def hparams_config():
return {
'kernel':
CategoricalParam(choices=['linear', 'poly', 'rbf', 'sigmoid']),
'C': RealParam(min=0.1, max=5),
'epsilon': RealParam()
}
def get_hparam_config(args):
return {'d_model': ConstantParam(1500),
'dropout': RealParam(min=0.),
'monte_carlo_N': ConstantParam(5),
'use_monte_carlo_sim': ConstantParam(True),
'no_mc_fill_val': ConstantParam(0.0),
'gamma': ConstantParam(0.97),
'episodes_to_train': DiscreteParam(min=5, max=20),
'gae_lambda': RealParam(0.9, max=0.999),
'ppo_eps': ConstantParam(0.2),
'ppo_batch': ConstantParam(1),
'ppo_epochs': DiscreteParam(2, max=10),
'entropy_beta': LogRealParam(),
'use_true_reward': ConstantParam(args.use_true_reward),
'baseline_reward': ConstantParam(args.baseline_reward),
'reward_params': DictParam({'num_layers': DiscreteParam(min=1, max=4),
'd_model': DiscreteParam(min=128, max=1024),
'unit_type': ConstantParam('lstm'),
'demo_batch_size': CategoricalParam([64, 128, 256]),
'irl_alg_num_iter': DiscreteParam(2, max=10),
'use_attention': ConstantParam(False),
'bidirectional': ConstantParam(True),
'dropout': RealParam(),
'use_validity_flag': ConstantParam(not args.no_smiles_validity_flag),
'optimizer': CategoricalParam(
choices=['sgd', 'adam', 'adadelta', 'adagrad', 'adamax', 'rmsprop']),
'optimizer__global__weight_decay': LogRealParam(),
'optimizer__global__lr': LogRealParam()}),
'agent_params': DictParam({'unit_type': ConstantParam('gru'),
'num_layers': ConstantParam(2),
'stack_width': ConstantParam(1500),
'stack_depth': ConstantParam(200),
'optimizer': ConstantParam('adadelta'),
'optimizer__global__weight_decay': LogRealParam(),
'optimizer__global__lr': LogRealParam()}),
'critic_params': DictParam({'num_layers': ConstantParam(2),
'd_model': ConstantParam(256),
'unit_type': ConstantParam('lstm'),
'optimizer': ConstantParam('adadelta'),
'optimizer__global__weight_decay': LogRealParam(),
'optimizer__global__lr': LogRealParam()}),
'expert_model_params': DictParam({'model_dir': ConstantParam('./model_dir/expert_rnn_bin'),
'd_model': ConstantParam(128),
'rnn_num_layers': ConstantParam(2),
'dropout': ConstantParam(0.8),
'is_bidirectional': ConstantParam(True),
'unit_type': ConstantParam('lstm')})
}
def get_hparam_config(args):
config = {
'unit_type':
CategoricalParam(choices=['gru', 'lstm']),
'num_layers':
DiscreteParam(min=1, max=10),
"d_model":
DiscreteParam(min=32, max=1024),
"stack_width":
DiscreteParam(min=10, max=128),
"stack_depth":
DiscreteParam(min=10, max=64),
"dropout":
RealParam(0.0, max=0.3),
"batch_size":
CategoricalParam(choices=[32, 64, 128]),
# optimizer params
"optimizer":
CategoricalParam(choices=[
"sgd", "adam", "adadelta", "adagrad", "adamax", "rmsprop"
]),
"optimizer__global__weight_decay":
LogRealParam(),
"optimizer__global__lr":
LogRealParam(),
}
return config
def hparams_config():
return {'batch': CategoricalParam(choices=[32, 64, 128]),
'd_model': DiscreteParam(min=32, max=256),
'rnn_num_layers': DiscreteParam(min=1, max=3),
'dropout': RealParam(min=0., max=0.8),
'is_bidirectional': CategoricalParam(choices=[True, False]),
'unit_type': CategoricalParam(choices=['gru', 'lstm']),
'optimizer': CategoricalParam(choices=['sgd', 'adam', 'adadelta', 'adagrad', 'adamax', 'rmsprop']),
'optimizer__global__weight_decay': LogRealParam(),
'optimizer__global__lr': LogRealParam()}
def get_hparam_config(args):
config = {
"attn_heads":
CategoricalParam([1, 2, 4, 8]),
"attn_layers":
DiscreteParam(min=1, max=4),
"lin_dims":
DiscreteParam(min=64, max=2048, size=DiscreteParam(min=1, max=3)),
"d_model":
CategoricalParam(choices=[128, 256, 512, 1024]),
"d_hidden":
DiscreteParam(min=10, max=64),
"stack_width":
DiscreteParam(min=10, max=64),
"stack_depth":
DiscreteParam(min=10, max=64),
"d_ff":
DiscreteParam(min=128, max=2048),
"d_ss":
DiscreteParam(min=128, max=2024),
"dropout":
RealParam(0.0, max=0.5),
"batch_size":
CategoricalParam(choices=[32, 64, 128]),
# optimizer params
"optimizer":
CategoricalParam(choices=[
"sgd", "adam", "adadelta", "adagrad", "adamax", "rmsprop"
]),
"optimizer__global__weight_decay":
LogRealParam(),
"optimizer__global__lr":
LogRealParam(),
}
return config
def get_hparam_config(args):
return {
'd_model':
ConstantParam(1500),
'dropout':
RealParam(min=0.),
'monte_carlo_N':
ConstantParam(5),
'use_monte_carlo_sim':
ConstantParam(True),
'no_mc_fill_val':
ConstantParam(0.0),
'gamma':
ConstantParam(0.97),
'episodes_to_train':
DiscreteParam(min=5, max=20),
'reinforce_max_norm':
ConstantParam(None),
'lr_decay_gamma':
RealParam(),
'lr_decay_step_size':
DiscreteParam(min=100, max=1000),
'xent_lambda':
ConstantParam(0.0),
'use_true_reward':
ConstantParam(args.use_true_reward),
'bias_mode':
ConstantParam(args.bias_mode),
'reward_params':
DictParam({
'num_layers':
ConstantParam(2),
'd_model':
ConstantParam(256),
'unit_type':
ConstantParam('lstm'),
'demo_batch_size':
ConstantParam(32),
'irl_alg_num_iter':
ConstantParam(5),
'use_attention':
ConstantParam(args.use_attention),
'use_validity_flag':
ConstantParam(not args.no_smiles_validity_flag),
'bidirectional':
ConstantParam(True),
'optimizer':
ConstantParam('adadelta'),
'optimizer__global__weight_decay':
ConstantParam(0.0000),
'optimizer__global__lr':
ConstantParam(0.001),
}),
'agent_params':
DictParam({
'unit_type': ConstantParam('gru'),
'num_layers': ConstantParam(2),
'stack_width': ConstantParam(1500),
'stack_depth': ConstantParam(200),
'optimizer': ConstantParam('adadelta'),
'optimizer__global__weight_decay': LogRealParam(),
'optimizer__global__lr': LogRealParam()
}),
'expert_model_dir':
ConstantParam('./model_dir/expert_xgb_reg')
}