/home/XXXX/miniconda2/envs/py36habnet/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
Testing: 100%|██████████| 1000/1000 [00:11<00:00, 88.66it/s]
/home/XXXX/miniconda2/envs/py36habnet/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
Testing accuracy = 23.40
Traceback (most recent call last):
File "train.py", line 290, in <module>
tf.app.run()
File "/home/XXXX/miniconda2/envs/py36habnet/lib/python3.6/site-packages/tensorflow_core/python/platform/app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "/home/XXXX/miniconda2/envs/py36habnet/lib/python3.6/site-packages/absl/app.py", line 303, in run
_run_main(main, args)
File "/home/XXXX/miniconda2/envs/py36habnet/lib/python3.6/site-packages/absl/app.py", line 251, in _run_main
sys.exit(main(argv))
File "train.py", line 276, in main
save_optimized_presicion(all_test_labels, all_test_y_pred, stats_graph_folder, 'test', epoch)
File "train.py", line 117, in save_optimized_presicion
diff = recalls_vstack -recalls_tile
ValueError: operands could not be broadcast together with shapes (9,10) (9,9)
Could you help? Thanks.
When I run train.py in HabNet_MC, I am facing the following issues:
Could you help? Thanks.