tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.pushed_above(unused_value_type)

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.popped_above(unused_value_type)

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.n

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.stop_gradient

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.value_type

class tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue Ask the StochasticTensor for n samples and reshape the result. Sampling from a StochasticTensor increases the rank of the value by 1 (because each sample represents a new outer dimension). This ValueType requests n samples from StochasticTensors run within its context that the outer two dimensions are reshaped to intermix the samples with the outermost (usually batch) dimension. Example: # mu and sigma are both shaped (2, 3) mu

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.value(name='value')

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

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.graph

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