tf.contrib.distributions.TransformedDistribution.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.

tf.contrib.distributions.BetaWithSoftplusAB.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.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.dtype

tf.contrib.distributions.BetaWithSoftplusAB.pdf(value, name='pdf') Probability density function. 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. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.DirichletMultinomial.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

class tf.TensorArray Class wrapping dynamic-sized, per-time-step, write-once Tensor arrays. This class is meant to be used with dynamic iteration primitives such as while_loop and map_fn. It supports gradient back-propagation via special "flow" control flow dependencies.

tf.contrib.learn.LinearRegressor.dnn_bias_ Returns bias of deep neural network part.

class tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor BetaWithSoftplusABTensor is a StochasticTensor backed by the distribution BetaWithSoftplusAB.

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.value(name='value')

tf.contrib.distributions.StudentT.dtype The DType of Tensors handled by this Distribution.