tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.learn.monitors.GraphDump.step_end(step, output)

static bool tensorflow::TensorShapeUtils::IsVectorOrHigher(const TensorShape &shape)

tf.contrib.distributions.BetaWithSoftplusAB.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.image.sample_distorted_bounding_box(image_size, bounding_boxes, seed=None, seed2=None, min_object_covered=None, aspect_ratio_range=None, area_range=None, max_attempts=None, use_image_if_no_bounding_boxes=None, name=None) Generate a single randomly distorted bounding box for an image. Bounding box annotations are often supplied in addition to ground-truth labels in image recognition or object localization tasks. A common technique for training such a system is to randomly distort an image wh

class tf.VarLenFeature Configuration for parsing a variable-length input feature. Fields: dtype: Data type of input.

TTypes< T, NDIMS >::UnalignedConstTensor tensorflow::Tensor::unaligned_shaped(gtl::ArraySlice< int64 > new_sizes) const

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

class tf.contrib.distributions.Categorical Categorical distribution. The categorical distribution is parameterized by the log-probabilities of a set of classes.

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