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Variable tracking object which applies function upon convert_to_tensor
.
tfp.substrates.numpy.util.DeferredTensor(
pretransformed_input,
transform_fn,
dtype=None,
shape=NONE_SPECIFIED,
also_track=None,
name=None
)
Example
from tensorflow_probability.python.internal.backend.numpy.compat import v2 as tf
import tensorflow_probability as tfp; tfp = tfp.substrates.numpy
tfb = tfp.bijectors
tfd = tfp.distributions
# Note: it'd be better to use `tfp.util.TransformedVariable`;
# this example is for illustration only.
trainable_normal = tfd.Normal(
loc=tf.Variable(0.),
scale=tfp.util.DeferredTensor(tf.Variable(0.), tf.math.exp))
trainable_normal.loc
# ==> <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=0.0>
trainable_normal.scale
# ==> <DeferredTensor: dtype=float32, shape=[], fn=exp>
# Operators work with `DeferredTensor`.
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), autograph=True)
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 parameterize a DeferredTensor
with a bijector, e.g.:
# Note: it'd be better to use `tfp.util.TransformedVariable`;
# this example is for illustration only.
d = tfd.Normal(loc=0.,
scale=tfp.util.DeferredTensor(tf.Variable([0.54, 1.85]),
tfb.Softplus()))
d.stddev()
# ==> [1., 2.]
tf.convert_to_tensor(d.scale)
# ==> [1., 2.]
Args | |
---|---|
pretransformed_input
|
object with shape , dtype properties (typically a
tf.Variable ) passed into transform_fn when this object is acted upon
in a Tensor context, eg, tf.convert_to_tensor , + , tf.math.exp ,
etc.
|
transform_fn
|
Python callable or tfp.bijectors.Bijector -like instance.
When callable , should take pretransformed_input and
return a Tensor (representing by this object).
|
dtype
|
Equivalent to what would otherwise be
transform_fn(pretransformed_input).dtype .
Default value: None (i.e.,
getattr(transform_fn, 'dtype', None) or pretransformed_input.dtype ).
|
shape
|
Equivalent to what would otherwise be
transform_fn(pretransformed_input).shape .
Default value: 'None' (i.e.,
getattr(transform_fn, 'forward_event_shape', lambda x: x)(
pretransformed_input.shape) ).
|
also_track
|
Optional instance or structure of instances of tf.Variable
and/or tf.Module , containing any additional trainable variables that
the transform_fn may access beyond the given
pretransformed_input . This ensures that such variables
will be correctly tracked in self.trainable_variables .
Default value: None .
|
name
|
Python str representing this object's name ; used only in graph
mode.
Default value: None (i.e.,
(getattr(transform_fn, 'name', None) or
transform_fn.__name__ + '_' + pretransformed_input.name) ).
|
Raises | |
---|---|
TypeError
|
if transform_fn is not callable .
|
TypeError
|
if pretransformed_input lacks dtype and/or shape
properties (and dtype and/or shape arguments are unspecified).
|
Methods
numpy
numpy()
Returns (copy of) deferred values as a NumPy array or scalar.
set_shape
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 .
|
__abs__
__abs__(
*args, **kwargs
)
__add__
__add__(
*args, **kwargs
)
__and__
__and__(
*args, **kwargs
)
__array__
__array__(
dtype=None
)
__bool__
__bool__()
True if self else False
__floordiv__
__floordiv__(
*args, **kwargs
)
__ge__
__ge__(
*args, **kwargs
)
__getitem__
__getitem__(
*args, **kwargs
)
__gt__
__gt__(
*args, **kwargs
)
__invert__
__invert__(
*args, **kwargs
)
__iter__
__iter__(
*args, **kwargs
)
__le__
__le__(
*args, **kwargs
)
__lt__
__lt__(
*args, **kwargs
)
__matmul__
__matmul__(
*args, **kwargs
)
__mod__
__mod__(
*args, **kwargs
)
__mul__
__mul__(
*args, **kwargs
)
__neg__
__neg__(
*args, **kwargs
)
__or__
__or__(
*args, **kwargs
)
__pow__
__pow__(
*args, **kwargs
)
__radd__
__radd__(
*args, **kwargs
)
__rand__
__rand__(
*args, **kwargs
)
__rfloordiv__
__rfloordiv__(
*args, **kwargs
)
__rmatmul__
__rmatmul__(
*args, **kwargs
)
__rmod__
__rmod__(
*args, **kwargs
)
__rmul__
__rmul__(
*args, **kwargs
)
__ror__
__ror__(
*args, **kwargs
)
__rpow__
__rpow__(
*args, **kwargs
)
__rsub__
__rsub__(
*args, **kwargs
)
__rtruediv__
__rtruediv__(
*args, **kwargs
)
__rxor__
__rxor__(
*args, **kwargs
)
__sub__
__sub__(
*args, **kwargs
)
__truediv__
__truediv__(
*args, **kwargs
)
__xor__
__xor__(
*args, **kwargs
)