class tf.contrib.bayesflow.stochastic_tensor.StochasticTensor StochasticTensor is a BaseStochasticTensor backed by a distribution.

tf.contrib.bayesflow.stochastic_tensor.SampleValue.__init__(n=1, stop_gradient=False) Sample n times and concatenate along a new outer dimension. Args: n: A python integer or int32 tensor. The number of samples to take. stop_gradient: If True, StochasticTensors' values are wrapped in stop_gradient, to avoid backpropagation through.

tf.contrib.bayesflow.stochastic_tensor.SampleValue.stop_gradient

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

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

tf.contrib.bayesflow.stochastic_tensor.SampleValue.n

tf.contrib.bayesflow.stochastic_tensor.SampleValue.declare_inputs(unused_stochastic_tensor, unused_inputs_dict)

class tf.contrib.bayesflow.stochastic_tensor.SampleValue Draw n samples along a new outer dimension. This ValueType draws n samples from StochasticTensors run within its context, increasing the rank by one along a new outer dimension. Example: mu = tf.zeros((2,3)) sigma = tf.ones((2, 3)) with sg.value_type(sg.SampleValue(n=4)): dt = sg.DistributionTensor( distributions.Normal, mu=mu, sigma=sigma) # draws 4 samples each with shape (2, 3) and concatenates assertEqual(dt.value().get_shape()

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.__init__(n=1, stop_gradient=False) Sample n times and reshape the outer 2 axes so rank does not change. Args: n: A python integer or int32 tensor. The number of samples to take. stop_gradient: If True, StochasticTensors' values are wrapped in stop_gradient, to avoid backpropagation through.

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.stop_gradient