huanzhang12/RecurJac-and-CROWN
Reference implementations for RecurJac, CROWN, FastLin and FastLip (Neural Network verification and robustness certification algorithms) [Do not use this repo, use https://github.com/Verified-Intelligence/auto_LiRPA instead]
PythonBSD-2-Clause
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# python3 main.py --task robustness --numimage 10 --targettype random --norm i --modelfile ../models/mnist_3layer_relu_1024_adv_retrain --layerbndalg crown-adaptive --jacbndalg recurjac --eps 0.2 Loading model ../models/mnist_3layer_relu_1024_adv_retrain 2021-07-26 08:37:45.812888: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually. Model imported using keras Traceback (most recent call last): File "/home/RecurJac-and-CROWN-master/main.py", line 145, in <module> if input_dim[2] == 28 or input_dim[2] == "28": IndexError: list index out of range
#2 opened by aspnetcs - 2
Certifying robustness on custom network
#1 opened by lhfowl