# tf.contrib.kfac.loss_functions.CategoricalLogitsNegativeLogProbLoss

## Class CategoricalLogitsNegativeLogProbLoss

Neg log prob loss for a categorical distribution parameterized by logits.

Note that the Fisher (for a single case) of a categorical distribution, with respect to the natural parameters (i.e. the logits), is given by:

F = diag(p) - p*p^T

where p = softmax(logits). F can be factorized as F = B * B^T where

B = diag(q) - p*q^T

where q is the entry-wise square root of p. This is easy to verify using the fact that q^T*q = 1.

## Methods

### __init__

__init__(
logits,
targets=None,
seed=None
)


Instantiates a CategoricalLogitsNegativeLogProbLoss.

#### Args:

• logits: Tensor of shape [batch_size, output_size]. Parameters for underlying distribution.
• targets: None or Tensor of shape [output_size]. Each elements contains an index in [0, output_size).
• seed: int or None. Default random seed when sampling.

### 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)