tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.distribution

class tf.contrib.distributions.Multinomial Multinomial distribution. This distribution is parameterized by a vector p of probability parameters for k classes and n, the counts per each class..

tf.contrib.distributions.MultivariateNormalDiag.sigma_det(name='sigma_det') Determinant of covariance matrix.

class tf.contrib.distributions.BetaWithSoftplusAB Beta with softplus transform on a and b.

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

tf.contrib.learn.TensorFlowRNNRegressor.get_params(deep=True) Get parameters for this estimator. Args: deep: boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns: params : mapping of string to any Parameter names mapped to their values.

tf.contrib.distributions.Binomial.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.Laplace.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.MultivariateNormalDiag.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.