Can this be problem Specific?

[pGit1]

Does this implementation only work with ImageNet like images or can it be tuned to your specific problem?

[jacobgil]

It can be tuned to your specific problem.
As long as you can specify a target cost function, you can use this.
I used this for visualizing the PilotNet network that outputs angles. This network is used for regression and not classification.
https://jacobgil.github.io/deeplearning/vehicle-steering-angle-visualizations

[pardha-fission]

InvalidArgumentError Traceback (most recent call last)
/home/pardha/anaconda3/envs/for_tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1326 try:
-> 1327 return fn(*args)
1328 except errors.OpError as e:

/home/pardha/anaconda3/envs/for_tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
1305 feed_dict, fetch_list, target_list,
-> 1306 status, run_metadata)
1307

/home/pardha/anaconda3/envs/for_tensorflow/lib/python3.6/contextlib.py in exit(self, type, value, traceback)
88 try:
---> 89 next(self.gen)
90 except StopIteration:

/home/pardha/anaconda3/envs/for_tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in raise_exception_on_not_ok_status()
465 compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 466 pywrap_tensorflow.TF_GetCode(status))
467 finally:

InvalidArgumentError: You must feed a value for placeholder tensor 'batch_normalization_1/keras_learning_phase' with dtype bool
[[Node: batch_normalization_1/keras_learning_phase = Placeholderdtype=DT_BOOL, shape=, _device="/job:localhost/replica:0/task:0/cpu:0"]]

During handling of the above exception, another exception occurred:

InvalidArgumentError Traceback (most recent call last)
in ()
1 for i,layer_name in enumerate([l.name for l in my_model.layers]):
----> 2 cam, heatmap = grad_cam(my_model, preprocessed_img, predicted_class, layer_name)
3 cam = cv2.cvtColor(cam, cv2.COLOR_BGR2RGB)
4 plt.figure(i)
5 plt.title(str(layer_name))

in grad_cam(input_model, image, category_index, layer_name)
14 gradient_function = K.function([model.layers[0].input], [conv_output, grads])
15 print('hellooooooooo',gradient_function)
---> 16 output, grads_val = gradient_function([image])
17 output, grads_val = output[0, :], grads_val[0, :, :, :]
18

/home/pardha/anaconda3/envs/for_tensorflow/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in call(self, inputs)
2266 updated = session.run(self.outputs + [self.updates_op],
2267 feed_dict=feed_dict,
-> 2268 **self.session_kwargs)
2269 return updated[:len(self.outputs)]
2270

/home/pardha/anaconda3/envs/for_tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
893 try:
894 result = self._run(None, fetches, feed_dict, options_ptr,
--> 895 run_metadata_ptr)
896 if run_metadata:
897 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/pardha/anaconda3/envs/for_tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1122 if final_fetches or final_targets or (handle and feed_dict_tensor):
1123 results = self._do_run(handle, final_targets, final_fetches,
-> 1124 feed_dict_tensor, options, run_metadata)
1125 else:
1126 results = []

/home/pardha/anaconda3/envs/for_tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1319 if handle is None:
1320 return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1321 options, run_metadata)
1322 else:
1323 return self._do_call(_prun_fn, self._session, handle, feeds, fetches)

/home/pardha/anaconda3/envs/for_tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1338 except KeyError:
1339 pass
-> 1340 raise type(e)(node_def, op, message)
1341
1342 def _extend_graph(self):

InvalidArgumentError: You must feed a value for placeholder tensor 'batch_normalization_1/keras_learning_phase' with dtype bool
[[Node: batch_normalization_1/keras_learning_phase = Placeholderdtype=DT_BOOL, shape=, _device="/job:localhost/replica:0/task:0/cpu:0"]]