Comments (2)
@lhfowl Thank you for your interests in our work!
Currently this repository only provides a proof-of-concept implementation, and its implementation does not include convolutional layers (this is absolutely within by our theoretical framework but just unimplemented). Following the instructions you will be able to certify any MLP networks.
We are currently working on a re-implementation of RecurJac and CROWN that works with Pytorch and supports most common layers. I will let you know once that is ready.
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Hi @lhfowl,
A new implementation of CROWN/Fast-Lin is available here:
https://github.com/huanzhang12/CROWN-IBP
It uses PyTorch and supports CNN layers. You can try it out - it should at least be useful for verifying a VGG like architecture.
from recurjac-and-crown.
Related Issues (6)
- # 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
- Local Lipschitz constant on customized Pytorch network HOT 2
- What's the different between crown_adaptive_bound and crown_general_bound HOT 2
- Does this repository support robust boundary calculations of CROWN-IBP type?
- A question about the back propagation algorithm in crown
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