659534c4f142c4e87acb4a4320f8387e6f755c93,foolbox/models/keras.py,KerasModel,__init__,#KerasModel#Any#Any#Any#Any#Any#,28

Before Change

        self._predictions_are_logits = predictions_are_logits

        if preprocess_fn is not None:
            self.preprocessing_fn = lambda x: preprocess_fn(x.copy())
        else:
            self.preprocessing_fn = lambda x: x

    def _as_logits(self, predictions):
        assert predictions.ndim in [1, 2]
        if self._predictions_are_logits:
            return predictions

After Change

        grads = K.gradients(loss, images_input)
        grad = grads[0]

        self._loss_fn = K.function(
            [images_input, label_input],
            [loss])
        self._batch_pred_fn = K.function(
            [images_input], [predictions])
        self._pred_grad_fn = K.function(
            [images_input, label_input],

Instances