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

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.name

tf.contrib.bayesflow.variational_inference.elbo(log_likelihood, variational_with_prior=None, keep_batch_dim=True, form=None, name='ELBO') Evidence Lower BOund. log p(x) >= ELBO. Optimization objective for inference of hidden variables by variational inference. This function is meant to be used in conjunction with DistributionTensor. The user should build out the inference network, using DistributionTensors as latent variables, and the generative network. elbo at minimum needs p(x|Z) and ass

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.mean(name='mean')

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

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.input_dict

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

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.graph

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.distribution