def test_works_with_multiple_controls(self):
history = trax_history.History()
history.append('eval', 'metrics/accuracy', step=0, value=0.8)
history.append(*online_tune.control_metric('learning_rate'),
step=0,
value=1e-4)
history.append(*online_tune.control_metric('weight_decay_rate'),
step=0,
value=1e-5)
schedule = self._make_schedule(
history,
observation_metrics=(('eval', 'metrics/accuracy'), ),
control_configs=(
('learning_rate', 1e-3, (1e-9, 1.0), False),
('weight_decay_rate', 1e-5, (1e-9, 1.0), False),
),
action_multipliers=(1.0, ),
)
new_controls = schedule(123)
self.assertIn('learning_rate', new_controls)
self.assertIn('weight_decay_rate', new_controls)
def evaluate(self, eval_steps):
del eval_steps
self.state.history.append(mode=HISTORY_MODE,
metric=METRIC,
step=self.step,
value=self.metrics_to_report.pop(0))
for (name, value) in self.nontrainable_params.items():
(mode, metric) = online_tune.control_metric(name)
self.state.history.append(mode=mode,
metric=metric,
step=self.step,
value=value)
def test_clips_updated_control_with_flipping(self):
config = ("momentum", None, (0.5, 0.99), True)
history = trax_history.History()
self._append_metrics(history, online_tune.control_metric("momentum"),
[0.985])
new_control = online_tune.update_control(
control_config=config,
action=0,
history=history,
action_multipliers=(0.5, 1.0, 2.0),
)
np.testing.assert_almost_equal(new_control, 0.99)
def test_clips_updated_control_without_flipping(self):
config = ("learning_rate", None, (1e-9, 10.0), False)
history = trax_history.History()
self._append_metrics(history,
online_tune.control_metric("learning_rate"),
[7.0])
new_control = online_tune.update_control(
control_config=config,
action=2,
history=history,
action_multipliers=(0.5, 1.0, 2.0),
)
np.testing.assert_almost_equal(new_control, 10.0)
def test_changes_lr_when_there_are_some_metrics(self):
history = trax_history.History()
history.append('eval', 'metrics/accuracy', step=0, value=0.8)
history.append(*online_tune.control_metric('learning_rate'),
step=0,
value=1e-4)
schedule = self._make_schedule(
history,
control_configs=(('learning_rate', 1e-3, (1e-9, 1.0), False), ),
observation_metrics=(('eval', 'metrics/accuracy'), ),
action_multipliers=(0.5, 2.0),
)
new_lr = schedule(123)['learning_rate']
self.assertTrue(np.allclose(new_lr, 5e-5) or np.allclose(new_lr, 2e-4))
def test_works_with_serialized_policy(self):
history = trax_history.History()
history.append('eval', 'metrics/accuracy', step=0, value=0.8)
history.append(*online_tune.control_metric('learning_rate'),
step=0,
value=1e-4)
schedule = self._make_schedule(
history,
control_configs=(('learning_rate', 1e-3, (1e-9, 1.0), False), ),
observation_metrics=(('eval', 'metrics/accuracy'), ),
action_multipliers=(0.5, 2.0),
vocab_size=16,
)
new_lr = schedule(123)['learning_rate']
self.assertTrue(np.allclose(new_lr, 5e-5) or np.allclose(new_lr, 2e-4))
