tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Stud

tensorflow::error::Code tensorflow::Status::code() const

tf.contrib.distributions.Binomial.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.graph_editor.get_walks_intersection_ops(forward_seed_ops, backward_seed_ops, forward_inclusive=True, backward_inclusive=True, within_ops=None, control_inputs=False, control_outputs=None, control_ios=None) Return the intersection of a foward and a backward walk. Args: forward_seed_ops: an iterable of operations from which the forward graph walk starts. If a list of tensors is given instead, the seed_ops are set to be the consumers of those tensors. backward_seed_ops: an iterable of

Manages the partially known dimensions of a Tensor and their sizes. Member Details tensorflow::PartialTensorShape::PartialTensorShape() Construct an unknown PartialTensorShape. tensorflow::PartialTensorShape::PartialTensorShape(gtl::ArraySlice< int64 > dim_sizes) Construct a PartialTensorShape from the provided sizes. REQUIRES: dim_sizes[i] >= 0 tensorflow::PartialTensorShape::PartialTensorShape(std::initializer_list< int64 > dim_sizes) tensorflow::PartialTensorShape::PartialTens

tf.atan(x, name=None) Computes atan 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.Normal.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`.

tf.contrib.framework.get_unique_variable(var_op_name) Gets the variable uniquely identified by that var_op_name. Args: var_op_name: the full name of the variable op, including the scope. Returns: a tensorflow variable. Raises: ValueError: if no variable uniquely identified by the name exists.

tf.contrib.distributions.Beta.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from Beta: Note that the argument x must be a non-negative floating point tensor whose shape can be broadcast with self.a and self.b. For fixed leading dimensions, the last dimension represents counts for the corresponding Beta distribution in self.a and self.b. x is only legal if 0 < x < 1. Args: value: float or double Tensor. name: The name t

tf.image.pad_to_bounding_box(image, offset_height, offset_width, target_height, target_width) Pad image with zeros to the specified height and width. Adds offset_height rows of zeros on top, offset_width columns of zeros on the left, and then pads the image on the bottom and right with zeros until it has dimensions target_height, target_width. This op does nothing if offset_* is zero and the image already has size target_height by target_width. Args: image: 3-D tensor with shape [height, widt