How to export model with custom metric

[filot97]

I wrote as following code :

clf = ak.StructuredDataClassifier(overwrite=True, max_trials=1, loss="sparse_categorical_crossentropy", objective=kt.Objective("val_log_loss", direction="min"), metrics=[tf.compat.v1.losses.log_loss], seed=2021)
clf.fit(X_train, y_train, epochs=1, batch_size=16, validation_data=(X_val, y_val))
**model = clf.export_model()**

But, this code print following message.
I want to know how to export a model with custom metric.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-13-a52f4320acf1> in <module>
     12     clf = ak.StructuredDataClassifier(overwrite=True, max_trials=1, loss="sparse_categorical_crossentropy", objective=kt.Objective("val_log_loss", direction="min"), metrics=[tf.compat.v1.losses.log_loss], seed=2021)
     13     clf.fit(X_train, y_train, epochs=1, batch_size=16, validation_data=(X_val, y_val), callbacks=[es, reLR, DisplayCallback()])
---> 14     model = clf.export_model()
     15     #models.append(clf)
     16     y_pred = model.predict(X_val)

~/anaconda3/envs/autokeras/lib/python3.8/site-packages/autokeras/auto_model.py in export_model(self)
    471             with trained weights.
    472         """
--> 473         return self.tuner.get_best_model()

~/anaconda3/envs/autokeras/lib/python3.8/site-packages/autokeras/engine/tuner.py in get_best_model(self)
     61     def get_best_model(self):
     62         with hm_module.maybe_distribute(self.distribution_strategy):
---> 63             model = tf.keras.models.load_model(self.best_model_path)
     64         return model
     65 

~/anaconda3/envs/autokeras/lib/python3.8/site-packages/tensorflow/python/keras/saving/save.py in load_model(filepath, custom_objects, compile, options)
    210       if isinstance(filepath, six.string_types):
    211         loader_impl.parse_saved_model(filepath)
--> 212         return saved_model_load.load(filepath, compile, options)
    213 
    214   raise IOError(

~/anaconda3/envs/autokeras/lib/python3.8/site-packages/tensorflow/python/keras/saving/saved_model/load.py in load(path, compile, options)
    157         'training_config', None)
    158     if training_config is not None:
--> 159       model.compile(**saving_utils.compile_args_from_training_config(
    160           training_config))
    161       saving_utils.try_build_compiled_arguments(model)

~/anaconda3/envs/autokeras/lib/python3.8/site-packages/tensorflow/python/keras/saving/saving_utils.py in compile_args_from_training_config(training_config, custom_objects)
    222     metrics_config = training_config.get('metrics', None)
    223     if metrics_config is not None:
--> 224       metrics = _deserialize_nested_config(_deserialize_metric, metrics_config)
    225 
    226     # Recover weighted metrics.

~/anaconda3/envs/autokeras/lib/python3.8/site-packages/tensorflow/python/keras/saving/saving_utils.py in _deserialize_nested_config(deserialize_fn, config)
    259     return deserialize_fn(config)
    260   elif isinstance(config, dict):
--> 261     return {
    262         k: _deserialize_nested_config(deserialize_fn, v)
    263         for k, v in config.items()

~/anaconda3/envs/autokeras/lib/python3.8/site-packages/tensorflow/python/keras/saving/saving_utils.py in <dictcomp>(.0)
    260   elif isinstance(config, dict):
    261     return {
--> 262         k: _deserialize_nested_config(deserialize_fn, v)
    263         for k, v in config.items()
    264     }

~/anaconda3/envs/autokeras/lib/python3.8/site-packages/tensorflow/python/keras/saving/saving_utils.py in _deserialize_nested_config(deserialize_fn, config)
    264     }
    265   elif isinstance(config, (tuple, list)):
--> 266     return [_deserialize_nested_config(deserialize_fn, obj) for obj in config]
    267 
    268   raise ValueError('Saved configuration not understood.')

~/anaconda3/envs/autokeras/lib/python3.8/site-packages/tensorflow/python/keras/saving/saving_utils.py in <listcomp>(.0)
    264     }
    265   elif isinstance(config, (tuple, list)):
--> 266     return [_deserialize_nested_config(deserialize_fn, obj) for obj in config]
    267 
    268   raise ValueError('Saved configuration not understood.')

~/anaconda3/envs/autokeras/lib/python3.8/site-packages/tensorflow/python/keras/saving/saving_utils.py in _deserialize_nested_config(deserialize_fn, config)
    257     return None
    258   if _is_single_object(config):
--> 259     return deserialize_fn(config)
    260   elif isinstance(config, dict):
    261     return {

~/anaconda3/envs/autokeras/lib/python3.8/site-packages/tensorflow/python/keras/saving/saving_utils.py in _deserialize_metric(metric_config)
    287     # case handling for these in compile, based on model output shape.
    288     return metric_config
--> 289   return metrics_module.deserialize(metric_config)
    290 
    291 

~/anaconda3/envs/autokeras/lib/python3.8/site-packages/tensorflow/python/keras/metrics.py in deserialize(config, custom_objects)
   3444       A Keras `Metric` instance or a metric function.
   3445   """
-> 3446   return deserialize_keras_object(
   3447       config,
   3448       module_objects=globals(),

~/anaconda3/envs/autokeras/lib/python3.8/site-packages/tensorflow/python/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    375       obj = module_objects.get(object_name)
    376       if obj is None:
--> 377         raise ValueError(
    378             'Unknown ' + printable_module_name + ': ' + object_name)
    379     # Classes passed by name are instantiated with no args, functions are

ValueError: Unknown metric function: log_loss

[haifeng-jin]

You will need to load the Keras model again with the metric as a custom_object.

[Charlespoole]

from tensorflow.keras.utils import CustomObjectScope
with CustomObjectScope({'r2': r2}):
model = clf.export_model()

This block of code works for me, with r2 being the custom metrics function.