tf.contrib.distributions.Exponential.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.

class tf.contrib.distributions.LaplaceWithSoftplusScale Laplace with softplus applied to scale.

tf.contrib.distributions.NormalWithSoftplusSigma.is_reparameterized

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.__init__(n=1, stop_gradient=False) Sample n times and reshape the outer 2 axes so rank does not change. Args: n: A python integer or int32 tensor. The number of samples to take. stop_gradient: If True, StochasticTensors' values are wrapped in stop_gradient, to avoid backpropagation through.

class tf.contrib.distributions.MultivariateNormalCholesky The multivariate normal distribution on R^k. This distribution is defined by a 1-D mean mu and a Cholesky factor chol. Providing the Cholesky factor allows for O(k^2) pdf evaluation and sampling, and requires O(k^2) storage.

tf.contrib.distributions.WishartFull.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.NormalWithSoftplusSigma.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.MultivariateNormalCholesky.sigma_det(name='sigma_det') Determinant of covariance matrix.

tf.contrib.distributions.WishartCholesky.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.WishartFull.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.