def setOptimizingParams(self,
damping_factor_obj: float = 1.0,
damping_update_factor_obj: float = 0.999,
damping_update_frequency_obj: int = 5,
damping_factor_probe: float = 1.0,
damping_update_factor_probe: float = 0.999,
damping_update_frequency_probe: float = 5):
with self.graph.as_default():
self._optparams.obj_optimizer = Curveball(
input_var=self._tf_obj,
predictions_fn=self._training_predictions_as_obj_fn,
loss_fn=self._training_loss_fn,
damping_factor=damping_factor_obj,
damping_update_factor=damping_update_factor_obj,
damping_update_frequency=damping_update_frequency_obj,
name='obj_opt')
self._optparams.obj_minimize_op = self._optparams.obj_optimizer.minimize(
)
if self._probe_recons:
self._optparams.probe_optimizer = Curveball(
input_var=self._tf_probe,
predictions_fn=self._training_predictions_as_probe_fn,
loss_fn=self._training_loss_fn,
damping_factor=damping_factor_probe,
damping_update_factor=damping_update_factor_probe,
damping_update_frequency=damping_update_frequency_probe,
name='probe_opt')
self._optparams.probe_minimize_op = self._optparams.probe_optimizer.minimize(
)
self._optparams.training_loss_tensor = self._optparams.obj_optimizer._loss_fn_tensor
self._optimizers_defined = True
def __init__(self,
input_var: tf.Variable,
predictions_fn: Callable,
loss_fn: Callable,
diag_hessian_fn: Callable = None,
initial_update_delay: int = 0,
update_frequency: int = 1,
**extra_init_kwargs: int):
super().__init__(initial_update_delay, update_frequency)
self._optimizer = Curveball(input_var=input_var,
predictions_fn=predictions_fn,
loss_fn=loss_fn,
diag_hessian_fn=diag_hessian_fn,
**extra_init_kwargs)
def setOptimizingParams(self,
damping_factor_obj: float = 1.0,
damping_update_factor_obj: float = 0.999,
damping_update_frequency_obj: int = 5,
damping_factor_probe: float = 1.0,
damping_update_factor_probe: float = 0.999,
damping_update_frequency_probe: float = 5,
update_cond_threshold_low: float = 0.5,
update_cond_threshold_high: float = 1.5):
if self._loss_type in ["poisson", "poisson_surrogate"]:
loss_hessian_fn = self._training_loss_hessian_fn
squared_loss = False
else:
loss_hessian_fn = None
squared_loss = True
with self.graph.as_default():
self._optparams.obj_optimizer = Curveball(
input_var=self._tf_obj,
predictions_fn=self._training_predictions_as_obj_fn,
loss_fn=self._training_loss_fn,
damping_factor=damping_factor_obj,
damping_update_factor=damping_update_factor_obj,
damping_update_frequency=damping_update_frequency_obj,
update_cond_threshold_low=update_cond_threshold_low,
update_cond_threshold_high=update_cond_threshold_high,
name='obj_opt',
diag_hessian_fn=loss_hessian_fn,
squared_loss=squared_loss)
self._optparams.obj_minimize_op = self._optparams.obj_optimizer.minimize(
)
if self._probe_recons:
self._optparams.probe_optimizer = Curveball(
input_var=self._tf_probe,
predictions_fn=self._training_predictions_as_probe_fn,
loss_fn=self._training_loss_fn,
damping_factor=damping_factor_probe,
damping_update_factor=damping_update_factor_probe,
damping_update_frequency=damping_update_frequency_probe,
name='probe_opt',
diag_hessian_fn=loss_hessian_fn,
squared_loss=squared_loss)
self._optparams.probe_minimize_op = self._optparams.probe_optimizer.minimize(
)
self._optparams.training_loss_tensor = self._optparams.obj_optimizer._loss_fn_tensor
self._optimizers_defined = True
def setOptimizingParams(self):
if self._loss_type in ["poisson", "poisson_surrogate"]:
loss_hessian_fn = self._training_loss_hessian_fn
squared_loss = False
else:
loss_hessian_fn = None
squared_loss = True
with self.graph.as_default():
self._optparams.optimizer = Curveball(
input_var=self._tf_var,
predictions_fn=self._training_predictions_fn,
loss_fn=self._training_loss_fn,
name='opt',
diag_hessian_fn=loss_hessian_fn,
squared_loss=squared_loss)
self._optparams.minimize_op = self._optparams.optimizer.minimize()
self._optparams.training_loss_tensor = self._optparams.optimizer._loss_fn_tensor
self._optimizers_defined = True
class CurveballOptimizer(Optimizer):
def __init__(self,
input_var: tf.Variable,
predictions_fn: Callable,
loss_fn: Callable,
diag_hessian_fn: Callable = None,
initial_update_delay: int = 0,
update_frequency: int = 1,
**extra_init_kwargs: int):
super().__init__(initial_update_delay, update_frequency)
self._optimizer = Curveball(input_var=input_var,
predictions_fn=predictions_fn,
loss_fn=loss_fn,
diag_hessian_fn=diag_hessian_fn,
**extra_init_kwargs)
def setupMinimizeOp(self):
self._minimize_op = self._optimizer.minimize()
@property
def minimize_op(self):
return self._minimize_op