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

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.graph

tf.nn.rnn_cell.LSTMStateTuple.__repr__() Return a nicely formatted representation string

tf.contrib.distributions.WishartCholesky.name Name prepended to all ops created by this Distribution.

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.name

tf.contrib.distributions.QuantizedDistribution.is_continuous

tf.contrib.distributions.Chi2.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.MultivariateNormalDiagPlusVDVT.mode(name='mode') Mode.

tf.random_crop(value, size, seed=None, name=None) Randomly crops a tensor to a given size. Slices a shape size portion out of value at a uniformly chosen offset. Requires value.shape >= size. If a dimension should not be cropped, pass the full size of that dimension. For example, RGB images can be cropped with size = [crop_height, crop_width, 3]. Args: value: Input tensor to crop. size: 1-D tensor with size the rank of value. seed: Python integer. Used to create a random seed. See set_ra

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.entropy(name='entropy')