In TensorFlow/Keras Batch Normalization, the exponential moving average of the population mean and variance are calculated as follows:

moving_mean = moving_mean * momentum + batch_mean * (1 - momentum)
moving_var = moving_var * momentum + batch_var * (1 - momentum)


where momentum is a number close to 1 (default is 0.99). In the actual code, the moving average are updated in a more efficient way:

moving_mean -= (moving_mean - batch_mean) * (1 - momentum)
moving_var -= (moving_var - batch_var) * (1 - momentum)


They are equivalent as shown below ($\mu$ is the moving mean, $\mu_{B}$ is the batch mean, $\alpha$ is the momentum):

\begin{align} \mu &= \alpha\mu + (1 - \alpha) \mu_{B} \\ &= \mu - (1 - \alpha) \mu + (1 - \alpha) \mu_{B} \\ &= \mu - (1 - \alpha) (\mu - \mu_{B}) \end{align}

Hence, the moving average will decay by the difference between the existing value and the new value, multiplied with a decay factor of (1 - momentum). A lower value of momentum means that older values are forgotten sooner. This results in a faster-changing moving average.