tf.contrib.distributions.moving_mean_variance

Compute exponentially weighted moving {mean,variance} of a streaming value.

tf.contrib.distributions.moving_mean_variance(
    value, decay, collections=None, name=None
)

The exponentially-weighting moving mean_var and variance_var are updated by value according to the following recurrence:

variance_var = decay * (variance_var + (1-decay) * (value - mean_var)**2)
mean_var     = decay * mean_var + (1 - decay) * value

For derivation justification, see [Finch (2009; Eq. 143)][1].

Unlike assign_moving_mean_variance, this function handles variable creation.

Args
`value`	`float`-like `Tensor`. Same shape as `mean_var` and `variance_var`.
`decay`	A `float`-like `Tensor`. The moving mean decay. Typically close to `1.`, e.g., `0.999`.
`collections`	Python list of graph-collections keys to which the internal variables `mean_var` and `variance_var` are added. Default value is `[GraphKeys.GLOBAL_VARIABLES]`.
`name`	Optional name of the returned operation.

Returns
`mean_var`	`Variable` representing the `value`-updated exponentially weighted moving mean.
`variance_var`	`Variable` representing the `value`-updated exponentially weighted moving variance.

Raises
`TypeError`	if `value_var` does not have float type `dtype`.
`TypeError`	if `value`, `decay` have different `base_dtype`.

[1]: Tony Finch. Incremental calculation of weighted mean and variance. Technical Report, 2009. http://people.ds.cam.ac.uk/fanf2/hermes/doc/antiforgery/stats.pdf

Args