text: ##############################################################################
  #The MIT License (MIT)
  #
  #Copyright (c) 2018 IBM Corporation, Carnegie Mellon University and others
  #
  #Permission is hereby granted, free of charge, to any person obtaining a copy
  #of this software and associated documentation files (the "Software"), to deal
  #in the Software without restriction, including without limitation the rights
  #to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  #copies of the Software, and to permit persons to whom the Software is
  #furnished to do so, subject to the following conditions:
  #
  #The above copyright notice and this permission notice shall be included in all
  #copies or substantial portions of the Software.
  #
  #THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  #IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  #FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  #AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  #LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  #OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  #SOFTWARE.
  ##############################################################################
  
  import numpy as np
  from keras import backend as K
  
  def euc_loss1x(y_true, y_pred):
      lx = K.sqrt(K.sum(K.square(y_true[:,:] - y_pred[:,:]), axis=1, keepdims=True))
      return (0.3 * lx)
  
  def euc_loss1q(y_true, y_pred):
      lq = K.sqrt(K.sum(K.square(y_true[:,:] - y_pred[:,:]), axis=1, keepdims=True))
      return (150 * lq)
  
  def euc_loss2x(y_true, y_pred):
      lx = K.sqrt(K.sum(K.square(y_true[:,:] - y_pred[:,:]), axis=1, keepdims=True))
      return (0.3 * lx)
  
  def euc_loss2q(y_true, y_pred):
      lq = K.sqrt(K.sum(K.square(y_true[:,:] - y_pred[:,:]), axis=1, keepdims=True))
      return (150 * lq)
  
  def euc_loss3x(y_true, y_pred):
      lx = K.sqrt(K.sum(K.square(y_true[:,:] - y_pred[:,:]), axis=1, keepdims=True))
      return (1 * lx)
  
  def euc_loss3q(y_true, y_pred):
      lq = K.sqrt(K.sum(K.square(y_true[:,:] - y_pred[:,:]), axis=1, keepdims=True))
      return (500 * lq)
  