def test_intermixed_prior_graph(self):
# Force graph mode
with tf.compat.v1.Graph().as_default():
spec = phoenix_spec_pb2.PhoenixSpec(
problem_type=phoenix_spec_pb2.PhoenixSpec.DNN)
spec.ensemble_spec.ensemble_search_type = (
ensembling_spec_pb2.EnsemblingSpec.
INTERMIXED_NONADAPTIVE_ENSEMBLE_SEARCH)
spec.ensemble_spec.intermixed_search.width = 2
spec.ensemble_spec.intermixed_search.try_ensembling_every = 4
spec.ensemble_spec.intermixed_search.num_trials_to_consider = 3
spec.is_input_shared = True
generator = prior_generator.PriorGenerator(
phoenix_spec=spec,
metadata=ml_metadata_db.MLMetaData(phoenix_spec=spec,
study_name='',
study_owner=''))
fake_config = collections.namedtuple('RunConfig', ['model_dir'])
# Should be multplication of 4.
run_config = fake_config(model_dir=flags.FLAGS.test_tmpdir +
'/10000')
tf.io.gfile.makedirs(run_config.model_dir)
# Best three trials checkpoint are generated. If the generator chooses
# the suboptimal (wrong) trials, the test will fail.
self._create_checkpoint(['search_generator'], 2)
self._create_checkpoint(['search_generator'], 3)
self._create_checkpoint(['search_generator'], 5)
logits, _ = generator.first_time_chief_generate(
features={},
input_layer_fn=lambda: None,
trial_mode=trial_utils.TrialMode.ENSEMBLE_SEARCH,
shared_input_tensor=tf.zeros([100, 32, 32, 3]),
shared_lengths=None,
logits_dimension=10,
hparams={},
run_config=run_config,
is_training=True,
trials=trial_utils.create_test_trials_intermixed(
flags.FLAGS.test_tmpdir))
self.assertLen(logits, 2)
all_nodes = [
node.name for node in
tf.compat.v1.get_default_graph().as_graph_def().node
]
self.assertAllInSet(_NONADAPTIVE_GRAPH_NODES, all_nodes)
def test_nonadaptive_prior(self, width, consider):
# Force graph mode
with tf.compat.v1.Graph().as_default():
spec = phoenix_spec_pb2.PhoenixSpec(
problem_type=phoenix_spec_pb2.PhoenixSpec.DNN)
spec.ensemble_spec.ensemble_search_type = (
ensembling_spec_pb2.EnsemblingSpec.NONADAPTIVE_ENSEMBLE_SEARCH)
spec.ensemble_spec.nonadaptive_search.width = width
spec.ensemble_spec.nonadaptive_search.num_trials_to_consider = consider
spec.is_input_shared = True
generator = prior_generator.PriorGenerator(
phoenix_spec=spec,
metadata=ml_metadata_db.MLMetaData(phoenix_spec=spec,
study_name='',
study_owner=''))
fake_config = collections.namedtuple('RunConfig', ['model_dir'])
run_config = fake_config(model_dir=flags.FLAGS.test_tmpdir +
'/10000')
tf.io.gfile.makedirs(run_config.model_dir)
# Best three trials checkpoint are generated. If the generator chooses
# the suboptimal (wrong) trials, the test will fail.
self._create_checkpoint(['search_generator'], 3)
self._create_checkpoint(['search_generator'], 4)
self._create_checkpoint(['search_generator'], 5)
logits, _ = generator.first_time_chief_generate(
features={},
input_layer_fn=lambda: None,
trial_mode=trial_utils.TrialMode.ENSEMBLE_SEARCH,
shared_input_tensor=tf.zeros([100, 32, 32, 3]),
shared_lengths=None,
logits_dimension=10,
hparams={},
run_config=run_config,
is_training=True,
trials=_create_trials(flags.FLAGS.test_tmpdir))
self.assertLen(logits, min(width, consider))
