def main(argv):
del argv
if FLAGS.hparams is not None:
with gfile.Open(FLAGS.hparams, 'r') as f:
hparams = deep_q_networks.get_hparams(**json.load(f))
else:
hparams = deep_q_networks.get_hparams()
hparams.override_from_dict(
{'max_steps_per_episode': FLAGS.max_steps_per_episode})
environment = Molecule(atom_types=set(hparams.atom_types),
init_mol=FLAGS.start_molecule,
allow_removal=hparams.allow_removal,
allow_no_modification=hparams.allow_no_modification,
max_steps=hparams.max_steps_per_episode)
dqn = deep_q_networks.DeepQNetwork(
input_shape=(hparams.batch_size, hparams.fingerprint_length),
q_fn=functools.partial(deep_q_networks.multi_layer_model,
hparams=hparams),
optimizer=hparams.optimizer,
grad_clipping=hparams.grad_clipping,
num_bootstrap_heads=hparams.num_bootstrap_heads,
gamma=hparams.gamma,
epsilon=1.0)
run_dqn.run_training(
hparams=hparams,
environment=environment,
dqn=dqn,
)
core.write_hparams(hparams, os.path.join(FLAGS.model_dir, 'config.json'))
def main(argv):
del argv # unused.
if FLAGS.hparams is not None:
with gfile.Open(FLAGS.hparams, 'r') as f:
hparams = deep_q_networks.get_hparams(**json.load(f))
else:
hparams = deep_q_networks.get_hparams()
filename = 'all_800_mols.json'
with gfile.Open(filename) as fp:
all_molecules = json.load(fp)
environment = LogPRewardWithSimilarityConstraintMolecule(
similarity_constraint=FLAGS.similarity_constraint,
discount_factor=hparams.discount_factor,
all_molecules=all_molecules,
atom_types=set(hparams.atom_types),
init_mol=None,
allow_removal=hparams.allow_removal,
allow_no_modification=hparams.allow_no_modification,
allow_bonds_between_rings=hparams.allow_bonds_between_rings,
allowed_ring_sizes=set(hparams.allowed_ring_sizes),
max_steps=hparams.max_steps_per_episode)
dqn = deep_q_networks.DeepQNetwork(
input_shape=(hparams.batch_size, hparams.fingerprint_length + 1),
q_fn=functools.partial(deep_q_networks.multi_layer_model,
hparams=hparams),
optimizer=hparams.optimizer,
grad_clipping=hparams.grad_clipping,
num_bootstrap_heads=hparams.num_bootstrap_heads,
gamma=hparams.gamma,
epsilon=1.0)
run_dqn.run_training(
hparams=hparams,
environment=environment,
dqn=dqn,
)
core.write_hparams(hparams, os.path.join(FLAGS.model_dir, 'config.json'))
def main(argv):
del argv
if FLAGS.hparams is not None:
with gfile.Open(FLAGS.hparams, 'r') as f:
hparams = run_dqn.get_hparams(**json.load(f))
else:
hparams = run_dqn.get_hparams()
environment = Molecule(target_weight=FLAGS.target_weight,
atom_types=set(hparams.atom_types),
init_mol=FLAGS.start_molecule,
allow_removal=hparams.allow_removal,
allow_no_modification=hparams.allow_no_modification,
max_steps=hparams.max_steps_per_episode)
if FLAGS.error_type.lower() == 'l2':
klass = deep_q_networks_noise.DeepQNetworkL2
else:
klass = deep_q_networks_noise.DeepQNetwork
dqn = klass(input_shape=(hparams.batch_size, hparams.fingerprint_length),
q_fn=functools.partial(deep_q_networks_noise.multi_layer_model,
hparams=hparams),
optimizer=hparams.optimizer,
grad_clipping=hparams.grad_clipping,
num_bootstrap_heads=hparams.num_bootstrap_heads,
gamma=hparams.gamma,
epsilon=1.0)
run_dqn.run_training(
hparams=hparams,
environment=environment,
dqn=dqn,
)
hparams.add_hparam('noise_std', FLAGS.noise_std)
hparams.add_hparam('error_type', FLAGS.error_type)
core.write_hparams(hparams, os.path.join(FLAGS.model_dir, 'config.json'))
def main(argv):
del argv
if FLAGS.hparams is not None:
with gfile.Open(FLAGS.hparams, 'r') as f:
hparams = deep_q_networks.get_hparams(**json.load(f))
else:
hparams = deep_q_networks.get_hparams()
hparams.add_hparam('target_qed', FLAGS.target_qed)
hparams.add_hparam('target_sas', FLAGS.target_sas)
environment = Molecule(
atom_types=set(hparams.atom_types),
init_mol='CCc1c(C)[nH]c2CCC(CN3CCOCC3)C(=O)c12',
allow_removal=hparams.allow_removal,
allow_no_modification=hparams.allow_no_modification,
allow_bonds_between_rings=False,
allowed_ring_sizes={3, 4, 5, 6},
max_steps=hparams.max_steps_per_episode)
dqn = deep_q_networks.DeepQNetwork(
input_shape=(hparams.batch_size, hparams.fingerprint_length + 1),
q_fn=functools.partial(
deep_q_networks.multi_layer_model, hparams=hparams),
optimizer=hparams.optimizer,
grad_clipping=hparams.grad_clipping,
num_bootstrap_heads=hparams.num_bootstrap_heads,
gamma=hparams.gamma,
epsilon=1.0)
run_dqn.run_training(
hparams=hparams,
environment=environment,
dqn=dqn,
)
core.write_hparams(hparams, os.path.join(FLAGS.model_dir, 'config.json'))
