tfp.experimental.nn.util.RandomVariable

RandomVariable supports random variable semantics for TFP distributions.

Inherits From: DeferredTensor

tfp.experimental.nn.util.RandomVariable(
    distribution,
    convert_to_tensor_fn=tfp.distributions.Distribution.sample,
    dtype=None,
    shape=None,
    name=None
)

The RandomVariable class memoizes concretizations of TFP distribution-like objects so that random draws can be re-triggered on-demand, i.e., by calling reset. For more details type help(tfp.util.DeferredTensor).

Examples

# In this example we see the memoization semantics in action.
tfd = tfp.distributions
tfn = tfp.experimental.nn
x = tfn.util.RandomVariable(tfd.Normal(0, 1))
x_ = tf.convert_to_tensor(x)
x _ + 1. == x + 1.
# ==> True; `x` always has the same value until reset.
x.reset()
tf.convert_to_tensor(x) == x_
# ==> False; `x` was reset which triggers a new sample.

# In this example we see how to concretize with different semantics.
tfd = tfp.distributions
tfn = tfp.experimental.nn
x = tfn.util.RandomVariable(
    tfd.Bernoulli(probs=[[0.25], [0.5]]),
    convert_to_tensor_fn=tfd.Distribution.mean,
    dtype=tf.float32,
    shape=[2, 1],
    name='x')
tf.convert_to_tensor(x)
# ==> [[0.25], [0.5]]
x.shape
# ==> [2, 1]
x.dtype
# ==> tf.float32
x.name
# ==> 'x'

# In this example we see a common pitfall: accessing the memoized value from a
# different graph context.
tfd = tfp.distributions
tfn = tfp.experimental.nn
x = tfn.util.RandomVariable(tfd.Normal(0, 1))
@tf.function(autograph=False, jit_compile=True)
def run():
  return tf.convert_to_tensor(x)
first = run()
second = tf.convert_to_tensor(x)
# raises ValueError:
#   "You are attempting to access a memoized value from a different
#   graph context. Please call `this.reset()` before accessing a
#   memoized value from a different graph context."
x.reset()
third = tf.convert_to_tensor(x)
# ==> No exception.
first == third
# ==> False

Args
distribution	TFP distribution-like object which is passed into the convert_to_tensor_fn whenever this object is evaluated in Tensor-like contexts.
convert_to_tensor_fn	Python callable which takes one argument, the distribution and returns a Tensor of type dtype and shape shape. Default value: tfp.distributions.Distribution.sample.
dtype	TF dtype equivalent to what would otherwise be convert_to_tensor_fn(distribution).dtype. Default value: None (i.e., distribution.dtype).
shape	tf.TensorShape-like object compatible with what would otherwise be convert_to_tensor_fn(distribution).shape. Default value: 'None' (i.e., unspecified static shape).
name	Python str representing this object's name; used only in graph mode. Default value: None (i.e., distribution.name)

Attributes
also_track	Additional variables tracked by tf.Module in self.trainable_variables.
convert_to_tensor_fn
distribution
dtype	Represents the type of the elements in a Tensor.
name	The string name of this object.
name_scope	Returns a tf.name_scope instance for this class.
non_trainable_variables	Sequence of non-trainable variables owned by this module and its submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.
pretransformed_input	Input to transform_fn.
shape	Represents the shape of a Tensor.
submodules	Sequence of all sub-modules. Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on). a = tf.Module() b = tf.Module() c = tf.Module() a.b = b b.c = c list(a.submodules) == [b, c] True list(b.submodules) == [c] True list(c.submodules) == [] True
trainable_variables	Sequence of trainable variables owned by this module and its submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.
transform_fn	Function which characterizes the Tensorization of this object.
variables	Sequence of variables owned by this module and its submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.

Methods

is_unset

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is_unset()

Returns True if there is no memoized value and False otherwise.

numpy

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numpy()

Returns (copy of) deferred values as a NumPy array or scalar.

reset

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reset()

Removes memoized value which triggers re-eval on subsequent reads.

set_shape

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set_shape(
    shape
)

Updates the shape of this pretransformed_input.

This method can be called multiple times, and will merge the given shape with the current shape of this object. It can be used to provide additional information about the shape of this object that cannot be inferred from the graph alone.

Args
shape	A TensorShape representing the shape of this pretransformed_input, a TensorShapeProto, a list, a tuple, or None.

Raises
ValueError	If shape is not compatible with the current shape of this pretransformed_input.

with_name_scope

@classmethod
with_name_scope(
    method
)

Decorator to automatically enter the module name scope.

class MyModule(tf.Module):
  @tf.Module.with_name_scope
  def __call__(self, x):
    if not hasattr(self, 'w'):
      self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
    return tf.matmul(x, self.w)

Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

mod = MyModule()
mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Args
method	The method to wrap.

Returns
The original method wrapped such that it enters the module's name scope.

__abs__

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__abs__(
    *args, **kwargs
)

__add__

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__add__(
    *args, **kwargs
)

__and__

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__and__(
    *args, **kwargs
)

__array__

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__array__(
    dtype=None
)

__bool__

__bool__()

Dummy method to prevent a tensor from being used as a Python bool.

This overload raises a TypeError when the user inadvertently treats a Tensor as a boolean (most commonly in an if or while statement), in code that was not converted by AutoGraph. For example:

if tf.constant(True):  # Will raise.
  # ...

if tf.constant(5) < tf.constant(7):  # Will raise.
  # ...

Raises
TypeError.

__div__

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__div__(
    *args, **kwargs
)

__floordiv__

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__floordiv__(
    *args, **kwargs
)

__ge__

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__ge__(
    *args, **kwargs
)

__getitem__

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__getitem__(
    *args, **kwargs
)

__gt__

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__gt__(
    *args, **kwargs
)

__invert__

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__invert__(
    *args, **kwargs
)

__iter__

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__iter__(
    *args, **kwargs
)

__le__

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__le__(
    *args, **kwargs
)

__lt__

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__lt__(
    *args, **kwargs
)

__matmul__

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__matmul__(
    *args, **kwargs
)

__mod__

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__mod__(
    *args, **kwargs
)

__mul__

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__mul__(
    *args, **kwargs
)

__neg__

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__neg__(
    *args, **kwargs
)

__nonzero__

__nonzero__()

Dummy method to prevent a tensor from being used as a Python bool.

This is the Python 2.x counterpart to __bool__() above.

Raises
TypeError.

__or__

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__or__(
    *args, **kwargs
)

__pow__

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__pow__(
    *args, **kwargs
)

__radd__

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__radd__(
    *args, **kwargs
)

__rand__

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__rand__(
    *args, **kwargs
)

__rdiv__

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__rdiv__(
    *args, **kwargs
)

__rfloordiv__

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__rfloordiv__(
    *args, **kwargs
)

__rmatmul__

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__rmatmul__(
    *args, **kwargs
)

__rmod__

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__rmod__(
    *args, **kwargs
)

__rmul__

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__rmul__(
    *args, **kwargs
)

__ror__

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__ror__(
    *args, **kwargs
)

__rpow__

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__rpow__(
    *args, **kwargs
)

__rsub__

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__rsub__(
    *args, **kwargs
)

__rtruediv__

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__rtruediv__(
    *args, **kwargs
)

__rxor__

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__rxor__(
    *args, **kwargs
)

__sub__

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__sub__(
    *args, **kwargs
)

__truediv__

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__truediv__(
    *args, **kwargs
)

__xor__

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__xor__(
    *args, **kwargs
)