def accuracy(logits, labels):
  """Calculates aggregated accuracy."""
  is_correct = tf.nn.in_top_k(logits, labels, 1)
  correct = tf.reduce_sum(tf.cast(is_correct, tf.int32))
  incorrect = tf.reduce_sum(tf.cast(tf.logical_not(is_correct), tf.int32))
  correct_count = tf.Variable(0, False)
  incorrect_count = tf.Variable(0, False)
  correct_count_update = tf.assign_add(correct_count, correct)
  incorrect_count_update = tf.assign_add(incorrect_count, incorrect)
  accuracy_op = tf.cast(correct_count, tf.float32) / tf.cast(
      correct_count + incorrect_count, tf.float32)
  return [correct_count_update, incorrect_count_update], accuracy_op
def accuracy ( logits , labels ) : is_correct = tf . nn . in_top_k ( logits , labels , 1 ) correct = tf . reduce_sum ( tf . cast ( is_correct , tf . int32 ) ) incorrect = tf . reduce_sum ( tf . cast ( tf . logical_not ( is_correct ) , tf . int32 ) ) correct_count = tf . Variable ( 0 , False ) incorrect_count = tf . Variable ( 0 , False ) correct_count_update = tf . assign_add ( correct_count , correct ) incorrect_count_update = tf . assign_add ( incorrect_count , incorrect ) accuracy_op = tf . cast ( correct_count , tf . float32 ) / tf . cast ( correct_count + incorrect_count , tf . float32 ) return [ correct_count_update , incorrect_count_update ] , accuracy_op
Calculates aggregated accuracy.