tf.contrib.distributions.Distribution.__init__()

tf.contrib.distributions.Distribution.__init__(dtype, parameters, is_continuous, is_reparameterized, validate_args, allow_nan_stats, name=None)

Constructs the Distribution.

This is a private method for subclass use.

Args:

• dtype: The type of the event samples. None implies no type-enforcement.
• parameters: Python dictionary of parameters used by this Distribution.
• is_continuous: Python boolean. If True this Distribution is continuous over its supported domain.
• is_reparameterized: Python boolean. If True this Distribution can be reparameterized in terms of some standard distribution with a function whose Jacobian is constant for the support of the standard distribution.
• validate_args: Python boolean. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
• allow_nan_stats: Pytho nboolean. If False, raise an exception if a statistic (e.g., mean, mode) 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 (optional).