old_contents: from __future__ import absolute_import
  import numpy as np
  from . import backend as K
  
  
  def mean_squared_error(y_true, y_pred):
      return K.mean(K.square(y_pred - y_true), axis=-1)
  
  
  def root_mean_squared_error(y_true, y_pred):
      return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))
  
  
  def mean_absolute_error(y_true, y_pred):
      return K.mean(K.abs(y_pred - y_true), axis=-1)
  
  
  def mean_absolute_percentage_error(y_true, y_pred):
      diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K._EPSILON, np.inf))
      return 100. * K.mean(diff, axis=-1)
  
  
  def mean_squared_logarithmic_error(y_true, y_pred):
      first_log = K.log(K.clip(y_pred, K._EPSILON, np.inf) + 1.)
      second_log = K.log(K.clip(y_true, K._EPSILON, np.inf) + 1.)
      return K.mean(K.square(first_log - second_log), axis=-1)
  
  
  def squared_hinge(y_true, y_pred):
      return K.mean(K.square(K.maximum(1. - y_true * y_pred, 0.)), axis=-1)
  
  
  def hinge(y_true, y_pred):
      return K.mean(K.maximum(1. - y_true * y_pred, 0.), axis=-1)
  
  
  def categorical_crossentropy(y_true, y_pred):
      '''Expects a binary class matrix instead of a vector of scalar classes
      '''
      return K.mean(K.categorical_crossentropy(y_pred, y_true), axis=-1)
  
  
  def binary_crossentropy(y_true, y_pred):
      return K.mean(K.binary_crossentropy(y_pred, y_true), axis=-1)
  
  
  def poisson_loss(y_true, y_pred):
      return K.mean(y_pred - y_true * K.log(y_pred + K._EPSILON), axis=-1)
  
  # aliases
  mse = MSE = mean_squared_error
  rmse = RMSE = root_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')
  
new_contents: from __future__ import absolute_import
  import numpy as np
  from . import backend as K
  
  
  def mean_squared_error(y_true, y_pred):
      return K.mean(K.square(y_pred - y_true), axis=-1)
  
  
  def root_mean_squared_error(y_true, y_pred):
      return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))
  
  
  def mean_absolute_error(y_true, y_pred):
      return K.mean(K.abs(y_pred - y_true), axis=-1)
  
  
  def mean_absolute_percentage_error(y_true, y_pred):
      diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), np.inf))
      return 100. * K.mean(diff, axis=-1)
  
  
  def mean_squared_logarithmic_error(y_true, y_pred):
      first_log = K.log(K.clip(y_pred, K.epsilon(), np.inf) + 1.)
      second_log = K.log(K.clip(y_true, K.epsilon(), np.inf) + 1.)
      return K.mean(K.square(first_log - second_log), axis=-1)
  
  
  def squared_hinge(y_true, y_pred):
      return K.mean(K.square(K.maximum(1. - y_true * y_pred, 0.)), axis=-1)
  
  
  def hinge(y_true, y_pred):
      return K.mean(K.maximum(1. - y_true * y_pred, 0.), axis=-1)
  
  
  def categorical_crossentropy(y_true, y_pred):
      '''Expects a binary class matrix instead of a vector of scalar classes
      '''
      return K.mean(K.categorical_crossentropy(y_pred, y_true), axis=-1)
  
  
  def binary_crossentropy(y_true, y_pred):
      return K.mean(K.binary_crossentropy(y_pred, y_true), axis=-1)
  
  
  def poisson_loss(y_true, y_pred):
      return K.mean(y_pred - y_true * K.log(y_pred + K.epsilon()), axis=-1)
  
  # aliases
  mse = MSE = mean_squared_error
  rmse = RMSE = root_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: Fix epsilon in objectives