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

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.distributions.BetaWithSoftplusAB.validate_args Python boolean indicated possibly expensive checks are enabled.

class tf.contrib.learn.monitors.LoggingTrainable Writes trainable variable values into log every N steps. Write the tensors in trainable variables every_n steps, starting with the first_nth step.

tf.contrib.learn.monitors.LoggingTrainable.every_n_post_step(step, session) Callback after a step is finished or end() is called. Args: step: int, the current value of the global step. session: Session object.

class tf.python_io.TFRecordWriter A class to write records to a TFRecords file. This class implements __enter__ and __exit__, and can be used in with blocks like a normal file.

tf.contrib.distributions.InverseGamma.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

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

tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, validate_args=False, allow_nan_stats=True, name='DirichletMultinomial') Initialize a batch of DirichletMultinomial distributions. Args: n: Non-negative floating point tensor, whose dtype is the same as alpha. The shape is broadcastable to [N1,..., Nm] with m >= 0. Defines this as a batch of N1 x ... x Nm different Dirichlet multinomial distributions. Its components should be equal to integer values. alpha: Positive floating

tf.image.non_max_suppression(boxes, scores, max_output_size, iou_threshold=None, name=None) Greedily selects a subset of bounding boxes in descending order of score, pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absol