tf.contrib.distributions.assign_log_moving_mean_exp

Compute the log of the exponentially weighted moving mean of the exp.

tf.contrib.distributions.assign_log_moving_mean_exp(
    log_mean_exp_var, log_value, decay, name=None
)

If log_value is a draw from a stationary random variable, this function approximates log(E[exp(log_value)]), i.e., a weighted log-sum-exp. More precisely, a tf.Variable, log_mean_exp_var, is updated by log_value using the following identity:

log_mean_exp_var =
= log(decay exp(log_mean_exp_var) + (1 - decay) exp(log_value))
= log(exp(log_mean_exp_var + log(decay)) + exp(log_value + log1p(-decay)))
= log_mean_exp_var

  + log(  exp(log_mean_exp_var   - log_mean_exp_var + log(decay))
        + exp(log_value - log_mean_exp_var + log1p(-decay)))
= log_mean_exp_var
  + log_sum_exp([log(decay), log_value - log_mean_exp_var + log1p(-decay)]).

In addition to numerical stability, this formulation is advantageous because log_mean_exp_var can be updated in a lock-free manner, i.e., using assign_add. (Note: the updates are not thread-safe; it's just that the update to the tf.Variable is presumed efficient due to being lock-free.)

Args
`log_mean_exp_var`	`float`-like `Variable` representing the log of the exponentially weighted moving mean of the exp. Same shape as `log_value`.
`log_value`	`float`-like `Tensor` representing a new (streaming) observation. Same shape as `log_mean_exp_var`.
`decay`	A `float`-like `Tensor`. The moving mean decay. Typically close to `1.`, e.g., `0.999`.
`name`	Optional name of the returned operation.

Returns
`log_mean_exp_var`	A reference to the input 'Variable' tensor with the `log_value`-updated log of the exponentially weighted moving mean of exp.

Raises
`TypeError`	if `log_mean_exp_var` does not have float type `dtype`.
`TypeError`	if `log_mean_exp_var`, `log_value`, `decay` have different `base_dtype`.

tf.contrib.distributions.assign_log_moving_mean_exp

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