diff --git a/src/HGQ/proxy/fixed_point_quantizer.py b/src/HGQ/proxy/fixed_point_quantizer.py
index 6588204..4518895 100644
--- a/src/HGQ/proxy/fixed_point_quantizer.py
+++ b/src/HGQ/proxy/fixed_point_quantizer.py
@@ -143,6 +143,7 @@ def fixed(bits, integer_bits, RND='TRN', SAT='WRAP') -> Callable:
     return gfixed(1, bits, integer_bits, RND, SAT)
 
 
+@keras.utils.register_keras_serializable(package='HGQ')
 class FixedPointQuantizer(keras.layers.Layer, metaclass=abc.ABCMeta):
 
     def __init__(self, keep_negative, bits, integers, RND: str = 'TRN', SAT: str = 'WRAP', overrides: dict | None = None, **kwargs):
@@ -174,7 +175,7 @@ def __init__(self, keep_negative, bits, integers, RND: str = 'TRN', SAT: str = '
 
         super().__init__(trainable=False, **kwargs)
 
-    def call(self, x, training=None):
+    def call(self, x, training=None):  # type:ignore
         assert not training, "Proxy model shall can not be trained!"
         if not self.built:
             self.build(x.shape)
diff --git a/src/HGQ/proxy/unary_lut.py b/src/HGQ/proxy/unary_lut.py
index 76e529a..93a7f72 100644
--- a/src/HGQ/proxy/unary_lut.py
+++ b/src/HGQ/proxy/unary_lut.py
@@ -13,6 +13,7 @@
 LUT_SIZE_LIMITATION = int(os.environ.get('LUT_SIZE_LIMITATION', 2**12))
 
 
+@keras.utils.register_keras_serializable(package='HGQ')
 class UnaryLUT(Layer):
     proxy_ready = True
 
@@ -33,7 +34,7 @@ def __init__(self, kif_in: tuple[int, int, int], kif_out: tuple[int, int, int],
             self.table = tf.Variable(table, dtype='float32', trainable=False, name='table')
         super().__init__(**kwargs)
 
-    def call(self, inputs, **kwargs):
+    def call(self, inputs, **kwargs):  # type:ignore
         if not self.built:
             self.build(inputs.shape)
         inputs = tf.round(inputs * self.scale)
diff --git a/test/helpers.py b/test/helpers.py
index e7bf442..44bc21f 100644
--- a/test/helpers.py
+++ b/test/helpers.py
@@ -16,7 +16,7 @@
 from tensorflow import keras
 
 from HGQ import trace_minmax
-from HGQ.proxy import FixedPointQuantizer, UnaryLUT, to_proxy_model
+from HGQ.proxy import FixedPointQuantizer, to_proxy_model
 
 tf.get_logger().setLevel('ERROR')
 
@@ -76,7 +76,7 @@ def _run_model_sl_test(model: keras.Model, proxy: keras.Model, data, output_dir:
     model_loaded: keras.Model = keras.models.load_model(output_dir + '/keras.h5')  # type: ignore
 
     proxy.save(output_dir + '/proxy.h5')
-    proxy_loaded: keras.Model = keras.models.load_model(output_dir + '/proxy.h5', custom_objects={'FixedPointQuantizer': FixedPointQuantizer, 'UnaryLUT': UnaryLUT})  # type: ignore
+    proxy_loaded: keras.Model = keras.models.load_model(output_dir + '/proxy.h5')  # type: ignore
     for l1, l2 in zip(proxy.layers, proxy_loaded.layers):
         if not isinstance(l1, FixedPointQuantizer):
             continue