parameters: (scores, labels)
return_statement: return loss
docstring: scores: a list of tensors [batch_size, height, width, num_classes]
      labels: a list of tensors [batch_size, height, width, num_classes]
docstring_summary: scores: a list of tensors [batch_size, height, width, num_classes]
      labels: a list of tensors [batch_size, height, width, num_classes]
docstring_tokens: scores : a list of tensors [ batch_size height width num_classes ] labels : a list of tensors [ batch_size height width num_classes ]
function: def loss_cross_entropy(scores, labels):
      """
      scores: a list of tensors [batch_size, height, width, num_classes]
      labels: a list of tensors [batch_size, height, width, num_classes]
      """
  
      with tf.name_scope('loss'):
          loss = 0
          for i in range(cfg.TRAIN.NUM_STEPS):
              score = scores[i]
              label = labels[i]
              cross_entropy = -tf.reduce_sum(label * score, reduction_indices=[3])
              loss += tf.div(tf.reduce_sum(cross_entropy), tf.reduce_sum(label))
          loss /= cfg.TRAIN.NUM_STEPS
      return loss
function_tokens: def loss_cross_entropy ( scores , labels ) : with tf . name_scope ( 'loss' ) : loss = 0 for i in range ( cfg . TRAIN . NUM_STEPS ) : score = scores [ i ] label = labels [ i ] cross_entropy = - tf . reduce_sum ( label * score , reduction_indices = [ 3 ] ) loss += tf . div ( tf . reduce_sum ( cross_entropy ) , tf . reduce_sum ( label ) ) loss /= cfg . TRAIN . NUM_STEPS return loss