def test_fgm_gradient_max(self):
input_dim = 2
nb_classes = 3
batch_size = 4
rng = np.random.RandomState([2017, 8, 23])
x = tf.placeholder(tf.float32, [batch_size, input_dim])
weights = tf.placeholder(tf.float32, [input_dim, nb_classes])
logits = tf.matmul(x, weights)
probs = tf.nn.softmax(logits)
adv_x = fgm(x, probs)
random_example = rng.randint(batch_size)
random_feature = rng.randint(input_dim)
output = tf.slice(adv_x, [random_example, random_feature], [1, 1])
(dx, ) = tf.gradients(output, x)
# The following line catches GitHub issue #243
self.assertIsNotNone(dx)
dx = self.sess.run(dx, feed_dict=random_feed_dict(rng, [x, weights]))
ground_truth = np.zeros((batch_size, input_dim))
ground_truth[random_example, random_feature] = 1.0
self.assertClose(dx, ground_truth)
def test_fgm_gradient_max():
input_dim = 2
num_classes = 3
batch_size = 4
loss_type = KEYWORDS.CE
rng = np.random.RandomState([2017, 8, 23])
x = tf.placeholder(tf.float32, [batch_size, input_dim])
weights = tf.placeholder(tf.float32, [input_dim, num_classes])
logits = tf.matmul(x, weights)
probs = tf.nn.softmax(logits)
adv_x = fgm(x, probs, loss_type=loss_type)
random_example = rng.randint(batch_size)
random_feature = rng.randint(input_dim)
output = tf.slice(adv_x, [random_example, random_feature], [1, 1])
dx, = tf.gradients(output, x)
# The following line catches GitHub issue #243
assert dx is not None
sess = tf.Session()
dx = sess.run(dx, feed_dict=random_feed_dict(rng, [x, weights]))
ground_truth = np.zeros((batch_size, input_dim))
ground_truth[random_example, random_feature] = 1.
assert np.allclose(dx, ground_truth), (dx, ground_truth)
