Minor updates on comment
@@ -764,6 +764,8 @@ def deepfool_attack(sess, x, predictions, logits, grads, sample, nb_candidate,
:param x: the input placeholder
:param predictions: the model's symbolic output (linear output,
pre-softmax)
+ :param logits: the model's unnormalized output tensor (the input to
+ the softmax layer)
:param grads: symbolic gradients
:param sample: numpy array with sample input
:param nb_candidate: the number of classes to test against
@@ -796,7 +798,6 @@ def deepfool_attack(sess, x, predictions, logits, grads, sample, nb_candidate,
current))
gradients = sess.run(grads, feed_dict={x: adv_x})
f = sess.run(predictions, feed_dict={x: adv_x})
- # pdb.set_trace()
for idx in range(sample.shape[0]):
pert = np.inf
if current[idx] != original[idx]:
