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

positive_variable = tfp.util.TransformedVariable(1., bijector=tfb.Exp())

positive_variable
# ==> <TransformedVariable: dtype=float32, shape=[], fn=exp>

# Note that the initial value corresponds to the transformed output.
tf.convert_to_tensor(positive_variable)
# ==> 1.

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

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

# It is also possible to assign values to a TransformedVariable
with tf.control_dependencies([positive_variable.assign_add(2.)]):
  positive_variable
# ==> 3.

A common use case for the `TransformedVariable` is to fit constrained
parameters. E.g.:

```python
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>

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

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

initial_value A Tensor, or Python object convertible to a Tensor, which is the initial value for the TransformedVariable. The underlying untransformed tf.Variable will be initialized with bijector.inverse(initial_value). Can also be a callable with no argument that returns the initial value when called.
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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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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__array__

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__bool__

True if self else False

__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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