tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.loss()

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

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.graph

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.graph

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.entropy()

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

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.dtype

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.clone()

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor

class tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor MultinomialTensor is a StochasticTensor backed by the distribution Multinomial.

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.__init__()

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

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.value_type