def get_model():
x0 = tf.keras.layers.Input(shape=(13, ))
x1 = Cross(projection_dim=None)(x0, x0)
x2 = Cross(projection_dim=None)(x0, x1)
logits = tf.keras.layers.Dense(units=1)(x2)
model = tf.keras.Model(x0, logits)
return model
def test_invalid_diag_scale(self):
with self.assertRaisesRegexp(ValueError,
r"`diag_scale` should be non-negative"):
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 = Cross(diag_scale=-1.)
layer(x0, x)
def test_invalid_proj_dim(self):
with self.assertRaisesRegexp(ValueError,
r"should be smaller than last_dim / 2"):
x0 = np.random.random((12, 5))
x = np.random.random((12, 5))
layer = Cross(projection_dim=6)
layer(x0, x)
def test_low_rank_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 = Cross(projection_dim=1, 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(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 = Cross(projection_dim=None,
diag_scale=1.,
kernel_initializer="ones")
output = layer(x0, x)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllClose(np.asarray([[0.59, 0.9, 1.23]]), output)
def test_serialization(self):
layer = Cross(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_unsupported_input_dim(self):
with self.assertRaisesRegexp(ValueError, r"dimension mismatch"):
x0 = np.random.random((12, 5))
x = np.random.random((12, 7))
layer = Cross()
layer(x0, x)
def test_one_input(self):
x0 = np.asarray([[0.1, 0.2, 0.3]]).astype(np.float32)
layer = Cross(projection_dim=None, kernel_initializer="ones")
output = layer(x0)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.assertAllClose(np.asarray([[0.16, 0.32, 0.48]]), output)