def test_graph_parameters(self):
np.random.seed(0)
net1 = Vector(10) >> Linear(10)
net2 = net1 >> Linear(10)
self.assertEqual(net1.get_state(as_list=True),
net2.left.get_state(as_list=True))
def __init__(self, input_size, embedding_size, q_network,
likelihood_model):
self.input_size = input_size
self.embedding_size = embedding_size
self.q_network = q_network >> (Linear(embedding_size),
Linear(embedding_size))
self.likelihood_model = likelihood_model
def test_shape_elementwise(self):
part1 = Linear()
part2 = Linear(20)
self.assertEqual(part1.get_shape(), (None, None))
self.assertEqual(part2.get_shape(), (None, [Shape(20)]))
part2.chain(part1)
self.assertEqual(part1.get_shape(), ([Shape(20)], [Shape(20)]))
self.assertEqual(part2.get_shape(), (None, [Shape(20)]))
def test_shape_inference2(self):
part1 = Linear(10, 100)
part2 = Linear(100, 20)
self.assertEqual(part1.get_shape(), ([Shape(10)], [Shape(100)]))
self.assertEqual(part2.get_shape(), ([Shape(100)], [Shape(20)]))
part1.chain(part2)
self.assertEqual(part1.get_shape(), ([Shape(10)], [Shape(100)]))
self.assertEqual(part2.get_shape(), ([Shape(100)], [Shape(20)]))
def test_graph_basic(self):
np.random.seed(0)
v1 = Vector(10)
v2 = Linear(10)
self.assertEqual((v1 >> v2).left, v1.chain(v2).left)
self.assertEqual((v1 >> v2).right, v1.chain(v2).right)
def test_freeze_parameters(self):
np.random.seed(0)
net1 = Vector(10) >> Linear(10)
net1.initialize()
self.assertEqual(net1.freeze().get_state(as_list=True),
net1.get_state(as_list=True))
def test_shape_elementwise(self):
part1 = Linear()
part2 = Linear(20)
self.assertEqual(part1.get_shape(), (None, None))
self.assertEqual(part2.get_shape(), (None, [Shape(20)]))
part2.chain(part1)
self.assertEqual(part1.get_shape(), ([Shape(20)], [Shape(20)]))
self.assertEqual(part2.get_shape(), (None, [Shape(20)]))
def test_shape_inference2(self):
part1 = Linear(10, 100)
part2 = Linear(100, 20)
self.assertEqual(part1.get_shape(), ([Shape(10)], [Shape(100)]))
self.assertEqual(part2.get_shape(), ([Shape(100)], [Shape(20)]))
part1.chain(part2)
self.assertEqual(part1.get_shape(), ([Shape(10)], [Shape(100)]))
self.assertEqual(part2.get_shape(), ([Shape(100)], [Shape(20)]))
def test_shape_elementwise2(self):
part1 = Linear(20)
part2 = Linear()
part3 = Linear(30, 40)
self.assertEqual(part1.get_shape(), (None, [Shape(20)]))
self.assertEqual(part2.get_shape(), (None, None))
self.assertEqual(part3.get_shape(), ([Shape(30)], [Shape(40)]))
part4 = part1.chain(part2)
self.assertEqual(part1.get_shape(), (None, [Shape(20)]))
self.assertEqual(part2.get_shape(), ([Shape(20)], [Shape(20)]))
self.assertEqual(part4.get_shape(), (None, [Shape(20)]))
with self.assertRaises(ShapeInError):
part4.chain(part3)
def test_freeze_parameters2(self):
np.random.seed(0)
gan = (Vector(10) >> Linear(20)) >> (Linear(10) >> Linear(2))
gan.initialize()
self.assertEqual(gan.left.freeze().get_state(as_list=True),
gan.left.get_state(as_list=True))
self.assertEqual(gan.right.freeze().get_state(as_list=True),
gan.right.get_state(as_list=True))
self.assertEqual(gan.right.freeze().get_graph_parameters(), [])
self.assertNotEqual(gan.right.get_graph_parameters(), [])
self.assertEqual(
(Vector(20) >> gan.right).freeze().get_graph_parameters(), [])
self.assertNotEqual((Vector(20) >> gan.right).get_graph_parameters(),
[])
self.assertEqual(
(Vector(20) >> gan.right).freeze().get_state(as_list=True),
(Vector(20) >> gan.right).get_state(as_list=True))
self.assertEqual(gan.left.freeze().get_graph_parameters(), [])
self.assertNotEqual(gan.left.get_graph_parameters(), [])
T.set_default_device('/cpu:0')
c = T.scalar(name='c')
segments = T.matrix(dtype='int32', name='segments')
a_idx = segments[:, 0]
b_idx = segments[:, 1]
leaf_segment = segments[:, 2]
m = segments[:, 3]
log_fac = segments[:, 4]
x = T.matrix(name='x')
e = T.matrix(name='e')
q_network = Vector(X.shape[1], placeholder=x, is_input=False) >> Repeat(Tanh(200), 2)
q_mu_network = q_network >> Linear(D)
q_mu = q_mu_network.get_outputs()[0].get_placeholder()
q_sigma_network = q_network >> Linear(D)
q_sigma = tf.sqrt(tf.exp(q_sigma_network.get_outputs()[0].get_placeholder()))
z = q_mu + e * q_sigma
values, times = T.variable(values), T.variable(times)
values = tf.concat(0, [z, values])
harmonic = T.variable(create_harmonic(M))
a_batch_values = T.gather(values, a_idx)
a_batch_times = T.gather(times, a_idx)
b_batch_values = T.gather(values, b_idx)
b_batch_times = T.gather(times, b_idx)
harmonic_m = T.gather(harmonic, m - 1)
def test_bad_shape2(self):
part1 = Linear(100)
part2 = Linear(20)
self.assertEqual(part1.get_shape(), (None, [Shape(100)]))
self.assertEqual(part2.get_shape(), (None, [Shape(20)]))
part1.chain(part2)
self.assertEqual(part1.get_shape(), (None, [Shape(100)]))
self.assertEqual(part2.get_shape(), ([Shape(100)], [Shape(20)]))
part3 = Linear(100)
part2.chain(part3)
part4 = Linear(90)
with self.assertRaises(ShapeInError):
part4.chain(part3)
def test_bad_shape(self):
part1 = Linear(10, 100)
part2 = Linear(90, 20)
with self.assertRaises(ShapeInError):
part1.chain(part2)
def test_freeze(self):
net1 = Vector(10) >> Linear(10)
self.assertEqual(Freeze(net1).get_graph_parameters(), [])
def test_shape_elementwise2(self):
part1 = Linear(20)
part2 = Linear()
part3 = Linear(30, 40)
self.assertEqual(part1.get_shape(), (None, [Shape(20)]))
self.assertEqual(part2.get_shape(), (None, None))
self.assertEqual(part3.get_shape(), ([Shape(30)], [Shape(40)]))
part4 = part1.chain(part2)
self.assertEqual(part1.get_shape(), (None, [Shape(20)]))
self.assertEqual(part2.get_shape(), ([Shape(20)], [Shape(20)]))
self.assertEqual(part4.get_shape(), (None, [Shape(20)]))
with self.assertRaises(ShapeInError):
part4.chain(part3)
def test_bad_shape2(self):
part1 = Linear(100)
part2 = Linear(20)
self.assertEqual(part1.get_shape(), (None, [Shape(100)]))
self.assertEqual(part2.get_shape(), (None, [Shape(20)]))
part1.chain(part2)
self.assertEqual(part1.get_shape(), (None, [Shape(100)]))
self.assertEqual(part2.get_shape(), ([Shape(100)], [Shape(20)]))
part3 = Linear(100)
part2.chain(part3)
part4 = Linear(90)
with self.assertRaises(ShapeInError):
part4.chain(part3)
def test_bad_shape(self):
part1 = Linear(10, 100)
part2 = Linear(90, 20)
with self.assertRaises(ShapeInError):
part1.chain(part2)
