tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.graph

tf.contrib.distributions.InverseGamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='InverseGamma') Construct InverseGamma distributions with parameters alpha and beta. The parameters alpha and beta must be shaped in a way that supports broadcasting (e.g. alpha + beta is a valid operation). Args: alpha: Floating point tensor, the shape params of the distribution(s). alpha must contain only positive values. beta: Floating point tensor, the scale params of the distribut

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.entropy(name='entropy')

tf.contrib.distributions.StudentT.std(name='std') Standard deviation.

tf.contrib.distributions.WishartCholesky.is_continuous

class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor MultivariateNormalDiagWithSoftplusStDevTensor is a StochasticTensor backed by the distribution MultivariateNormalDiagWithSoftplusStDev.

tf.contrib.framework.reduce_sum_n(tensors, name=None) Reduce tensors to a scalar sum. This reduces each tensor in tensors to a scalar via tf.reduce_sum, then adds them via tf.add_n. Args: tensors: List of tensors, all of the same numeric type. name: Tensor name, and scope for all other ops. Returns: Total loss tensor, or None if no losses have been configured. Raises: ValueError: if losses is missing or empty.

tf.contrib.distributions.Chi2WithAbsDf.variance(name='variance') Variance.

tf.contrib.distributions.MultivariateNormalCholesky.mode(name='mode') Mode.

tf.FixedLenFeature.__repr__() Return a nicely formatted representation string