Made changes to some errors prone lines #272
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| save_best_only=True), | ||
| TensorBoard(log_dir="logs/" + stub, update_freq="batch") | ||
| ] | ||
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| if os.path.exists(file_path): |
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Is this check needed? It seems more intuitive to "start training again" than to "train a certain number of epochs more".
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It's indeed used for "start training again" as an individual I have no computational resources to continue 75 epochs in single go this will be helpful.
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I see.
Are we talking about the tinyAES dataset or ASCADv2? Because the tinyAES should be quite fast even on a moderate computer without a GPU (using the sample configs). Also there are fewer epochs in the configs. In case of ASCADv2 please use the GPAM model (https://github.com/google/scaaml/tree/main/papers/2024/GPAM#models) since I doubt the initial model is powerful enough for this dataset.
tinyAES: There is a possibility to use https://colab.google/ (something like jupyterlab running on cloud). One can request a GPU or a TPU (K8 IIRC) and run for a limited time (8 hours IIRC). The problem is that the saved files count into your Google account quota. This might be ok for the case of tinyAES but ASCADv2 is larger than the free quota (unprocessed). I never tried mounting a Google cloud bucket into a colab (could be very slow, downloaded data might be limited).
As far as the code change goes: if you find the feature useful I would prefer adding an argument allow_model_reload to train_model and omit the print statements. And then a command line argument defaulting to false: https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse
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yes I was talking about ASCAD dataset
| stub = get_model_stub(attack_point, attack_byte, config) | ||
| file_path = f"models/{stub}.keras" |
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This modernises the code. However we would be silently losing backwards compatibility with pretrained models. Since this code is for the 2019 Defcon presentation I would prefer to pin a TensorFlow version here (say 2.12) and have more up to date code for the more recent projects. What do you think?
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you're right back compatibility is a concern but when I tried to run the code it threw an error asking to add a keras extension so I did. I was using the latest version of everything.
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ok this might not be an issue if we install the version mentioned in requirements.
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And sorry the reply took me so long, we were quite busy last week. |
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train.pyso one can load the model if it already exists and continue training..kerasfile extension so it shows no error while saving the model or loading the model.