tf.contrib.distributions.WishartFull.df Wishart distribution degree(s) of freedom.

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

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

tf.IdentityReader.num_records_produced(name=None) Returns the number of records this reader has produced. This is the same as the number of Read executions that have succeeded. Args: name: A name for the operation (optional). Returns: An int64 Tensor.

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

tf.contrib.distributions.BetaWithSoftplusAB.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: float or

tf.image.random_contrast(image, lower, upper, seed=None) Adjust the contrast of an image by a random factor. Equivalent to adjust_contrast() but uses a contrast_factor randomly picked in the interval [lower, upper]. Args: image: An image tensor with 3 or more dimensions. lower: float. Lower bound for the random contrast factor. upper: float. Upper bound for the random contrast factor. seed: A Python integer. Used to create a random seed. See set_random_seed for behavior. Returns: The con

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.clone(name=None, **dist_args)

tf.accumulate_n(inputs, shape=None, tensor_dtype=None, name=None) Returns the element-wise sum of a list of tensors. Optionally, pass shape and tensor_dtype for shape and type checking, otherwise, these are inferred. NOTE: This operation is not differentiable and cannot be used if inputs depend on trainable variables. Please use tf.add_n for such cases. For example: # tensor 'a' is [[1, 2], [3, 4]] # tensor `b` is [[5, 0], [0, 6]] tf.accumulate_n([a, b, a]) ==> [[7, 4], [6, 14]] # Explicit

tf.contrib.distributions.Poisson.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.