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# tfp.stats.moving_mean_variance_zero_debiased

Compute zero debiased versions of `moving_mean` and `moving_variance`.

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].

`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').

`moving_mean` The zero debiased exponentially weighted moving mean.
`moving_variance` The zero debiased exponentially weighted moving variance.

`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

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