def register_normal_predictive_distribution(self,
mean,
var=0.5,
seed=None,
targets=None,
name=None,
reuse=VARIABLE_SCOPE):
"""Registers a normal predictive distribution.
Args:
mean: The mean vector defining the distribution.
var: The variance (must be a scalar). Note that the default value of
0.5 corresponds to a standard squared error loss (target -
prediction)**2. If your squared error loss is of the form
0.5*(target - prediction)**2 you should use var=1.0. (Default: 0.5)
seed: The seed for the RNG (for debugging) (Default: None)
targets: (OPTIONAL) The targets for the loss function. Only required if
one wants to call total_loss() instead of total_sampled_loss().
total_loss() is required, for example, to estimate the
"empirical Fisher" (instead of the true Fisher).
(Default: None)
name: (OPTIONAL) str or None. Unique name for this loss function. If None,
a new name is generated. (Default: None)
reuse: (OPTIONAL) bool or str. If True, reuse an existing FisherBlock.
If False, create a new FisherBlock. If VARIABLE_SCOPE, use
tf.get_variable_scope().reuse.
"""
loss = lf.NormalMeanNegativeLogProbLoss(mean, var, targets=targets,
seed=seed)
self.register_loss_function(loss, mean,
"normal_predictive_distribution",
name=name, reuse=reuse)
def register_normal_predictive_distribution(self,
mean,
var=0.5,
seed=None,
targets=None,
name=None):
"""Registers a normal predictive distribution.
Args:
mean: The mean vector defining the distribution.
var: The variance (must be a scalar). Note that the default value of
0.5 corresponds to a standard squared error loss (target -
prediction)**2. If your squared error loss is of the form
0.5*(target - prediction)**2 you should use var=1.0. (Default: 0.5)
seed: The seed for the RNG (for debugging) (Default: None)
targets: (OPTIONAL) The targets for the loss function. Only required if
one wants to call total_loss() instead of total_sampled_loss().
total_loss() is required, for example, to estimate the
"empirical Fisher" (instead of the true Fisher).
(Default: None)
name: (OPTIONAL) str or None. Unique name for this loss function. If None,
a new name is generated. (Default: None)
"""
name = name or self._graph.unique_name(
"register_normal_predictive_distribution")
if name in self._loss_dict:
raise NotImplementedError(
"Adding logits to an existing LossFunction not yet supported.")
loss = lf.NormalMeanNegativeLogProbLoss(mean,
var,
targets=targets,
seed=seed)
self._loss_dict[name] = loss
def register_normal_predictive_distribution(self,
mean,
var=0.5,
seed=None,
targets=None):
"""Registers a normal predictive distribution.
Args:
mean: The mean vector defining the distribution.
var: The variance (must be a scalar). Note that the default value of
0.5 corresponds to a standard squared error loss (target -
prediction)**2. If your squared error loss is of the form
0.5*(target - prediction)**2 you should use var=1.0. (Default: 0.5)
seed: The seed for the RNG (for debugging) (Default: None)
targets: (OPTIONAL) The targets for the loss function. Only required if
one wants to call total_loss() instead of total_sampled_loss().
total_loss() is required, for example, to estimate the
"empirical Fisher" (instead of the true Fisher).
(Default: None)
"""
loss = lf.NormalMeanNegativeLogProbLoss(mean,
var,
targets=targets,
seed=seed)
self.losses.append(loss)
