class tf.contrib.distributions.TransformedDistribution A Transformed Distribution. A Transformed Distribution models p(y) given a base distribution p(x), an invertible transform, y = f(x), and the determinant of the Jacobian of f(x). Shapes, type, and reparameterization are taken from the base distribution.

tf.contrib.distributions.Chi2WithAbsDf.alpha Shape parameter.

tf.contrib.distributions.Exponential.parameters Dictionary of parameters used by this Distribution.

tf.FixedLenSequenceFeature.__new__(_cls, shape, dtype, allow_missing=False) Create new instance of FixedLenSequenceFeature(shape, dtype, allow_missing)

tf.contrib.distributions.NormalWithSoftplusSigma.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.WishartFull.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.ExponentialWithSoftplusLam.__init__(lam, validate_args=False, allow_nan_stats=True, name='ExponentialWithSoftplusLam')

class tf.contrib.distributions.RegisterKL Decorator to register a KL divergence implementation function. Usage: @distributions.RegisterKL(distributions.Normal, distributions.Normal) def _kl_normal_mvn(norm_a, norm_b): # Return KL(norm_a || norm_b)

tf.contrib.learn.DNNRegressor.linear_weights_ Returns weights per feature of the linear part.

tf.contrib.distributions.Normal.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.