def _get_output(self, a, image):
        """ Looks up the precomputed adversarial image for a given image.

        """
        sd = np.square(self._input_images - image)
        mses = np.mean(sd, axis=tuple(range(1, sd.ndim)))
        index = np.argmin(mses)

        # if we run into numerical problems with this approach, we might
        # need to add a very tiny threshold here
        if mses[index] > 0:
            raise ValueError('No precomputed output image for this image')
        return self._output_images[index]
def _get_output(self, a, image):
        """ Looks up the precomputed adversarial image for a given image.

        """
        sd = np.square(self._input_images - image)
        mses = np.mean(sd, axis=tuple(range(1, sd.ndim)))
        index = np.argmin(mses)

        # if we run into numerical problems with this approach, we might
        # need to add a very tiny threshold here
        if mses[index] > 0:
            raise ValueError('No precomputed output image for this image')
        return self._output_images[index]
def _get_output ( self , a , image ) : sd = np . square ( self . _input_images - image ) mses = np . mean ( sd , axis = tuple ( range ( 1 , sd . ndim ) ) ) index = np . argmin ( mses ) # if we run into numerical problems with this approach, we might # need to add a very tiny threshold here if mses [ index ] > 0 : raise ValueError ( 'No precomputed output image for this image' ) return self . _output_images [ index ]
Looks up the precomputed adversarial image for a given image.
Looks up the precomputed adversarial image for a given image .