def test_invalid_inputs():
with pytest.raises(ValueError, match="must be a tuple or list of size 2"):
x0 = np.random.random((12, 5))
x = np.random.random((12, 5))
x1 = np.random.random((12, 5))
layer = PolynomialCrossing(projection_dim=6)
layer([x0, x, x1])
def test_invalid_proj_dim():
with pytest.raises(ValueError) as exception_info:
x0 = np.random.random((12, 5))
x = np.random.random((12, 5))
layer = PolynomialCrossing(projection_dim=6)
layer([x0, x])
assert "is not supported yet" in str(exception_info.value)
def test_invalid_proj_dim():
with pytest.raises(ValueError,
match="should be smaller than last_dim / 2"):
x0 = np.random.random((12, 5))
x = np.random.random((12, 5))
layer = PolynomialCrossing(projection_dim=6)
layer([x0, x])
def test_invalid_inputs():
with pytest.raises(ValueError) as exception_info:
x0 = np.random.random((12, 5))
x = np.random.random((12, 5))
x1 = np.random.random((12, 5))
layer = PolynomialCrossing(projection_dim=6)
layer([x0, x, x1])
assert "must be a tuple or list of size 2" in str(exception_info.value)
def test_invalid_inputs(self):
with self.assertRaisesRegexp(ValueError,
r"must be a tuple or list of size 2"):
x0 = np.random.random((12, 5))
x = np.random.random((12, 5))
x1 = np.random.random((12, 5))
layer = PolynomialCrossing(projection_dim=6)
layer([x0, x, x1])
def test_full_matrix(self):
x0 = np.asarray([[0.1, 0.2, 0.3]]).astype(np.float32)
x = np.asarray([[0.4, 0.5, 0.6]]).astype(np.float32)
layer = PolynomialCrossing(projection_dim=None,
kernel_initializer="ones")
output = layer([x0, x])
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllClose(np.asarray([[0.55, 0.8, 1.05]]), output)
def test_diag_scale():
x0 = np.asarray([[0.1, 0.2, 0.3]]).astype(np.float32)
x = np.asarray([[0.4, 0.5, 0.6]]).astype(np.float32)
layer = PolynomialCrossing(projection_dim=None,
diag_scale=1.0,
kernel_initializer="ones")
output = layer([x0, x])
np.testing.assert_allclose([[0.59, 0.9, 1.23]], output)
def test_serialization():
layer = PolynomialCrossing(projection_dim=None)
serialized_layer = tf.keras.layers.serialize(layer)
new_layer = tf.keras.layers.deserialize(serialized_layer)
assert layer.get_config() == new_layer.get_config()
def test_full_matrix():
x0 = np.asarray([[0.1, 0.2, 0.3]]).astype(np.float32)
x = np.asarray([[0.4, 0.5, 0.6]]).astype(np.float32)
layer = PolynomialCrossing(projection_dim=None, kernel_initializer="ones")
output = layer([x0, x])
np.testing.assert_allclose([[0.55, 0.8, 1.05]], output)
def test_serialization(self):
layer = PolynomialCrossing(projection_dim=None)
serialized_layer = tf.keras.layers.serialize(layer)
new_layer = tf.keras.layers.deserialize(serialized_layer)
self.assertEqual(layer.get_config(), new_layer.get_config())
def test_invalid_proj_dim(self):
with self.assertRaisesRegexp(ValueError, r"is not supported yet"):
x0 = np.random.random((12, 5))
x = np.random.random((12, 5))
layer = PolynomialCrossing(projection_dim=6)
layer([x0, x])
