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
        if self.samplewise_center:
            x -= np.mean(x, keepdims=True)
        if self.samplewise_std_normalization:
            x /= np.std(x, keepdims=True) + 1e-7

        if self.featurewise_center:
            if self.mean is not None:
                x -= self.mean
            else:
                warnings.warn('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:
                warnings.warn('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:
                warnings.warn('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
def standardize ( self , x ) : if self . preprocessing_function : x = self . preprocessing_function ( x ) if self . rescale : x *= self . rescale if self . samplewise_center : x -= np . mean ( x , keepdims = True ) if self . samplewise_std_normalization : x /= np . std ( x , keepdims = True ) + 1e-7 if self . featurewise_center : if self . mean is not None : x -= self . mean else : warnings . warn ( '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 : warnings . warn ( '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 : warnings . warn ( '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
Apply the normalization configuration to a batch of inputs.

        # Arguments
            x: batch of inputs to be normalized.

        # Returns
            The inputs, normalized.