def setOptimizingParams(self,
damping_factor_obj: float = 1.0,
damping_update_factor_obj: float = 2 / 3,
damping_factor_probe: float = 1.0,
damping_update_factor_probe: float = 2 / 3):
with self.graph.as_default():
self._optparams.obj_optimizer = LMA(
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_expansion=damping_update_factor_obj,
name='obj_opt')
self._optparams.obj_minimize_op = self._optparams.obj_optimizer.minimize(
)
if self._probe_recons:
self._optparams.probe_optimizer = LMA(
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_expansion=damping_update_factor_probe,
name='probe_opt')
self._optparams.probe_minimize_op = self._optparams.probe_optimizer.minimize(
)
self._optparams.training_loss_tensor = self._optparams.obj_optimizer._loss_t
self._optimizers_defined = True
def setOptimizingParams(self):
if self._loss_type in ["poisson", "poisson_surrogate"]:
loss_hessian_fn = self._training_loss_hessian_fn
else:
loss_hessian_fn = None
size = self._batch_train_mods.shape.as_list()[0]
with self.graph.as_default():
self._optparams.obj_optimizer = LMA(input_var=self._tf_obj,
predictions_fn=self._training_predictions_as_obj_fn,
loss_fn=self._training_loss_fn,
name='obj_opt',
diag_hessian_fn=loss_hessian_fn,
assert_tolerances=False)
self._optparams.obj_minimize_op = self._optparams.obj_optimizer.minimize()
if self._probe_recons:
self._optparams.probe_optimizer = LMA(input_var=self._tf_probe,
predictions_fn=self._training_predictions_as_probe_fn,
loss_fn=self._training_loss_fn,
name='probe_opt',
diag_hessian_fn=loss_hessian_fn,
assert_tolerances=False)
self._optparams.probe_minimize_op = self._optparams.probe_optimizer.minimize()
self._optparams.training_loss_tensor = self._optparams.obj_optimizer._loss_t
self._optimizers_defined = True
def setOptimizingParams(self,
cg_tol: float = 1e-5,
max_cg_iter: int = 10,
grad_norm_reg_pow: int = 0):
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
size = self._batch_train_mods.shape.as_list()[0]
with self.graph.as_default():
self._optparams.optimizer = LMA(
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,
max_cg_iter=max_cg_iter,
min_cg_tol=cg_tol,
grad_norm_reg_pow=grad_norm_reg_pow)
self._optparams.minimize_op = self._optparams.optimizer.minimize()
self._optparams.training_loss_tensor = self._optparams.optimizer._loss_t
self._optimizers_defined = True
class LMAOptimizer(Optimizer):
def __init__(self,
input_var: tf.Variable,
predictions_fn: Callable,
loss_fn: Callable,
diag_hessian_fn: Callable = None,
max_cg_iter: int = 100,
initial_update_delay: int = 0,
update_frequency: int = 1,
**extra_init_kwargs: int):
super().__init__(initial_update_delay, update_frequency)
print('Extra initial parameters:', extra_init_kwargs)
self._optimizer = LMA(input_var=input_var,
predictions_fn=predictions_fn,
loss_fn=loss_fn,
diag_hessian_fn=diag_hessian_fn,
max_cg_iter=max_cg_iter,
**extra_init_kwargs)
def setupMinimizeOp(self):
self._minimize_op = self._optimizer.minimize()
@property
def minimize_op(self):
return self._minimize_op
def __init__(self,
input_var: tf.Variable,
predictions_fn: Callable,
loss_fn: Callable,
diag_hessian_fn: Callable = None,
max_cg_iter: int = 100,
initial_update_delay: int = 0,
update_frequency: int = 1,
**extra_init_kwargs: int):
super().__init__(initial_update_delay, update_frequency)
print('Extra initial parameters:', extra_init_kwargs)
self._optimizer = LMA(input_var=input_var,
predictions_fn=predictions_fn,
loss_fn=loss_fn,
diag_hessian_fn=diag_hessian_fn,
max_cg_iter=max_cg_iter,
**extra_init_kwargs)