# Bernoulli

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

```python
class tensorplay.distributions.Bernoulli(probs: Tensor | bool | int | float | None = None, logits: Tensor | bool | int | float | None = None, validate_args: bool | None = None)
```

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

Samples are binary (0 or 1). They take the value 1 with probability p
and 0 with probability 1 - p.

Example:

```
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = Bernoulli(tensorplay.tensor([0.3]))
>>> m.sample()  # 30% chance 1; 70% chance 0
tensor([ 0.])
```

Parameters:

- probs (Number, [Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the probability of sampling 1

- logits (Number, [Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the log-odds of sampling 1

- validate_args ([bool](https://docs.python.org/3/builtins/functions.html#bool), optional) – whether to validate arguments, None by default

```python
property batch_shape: Size
```

Returns the shape over which parameters are batched.

```python
cdf(value: Tensor) → Tensor
```

Returns the cumulative density/mass function evaluated at
value.

Parameters:

value ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor))

```python
property event_shape: Size
```

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

```python
icdf(value: Tensor) → Tensor
```

Returns the inverse cumulative density/mass function evaluated at
value.

Parameters:

value ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor))

```python
perplexity() → Tensor
```

Returns perplexity of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

```python
rsample(sample_shape: Size | Sequence[int] = ()) → Tensor
```

Generates a sample_shape shaped reparameterized sample or sample_shape
shaped batch of reparameterized samples if the distribution parameters
are batched.

```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.

```python
property stddev: Tensor
```

Returns the standard deviation of the distribution.