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

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

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

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

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

class tf.errors.UnimplementedError Raised when an operation has not been implemented. Some operations may raise this error when passed otherwise-valid arguments that it does not currently support. For example, running the tf.nn.max_pool() operation would raise this error if pooling was requested on the batch dimension, because this is not yet supported.

virtual Status tensorflow::Session::Close(const RunOptions &run_options)

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.stop_gradient

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

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