def test_full_ising_model_single(self):
connected = simple_ising_model.IsingModel(
length=5, vocab_size=3, fully_connected=True)
output = connected(np.array([[1, 1, 1, 1, 1]]))
self.assertEqual(3, connected.domain.vocab_size)
self.assertEqual(5, connected.domain.length)
self.assertAlmostEqual(-2.47013903, output[0].numpy(), delta=1e-5)
def test_full_ising_model(self, batch_size, length, vocab_size):
full = simple_ising_model.IsingModel(
length=length, vocab_size=vocab_size, fully_connected=True)
variables = tf.random_uniform(
shape=[batch_size, length], minval=0, maxval=vocab_size, dtype=tf.int32)
output = full(variables)
expected = _manual_full_model(variables, full.potentials)
self.assertAllClose(expected, output.numpy())
# Check that it can run with one-hot inputs.
output = full(tf.one_hot(variables, depth=vocab_size))
self.assertAllClose(expected, output.numpy())
def test_local_ising_model_single(self):
local = simple_ising_model.IsingModel(
length=5, vocab_size=3, fully_connected=False)
output = local(np.array([[1, 1, 1, 1, 1]]))
self.assertAlmostEqual(3.23183179, output[0].numpy(), delta=1e-5)