(sess, generated, num_accu_examples)
return True
Returns True if the accumlators for batch norm were updated.

  Args:
    sess: `tf.Session` object. Checkpoint should already be loaded.
    generated: Output tensor of the generator.
    num_accu_examples: How many examples should be used to update accumulators.

  Returns:
    True if there were accumlators.
Returns True if the accumlators for batch norm were updated.
Returns True if the accumlators for batch norm were updated .
def _update_bn_accumulators(sess, generated, num_accu_examples):
  """Returns True if the accumlators for batch norm were updated.

  Args:
    sess: `tf.Session` object. Checkpoint should already be loaded.
    generated: Output tensor of the generator.
    num_accu_examples: How many examples should be used to update accumulators.

  Returns:
    True if there were accumlators.
  """
  # Create update ops for batch statistic updates for each batch normalization
  # with accumlators.
  update_accu_switches = [v for v in tf.global_variables()
                          if "accu/update_accus" in v.name]
  logging.info("update_accu_switches: %s", update_accu_switches)
  if not update_accu_switches:
    return False
  sess.run([tf.assign(v, 1) for v in update_accu_switches])
  batch_size = generated.shape[0].value
  num_batches = num_accu_examples // batch_size
  for i in range(num_batches):
    if i % 500 == 0:
      logging.info("Updating BN accumulators %d/%d steps.", i, num_batches)
    sess.run(generated)
  sess.run([tf.assign(v, 0) for v in update_accu_switches])
  logging.info("Done updating BN accumulators.")
  return True
def _update_bn_accumulators ( sess , generated , num_accu_examples ) : # Create update ops for batch statistic updates for each batch normalization # with accumlators. update_accu_switches = [ v for v in tf . global_variables ( ) if "accu/update_accus" in v . name ] logging . info ( "update_accu_switches: %s" , update_accu_switches ) if not update_accu_switches : return False sess . run ( [ tf . assign ( v , 1 ) for v in update_accu_switches ] ) batch_size = generated . shape [ 0 ] . value num_batches = num_accu_examples // batch_size for i in range ( num_batches ) : if i % 500 == 0 : logging . info ( "Updating BN accumulators %d/%d steps." , i , num_batches ) sess . run ( generated ) sess . run ( [ tf . assign ( v , 0 ) for v in update_accu_switches ] ) logging . info ( "Done updating BN accumulators." ) return True