def objective(trial):
# type: (optuna.trial.Trial) -> float
clf = tf.estimator.DNNClassifier(
hidden_units=[],
feature_columns=[
tf.feature_column.numeric_column(key="x", shape=[20])
],
model_dir=None,
n_classes=2,
config=tf.estimator.RunConfig(save_summary_steps=10,
save_checkpoints_steps=10),
)
hook = TensorFlowPruningHook(
trial=trial,
estimator=clf,
metric="accuracy",
run_every_steps=5,
)
train_spec = tf.estimator.TrainSpec(input_fn=fixed_value_input_fn,
max_steps=100,
hooks=[hook])
eval_spec = tf.estimator.EvalSpec(input_fn=fixed_value_input_fn,
steps=1,
hooks=[])
tf.estimator.train_and_evaluate(estimator=clf,
train_spec=train_spec,
eval_spec=eval_spec)
return 1.0
def test_init_with_is_higher_better(is_higher_better):
# type: (bool) -> None
clf = tf.estimator.DNNClassifier(
hidden_units=[],
feature_columns=[
tf.feature_column.numeric_column(key="x", shape=[20])
],
model_dir=None,
n_classes=2,
config=tf.estimator.RunConfig(save_summary_steps=10,
save_checkpoints_steps=10),
)
study = optuna.create_study()
trial_id = study._storage.create_new_trial(study._study_id)
with pytest.raises(ValueError):
TensorFlowPruningHook(
trial=optuna.trial.Trial(study, trial_id),
estimator=clf,
metric="accuracy",
run_every_steps=5,
is_higher_better=is_higher_better,
)
