tf.get_default_session() Returns the default session for the current thread. The returned Session will be the innermost session on which a Session or Session.as_default() context has been entered. NOTE: The default session is a property of the current thread. If you create a new thread, and wish to use the default session in that thread, you must explicitly add a with sess.as_default(): in that thread's function. Returns: The default Session being used in the current thread.

tf.contrib.learn.monitors.EveryN.post_step(step, session)

tf.contrib.distributions.ExponentialWithSoftplusLam.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.

tf.contrib.distributions.InverseGamma.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mean(name='mean') Mean.

tf.asin(x, name=None) Computes asin of x element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.distributions.Exponential.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.distribution

tf.contrib.distributions.InverseGamma.entropy(name='entropy') Shanon entropy in nats. Additional documentation from InverseGamma: This is defined to be entropy = alpha - log(beta) + log(Gamma(alpha)) + (1-alpha)digamma(alpha) where digamma(alpha) is the digamma function.

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.loss(final_loss, name='Loss')