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

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

class tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor BernoulliTensor is a StochasticTensor backed by the distribution Bernoulli.

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.value(name=None)

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.loss(sample_loss) Returns the term to add to the surrogate loss. This method is called by surrogate_loss. The input sample_loss should have already had stop_gradient applied to it. This is because the surrogate_loss usually provides a Monte Carlo sample term of the form differentiable_surrogate * sample_loss where sample_loss is considered constant with respect to the input for purposes of the gradient. Args: sample_loss: Tensor, sam

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.graph

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.name

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.input_dict

tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace(log_f, log_p, sampling_dist_q, z=None, n=None, seed=None, name='expectation_importance_sampler_logspace') Importance sampling with a positive function, in log-space. With p(z) := exp{log_p(z)}, and f(z) = exp{log_f(z)}, this Op returns Log[ n^{-1} sum_{i=1}^n [ f(z_i) p(z_i) / q(z_i) ] ], z_i ~ q, \approx Log[ E_q[ f(Z) p(Z) / q(Z) ] ] = Log[E_p[f(Z)]] This integral is done in log-space with max-subtraction to bet

class tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor Base Class for Tensor-like objects that emit stochastic values.