5f4ae6c7f2e71382ef42e18bd33e1edb64e30b8c,foolbox/models/tensorflow.py,TensorFlowModel,__init__,#TensorFlowModel#Any#Any#Any#Any#Any#,30

Before Change

            loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
                labels=self._label[tf.newaxis],
                logits=self._logits[tf.newaxis])
            self._loss = tf.squeeze(loss, axis=0)
            gradients = tf.gradients(loss, images)
            assert len(gradients) == 1

After Change

                bw_gradients[0] = tf.zeros_like(images)
            self._bw_gradient = tf.squeeze(bw_gradients[0], axis=0)

            self._bw_gradients_pre = tf.placeholder(tf.float32, self._batch_logits.shape)  // noqa: E501
            batch_bw_loss = tf.reduce_sum(self._batch_logits * self._bw_gradients_pre)  // noqa: E501
            batch_bw_gradients = tf.gradients(batch_bw_loss, images)
            assert len(batch_bw_gradients) == 1
            if batch_bw_gradients[0] is None:
                batch_bw_gradients[0] = tf.zeros_like(images)
            self._bw_gradients = batch_bw_gradients[0]

    @classmethod
    def from_keras(cls, model, bounds, input_shape=None,

Instances