text: import tensorflow as tf
  
  
  def mse_loss_mean(output_tensor, label):
      """"[summary]
      
      Arguments:
          output_tensor {tensor} -- [description]
          label {[type]} -- [description]
      
      Returns:
          [type] -- [description]
      """
      loss = tf.square(tf.subtract(output_tensor, label))
      loss = tf.reduce_mean(loss)
      return loss
  
  
  def mse_loss_sum(output_tensor, label):
      """"[summary]
      
      Arguments:
          output_tensor {tensor} -- [description]
          label {[type]} -- [description]
      
      Returns:
          [type] -- [description]
      """
      loss = tf.square(tf.subtract(output_tensor, label))
      loss = tf.reduce_sum(loss)
      return loss
  
  
  def softmax_crosentropy_mean(output_tensor, label):
      """[summary]
      
      Arguments:
          output_tensor {[type]} -- [description]
          label {[type]} -- [description]
      
      Returns:
          [type] -- [description]
      """
      loss = tf.nn.softmax_cross_entropy_with_logits_v2(labels=label, logits=output_tensor)
      loss = tf.reduce_mean(loss)
      return loss
  
  
  def softmax_crosentropy_sum(output_tensor, label):
      """[summary]
      
      Arguments:
          output_tensor {[type]} -- [description]
          label {[type]} -- [description]
      
      Returns:
          [type] -- [description]
      """
      loss = tf.nn.softmax_cross_entropy_with_logits_v2(labels=label, logits=output_tensor)
      loss = tf.reduce_sum(loss)
      return loss
  
  
  def sigmoid_crossentropy_mean(output_tensor, label):
      """[summary]
      
      Arguments:
          output_tensor {[type]} -- [description]
          label {[type]} -- [description]
      
      Returns:
          [type] -- [description]
      """
      loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=label, logits=output_tensor)
      loss = tf.reduce_mean(loss)
      return loss
  
  
  def sigmoid_crossentropy_sum(output_tensor, label):
      """[summary]
      
      Arguments:
          output_tensor {[type]} -- [description]
          label {[type]} -- [description]
      
      Returns:
          [type] -- [description]
      """
      loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=label, logits=output_tensor)
      loss = tf.reduce_sum(loss)
      return loss
  
      
  