def predictions_and_gradient(
            self, image=None, label=None, strict=True, return_details=False):
        """Interface to model.predictions_and_gradient for attacks.

        Parameters
        ----------
        image : `numpy.ndarray`
            Single input with shape as expected by the model
            (without the batch dimension).
            Defaults to the original image.
        label : int
            Label used to calculate the loss that is differentiated.
            Defaults to the original label.
        strict : bool
            Controls if the bounds for the pixel values should be checked.

        """
        assert self.has_gradient()

        if image is None:
            image = self.__original_image
        if label is None:
            label = self.__original_class

        in_bounds = self.in_bounds(image)
        assert not strict or in_bounds

        self._total_prediction_calls += 1
        self._total_gradient_calls += 1
        predictions, gradient = self.__model.predictions_and_gradient(image, label)  # noqa: E501
        is_adversarial, is_best, distance = self.__is_adversarial(
            image, predictions, in_bounds)

        assert predictions.ndim == 1
        assert gradient.shape == image.shape
        if return_details:
            return predictions, gradient, is_adversarial, is_best, distance
        else:
            return predictions, gradient, is_adversarial
def predictions_and_gradient(
            self, image=None, label=None, strict=True, return_details=False):
        """Interface to model.predictions_and_gradient for attacks.

        Parameters
        ----------
        image : `numpy.ndarray`
            Single input with shape as expected by the model
            (without the batch dimension).
            Defaults to the original image.
        label : int
            Label used to calculate the loss that is differentiated.
            Defaults to the original label.
        strict : bool
            Controls if the bounds for the pixel values should be checked.

        """
        assert self.has_gradient()

        if image is None:
            image = self.__original_image
        if label is None:
            label = self.__original_class

        in_bounds = self.in_bounds(image)
        assert not strict or in_bounds

        self._total_prediction_calls += 1
        self._total_gradient_calls += 1
        predictions, gradient = self.__model.predictions_and_gradient(image, label)  # noqa: E501
        is_adversarial, is_best, distance = self.__is_adversarial(
            image, predictions, in_bounds)

        assert predictions.ndim == 1
        assert gradient.shape == image.shape
        if return_details:
            return predictions, gradient, is_adversarial, is_best, distance
        else:
            return predictions, gradient, is_adversarial
def predictions_and_gradient ( self , image = None , label = None , strict = True , return_details = False ) : assert self . has_gradient ( ) if image is None : image = self . __original_image if label is None : label = self . __original_class in_bounds = self . in_bounds ( image ) assert not strict or in_bounds self . _total_prediction_calls += 1 self . _total_gradient_calls += 1 predictions , gradient = self . __model . predictions_and_gradient ( image , label ) # noqa: E501 is_adversarial , is_best , distance = self . __is_adversarial ( image , predictions , in_bounds ) assert predictions . ndim == 1 assert gradient . shape == image . shape if return_details : return predictions , gradient , is_adversarial , is_best , distance else : return predictions , gradient , is_adversarial
Interface to model.predictions_and_gradient for attacks.

        Parameters
        ----------
        image : `numpy.ndarray`
            Single input with shape as expected by the model
            (without the batch dimension).
            Defaults to the original image.
        label : int
            Label used to calculate the loss that is differentiated.
            Defaults to the original label.
        strict : bool
            Controls if the bounds for the pixel values should be checked.
Interface to model . predictions_and_gradient for attacks .