def test_unweighted_ragged_reduction(self, reduction_str, expected_output):
data = ragged_factory_ops.constant([[[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]],
[[3.0, 1.0], [1.0, 2.0]]])
input_tensor = keras.Input(shape=(None, None), ragged=True)
output_tensor = reduction.Reduction(reduction=reduction_str)(input_tensor)
model = keras.Model(input_tensor, output_tensor)
output = model.predict(data)
self.assertAllClose(expected_output, output)
def test_unweighted_dense_reduction(self, reduction_str, expected_output):
data = np.array([[[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]],
[[3.0, 1.0], [1.0, 2.0], [0.0, 0.0]]])
input_tensor = keras.Input(shape=(None, None))
output_tensor = reduction.Reduction(reduction=reduction_str)(input_tensor)
model = keras.Model(input_tensor, output_tensor)
output = model.predict(data)
self.assertAllClose(expected_output, output)
def test_weighted_dense_reduction_with_different_dimensionality(self):
data = np.array([[[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]],
[[3.0, 1.0], [1.0, 2.0], [0.0, 0.0]]])
input_tensor = keras.Input(shape=(None, None))
weights = np.array([[1.0, 2.0, 1.0], [1.0, 1.0, 0.0]])
weight_input_tensor = keras.Input(shape=(None, ))
output_tensor = reduction.Reduction(reduction="mean")(
input_tensor, weights=weight_input_tensor)
model = keras.Model([input_tensor, weight_input_tensor], output_tensor)
output = model.predict([data, weights])
expected_output = [[2.0, 2.0], [2.0, 1.5]]
self.assertAllClose(expected_output, output)
def test_weighted_ragged_reduction_with_different_dimensionality(self):
data = ragged_factory_ops.constant([[[1.0, 1.0], [2.0, 2.0],
[3.0, 3.0]],
[[3.0, 1.0], [1.0, 2.0]]])
input_tensor = keras.Input(shape=(None, None), ragged=True)
weights = ragged_factory_ops.constant([[1.0, 2.0, 1.0], [1.0, 1.0]])
weight_input_tensor = keras.Input(shape=(None, ), ragged=True)
output_tensor = reduction.Reduction(reduction="mean")(
input_tensor, weights=weight_input_tensor)
model = keras.Model([input_tensor, weight_input_tensor], output_tensor)
output = model.predict([data, weights])
expected_output = [[2.0, 2.0], [2.0, 1.5]]
self.assertAllClose(expected_output, output)
def test_sqrtn_fails_on_unweighted_dense(self):
input_tensor = keras.Input(shape=(None, None))
with self.assertRaisesRegex(ValueError, ".*sqrtn.*"):
_ = reduction.Reduction(reduction="sqrtn")(input_tensor)
