def create_normalizer_update(
vector_input: tf.Tensor,
steps: tf.Tensor,
running_mean: tf.Tensor,
running_variance: tf.Tensor,
) -> tf.Operation:
"""
Creates the update operation for the normalizer.
:param vector_input: Vector observation to use for updating the running mean and variance.
:param running_mean: Tensorflow tensor representing the current running mean.
:param running_variance: Tensorflow tensor representing the current running variance.
:param steps: Tensorflow tensor representing the current number of steps that have been normalized.
:return: A TF operation that updates the normalization based on vector_input.
"""
# Based on Welford's algorithm for running mean and standard deviation, for batch updates. Discussion here:
# https://stackoverflow.com/questions/56402955/whats-the-formula-for-welfords-algorithm-for-variance-std-with-batch-updates
steps_increment = tf.shape(vector_input)[0]
total_new_steps = tf.add(steps, steps_increment)
# Compute the incremental update and divide by the number of new steps.
input_to_old_mean = tf.subtract(vector_input, running_mean)
new_mean = running_mean + tf.reduce_sum(
input_to_old_mean / tf.cast(total_new_steps, dtype=tf.float32),
axis=0)
# Compute difference of input to the new mean for Welford update
input_to_new_mean = tf.subtract(vector_input, new_mean)
new_variance = running_variance + tf.reduce_sum(
input_to_new_mean * input_to_old_mean, axis=0)
update_mean = tf.assign(running_mean, new_mean)
update_variance = tf.assign(running_variance, new_variance)
update_norm_step = tf.assign(steps, total_new_steps)
return tf.group([update_mean, update_variance, update_norm_step])
def create_normalizer_update(self, vector_input):
# Based on Welford's algorithm for running mean and standard deviation, for batch updates. Discussion here:
# https://stackoverflow.com/questions/56402955/whats-the-formula-for-welfords-algorithm-for-variance-std-with-batch-updates
steps_increment = tf.shape(vector_input)[0]
total_new_steps = tf.add(self.normalization_steps, steps_increment)
# Compute the incremental update and divide by the number of new steps.
input_to_old_mean = tf.subtract(vector_input, self.running_mean)
new_mean = self.running_mean + tf.reduce_sum(
input_to_old_mean / tf.cast(total_new_steps, dtype=tf.float32),
axis=0)
# Compute difference of input to the new mean for Welford update
input_to_new_mean = tf.subtract(vector_input, new_mean)
new_variance = self.running_variance + tf.reduce_sum(
input_to_new_mean * input_to_old_mean, axis=0)
update_mean = tf.assign(self.running_mean, new_mean)
update_variance = tf.assign(self.running_variance, new_variance)
update_norm_step = tf.assign(self.normalization_steps, total_new_steps)
return tf.group([update_mean, update_variance, update_norm_step])