tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mode()

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

tf.contrib.distributions.Chi2.mode()

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.QuantizedDistribution.is_continuous

tf.contrib.distributions.QuantizedDistribution.is_continuous

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.name

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.name

tf.contrib.graph_editor.matcher.__call__()

tf.contrib.graph_editor.matcher.__call__(op) Evaluate if the op matches or not.

tf.contrib.distributions.Dirichlet.event_shape()

tf.contrib.distributions.Dirichlet.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.bayesflow.stochastic_tensor.StudentTTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.name

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.name

tf.contrib.distributions.WishartCholesky.name

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

tf.nn.rnn_cell.LSTMStateTuple.__repr__()

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