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

tf.contrib.distributions.Beta.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Additional documentation from Beta: Note that the argument x must be a non-negative floating point tensor whose shape can be broadcast

tf.IdentityReader.restore_state(state, name=None) Restore a reader to a previously saved state. Not all Readers support being restored, so this can produce an Unimplemented error. Args: state: A string Tensor. Result of a SerializeState of a Reader with matching type. name: A name for the operation (optional). Returns: The created Operation.

tf.contrib.learn.Estimator.get_variable_value(name) Returns value of the variable given by name. Args: name: string, name of the tensor. Returns: Numpy array - value of the tensor.

tf.contrib.distributions.Chi2.variance(name='variance') Variance.

tf.contrib.distributions.Mixture.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

class tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor ExponentialWithSoftplusLamTensor is a StochasticTensor backed by the distribution ExponentialWithSoftplusLam.

tf.contrib.distributions.Normal.entropy(name='entropy') Shanon entropy in nats.

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