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

tf.contrib.distributions.Dirichlet.alpha_sum Sum of shape parameter.

tf.contrib.learn.monitors.StopAtStep.post_step(step, session) Callback after the step is finished. Called after step_end and receives session to perform extra session.run calls. If failure occurred in the process, will be called as well. Args: step: int, global step of the model. session: Session object.

tf.contrib.distributions.Categorical.is_reparameterized

void tensorflow::TensorShape::AddDim(int64 size) Add a dimension to the end ("inner-most"). REQUIRES: size >= 0

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.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.BetaWithSoftplusAB.parameters Dictionary of parameters used by this Distribution.

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

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