class tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue Ask the StochasticTensor for n samples and reshape the result. Sampling from a StochasticTensor increases the rank of the value by 1 (because each sample represents a new outer dimension). This ValueType requests n samples from StochasticTensors run within its context that the outer two dimensions are reshaped to intermix the samples with the outermost (usually batch) dimension. Example: # mu and sigma are both shaped (2, 3) mu

tf.reduce_min(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes the minimum of elements across dimensions of a tensor. Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is return

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

tf.assert_integer(x, message=None, name=None) Assert that x is of integer dtype. Example of adding a dependency to an operation: with tf.control_dependencies([tf.assert_integer(x)]): output = tf.reduce_sum(x) Example of adding dependency to the tensor being checked: x = tf.with_dependencies([tf.assert_integer(x)], x) Args: x: Tensor whose basetype is integer and is not quantized. message: A string to prefix to the default message. name: A name for this operation (optional). Defaults to

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Stud

tf.random_gamma(shape, alpha, beta=None, dtype=tf.float32, seed=None, name=None) Draws shape samples from each of the given Gamma distribution(s). alpha is the shape parameter describing the distribution(s), and beta is the inverse scale parameter(s). Example: samples = tf.random_gamma([10], [0.5, 1.5]) # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents # the samples drawn from each distribution samples = tf.random_gamma([7, 5], [0.5, 1.5]) # samples has shape [7, 5, 2]

tf.SparseTensor.__mul__(sp_x, y) Component-wise multiplies a SparseTensor by a dense Tensor. The output locations corresponding to the implicitly zero elements in the sparse tensor will be zero (i.e., will not take up storage space), regardless of the contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN). Limitation: this Op only broadcasts the dense side to the sparse side, but not the other direction. Args: sp_indices: A Tensor of type int64. 2-D. N x R matrix with the in

tf.contrib.distributions.Laplace.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.framework.variable(*args, **kwargs) Gets an existing variable with these parameters or creates a new one. Args: name: the name of the new or existing variable. shape: shape of the new or existing variable. dtype: type of the new or existing variable (defaults to DT_FLOAT). initializer: initializer for the variable if one is created. regularizer: a (Tensor -> Tensor or None) function; the result of applying it on a newly created variable will be added to the collection GraphK

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.name