tf.contrib.distributions.WishartFull.mean(name='mean') Mean.

tf.contrib.distributions.Multinomial.__init__(n, logits=None, p=None, validate_args=False, allow_nan_stats=True, name='Multinomial') Initialize

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.cdf(value, name='cdf') Cumulative distribution function.

tf.contrib.distributions.Dirichlet.is_reparameterized

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.get_batch_shape() Shape of a single sample from a single event index

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.name

tf.nn.rnn_cell.OutputProjectionWrapper.__call__(inputs, state, scope=None) Run the cell and output projection on inputs, starting

bool tensorflow::Tensor::IsInitialized() const If necessary, has this Tensor been initialized? Zero-element

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.learn.DNNRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) See