def _test_base_tuner():
def build_model(hp):
return hp.Int("a", 1, 100)
tuner = SimpleTuner(
oracle=kt.oracles.RandomSearch(objective=kt.Objective(
"score", "max"),
max_trials=10),
hypermodel=build_model,
directory=tmp_dir,
)
tuner.search()
# Only worker makes it to this point, server runs until thread stops.
assert dist_utils.has_chief_oracle()
assert not dist_utils.is_chief_oracle()
assert isinstance(tuner.oracle,
kt.distribute.oracle_client.OracleClient)
barrier.wait(60)
# Model is just a score.
scores = tuner.get_best_models(10)
assert len(scores)
assert scores == sorted(copy.copy(scores), reverse=True)
def _test_random_search():
def build_model(hp):
model = keras.Sequential()
model.add(keras.layers.Dense(3, input_shape=(5, )))
for i in range(hp.Int("num_layers", 1, 3)):
model.add(
keras.layers.Dense(hp.Int("num_units_%i" % i, 1, 3),
activation="relu"))
model.add(keras.layers.Dense(1, activation="sigmoid"))
model.compile("sgd", "binary_crossentropy")
return model
x = np.random.uniform(-1, 1, size=(2, 5))
y = np.ones((2, 1))
tuner = kt.tuners.RandomSearch(
hypermodel=build_model,
objective="val_loss",
max_trials=10,
directory=tmp_dir,
)
# Only worker makes it to this point, server runs until thread stops.
assert dist_utils.has_chief_oracle()
assert not dist_utils.is_chief_oracle()
assert isinstance(tuner.oracle,
kt.distribute.oracle_client.OracleClient)
tuner.search(x, y, validation_data=(x, y), epochs=1, batch_size=2)
# Suppress warnings about optimizer state not being restored by tf.keras.
tf.get_logger().setLevel(logging.ERROR)
trials = tuner.oracle.get_best_trials(2)
assert trials[0].score <= trials[1].score
models = tuner.get_best_models(2)
assert models[0].evaluate(x, y) <= models[1].evaluate(x, y)