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StochasticTensor is a BaseStochasticTensor backed by a distribution.
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#### `tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.__init__(dist, name='StochasticTensor', dist_value_type=None, loss_fn=score_function)` {#StochasticTensor.__init__}
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 `StochasticTensor`s backed by
reparameterized distributions; it will also return None if the value type is
`MeanValueType` or if `loss_fn=None`.
##### Args:
* <b>`dist`</b>: an instance of `Distribution`.
* <b>`name`</b>: a name for this `StochasticTensor` and its ops.
* <b>`dist_value_type`</b>: 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.
* <b>`loss_fn`</b>: 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:
* <b>`TypeError`</b>: if `dist` is not an instance of `Distribution`.
* <b>`TypeError`</b>: if `loss_fn` is not `callable`.
- - -
#### `tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.distribution` {#StochasticTensor.distribution}
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#### `tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.dtype` {#StochasticTensor.dtype}
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#### `tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.entropy(name='entropy')` {#StochasticTensor.entropy}
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#### `tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.graph` {#StochasticTensor.graph}
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#### `tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.loss(final_loss, name='Loss')` {#StochasticTensor.loss}
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#### `tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.mean(name='mean')` {#StochasticTensor.mean}
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#### `tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.name` {#StochasticTensor.name}
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#### `tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.value(name='value')` {#StochasticTensor.value}
- - -
#### `tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.value_type` {#StochasticTensor.value_type}
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