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Third Week into GSoC 2024: Replicating training parameters, approaching replication | ||
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.. post:: June 14 2024 | ||
:author: Iñigo Tellaetxe | ||
:tags: google | ||
:category: gsoc | ||
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What I did this week | ||
~~~~~~~~~~~~~~~~~~~~ | ||
This week was slightly less productive because I was really busy with my PhD tasks, but I managed to progress nevertheless. | ||
After implementing custom weight initializers (with He Initialization) for the ``Dense`` and ``Conv1D`` layers in the AutoEncoder (AE), I launched some experiments to try to replicate the training process of the original model. | ||
This yielded better results than last week, this time setting the weight decay, the learning rate, and the latent space dimensionality as shown in the `FINTA paper <https://doi.org/10.1016/j.media.2021.102126>`_. | ||
Now the AE has no problem learning that the bundles have depth, and the number of broken streamlines decreased a lot compared to the previous results. | ||
I also worked on trying to monitor the training experiments using TensorBoard, but I did not succeed because it was a last minute idea and I did not have time to implement it properly. | ||
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.. image:: /_static/images/fibercup_better_results.png | ||
:alt: Preliminary results of the AutoEncoder training for a subset of plausible fibers of the FiberCup dataset, approaching better replication compared to the PyTorch model. | ||
:width: 600 | ||
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What is coming up next week | ||
~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
My mentors and I agreed on trying to transfer the weights of the pre-trained PyTorch model to my Keras implementation, because it may take less time than actually training the model. Thus, the strategy we devised for this to work is the following: | ||
1. Implement dataset loading using HDF5 files, as the original model uses them, and the TractoInferno dataset is contained in such files (it is approximately 75 GB)/ | ||
2. Launch the training in Keras in the Donostia International Physics Center (DIPC) cluster, which has GPU accelerated nodes that I can use for speeding up training. Unlike PyTorch, I don't need to adjust the code for GPU usage, as TF takes care of that for speeding up training. | ||
3. While the previous step is running, I will work on transferring the weights from the PyTorch format to the Keras model. This will be a bit tricky but my mentor Jong Sung gave me a code snippet that was used in the past for this purpose, so I will try to adapt it to my needs. | ||
4. In parallel, I will try to read about the streamline sampling and filtering strategy Jon Haitz used for `GESTA <https://doi.org/10.1016/j.media.2023.102761>`_ and FINTA, respectively, to implement them in DIPY. I think the code is hosted in the TractoLearn repository, but I need to look it up. | ||
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Did I get stuck anywhere | ||
~~~~~~~~~~~~~~~~~~~~~~~~ | ||
It was not easy to implement the custom weight initializers for the Keras layers because the He initialization is not described in the Keras documentation as in the PyTorch one, so I had to make a mix of both. | ||
Otherwise, I did not get stuck this week, but I am a bit worried about the weight transfer process, as it may be a bit tricky to implement. | ||
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Until next week! |