# ContinuousBernoulli

Source: https://www.tensorplay.cn/docs/generated/tensorplay.distributions.ContinuousBernoulli.html

```python
class tensorplay.distributions.ContinuousBernoulli(probs: Tensor | bool | int | float | None = None, logits: Tensor | bool | int | float | None = None, lims: tuple[float, float] = (0.499, 0.501), validate_args: bool | None = None)
```

Creates a continuous Bernoulli distribution parameterized by probs
or logits (but not both).

The distribution is supported in [0, 1] and parameterized by ‘probs’ (in
(0,1)) or ‘logits’ (real-valued). Note that, unlike the Bernoulli, ‘probs’
does not correspond to a probability and ‘logits’ does not correspond to
log-odds, but the same names are used due to the similarity with the
Bernoulli. See [1] for more details.

Example:

```
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = ContinuousBernoulli(tensorplay.tensor([0.3]))
>>> m.sample()
tensor([ 0.2538])
```

Parameters:

- probs (Number, [Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – (0,1) valued parameters

- logits (Number, [Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – real valued parameters whose sigmoid matches ‘probs’

[1] The continuous Bernoulli: fixing a pervasive error in variational
autoencoders, Loaiza-Ganem G and Cunningham JP, NeurIPS 2019.
[https://arxiv.org/abs/1907.06845](https://arxiv.org/abs/1907.06845)

```python
property batch_shape: Size
```

Returns the shape over which parameters are batched.

```python
enumerate_support(expand: bool = True) → Tensor
```

Returns tensor containing all values supported by a discrete
distribution. The result will enumerate over dimension 0, so the shape
of the result will be (cardinality,) + batch_shape + event_shape
(where event_shape = () for univariate distributions).

Note that this enumerates over all batched tensors in lock-step
[[0, 0], [1, 1], …]. With expand=False, enumeration happens
along dim 0, but with the remaining batch dimensions being
singleton dimensions, [[0], [1], ...

To iterate over the full Cartesian product use
itertools.product(m.enumerate_support()).

Parameters:

expand ([bool](https://docs.python.org/3/builtins/functions.html#bool)) – whether to expand the support over the
batch dims to match the distribution’s batch_shape.

Returns:

Tensor iterating over dimension 0.

```python
property event_shape: Size
```

Returns the shape of a single sample (without batching).

```python
property mode: Tensor
```

Returns the mode of the distribution.

```python
perplexity() → Tensor
```

Returns perplexity of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

```python
sample_n(n: int) → Tensor
```

Generates n samples or n batches of samples if the distribution
parameters are batched.

```python
static set_default_validate_args(value: bool) → None
```

Sets whether validation is enabled or disabled.

The default behavior mimics Python’s assert statement: validation
is on by default, but is disabled if Python is run in optimized mode
(via python -O). Validation may be expensive, so you may want to
disable it once a model is working.

Parameters:

value ([bool](https://docs.python.org/3/builtins/functions.html#bool)) – Whether to enable validation.