OnehotCategorical¶

class tfsnippet.OnehotCategorical(logits, dtype=None)¶

Bases: tfsnippet.distributions.wrapper.ZhuSuanDistribution

One-hot multivariate Categorical distribution.

A batch of samples is an N-D Tensor with dtype values in range [0, n_categories).

See also

tfsnippet.distributions.Distribution, zhusuan.distributions.Distribution, zhusuan.distributions.OnehotCategorical

Attributes Summary

`batch_shape`	Get the batch shape of the samples.
`dtype`	Get the data type of samples.
`is_continuous`	Whether or not the distribution is continuous?
`is_reparameterized`	Whether or not the distribution is re-parameterized?
`logits`	The un-normalized log probabilities.
`n_categories`	The number of categories in the distribution.
`value_shape`	Get the value shape of an individual sample.

Methods Summary

`get_batch_shape`()	Get the static batch shape of the samples.
`get_value_shape`()	Get the static value shape of an individual sample.
`log_prob`(given[, group_ndims, name])	Compute the log-densities of x against the distribution.
`prob`(given[, group_ndims, name])	Compute the densities of x against the distribution.
`sample`([n_samples, is_reparameterized, …])	Generate samples from the distribution.

Attributes Documentation

batch_shape¶

Get the batch shape of the samples.

Returns:	The batch shape as tensor.
Return type:	tf.Tensor

dtype¶

Get the data type of samples.

Returns:	Data type of the samples.
Return type:	tf.DType

is_continuous¶

Whether or not the distribution is continuous?

Returns:	A boolean indicating whether it is continuous.
Return type:	bool

is_reparameterized¶

Whether or not the distribution is re-parameterized?

The re-parameterization trick is proposed in “Auto-Encoding Variational Bayes” (Kingma, D.P. and Welling), allowing the gradients to be propagated back along the samples. Note that the re-parameterization can be disabled by specifying is_reparameterized = False as an argument of sample().

Returns:	A boolean indicating whether it is re-parameterized.
Return type:	bool

logits¶: The un-normalized log probabilities.

n_categories¶: The number of categories in the distribution.

value_shape¶

Get the value shape of an individual sample.

Returns:	The value shape as tensor.
Return type:	tf.Tensor

Methods Documentation

get_batch_shape()¶

Get the static batch shape of the samples.

Returns:	The batch shape.
Return type:	tf.TensorShape

get_value_shape()¶

Get the static value shape of an individual sample.

Returns:	The static value shape.
Return type:	tf.TensorShape

log_prob(given, group_ndims=0, name=None)¶

Compute the log-densities of x against the distribution.

Parameters:	given (Tensor) – The samples to be tested. group_ndims (int or tf.Tensor) – If specified, the last group_ndims dimensions of the log-densities will be summed up. (default 0) name – TensorFlow name scope of the graph nodes. (default “log_prob”).
Returns:	The log-densities of given.
Return type:	tf.Tensor

prob(given, group_ndims=0, name=None)¶

Compute the densities of x against the distribution.

Parameters:	given (Tensor) – The samples to be tested. group_ndims (int or tf.Tensor) – If specified, the last group_ndims dimensions of the log-densities will be summed up. (default 0) name – TensorFlow name scope of the graph nodes. (default “prob”).
Returns:	The densities of given.
Return type:	tf.Tensor

sample(n_samples=None, is_reparameterized=None, group_ndims=0, compute_density=None, name=None)¶

Generate samples from the distribution.

Parameters:

n_samples (int or tf.Tensor or None) – A 0-D int32 Tensor or None. How many independent samples to draw from the distribution. The samples will have shape [n_samples] + batch_shape + value_shape, or batch_shape + value_shape if n_samples is None.
group_ndims (int or tf.Tensor) – Number of dimensions at the end of [n_samples] + batch_shape to be considered as events group. This will effect the behavior of log_prob() and prob(). (default 0)
is_reparameterized (bool) – If True, raises RuntimeError if the distribution is not re-parameterized. If False, disable re-parameterization even if the distribution is re-parameterized. (default None, following the setting of distribution)
compute_density (bool) – Whether or not to immediately compute the log-density for the samples? (default None, determine by the distribution class itself)
name – TensorFlow name scope of the graph nodes. (default “sample”).

Returns:

The samples as: StochasticTensor.

Return type:

tfsnippet.stochastic.StochasticTensor