def test_update_usage_vector(self):
graph = tf.Graph()
with graph.as_default():
with tf.Session(graph=graph) as session:
mem = Memory(4, 5, 2, 2)
free_gates = np.random.uniform(0, 1, (2, 2)).astype(np.float32)
init_read_weightings = random_softmax((2, 4, 2), axis=1)
init_write_weightings = random_softmax((2, 4), axis=1)
init_usage = np.random.uniform(0, 1, (2, 4)).astype(np.float32)
psi = np.product(
1 - init_read_weightings * free_gates[:, np.newaxis, :],
axis=2)
predicted_usage = (init_usage + init_write_weightings -
init_usage * init_write_weightings) * psi
mem.read_weightings = tf.convert_to_tensor(
init_read_weightings)
mem.write_weighting = tf.convert_to_tensor(
init_write_weightings)
mem.usage_vector = tf.convert_to_tensor(init_usage)
op = mem.update_usage_vector(free_gates)
u = session.run(op)
#updated_usage = session.run(mem.usage_vector.value())
self.assertEqual(u.shape, (2, 4))
self.assertTrue(np.array_equal(u, predicted_usage))
def test_get_directional_weightings(self):
graph = tf.Graph()
with graph.as_default():
with tf.Session(graph=graph) as session:
mem = Memory(4, 5, 2, 2)
_link_matrix = np.random.uniform(0, 1,
(2, 4, 4)).astype(np.float32)
_read_weightings = random_softmax((2, 4, 2), axis=1)
predicted_forward = np.matmul(_link_matrix, _read_weightings)
predicted_backward = np.matmul(
np.transpose(_link_matrix, [0, 2, 1]), _read_weightings)
mem.read_weightings = tf.convert_to_tensor(_read_weightings)
fop, bop = mem.get_directional_weightings(_link_matrix)
forward_weighting, backward_weighting = session.run([fop, bop])
self.assertTrue(
np.allclose(forward_weighting, predicted_forward))
self.assertTrue(
np.allclose(backward_weighting, predicted_backward))
