tf.contrib.distributions.WishartCholesky.variance(name='variance') Variance.

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mu

tf.contrib.bayesflow.stochastic_tensor.SampleValue.__init__(n=1, stop_gradient=False) Sample n times and concatenate

tf.nn.rnn_cell.InputProjectionWrapper.output_size

tf.diag_part(input, name=None) Returns the diagonal part of the tensor. This operation returns

tf.nn.rnn_cell.RNNCell.state_size size(s) of state(s) used by this cell. It can be represented

tf.nn.rnn_cell.BasicLSTMCell.__init__(num_units, forget_bias=1.0, input_size=None, state_is_tuple=True, activation=tanh) Initialize

tf.contrib.distributions.MultivariateNormalDiag.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.loss(final_loss, name='Loss')