A unified framework of perturbation and gradient-based attribution methods for Deep Neural Networks interpretability. DeepExplain also includes support for Shapley Values sampling. (ICLR 2018)
I am trying to use DeepExplain with the Concept Saliency Map. However, I run into the following issue:
import keras
sess = K.get_session()
print('sess: ',sess)
from ConceptSaliencyMaps.deepexplain.tensorflow import DeepExplain
from ConceptSaliencyMaps.deepexplain.utils import preprocess
list_files = []
all_files = train_files + test_files
for file_name in files_max:
for file_name2 in all_files:
if file_name in file_name2:
list_files.append(file_name2)
test_set2 = zfish_age(list_files, path_to_save = path_to_augmented, test=True, transform = True, new_channel=new_channel, new_size_frame=size_frame,
verbose=False)
test_generator2 = data.DataLoader(test_set2,batch_size=1,
shuffle=False,
num_workers=20)
input_img = keras.Input(shape=(50, 128, 128))
with DeepExplain(session=sess, graph=sess.graph) as de:
with torch.no_grad():
for i, d in enumerate(test_generator2):
xis, _, _, labels_name = d
print('labels_name: {}'.format(labels_name))
input_tensor = input_img
img_array = xis.reshape([1,50,128,128])
ris, zis = model(xis.to(device))
print('zis.shape: ',zis.shape) # torch.Size([1, 256])
latents = reducer.transform(zis.cpu().detach())
print('latents.shape: ',latents.shape) # (1, 2)
method = 'guidedbp'
concept_score = [K.sum(latents*i) for i in concept_vectors[attr]]
attributions_guided = [de.explain(method, i, input_tensor, img_array) for i in concept_score]```
Error:
TypeError Traceback (most recent call last)
<ipython-input-169-177871cfe4fc> in <module>
73
74 concept_score = [K.sum(latents*i) for i in concept_vectors[attr]]
---> 75 attributions_guided = [de.explain(method, i, input_tensor, img_array) for i in concept_score]
<ipython-input-169-177871cfe4fc> in <listcomp>(.0)
73
74 concept_score = [K.sum(latents*i) for i in concept_vectors[attr]]
---> 75 attributions_guided = [de.explain(method, i, input_tensor, img_array) for i in concept_score]
../ConceptSaliencyMaps/deepexplain/tensorflow/methods.py in explain(self, method, T, X, xs, **kwargs)
733 _ENABLED_METHOD_CLASS = method_class
734 method = _ENABLED_METHOD_CLASS(T, X, xs, self.session, self.keras_phase_placeholder, **kwargs)
--> 735 result = method.run()
736 if issubclass(_ENABLED_METHOD_CLASS, GradientBasedMethod) and _GRAD_OVERRIDE_CHECKFLAG == 0:
737 warnings.warn('DeepExplain detected you are trying to use an attribution method that requires '
../ConceptSaliencyMaps/deepexplain/tensorflow/methods.py in run(self)
463 for alpha in list(np.linspace(1. / self.steps, 1.0, self.steps)):
464 xs_mod = [xs * alpha for xs in self.xs] if self.has_multiple_inputs else self.xs * alpha
--> 465 _attr = self.session_run(attributions, xs_mod)
466 if gradient is None: gradient = _attr
467 else: gradient = [g + a for g, a in zip(gradient, _attr)]
../ConceptSaliencyMaps/deepexplain/tensorflow/methods.py in session_run(self, T, xs)
94 if self.keras_learning_phase is not None:
95 feed_dict[self.keras_learning_phase] = 0
---> 96 return self.session.run(T, feed_dict)
97
98 def _set_check_baseline(self):
../lib/python3.7/site-packages/tensorflow_core/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
954 try:
955 result = self._run(None, fetches, feed_dict, options_ptr,
--> 956 run_metadata_ptr)
957 if run_metadata:
958 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
../lib/python3.7/site-packages/tensorflow_core/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1163 # Create a fetch handler to take care of the structure of fetches.
1164 fetch_handler = _FetchHandler(
-> 1165 self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)
1166
1167 # Run request and get response.
..lib/python3.7/site-packages/tensorflow_core/python/client/session.py in __init__(self, graph, fetches, feeds, feed_handles)
472 """
473 with graph.as_default():
--> 474 self._fetch_mapper = _FetchMapper.for_fetch(fetches)
475 self._fetches = []
476 self._targets = []
../lib/python3.7/site-packages/tensorflow_core/python/client/session.py in for_fetch(fetch)
264 elif isinstance(fetch, (list, tuple)):
265 # NOTE(touts): This is also the code path for namedtuples.
--> 266 return _ListFetchMapper(fetch)
267 elif isinstance(fetch, collections_abc.Mapping):
268 return _DictFetchMapper(fetch)
../lib/python3.7/site-packages/tensorflow_core/python/client/session.py in __init__(self, fetches)
373 """
374 self._fetch_type = type(fetches)
--> 375 self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
376 self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)
377
../lib/python3.7/site-packages/tensorflow_core/python/client/session.py in <listcomp>(.0)
373 """
374 self._fetch_type = type(fetches)
--> 375 self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
376 self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)
377
../lib/python3.7/site-packages/tensorflow_core/python/client/session.py in for_fetch(fetch)
261 if fetch is None:
262 raise TypeError('Fetch argument %r has invalid type %r' %
--> 263 (fetch, type(fetch)))
264 elif isinstance(fetch, (list, tuple)):
265 # NOTE(touts): This is also the code path for namedtuples.
TypeError: Fetch argument None has invalid type <class 'NoneType'>
I have reported the issue in the Concept Sal. Maps github as well, but both the developer and I believe the issue is related to DeepExplain. Any insights into this problem?
Please let me know if you need any further info related to the problem. Thanks!
Hi,
I am trying to use DeepExplain with the Concept Saliency Map. However, I run into the following issue:
Error:
I have reported the issue in the Concept Sal. Maps github as well, but both the developer and I believe the issue is related to DeepExplain. Any insights into this problem?
Please let me know if you need any further info related to the problem. Thanks!