tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.mean(name='mean')

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

tf.contrib.learn.monitors.EveryN.run_on_all_workers

tf.SparseTensorValue.__new__(_cls, indices, values, shape) Create new instance of SparseTensorValue(indices, values, shape)

tf.contrib.distributions.Poisson.mode(name='mode') Mode. Additional documentation from Poisson: Note that when lam is an integer, there are actually two modes. Namely, lam and lam - 1 are both modes. Here we return only the larger of the two modes.

class tf.contrib.distributions.RegisterKL Decorator to register a KL divergence implementation function. Usage: @distributions.RegisterKL(distributions.Normal, distributions.Normal) def _kl_normal_mvn(norm_a, norm_b): # Return KL(norm_a || norm_b)

tf.contrib.distributions.ExponentialWithSoftplusLam.__init__(lam, validate_args=False, allow_nan_stats=True, name='ExponentialWithSoftplusLam')

tf.contrib.distributions.InverseGamma.beta Scale parameter.

tf.contrib.learn.monitors.ExportMonitor.every_n_step_begin(step) Callback before every n'th step begins. Args: step: int, the current value of the global step. Returns: A list of tensors that will be evaluated at this step.

tf.contrib.distributions.Binomial.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.