contrib.bayesflow.stochastic_tensor.StochasticTensor

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor

class tf.contrib.bayesflow.stochastic_tensor.StochasticTensor

Defined in tensorflow/contrib/bayesflow/python/ops/stochastic_tensor_impl.py.

See the guide: BayesFlow Stochastic Tensors (contrib) > Stochastic Tensor Classes

StochasticTensor is a BaseStochasticTensor backed by a distribution.

Properties

distribution

dtype

graph

name

value_type

Methods

__init__

__init__(
    dist,
    name='StochasticTensor',
    dist_value_type=None,
    loss_fn=sge.score_function
)

Construct a StochasticTensor.

StochasticTensor is backed by the dist distribution and its value method will return the same value each time it is called. What value is returned is controlled by the dist_value_type (defaults to SampleValue).

Some distributions' sample functions are not differentiable (e.g. a sample from a discrete distribution like a Bernoulli) and so to differentiate wrt parameters upstream of the sample requires a gradient estimator like the score function estimator. This is accomplished by passing a differentiable loss_fn to the StochasticTensor, which defaults to a function whose derivative is the score function estimator. Calling stochastic_graph.surrogate_loss(final_losses) will call loss() on every StochasticTensor upstream of final losses.

loss() will return None for StochasticTensors backed by reparameterized distributions; it will also return None if the value type is MeanValueType or if loss_fn=None.

Args:

• dist: an instance of Distribution.
• name: a name for this StochasticTensor and its ops.
• dist_value_type: a _StochasticValueType, which will determine what the value of this StochasticTensor will be. If not provided, the value type set with the value_type context manager will be used.
• loss_fn: callable that takes (st, st.value(), influenced_loss), where st is this StochasticTensor, and returns a Tensor loss. By default, loss_fn is the score_function, or more precisely, the integral of the score function, such that when the gradient is taken, the score function results. See the stochastic_gradient_estimators module for additional loss functions and baselines.

Raises:

• TypeError: if dist is not an instance of Distribution.
• TypeError: if loss_fn is not callable.

entropy

entropy(name='entropy')

loss

loss(
    final_loss,
    name='Loss'
)

mean

mean(name='mean')

value

value(name='value')