tf.contrib.distributions.TransformedDistribution.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

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

tf.contrib.distributions.InverseGamma.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from _MultivariateNormalOperatorPD: x is a batch vector with compatible shape if x is a Tensor whose shape can be broadcast up to either: self.batch_shape + self.event_shape or [M1,...,Mm] + self.batch_shape + self.event_shape Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor o

tf.image.rgb_to_grayscale(images, name=None) Converts one or more images from RGB to Grayscale. Outputs a tensor of the same DType and rank as images. The size of the last dimension of the output is 1, containing the Grayscale value of the pixels. Args: images: The RGB tensor to convert. Last dimension must have size 3 and should contain RGB values. name: A name for the operation (optional). Returns: The converted grayscale image(s).

tf.contrib.distributions.Uniform.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.LaplaceWithSoftplusScale.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 Student's

tf.contrib.learn.LinearClassifier.get_variable_names()

tf.contrib.framework.variable(*args, **kwargs) Gets an existing variable with these parameters or creates a new one. Args: name: the name of the new or existing variable. shape: shape of the new or existing variable. dtype: type of the new or existing variable (defaults to DT_FLOAT). initializer: initializer for the variable if one is created. regularizer: a (Tensor -> Tensor or None) function; the result of applying it on a newly created variable will be added to the collection GraphK