tfp.substrates.jax.util.TransformedVariable

Variable tracking object which applies a bijector upon convert_to_tensor.

Inherits From: DeferredTensor

Example

from tensorflow_probability.python.internal.backend.jax.compat import v2 as tf
import tensorflow_probability as tfp; tfp = tfp.substrates.jax
tfb = tfp.bijectors
tfd = tfp.distributions

trainable_normal = tfd.Normal(
    loc=tf.Variable(0.),
    scale=tfp.util.TransformedVariable(1., bijector=tfb.Exp()))

trainable_normal.loc
# ==> <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=0.0>

trainable_normal.scale
# ==> <TransformedVariable: dtype=float32, shape=[], fn=exp>

tf.convert_to_tensor(trainable_normal.scale)
# ==> 1.

# Operators work with `TransformedVariable`.
trainable_normal.scale + 1.
# ==> 2.

with tf.GradientTape() as tape:
  negloglik = -trainable_normal.log_prob(0.5)
g = tape.gradient(negloglik, trainable_normal.trainable_variables)
# ==> (-0.5, 0.75)

Which we could then fit as:

opt = tf.optimizers.Adam(learning_rate=0.05)
loss = tf.function(lambda: -trainable_normal.log_prob(0.5))
for _ in range(int(1e3)):
  opt.minimize(loss, trainable_normal.trainable_variables)
trainable_normal.mean()
# ==> 0.5
trainable_normal.stddev()
# ==> (approximately) 0.0075

It is also possible to assign values to a TransformedVariable, e.g.,

d = tfd.Normal(
    loc=tf.Variable(0.),
    scale=tfp.util.TransformedVariable([1., 2.], bijector=tfb.Softplus()))
d.stddev()
# ==> [1., 2.]
with tf.control_dependencies([x.scale.assign_add([0.5, 1.])]):
  d.stddev()
  # ==> [1.5, 3.]

initial_value A Tensor, or Python object convertible to a Tensor, which is the initial value for the Variable. Can also be a callable with no argument that returns the initial value when called. Note: if initial_value is a TransformedVariable then the instantiated object does not create a new tf.Variable, but rather points to the underlying Variable and chains the bijector arg with the underlying bijector as tfb.Chain([bijector, initial_value.bijector]).
bijector A Bijector-like instance which defines the transformations applied to the underlying tf.Variable.
dtype tf.dtype.DType instance or otherwise valid dtype value to tf.convert_to_tensor(..., dtype). Default value: None (i.e., bijector.dtype).
name Python str representing the underlying tf.Variable's name. Default value: None.
**kwargs Keyword arguments forward to tf.Variable.

also_track Additional variables tracked by tf.Module in self.trainable_variables.
bijector

dtype Represents the type of the elements in a Tensor.
initializer The initializer operation for the underlying variable.
name The string name of this object.
pretransformed_input Input to transform_fn.
shape Represents the shape of a Tensor.
trainable_variables

transform_fn Function which characterizes the Tensorization of this object.
variables

Methods

assign

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assign_add

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assign_sub

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get_shape

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Legacy means of getting Tensor shape, for compat with 2.0.0 LinOp.

numpy

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

set_shape

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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.

__abs__

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__add__

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__and__

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__bool__

self != 0

__floordiv__

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__ge__

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__getitem__

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__gt__

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__invert__

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__iter__

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__le__

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__lt__

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__matmul__

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__mod__

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__mul__

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__neg__

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__or__

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__pow__

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__radd__

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__rand__

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__rfloordiv__

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__rmatmul__

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__rmod__

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__rmul__

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__ror__

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__rpow__

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__rsub__

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__rtruediv__

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__rxor__

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__sub__

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__truediv__

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__xor__

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