tf.contrib.distributions.Bernoulli.__init__()

tf.contrib.distributions.Bernoulli.__init__(logits=None, p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='Bernoulli')

Construct Bernoulli distributions.

Args:

• logits: An N-D Tensor representing the log-odds of a positive event. Each entry in the Tensor parametrizes an independent Bernoulli distribution where the probability of an event is sigmoid(logits).
• p: An N-D Tensor representing the probability of a positive event. Each entry in the Tensor parameterizes an independent Bernoulli distribution.
• dtype: dtype for samples.
• validate_args: Boolean, default False. Whether to validate that 0 <= p <= 1. If validate_args is False, and the inputs are invalid, methods like log_pmf may return NaN values.
• allow_nan_stats: Boolean, default True. If False, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
• name: A name for this distribution.

Raises:

• ValueError: If p and logits are passed, or if neither are passed.