tf.contrib.kfac.loss_functions.NormalMeanNegativeLogProbLoss

Class NormalMeanNegativeLogProbLoss

Inherits From: DistributionNegativeLogProbLoss, NaturalParamsNegativeLogProbLoss

Defined in tensorflow/contrib/kfac/python/ops/loss_functions.py.

Neg log prob loss for a normal distribution parameterized by a mean vector.

Note that the covariance is treated as a constant 'var' times the identity. Also note that the Fisher for such a normal distribution with respect the mean parameter is given by:

F = (1/var) * I

See for example https://www.ii.pwr.edu.pl/~tomczak/PDF/[JMT]Fisher_inf.pdf.

Properties

dist

fisher_factor_inner_shape

fisher_factor_inner_static_shape

hessian_factor_inner_shape

hessian_factor_inner_static_shape

inputs

params

targets

Methods

__init__

__init__(
    mean,
    var=0.5,
    targets=None,
    seed=None
)

evaluate

evaluate()

Evaluate the loss function on the targets.

evaluate_on_sample

evaluate_on_sample(seed=None)

Evaluates the log probability on a random sample.

Args:

  • seed: int or None. Random seed for this draw from the distribution.

Returns:

Log probability of sampled targets, summed across examples.

multiply_fisher

multiply_fisher(vector)

multiply_fisher_factor

multiply_fisher_factor(vector)

multiply_fisher_factor_replicated_one_hot

multiply_fisher_factor_replicated_one_hot(index)

multiply_fisher_factor_transpose

multiply_fisher_factor_transpose(vector)

multiply_hessian

multiply_hessian(vector)

multiply_hessian_factor

multiply_hessian_factor(vector)

multiply_hessian_factor_replicated_one_hot

multiply_hessian_factor_replicated_one_hot(index)

multiply_hessian_factor_transpose

multiply_hessian_factor_transpose(vector)

sample

sample(seed)