old_contents: from __future__ import absolute_import
  import theano
  import theano.tensor as T
  import numpy as np
  from six.moves import range
  
  epsilon = 1.0e-9
  
  def mean_squared_error(y_true, y_pred):
      return T.sqr(y_pred - y_true).mean(axis=-1)
  
  def mean_absolute_error(y_true, y_pred):
      return T.abs_(y_pred - y_true).mean(axis=-1)
  
  def mean_squared_logarithmic_error(y_true, y_pred):
      return T.sqr(T.log(T.clip(y_pred, epsilon, np.inf) + 1.) - T.log(T.clip(y_true, epsilon, np.inf) + 1.)).mean(axis=-1)
  
  def squared_hinge(y_true, y_pred):
      return T.sqr(T.maximum(1. - y_true * y_pred, 0.)).mean(axis=-1)
  
  def hinge(y_true, y_pred):
      return T.maximum(1. - y_true * y_pred, 0.).mean(axis=-1)
  
  def categorical_crossentropy(y_true, y_pred):
      '''Expects a binary class matrix instead of a vector of scalar classes
      '''
      y_pred = T.clip(y_pred, epsilon, 1.0 - epsilon)
      # scale preds so that the class probas of each sample sum to 1
      y_pred /= y_pred.sum(axis=-1, keepdims=True) 
      cce = T.nnet.categorical_crossentropy(y_pred, y_true)
      return cce
  
  def binary_crossentropy(y_true, y_pred):
      y_pred = T.clip(y_pred, epsilon, 1.0 - epsilon)
      bce = T.nnet.binary_crossentropy(y_pred, y_true)
      return bce
  
  # aliases
  mse = MSE = mean_squared_error
  mae = MAE = mean_absolute_error
  msle = MSLE = mean_squared_logarithmic_error
  
  from .utils.generic_utils import get_from_module
  def get(identifier):
      return get_from_module(identifier, globals(), 'objective')
  
  
new_contents: from __future__ import absolute_import
  import theano
  import theano.tensor as T
  import numpy as np
  from six.moves import range
  
  epsilon = 1.0e-9
  
  def mean_squared_error(y_true, y_pred):
      return T.sqr(y_pred - y_true).mean(axis=-1)
  
  def mean_absolute_error(y_true, y_pred):
      return T.abs_(y_pred - y_true).mean(axis=-1)
  
  def mean_absolute_percentage_error(y_true, y_pred):
      return T.abs_((y_true - y_pred) / y_true).mean() * 100
  
  def mean_squared_logarithmic_error(y_true, y_pred):
      return T.sqr(T.log(T.clip(y_pred, epsilon, np.inf) + 1.) - T.log(T.clip(y_true, epsilon, np.inf) + 1.)).mean(axis=-1)
  
  def squared_hinge(y_true, y_pred):
      return T.sqr(T.maximum(1. - y_true * y_pred, 0.)).mean(axis=-1)
  
  def hinge(y_true, y_pred):
      return T.maximum(1. - y_true * y_pred, 0.).mean(axis=-1)
  
  def categorical_crossentropy(y_true, y_pred):
      '''Expects a binary class matrix instead of a vector of scalar classes
      '''
      y_pred = T.clip(y_pred, epsilon, 1.0 - epsilon)
      # scale preds so that the class probas of each sample sum to 1
      y_pred /= y_pred.sum(axis=-1, keepdims=True) 
      cce = T.nnet.categorical_crossentropy(y_pred, y_true)
      return cce
  
  def binary_crossentropy(y_true, y_pred):
      y_pred = T.clip(y_pred, epsilon, 1.0 - epsilon)
      bce = T.nnet.binary_crossentropy(y_pred, y_true)
      return bce
  
  # aliases
  mse = MSE = mean_squared_error
  mae = MAE = mean_absolute_error
  mape = MAPE = mean_absolute_percentage_error
  msle = MSLE = mean_squared_logarithmic_error
  
  from .utils.generic_utils import get_from_module
  def get(identifier):
      return get_from_module(identifier, globals(), 'objective')
  
  
subject: Add MAPE objective