tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.distribution

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

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

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.distribution

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

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.input_dict

tf.contrib.distributions.NormalWithSoftplusSigma.is_continuous

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.value_type

tf.contrib.distributions.Laplace.sample_n(n, seed=None, name='sample_n') Generate n samples.

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.dtype