I implemented and trained & saved model in tf.keras. I need to visualize some layers through lrp and other visualisation techniques that are only supported in original keras, therefore I need to load model in keras.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-65-08488a893466> in <module>
10
11 with CustomObjectScope({'GlorotUniform': glorot_uniform(), "BinaryAccuracy":binary_accuracy}):
---> 12 model = load_model(os.getcwd() + "/models/saved_models_for_fusion/0_FusionVGGMnistToPS.h5")
13 img_path = str(Path(os.getcwd() + "/models/scripts/datasets/parkinson_spiral_s/test/0/00027_w.cz.fnusa.1_1.svc.jpg"))
14 img = load_img(img_path)
d:\users\*\anaconda3\envs\tl\lib\site-packages\keras\engine\saving.py in load_wrapper(*args, **kwargs)
490 os.remove(tmp_filepath)
491 return res
--> 492 return load_function(*args, **kwargs)
493
494 return load_wrapper
d:\users\*\anaconda3\envs\tl\lib\site-packages\keras\engine\saving.py in load_model(filepath, custom_objects, compile)
582 if H5Dict.is_supported_type(filepath):
583 with H5Dict(filepath, mode='r') as h5dict:
--> 584 model = _deserialize_model(h5dict, custom_objects, compile)
585 elif hasattr(filepath, 'write') and callable(filepath.write):
586 def load_function(h5file):
d:\users\*\anaconda3\envs\tl\lib\site-packages\keras\engine\saving.py in _deserialize_model(h5dict, custom_objects, compile)
367 weighted_metrics=weighted_metrics,
368 loss_weights=loss_weights,
--> 369 sample_weight_mode=sample_weight_mode)
370
371 # Set optimizer weights.
d:\users\*\anaconda3\envs\tl\lib\site-packages\keras\engine\training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, **kwargs)
209
210 # Save all metric attributes per output of the model.
--> 211 self._cache_output_metric_attributes(metrics, weighted_metrics)
212
213 # Set metric attributes on model.
d:\users\*\anaconda3\envs\tl\lib\site-packages\keras\engine\training.py in _cache_output_metric_attributes(self, metrics, weighted_metrics)
736 output_shapes.append(list(output.shape))
737 self._per_output_metrics = training_utils.collect_per_output_metric_info(
--> 738 metrics, self.output_names, output_shapes, self.loss_functions)
739 self._per_output_weighted_metrics = (
740 training_utils.collect_per_output_metric_info(
d:\users\*\anaconda3\envs\tl\lib\site-packages\keras\engine\training_utils.py in collect_per_output_metric_info(metrics, output_names, output_shapes, loss_fns, is_weighted)
939 metrics_dict = OrderedDict()
940 for metric in metrics:
--> 941 metric_name = get_metric_name(metric, is_weighted)
942 metric_fn = get_metric_function(
943 metric, output_shape=output_shapes[i], loss_fn=loss_fns[i])
d:\users\*\anaconda3\envs\tl\lib\site-packages\keras\engine\training_utils.py in get_metric_name(metric, weighted)
967 return metric
968
--> 969 metric = metrics_module.get(metric)
970 return metric.name if hasattr(metric, 'name') else metric.__name__
971
d:\users\*\anaconda3\envs\tl\lib\site-packages\keras\metrics.py in get(identifier)
1974 if isinstance(identifier, dict):
1975 config = {'class_name': str(identifier), 'config': {}}
-> 1976 return deserialize(config)
1977 elif isinstance(identifier, six.string_types):
1978 return deserialize(str(identifier))
d:\users\*\anaconda3\envs\tl\lib\site-packages\keras\metrics.py in deserialize(config, custom_objects)
1968 module_objects=globals(),
1969 custom_objects=custom_objects,
-> 1970 printable_module_name='metric function')
1971
1972
d:\users\*\anaconda3\envs\tl\lib\site-packages\keras\utils\generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
138 if cls is None:
139 raise ValueError('Unknown ' + printable_module_name +
--> 140 ': ' + class_name)
141 if hasattr(cls, 'from_config'):
142 custom_objects = custom_objects or {}
ValueError: Unknown metric function: {'class_name': 'BinaryAccuracy', 'config': {'name': 'binary_accuracy', 'dtype': 'float32', 'threshold': 0.5}}
I had some troubles before with the GlorotUniform and this https://stackoverflow.com/a/53689541/5722894 comment fixed that. I need to somehow create that custom object, but no clue how. I tried to import BinaryAccuracy from tf.keras and just pass it as custom object, but that doesn't work either.
