Unknown loss function. How to pass object to custom_objects argument ?

[shreyapatilu]

Hello,

I am trying to run the following code with custom loss function RMSE. After running the predict code it is giving me following error.

How do I pass this custom loss function to the 'custom_objects'?

Thanks.

def my_objective(y_pred,  y_true):
    return tf.sqrt(tf.reduce_mean(tf.square(y_pred-y_true)))

input_node = ak.Input() 
output_node2 = ak.RNNBlock(return_sequences=True)(input_node) 
output_node = ak.RegressionHead(loss=my_objective)(output_node2)

auto_model = ak.AutoModel(inputs=input_node, outputs=output_node, overwrite=True, max_trials=100)

auto_model.fit(X_train, y_train, epochs=50)

predicted_y=auto_model.predict(X_test)```



---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/var/tmp/ipykernel_20393/2947224539.py in <module>
      1 start_time_prediction = time.time()
----> 2 predicted_y=auto_model.predict(X_test)
      3 
      4 
      5 print("--- %s seconds to predict the value ---" % (time.time() - start_time_prediction))

~/miniconda3/envs/renv/lib/python3.7/site-packages/autokeras/auto_model.py in predict(self, x, batch_size, verbose, **kwargs)
    441         dataset = self._adapt(x, self.inputs, batch_size)
    442         pipeline = self.tuner.get_best_pipeline()
--> 443         model = self.tuner.get_best_model()
    444         dataset = pipeline.transform_x(dataset)
    445         dataset = tf.data.Dataset.zip((dataset, dataset))

~/miniconda3/envs/renv/lib/python3.7/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 

~/miniconda3/envs/renv/lib/python3.7/site-packages/tensorflow/python/keras/saving/save.py in load_model(filepath, custom_objects, compile, options)
    204         filepath = path_to_string(filepath)
    205         if isinstance(filepath, str):
--> 206           return saved_model_load.load(filepath, compile, options)
    207 
    208   raise IOError(

~/miniconda3/envs/renv/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py in load(path, compile, options)
    166     if training_config is not None:
    167       model.compile(**saving_utils.compile_args_from_training_config(
--> 168           training_config), from_serialized=True)
    169       saving_utils.try_build_compiled_arguments(model)
    170     else:

~/miniconda3/envs/renv/lib/python3.7/site-packages/tensorflow/python/keras/saving/saving_utils.py in compile_args_from_training_config(training_config, custom_objects)
    211     loss_config = training_config.get('loss', None)
    212     if loss_config is not None:
--> 213       loss = _deserialize_nested_config(losses.deserialize, loss_config)
    214 
    215     # Recover metrics.

~/miniconda3/envs/renv/lib/python3.7/site-packages/tensorflow/python/keras/saving/saving_utils.py in _deserialize_nested_config(deserialize_fn, config)
    256     return {
    257         k: _deserialize_nested_config(deserialize_fn, v)
--> 258         for k, v in config.items()
    259     }
    260   elif isinstance(config, (tuple, list)):

~/miniconda3/envs/renv/lib/python3.7/site-packages/tensorflow/python/keras/saving/saving_utils.py in <dictcomp>(.0)
    256     return {
    257         k: _deserialize_nested_config(deserialize_fn, v)
--> 258         for k, v in config.items()
    259     }
    260   elif isinstance(config, (tuple, list)):

~/miniconda3/envs/renv/lib/python3.7/site-packages/tensorflow/python/keras/saving/saving_utils.py in _deserialize_nested_config(deserialize_fn, config)
    252     return None
    253   if _is_single_object(config):
--> 254     return deserialize_fn(config)
    255   elif isinstance(config, dict):
    256     return {

~/miniconda3/envs/renv/lib/python3.7/site-packages/tensorflow/python/keras/losses.py in deserialize(name, custom_objects)
   2035       module_objects=globals(),
   2036       custom_objects=custom_objects,
-> 2037       printable_module_name='loss function')
   2038 
   2039 

~/miniconda3/envs/renv/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    701             'https://www.tensorflow.org/guide/keras/save_and_serialize'
    702             '#registering_the_custom_object for details.'
--> 703             .format(printable_module_name, object_name))
    704 
    705     # Classes passed by name are instantiated with no args, functions are

ValueError: Unknown loss function: my_objective. Please ensure this object is passed to the `custom_objects` argument. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.