4887ef8baecbf5315ec0f235e56a4f93cd05aad7,cleverhans/attacks_tf.py,,spm,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,1947

Before Change

    preds_adv = model.get_logits(adv_xs[-1])

    // Compute accuracy
    accs.append(tf.count_nonzero(tf.equal(tf.argmax(y, axis=-1),
                                          tf.argmax(preds_adv, axis=-1))))
  // Return the adv_x with worst accuracy
  adv_xs = tf.stack(adv_xs)
  accs = tf.stack(accs)

After Change

  // We want the worst case sample, with the largest xent_loss
  worst_sample_idx = tf.argmax(all_xents, axis=0)  // B

  batch_size = tf.shape(x)[0]
  keys = tf.stack([
      tf.range(batch_size, dtype=tf.int32),
      tf.cast(worst_sample_idx, tf.int32)
  ], axis=1)

Instances