(loss, running_avg_loss, summary_writer, step, decay=0.99)
return running_avg_loss
Calculate the running average loss via exponential decay.
  This is used to implement early stopping w.r.t. a more smooth loss curve than the raw loss curve.

  Args:
    loss: loss on the most recent eval step
    running_avg_loss: running_avg_loss so far
    summary_writer: FileWriter object to write for tensorboard
    step: training iteration step
    decay: rate of exponential decay, a float between 0 and 1. Larger is smoother.

  Returns:
    running_avg_loss: new running average loss
Calculate the running average loss via exponential decay.
  This is used to implement early stopping w.r.t. a more smooth loss curve than the raw loss curve.
Calculate the running average loss via exponential decay . This is used to implement early stopping w . r . t . a more smooth loss curve than the raw loss curve .
def calc_running_avg_loss(loss, running_avg_loss, summary_writer, step, decay=0.99):
  """Calculate the running average loss via exponential decay.
  This is used to implement early stopping w.r.t. a more smooth loss curve than the raw loss curve.

  Args:
    loss: loss on the most recent eval step
    running_avg_loss: running_avg_loss so far
    summary_writer: FileWriter object to write for tensorboard
    step: training iteration step
    decay: rate of exponential decay, a float between 0 and 1. Larger is smoother.

  Returns:
    running_avg_loss: new running average loss
  """
  if running_avg_loss == 0:  # on the first iteration just take the loss
    running_avg_loss = loss
  else:
    running_avg_loss = running_avg_loss * decay + (1 - decay) * loss
  running_avg_loss = min(running_avg_loss, 12)  # clip
  loss_sum = tf.Summary()
  tag_name = 'running_avg_loss/decay=%f' % (decay)
  loss_sum.value.add(tag=tag_name, simple_value=running_avg_loss)
  summary_writer.add_summary(loss_sum, step)
  tf.logging.info('running_avg_loss: %f', running_avg_loss)
  return running_avg_loss
def calc_running_avg_loss ( loss , running_avg_loss , summary_writer , step , decay = 0.99 ) : if running_avg_loss == 0 : # on the first iteration just take the loss running_avg_loss = loss else : running_avg_loss = running_avg_loss * decay + ( 1 - decay ) * loss running_avg_loss = min ( running_avg_loss , 12 ) # clip loss_sum = tf . Summary ( ) tag_name = 'running_avg_loss/decay=%f' % ( decay ) loss_sum . value . add ( tag = tag_name , simple_value = running_avg_loss ) summary_writer . add_summary ( loss_sum , step ) tf . logging . info ( 'running_avg_loss: %f' , running_avg_loss ) return running_avg_loss