def test_update_precedence_vector(self):
graph = tf.Graph()
with graph.as_default():
with tf.Session(graph=graph) as session:
mem = Memory(4, 5, 2, 2)
write_weighting = random_softmax((2, 4), axis=1)
initial_precedence = random_softmax((2, 4), axis=1)
predicted = (1 - write_weighting.sum(axis=1, keepdims=True)
) * initial_precedence + write_weighting
mem.precedence_vector = tf.convert_to_tensor(
initial_precedence)
op = mem.update_precedence_vector(write_weighting)
p = session.run(op)
#updated_precedence_vector = session.run(mem.precedence_vector.value())
self.assertEqual(p.shape, (2, 4))
self.assertTrue(np.allclose(p, predicted))
def test_update_link_matrix(self):
graph = tf.Graph()
with graph.as_default():
with tf.Session(graph=graph) as session:
mem = Memory(4, 5, 2, 2)
_write_weighting = random_softmax((2, 4), axis=1)
_precedence_vector = random_softmax((2, 4), axis=1)
initial_link = np.random.uniform(0, 1,
(2, 4, 4)).astype(np.float32)
np.fill_diagonal(initial_link[0, :], 0)
np.fill_diagonal(initial_link[1, :], 0)
# calculate the updated link iteratively as in paper
# to check the correcteness of the vectorized implementation
predicted = np.zeros((2, 4, 4), dtype=np.float32)
for i in range(4):
for j in range(4):
if i != j:
reset_factor = (1 - _write_weighting[:, i] -
_write_weighting[:, j])
predicted[:, i,
j] = reset_factor * initial_link[:, i,
j] + _write_weighting[:,
i] * _precedence_vector[:,
j]
mem.link_matrix = tf.convert_to_tensor(initial_link)
mem.precedence_vector = tf.convert_to_tensor(
_precedence_vector)
write_weighting = tf.constant(_write_weighting)
op = mem.update_link_matrix(write_weighting)
L = session.run(op)
#updated_link_matrix = session.run(mem.link_matrix.value())
self.assertTrue(np.allclose(L, predicted))
