# tf.contrib.kfac.loss_functions.NormalMeanNegativeLogProbLoss

## Class NormalMeanNegativeLogProbLoss

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.

## 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)