tf.contrib.distributions.Binomial.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.learn.monitors.ExportMonitor.every_n_step_begin(step) Callback before every n'th step begins. Args: step: int, the current value of the global step. Returns: A list of tensors that will be evaluated at this step.

tf.contrib.distributions.InverseGamma.beta Scale parameter.

TTypes<T>::UnalignedFlat tensorflow::Tensor::unaligned_flat()

tf.ReaderBase.serialize_state(name=None) Produce a string tensor that encodes the state of a reader. Not all Readers support being serialized, so this can produce an Unimplemented error. Args: name: A name for the operation (optional). Returns: A string Tensor.

tf.contrib.distributions.WishartCholesky.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.MultivariateNormalCholesky.sigma_det(name='sigma_det') Determinant of covariance matrix.

class tf.contrib.distributions.TransformedDistribution A Transformed Distribution. A Transformed Distribution models p(y) given a base distribution p(x), an invertible transform, y = f(x), and the determinant of the Jacobian of f(x). Shapes, type, and reparameterization are taken from the base distribution.

tf.contrib.distributions.Chi2WithAbsDf.alpha Shape parameter.

tf.contrib.distributions.Exponential.parameters Dictionary of parameters used by this Distribution.