tfp.substrates.jax.stats.moving_mean_variance_zero_debiased
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Compute zero debiased versions of moving_mean
and moving_variance
.
tfp.substrates.jax.stats.moving_mean_variance_zero_debiased(
moving_mean,
moving_variance=None,
zero_debias_count=None,
decay=0.99,
name=None
)
Since moving_*
variables initialized with 0
s will be biased (toward 0
),
this function rescales the moving_mean
and moving_variance
by the factor
1 - decay**zero_debias_count
, i.e., such that the moving_mean
is unbiased.
For more details, see [Kingma (2014)][1].
Args |
moving_mean
|
float -like tf.Variable representing the exponentially
weighted moving mean. Same shape as moving_variance and value . This
function presumes the tf.Variable was created with all zero initial
value(s).
|
moving_variance
|
float -like tf.Variable representing the exponentially
weighted moving variance. Same shape as moving_mean and value . This
function presumes the tf.Variable was created with all zero initial
value(s).
Default value: None (i.e., no moving variance is computed).
|
zero_debias_count
|
int -like tf.Variable representing the number of times
this function has been called on streaming input (not the number of
reduced values used in this functions computation). When not None (the
default) the returned values for moving_mean and moving_variance are
"zero debiased", i.e., corrected for their presumed all zeros
intialization. Note: the tf.Variable s moving_mean and
moving_variance always store the unbiased calculation, regardless of
setting this argument. To obtain unbiased calculations from these
tf.Variable s, see tfp.stats.moving_mean_variance_zero_debiased .
Default value: None (i.e., no zero debiasing calculation is made).
|
decay
|
A float -like Tensor representing the moving mean decay. Typically
close to 1. , e.g., 0.99 .
Default value: 0.99 .
|
name
|
Python str prepended to op names created by this function.
Default value: None (i.e., 'moving_mean_variance_zero_debiased').
|
Returns |
moving_mean
|
The zero debiased exponentially weighted moving mean.
|
moving_variance
|
The zero debiased exponentially weighted moving variance.
|
Raises |
TypeError
|
if moving_mean does not have float type dtype .
|
TypeError
|
if moving_mean , moving_variance , decay have different
base_dtype .
|
References
[1]: Diederik P. Kingma, Jimmy Ba. Adam: A Method for Stochastic Optimization.
arXiv preprint arXiv:1412.6980, 2014.
https://arxiv.org/abs/1412.6980
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2023-11-21 UTC.
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