def test_call_without_circular_padding(self):
context_features = {
'context_feature_1': tf.constant([[1], [0]], dtype=tf.float32)
}
example_features = {
'example_feature_1':
tf.constant([[[1], [0], [-1]], [[0], [1], [0]]], dtype=tf.float32)
}
mask = tf.constant([[True, True, False], [True, False, False]],
dtype=tf.bool)
expected_concat_tensor = tf.constant(
[[[1., 1.], [1., 0.], [1., -1.]], [[0., 0.], [0., 1.], [0., 0.]]],
dtype=tf.float32)
concat_tensor = layers.ConcatFeatures(circular_padding=False)(
(context_features, example_features, mask))
self.assertAllClose(expected_concat_tensor, concat_tensor)
def test_serialization(self):
layer = layers.ConcatFeatures()
serialized = tf.keras.layers.serialize(layer)
loaded = tf.keras.layers.deserialize(serialized)
self.assertAllEqual(loaded.get_config(), layer.get_config())
