log_every_n_steps: 5           # print training log frequency
fp16_precision: False           # float precision 16 (i.e. True/False)
init_lr: 0.0005                 # initial learning rate for the prediction head
init_base_lr: 0.0005            # initial learning rate for the base GNN encoder
warmup: 250
weight_decay: 1e-6              # weight decay of Adam
gpu: cuda:0                     # training GPU
task_name: visc                 # name of fine-tuning benchmark, inlcuding
                                # classifications: BBBP/BACE/ClinTox/Tox21/HIV/SIDER/MUV
                                # regressions: FreeSolv/ESOL/Lipo/qm7/qm8/qm9

model_type: gcn                 # GNN backbone (i.e., gin/gcn)
model: 
  num_layer: 3                  # number of graph conv layers
  emb_dim: 300                  # embedding dimension in graph conv layers
  feat_dim: 512                 # output feature dimention
  drop_ratio: 0.1               # dropout ratio
  pool: mean                    # readout pooling (i.e., mean/max/add)

dataset:
  num_workers: 0                # dataloader number of workers
  valid_size: 0.1               # ratio of validation data
  test_size: 0.2                # ratio of test data
  train_size: 0.7              # ratio of training data
  splitting: random           # data splitting (i.e., random/scaffold)