def test_tuner_not_call_super_search_with_overwrite(final_fit, super_search,
tmp_path):
tuner = greedy.Greedy(hypermodel=utils.build_graph(), directory=tmp_path)
tuner.search(epochs=10)
tuner.save()
super_search.reset_mock()
tuner = greedy.Greedy(hypermodel=utils.build_graph(), directory=tmp_path)
tuner.search(epochs=10)
super_search.assert_not_called()
def test_tuner_not_call_super_search_with_overwrite(_, final_fit, super_search,
tmp_path):
tuner = greedy.Greedy(hypermodel=utils.build_graph(), directory=tmp_path)
final_fit.return_value = mock.Mock(), mock.Mock(), mock.Mock()
tuner.search(x=None, epochs=10, validation_data=None)
tuner.save()
super_search.reset_mock()
tuner = greedy.Greedy(hypermodel=utils.build_graph(), directory=tmp_path)
tuner.search(x=None, epochs=10, validation_data=None)
super_search.assert_not_called()
def test_greedy_oracle_populate_different_values(get_best_trials):
hp = keras_tuner.HyperParameters()
utils.build_graph().build(hp)
oracle = greedy.GreedyOracle(objective="val_loss", seed=utils.SEED)
trial = mock.Mock()
trial.hyperparameters = hp
get_best_trials.return_value = [trial]
oracle.update_space(hp)
values_a = oracle.populate_space("a")["values"]
values_b = oracle.populate_space("b")["values"]
assert not all([values_a[key] == values_b[key] for key in values_a])
def test_greedy_oracle_state_hypermodel_is_graph():
oracle = greedy.GreedyOracle(
hypermodel=utils.build_graph(),
objective='val_loss',
)
oracle.set_state(oracle.get_state())
assert isinstance(oracle.hypermodel, graph_module.Graph)
def test_greedy_oracle_stop_reach_max_collision(get_best_trials,
compute_values_hash):
hp = keras_tuner.HyperParameters()
utils.build_graph().build(hp)
oracle = greedy.GreedyOracle(objective="val_loss", seed=utils.SEED)
trial = mock.Mock()
trial.hyperparameters = hp
get_best_trials.return_value = [trial]
compute_values_hash.return_value = 1
oracle.update_space(hp)
oracle.populate_space("a")["values"]
assert (oracle.populate_space("b")["status"] ==
keras_tuner.engine.trial.TrialStatus.STOPPED)
def test_no_final_fit_without_epochs_and_fov(get_best_models, final_fit,
super_search, tmp_path):
tuner = greedy.Greedy(hypermodel=utils.build_graph(), directory=tmp_path)
tuner.search(x=None, epochs=None, fit_on_val_data=False)
final_fit.assert_not_called()
def test_tuner_does_not_crash_with_distribution_strategy(tmp_path):
tuner = greedy.Greedy(
hypermodel=utils.build_graph(),
directory=tmp_path,
distribution_strategy=tf.distribute.MirroredStrategy(),
)
tuner.hypermodel.build(tuner.oracle.hyperparameters)
def test_final_fit_with_specified_epochs(_, final_fit, super_search, tmp_path):
tuner = greedy.Greedy(hypermodel=utils.build_graph(), directory=tmp_path)
final_fit.return_value = mock.Mock(), mock.Mock(), mock.Mock()
tuner.search(x=None, epochs=10, validation_data=None)
assert final_fit.call_args_list[0][1]["epochs"] == 10
def test_tuner_call_super_with_early_stopping(_, final_fit, super_search, tmp_path):
tuner = greedy.Greedy(hypermodel=utils.build_graph(), directory=tmp_path)
final_fit.return_value = mock.Mock(), mock.Mock()
tuner.search(x=None, epochs=10, validation_data=None)
assert called_with_early_stopping(super_search)
def test_tuner_call_super_with_early_stopping(final_fit, super_search,
tmp_path):
tuner = greedy.Greedy(hypermodel=utils.build_graph(), directory=tmp_path)
tuner.search(x=None, epochs=10)
assert called_with_early_stopping(super_search)
def test_random_oracle_state():
graph = utils.build_graph()
oracle = greedy.GreedyOracle(
hypermodel=graph,
objective='val_loss',
)
oracle.hypermodel = graph
oracle.set_state(oracle.get_state())
assert oracle.hypermodel is graph
def test_final_fit_with_specified_epochs(
final_fit, super_search, tmp_path):
tuner = greedy.Greedy(
hypermodel=utils.build_graph(),
directory=tmp_path)
tuner.search(x=None, epochs=10)
assert final_fit.call_args_list[0][1]['epochs'] == 10
def test_greedy_oracle_get_state_update_space_can_run():
oracle = greedy.GreedyOracle(
hypermodel=utils.build_graph(),
objective='val_loss',
)
oracle.set_state(oracle.get_state())
hp = kerastuner.HyperParameters()
hp.Boolean('test')
oracle.update_space(hp)
def test_final_fit_best_epochs_if_epoch_unspecified(best_epochs, final_fit,
super_search, tmp_path):
tuner = greedy.Greedy(hypermodel=utils.build_graph(), directory=tmp_path)
tuner.search(x=mock.Mock(),
epochs=None,
fit_on_val_data=True,
validation_data=mock.Mock())
assert final_fit.call_args_list[0][1]["epochs"] == 2
def test_overwrite_search(fit_fn, base_tuner_search, tmp_path):
graph = utils.build_graph()
tuner = tuner_module.AutoTuner(
oracle=greedy.GreedyOracle(graph, objective='val_loss'),
hypermodel=graph,
directory=tmp_path)
tuner.search(epochs=10)
assert tuner._finished
def test_super_with_1k_epochs_if_epoch_unspecified(best_epochs, final_fit,
super_search, tmp_path):
tuner = greedy.Greedy(hypermodel=utils.build_graph(), directory=tmp_path)
tuner.search(x=mock.Mock(),
epochs=None,
fit_on_val_data=True,
validation_data=mock.Mock())
assert super_search.call_args_list[0][1]['epochs'] == 1000
assert called_with_early_stopping(super_search)
def test_add_early_stopping(fit_fn, base_tuner_search, tmp_path):
graph = utils.build_graph()
tuner = tuner_module.AutoTuner(
oracle=greedy.GreedyOracle(graph, objective='val_loss'),
hypermodel=graph,
directory=tmp_path)
tuner.search(x=None, epochs=10)
callbacks = base_tuner_search.call_args_list[0][1]['callbacks']
assert any([isinstance(callback, tf.keras.callbacks.EarlyStopping)
for callback in callbacks])
def test_no_epochs(best_epochs, fit_fn, base_tuner_search, tmp_path):
best_epochs.return_value = 2
graph = utils.build_graph()
tuner = tuner_module.AutoTuner(
oracle=greedy.GreedyOracle(graph, objective='val_loss'),
hypermodel=graph,
directory=tmp_path)
tuner.search(x=mock.Mock(), epochs=None, fit_on_val_data=True,
validation_data=mock.Mock())
callbacks = fit_fn.call_args_list[0][1]['callbacks']
print(callbacks)
assert not any([isinstance(callback, tf.keras.callbacks.EarlyStopping)
for callback in callbacks])
def test_overwrite_search(fit_fn, base_tuner_search, init, tmp_dir):
graph = utils.build_graph()
tuner = tuner_module.AutoTuner(oracle=mock.Mock(), hypermodel=graph)
tuner.hypermodel = graph
tuner.oracle = mock.Mock()
tuner.directory = tmp_dir
tuner.project_name = ''
hp = kerastuner.HyperParameters()
trial = mock.Mock()
trial.hyperparameters = hp
oracle = mock.Mock()
oracle.get_best_trials.return_value = (trial,)
tuner.oracle = oracle
tuner.search()
assert tuner._finished
def test_greedy_oracle(fn):
oracle = greedy.GreedyOracle(
hypermodel=utils.build_graph(),
objective='val_loss',
)
trial = mock.Mock()
hp = kerastuner.HyperParameters()
trial.hyperparameters = hp
fn.return_value = [trial]
oracle.update_space(hp)
for i in range(2000):
oracle._populate_space(str(i))
assert 'optimizer' in oracle._hp_names[greedy.GreedyOracle.OPT]
assert 'classification_head_1/dropout' in oracle._hp_names[
greedy.GreedyOracle.ARCH]
assert 'image_block_1/block_type' in oracle._hp_names[
greedy.GreedyOracle.HYPER]
def test_add_early_stopping(fit_fn, base_tuner_search, init, tmp_dir):
graph = utils.build_graph()
tuner = tuner_module.AutoTuner(oracle=mock.Mock(), hypermodel=graph)
tuner.hypermodel = graph
tuner.oracle = mock.Mock()
tuner.directory = tmp_dir
tuner.project_name = ''
hp = kerastuner.HyperParameters()
trial = mock.Mock()
trial.hyperparameters = hp
oracle = mock.Mock()
oracle.get_best_trials.return_value = (trial,)
tuner.oracle = oracle
tuner.search()
callbacks = base_tuner_search.call_args_list[0][1]['callbacks']
assert any([isinstance(callback, tf.keras.callbacks.EarlyStopping)
for callback in callbacks])
def test_greedy_oracle_populate_doesnt_crash_with_init_hps(get_best_trials):
hp = kerastuner.HyperParameters()
graph = utils.build_graph()
graph.build(hp)
oracle = greedy.GreedyOracle(
initial_hps=task_specific.IMAGE_CLASSIFIER,
objective="val_loss",
seed=utils.SEED,
)
trial = mock.Mock()
trial.hyperparameters = hp
get_best_trials.return_value = [trial]
for i in range(10):
tf.keras.backend.clear_session()
values = oracle._populate_space("a")["values"]
hp = oracle.hyperparameters.copy()
hp.values = values
graph.build(hp)
oracle.update_space(hp)
