tf.contrib.distributions.ExponentialWithSoftplusLam.mode(name='mode') Mode. Additional documentation from Gamma: The mode of a gamma distribution is (alpha - 1) / beta when alpha > 1, and NaN otherwise. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN.

tf.contrib.distributions.Chi2WithAbsDf.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.MultivariateNormalDiagWithSoftplusStDev.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.

tensorflow::PartialTensorShape::PartialTensorShape() Construct an unknown PartialTensorShape.

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

tf.contrib.distributions.Multinomial.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.metrics.set_size(a, validate_indices=True) Compute number of unique elements along last dimension of a. Args: a: SparseTensor, with indices sorted in row-major order. validate_indices: Whether to validate the order and range of sparse indices in a. Returns: int32 Tensor of set sizes. For a ranked n, this is a Tensor with rank n-1, and the same 1st n-1 dimensions as a. Each value is the number of unique elements in the corresponding [0...n-1] dimension of a. Raises: TypeError: I

tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.framework.has_arg_scope(func) Checks whether a func has been decorated with @add_arg_scope or not. Args: func: function to check. Returns: a boolean.