class tf.errors.InternalError Raised when the system experiences an internal error. This exception is raised when some invariant expected by the runtime has been broken. Catching this exception is not recommended.

tf.contrib.distributions.Exponential.is_continuous

tf.FixedLenFeature.__new__(_cls, shape, dtype, default_value=None) Create new instance of FixedLenFeature(shape, dtype, default_value)

tf.contrib.distributions.WishartCholesky.dimension Dimension of underlying vector space. The p in R^(p*p).

tf.contrib.distributions.DirichletMultinomial.is_continuous

tf.QueueBase.close(cancel_pending_enqueues=False, name=None) Closes this queue. This operation signals that no more elements will be enqueued in the given queue. Subsequent enqueue and enqueue_many operations will fail. Subsequent dequeue and dequeue_many operations will continue to succeed if sufficient elements remain in the queue. Subsequent dequeue and dequeue_many operations that would block will fail immediately. If cancel_pending_enqueues is True, all pending requests will also be cance

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.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.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.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.WishartCholesky.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 do