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Can't save and load lattice model
hello, I'm having a problem with loading premade models (https://www.tensorflow.org/lattice/tutorials/premade_models)
when I save the model as .tf format and load the model I would get
KeyError: 'layers'
when I save the model as keras format and load the model I would get
ValueError: Unknown object: 'CalibratedLatticeEnsembleConfig'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.
I'm seeing this issue running on the colab given by the page (https://colab.research.google.com/github/tensorflow/lattice/blob/master/docs/tutorials/premade_models.ipynb), on Databricks, and also local run
code I use to save and load the model (https://www.tensorflow.org/lattice/api_docs/python/tfl/premade/CalibratedLattice#save)
rtl_layer_ensemble_model.save("model.keras")
loaded_model = tf.keras.models.load_model("model.keras")
rtl_layer_ensemble_model.save("model.tf")
loaded_model = tf.keras.models.load_model("model.tf")
rtl_layer_ensemble_model.save("/")
loaded_model = tf.keras.models.load_model("/")
all the models in the example can't be loaded
linear_model, lattice_model, explicit_ensemble_model, random_ensemble_model, rtl_layer_ensemble_model, prefitting_model, crystals_ensemble_model
full stacktrace error:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-40-bc47832831ee>](https://localhost:8080/#) in <cell line: 2>()
1 rtl_layer_ensemble_model.save("model.tf")
----> 2 loaded_model = tf.keras.models.load_model("model.tf")
2 frames
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode, **kwargs)
236
237 # Legacy case.
--> 238 return legacy_sm_saving_lib.load_model(
239 filepath, custom_objects=custom_objects, compile=compile, **kwargs
240 )
[/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py](https://localhost:8080/#) in error_handler(*args, **kwargs)
68 # To get the full stack trace, call:
69 # `tf.debugging.disable_traceback_filtering()`
---> 70 raise e.with_traceback(filtered_tb) from None
71 finally:
72 del filtered_tb
[/usr/local/lib/python3.10/dist-packages/keras/src/engine/functional.py](https://localhost:8080/#) in reconstruct_from_config(config, custom_objects, created_layers)
1487
1488 # First, we create all layers and enqueue nodes to be processed
-> 1489 for layer_data in config["layers"]:
1490 process_layer(layer_data)
1491 # Then we process nodes in order of layer depth.
KeyError: 'layers'
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-39-c20ad91f21ca>](https://localhost:8080/#) in <cell line: 2>()
1 rtl_layer_ensemble_model.save("model.keras")
----> 2 loaded_model = tf.keras.models.load_model("model.keras")
6 frames
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode, **kwargs)
228 f"with the native Keras format: {list(kwargs.keys())}"
229 )
--> 230 return saving_lib.load_model(
231 filepath,
232 custom_objects=custom_objects,
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode)
273
274 except Exception as e:
--> 275 raise e
276 else:
277 return model
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode)
238 # Construct the model from the configuration file in the archive.
239 with ObjectSharingScope():
--> 240 model = deserialize_keras_object(
241 config_dict, custom_objects, safe_mode=safe_mode
242 )
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/serialization_lib.py](https://localhost:8080/#) in deserialize_keras_object(config, custom_objects, safe_mode, **kwargs)
702 safe_mode_scope = SafeModeScope(safe_mode)
703 with custom_obj_scope, safe_mode_scope:
--> 704 instance = cls.from_config(inner_config)
705 build_config = config.get("build_config", None)
706 if build_config:
[/usr/local/lib/python3.10/dist-packages/tensorflow_lattice/python/premade.py](https://localhost:8080/#) in from_config(cls, config, custom_objects)
146 @classmethod
147 def from_config(cls, config, custom_objects=None):
--> 148 model_config = tf.keras.utils.legacy.deserialize_keras_object(
149 config.get('model_config'), custom_objects=custom_objects
150 )
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/legacy/serialization.py](https://localhost:8080/#) in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
478 # In this case we are dealing with a Keras config dictionary.
479 config = identifier
--> 480 (cls, cls_config) = class_and_config_for_serialized_keras_object(
481 config, module_objects, custom_objects, printable_module_name
482 )
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/legacy/serialization.py](https://localhost:8080/#) in class_and_config_for_serialized_keras_object(config, module_objects, custom_objects, printable_module_name)
363 )
364 if cls is None:
--> 365 raise ValueError(
366 f"Unknown {printable_module_name}: '{class_name}'. "
367 "Please ensure you are using a `keras.utils.custom_object_scope` "
ValueError: Unknown object: 'CalibratedLatticeEnsembleConfig'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.
when I changed the version to 2.0.11, the keras failed with the error below and tf passed I think the issue happens in all version after 2.0.11
!pip install tensorflow-lattice==2.0.11 pydot
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-23-c20ad91f21ca>](https://localhost:8080/#) in <cell line: 2>()
1 rtl_layer_ensemble_model.save("model.keras")
----> 2 loaded_model = tf.keras.models.load_model("model.keras")
6 frames
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_api.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode, **kwargs)
228 f"with the native Keras format: {list(kwargs.keys())}"
229 )
--> 230 return saving_lib.load_model(
231 filepath,
232 custom_objects=custom_objects,
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode)
273
274 except Exception as e:
--> 275 raise e
276 else:
277 return model
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py](https://localhost:8080/#) in load_model(filepath, custom_objects, compile, safe_mode)
238 # Construct the model from the configuration file in the archive.
239 with ObjectSharingScope():
--> 240 model = deserialize_keras_object(
241 config_dict, custom_objects, safe_mode=safe_mode
242 )
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/serialization_lib.py](https://localhost:8080/#) in deserialize_keras_object(config, custom_objects, safe_mode, **kwargs)
702 safe_mode_scope = SafeModeScope(safe_mode)
703 with custom_obj_scope, safe_mode_scope:
--> 704 instance = cls.from_config(inner_config)
705 build_config = config.get("build_config", None)
706 if build_config:
[/usr/local/lib/python3.10/dist-packages/tensorflow_lattice/python/premade.py](https://localhost:8080/#) in from_config(cls, config, custom_objects)
145 @classmethod
146 def from_config(cls, config, custom_objects=None):
--> 147 model = super(CalibratedLatticeEnsemble, cls).from_config(
148 config, custom_objects=custom_objects)
149 try:
[/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py](https://localhost:8080/#) in from_config(cls, config, custom_objects)
3242 # constructor of the class.
3243 try:
-> 3244 model = cls(**config)
3245 except TypeError as e:
3246 raise TypeError(
[/usr/local/lib/python3.10/dist-packages/tensorflow_lattice/python/premade.py](https://localhost:8080/#) in __init__(self, model_config, dtype, **kwargs)
98 # Check that proper config has been given.
99 if not isinstance(model_config, configs.CalibratedLatticeEnsembleConfig):
--> 100 raise ValueError('Invalid config type: {}'.format(type(model_config)))
101 # Verify that the config is fully specified.
102 premade_lib.verify_config(model_config)
ValueError: Invalid config type: <class 'dict'>
Try
rtl_layer_ensemble_model.save("model_keras")
loaded_model = tf.keras.models.load_model(
"model_keras",
custom_objects=tfl.premade.get_custom_objects(),
)
-
tf.keras.models.load_modelreconstructs the Keras model, thus you need to pass in the custom objects used by the model in order to be able to recompile it.tfl.premade.get_custom_objects()returns all Tensorflow Lattice custom objects. If you only need the model for inference, i.e. a functional__call__, you can instead use
rtl_layer_ensemble_model.save("model_keras")
loaded_model = tf.saved_model.load("model_keras")
- The period in
keras.modelis likely causing problems during variable name matching / parsing. Escaping it solves the problem.
thank you for the suggestion
load_model with custom_objects=tfl.premade.get_custom_objects() doesn't throw error for when load model save with .save("model.tf") and .save("model") .save("model.keras") gave the error
ValueError: Input keypoints are invalid for feature age: {'class_name': '__numpy__', 'config': {'value': [29.0, 44.0, 54.0, 65.0, 100.0], 'dtype': 'float64'}}
however in version 2.0.13, the loaded model with custom_objects gave the following error when call .evaluate(x, y), .predict(x) seems to work
RuntimeError: You must compile your model before training/testing. Use `model.compile(optimizer, loss)`.
in version 2.0.11, saved and loaded model can call .evaluate(x, y)
escaping period doesn't seems to do anything or is there any special syntax for escape period besides
"model\.keras"
Hi, I am also having a problem with loading my tensorflow model with a Lattice layer. My model was able to compile and predict without a problem, and I saved it to json. I used model_from_json to load the saved model but I am getting an error about the lattice sizes:
ValueError: If lattice input is provided as list of tensors their number must match lattice_sizes. 'input list': [None, 5, 5], 'lattice_sizes': ListWrapper([5, 5, 5, 5, 5])
Hi @expeon07 in case the problem still persists could you provide a full stack trace?
Hi, I am also having a problem with loading my tensorflow model with a Lattice layer. My model was able to compile and predict without a problem, and I saved it to json. I used model_from_json to load the saved model but I am getting an error about the lattice sizes:
ValueError: If lattice input is provided as list of tensors their number must match lattice_sizes. 'input list': [None, 5, 5], 'lattice_sizes': ListWrapper([5, 5, 5, 5, 5])
We just release TFL 2.1.1 with a fix for this. Can you please try to see if you can load without an issue?
