def test_cast_float() -> None:
def objective(trial: optuna.trial.Trial) -> float:
x = trial.suggest_float("x", 1, 2)
y = trial.suggest_float("y", 1, 2, log=True)
assert isinstance(x, float)
assert isinstance(y, float)
return x + y
dirname = tempfile.mkdtemp()
metric_name = "target"
study_name = "test_tensorboard_integration"
tbcallback = TensorBoardCallback(dirname, metric_name)
study = optuna.create_study(study_name=study_name)
study.optimize(objective, n_trials=1, callbacks=[tbcallback])
def test_study_name() -> None:
dirname = tempfile.mkdtemp()
metric_name = "target"
study_name = "test_tensorboard_integration"
tbcallback = TensorBoardCallback(dirname, metric_name)
study = optuna.create_study(study_name=study_name)
study.optimize(_objective_func, n_trials=1, callbacks=[tbcallback])
event_acc = EventAccumulator(os.path.join(dirname, "trial-0"))
event_acc.Reload()
try:
assert len(event_acc.Tensors("target")) == 1
except Exception as e:
raise e
finally:
shutil.rmtree(dirname)
def test_experimental_warning() -> None:
with pytest.warns(optuna.exceptions.ExperimentalWarning):
TensorBoardCallback(dirname="", metric_name="")
tf.keras.layers.Dense(10, activation=tf.nn.softmax),
])
model.compile(optimizer=optimizer,
loss="sparse_categorical_crossentropy",
metrics=["accuracy"])
model.fit(
x_train, y_train,
epochs=1) # Run with 1 epoch to speed things up for demo purposes
_, accuracy = model.evaluate(x_test, y_test)
return accuracy
def objective(trial: optuna.trial.Trial) -> float:
num_units = trial.suggest_int("NUM_UNITS", 16, 32)
dropout_rate = trial.suggest_float("DROPOUT_RATE", 0.1, 0.2)
optimizer = trial.suggest_categorical("OPTIMIZER", ["sgd", "adam"])
accuracy = train_test_model(num_units, dropout_rate,
optimizer) # type: ignore
return accuracy
tensorboard_callback = TensorBoardCallback("logs/", metric_name="accuracy")
study = optuna.create_study(direction="maximize")
study.optimize(objective,
n_trials=10,
timeout=600,
callbacks=[tensorboard_callback])