parameters: (self, x)
return_statement: return x
docstring: Apply the normalization configuration to a batch of inputs.
  
      Arguments:
          x: batch of inputs to be normalized.
  
      Returns:
          The inputs, normalized.
docstring_summary: Apply the normalization configuration to a batch of inputs.
docstring_tokens: Apply the normalization configuration to a batch of inputs .
function: def standardize(self, x):
      """Apply the normalization configuration to a batch of inputs.
  
      Arguments:
          x: batch of inputs to be normalized.
  
      Returns:
          The inputs, normalized.
      """
      if self.preprocessing_function:
        x = self.preprocessing_function(x)
      if self.rescale:
        x *= self.rescale
      # x is a single image, so it doesn't have image number at index 0
      img_channel_axis = self.channel_axis - 1
      if self.samplewise_center:
        x -= np.mean(x, axis=img_channel_axis, keepdims=True)
      if self.samplewise_std_normalization:
        x /= (np.std(x, axis=img_channel_axis, keepdims=True) + 1e-7)
  
      if self.featurewise_center:
        if self.mean is not None:
          x -= self.mean
        else:
          logging.warning('This ImageDataGenerator specifies '
                          '`featurewise_center`, but it hasn\'t'
                          'been fit on any training data. Fit it '
                          'first by calling `.fit(numpy_data)`.')
      if self.featurewise_std_normalization:
        if self.std is not None:
          x /= (self.std + 1e-7)
        else:
          logging.warning('This ImageDataGenerator specifies '
                          '`featurewise_std_normalization`, but it hasn\'t'
                          'been fit on any training data. Fit it '
                          'first by calling `.fit(numpy_data)`.')
      if self.zca_whitening:
        if self.principal_components is not None:
          flatx = np.reshape(x, (-1, np.prod(x.shape[-3:])))
          whitex = np.dot(flatx, self.principal_components)
          x = np.reshape(whitex, x.shape)
        else:
          logging.warning('This ImageDataGenerator specifies '
                          '`zca_whitening`, but it hasn\'t'
                          'been fit on any training data. Fit it '
                          'first by calling `.fit(numpy_data)`.')
      return x
function_tokens: def standardize ( self , x ) : if self . preprocessing_function : x = self . preprocessing_function ( x ) if self . rescale : x *= self . rescale # x is a single image, so it doesn't have image number at index 0 img_channel_axis = self . channel_axis - 1 if self . samplewise_center : x -= np . mean ( x , axis = img_channel_axis , keepdims = True ) if self . samplewise_std_normalization : x /= ( np . std ( x , axis = img_channel_axis , keepdims = True ) + 1e-7 ) if self . featurewise_center : if self . mean is not None : x -= self . mean else : logging . warning ( 'This ImageDataGenerator specifies ' '`featurewise_center`, but it hasn\'t' 'been fit on any training data. Fit it ' 'first by calling `.fit(numpy_data)`.' ) if self . featurewise_std_normalization : if self . std is not None : x /= ( self . std + 1e-7 ) else : logging . warning ( 'This ImageDataGenerator specifies ' '`featurewise_std_normalization`, but it hasn\'t' 'been fit on any training data. Fit it ' 'first by calling `.fit(numpy_data)`.' ) if self . zca_whitening : if self . principal_components is not None : flatx = np . reshape ( x , ( - 1 , np . prod ( x . shape [ - 3 : ] ) ) ) whitex = np . dot ( flatx , self . principal_components ) x = np . reshape ( whitex , x . shape ) else : logging . warning ( 'This ImageDataGenerator specifies ' '`zca_whitening`, but it hasn\'t' 'been fit on any training data. Fit it ' 'first by calling `.fit(numpy_data)`.' ) return x