def loss ( loss_value ) : total_loss = tf . Variable ( 0.0 , False ) loss_count = tf . Variable ( 0 , False ) total_loss_update = tf . assign_add ( total_loss , loss_value ) loss_count_update = tf . assign_add ( loss_count , 1 ) loss_op = total_loss / tf . cast ( loss_count , tf . float32 ) return [ total_loss_update , loss_count_update ] , loss_op
how to get the value of loss at every iteration in python
Calculates aggregated mean loss .
def loss(loss_value):
  """Calculates aggregated mean loss."""
  total_loss = tf.Variable(0.0, False)
  loss_count = tf.Variable(0, False)
  total_loss_update = tf.assign_add(total_loss, loss_value)
  loss_count_update = tf.assign_add(loss_count, 1)
  loss_op = total_loss / tf.cast(loss_count, tf.float32)
  return [total_loss_update, loss_count_update], loss_op