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

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.value_type

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

return tensorflow::Status::OK()

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.value(name='value')

tf.contrib.distributions.Exponential.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.bayesflow.stochastic_tensor.BinomialTensor.input_dict

tf.contrib.distributions.LaplaceWithSoftplusScale.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.graph_editor.matcher.input_ops(*args) Add input matches.