def main(argv):
del argv # unused.
config_name = '/data/junyoung/workspace/Mol_DQN/models/multi_logp_qed_model/config_2'
all_cid = '/data/junyoung/workspace/Mol_DQN/Config/all_cid'
with open(config_name) as f:
hparams = json.load(f)
with open(all_cid) as f:
all_mols = json.load(f)
# init_mol = ["CNC(=O)/C(C#N)=C(/[O-])C1=NN(c2cc(C)ccc2C)C(=O)CC1"]
environment = Multi_LogP_QED_Molecule(hparams=hparams,
molecules=all_mols)
dqn = deep_q_networks.DeepQNetwork(
hparams=hparams,
q_fn=functools.partial(
deep_q_networks.Q_fn_neuralnet_model,
hparams=hparams))
Trainer =trainer.Trainer(
hparams=hparams,
environment=environment,
model=dqn)
Trainer.run_training()
config.write_hparams(hparams, os.path.join(hparams['save_param']['model_path'], 'config.json'))
def main(argv):
del argv # unused.
config_name = '/home/junyoung/workspace/Mol_DQN/models/logp_model/config'
all_cid = '/home/junyoung/workspace/Mol_DQN/Config/all_cid'
with open(config_name) as f:
hparams = json.load(f)
# with open(all_cid) as f:
# all_mols = json.load(f)
environment = LogP_Molecule(hparams=hparams,
molecules=None)
dqn = deep_q_networks.DeepQNetwork(
hparams=hparams,
q_fn=functools.partial(
deep_q_networks.Q_fn_neuralnet_model, hparams=hparams))
Trainer =trainer.Trainer(
hparams=hparams,
environment=environment,
model=dqn)
Trainer.run_training()
config.write_hparams(hparams, os.path.join(hparams['save_param']['model_dir'], 'config.json'))