tf.contrib.distributions.Poisson.mode(name='mode') Mode. Additional documentation from Poisson: Note that when lam is an integer, there are actually two modes. Namely, lam and lam - 1 are both modes. Here we return only the larger of the two modes.

tf.contrib.distributions.LaplaceWithSoftplusScale.std(name='std') Standard deviation.

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.

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.FixedLenSequenceFeature.__new__(_cls, shape, dtype, allow_missing=False) Create new instance of FixedLenSequenceFeature(shape, dtype, allow_missing)

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