tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.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. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.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.Multinomial.mode(name='mode') Mode.

tf.contrib.distributions.Laplace.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). 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.

tf.contrib.distributions.Mixture.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.Laplace.loc Distribution parameter for the location.

tf.contrib.distributions.Laplace.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.metrics.set_union(a, b, validate_indices=True) Compute set union of elements in last dimension of a and b. All but the last dimension of a and b must match. Args: a: Tensor or SparseTensor of the same type as b. If sparse, indices must be sorted in row-major order. b: Tensor or SparseTensor of the same type as a. Must be SparseTensor if a is SparseTensor. If sparse, indices must be sorted in row-major order. validate_indices: Whether to validate the order and range of sparse indi

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.input_dict

tf.contrib.training.NextQueuedSequenceBatch.insertion_index The insertion indices of the examples (when they were first added). These indices start with the value -2**63 and increase with every call to the prefetch op. Each whole example gets its own insertion index, and this is used to prioritize the example so that its truncated segments appear in adjacent iterations, even if new examples are inserted by the prefetch op between iterations. Returns: An int64 vector of length batch_size, the i