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[DOC] Adds Iñigo's blogposts of Weeks 1 and 2 #39

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Jun 10, 2024
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[DOC] Adds Iñigo's blogposts of Weeks 1 and 2 #39

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81d7cee
[DOC] Adds blogpost Iñigo's blogpost of Week 1
itellaetxe Jun 7, 2024
7483a03
FIX: RCorrects bad RST URL syntax & deletes mentions to week 0 work
itellaetxe Jun 9, 2024
c6bfe87
DOC: Adds Iñigo's week 1 and 2 blogposts
itellaetxe Jun 9, 2024
9ead66d
FIX: Single underscore instead of two for RST URLs
itellaetxe Jun 9, 2024
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29 changes: 29 additions & 0 deletions posts/2024/2024_06_07_Inigo_week_1.rst
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First Week into GSoC 2024: Building the AutoEncoder, preliminary results
========================================================================

.. post:: June 7 2024
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There is still confusion in dates and weeks. If I'm not mistaken there was a blog post (the first one) that corresponded to the community bonding period (week prior to May 27), and since then there have been two weeks: week 1 (ended Fri May 31), and week 2 (ended Fri Jun 7). So I find that this blog post still confuses dates and contents. Can this please be addressed? 2 blog posts are requested. To avoid this in the future, best is to allocate 30-45 minutes every Friday to write about the work that week. I know it is the most scientifically interesting part for the contributor, but it is important for the mentors, to keep track of the project, and the program.

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You are right, I messed up the dates. I will split the info into two blogs. Sorry for the confusion.

:author: Iñigo Tellaetxe
:tags: google
:category: gsoc


What I did this week
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Hi @itellaetxe!

Nice blog, clear and concise, Great job.

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Thanks Wachiou!

~~~~~~~~~~~~~~~~~~~~
This week I refactored the AutoEncoder code to match the design patterns and the organization of other Deep Learning models in the DIPY repo. I transferred my code to a `separate repo <https://github.com/itellaetxe/tractoencoder_gsoc>`_ to keep the DIPY repo clean and to experiment freely. Once the final product is working, I will merge it into DIPY. I also packaged the whole repo so I can use it as a library.
Training experiments were run for a maximum of a 150 epochs, with variable results. They are not amazing, but at least we get some reconstruction of the input tracts from FiberCup, which seems to be on the right track. I also implemented training logs that report the parameters I used for training, so I can reproduce the results at any time. This still needs work though, because not all parameters are stored. Need to polish!
The left image shows the input tracts, and the middle and right images show two reconstructions from two different training experiments.

.. image:: /_static/images/fibercup_preliminary_results.png
:alt: Preliminary results of the AutoEncoder training for a subset of plausible fibers of the FiberCup dataset.
:width: 600

What is coming up next week
~~~~~~~~~~~~~~~~~~~~~~~~~~~
With the help of my mentors, we identified possible improvements to the AutoEncoder training process. Yesterday I investigated how PyTorch weights are initialized in convolutional kernels and in Keras Dense layers using the `He Initialization <https://paperswithcode.com/paper/delving-deep-into-rectifiers-surpassing-human>`_. Using custom initializers, one can mimic the same behavior in TensorFlow, which I started to implement also yesterday.
This week should focus on trying to reproduce the small implementation differences that might be causing the model to not converge as the PyTorch one. I will also try to finish implementing the He Initialization in TensorFlow.


Did I get stuck anywhere
~~~~~~~~~~~~~~~~~~~~~~~~
I got a bit stuck refactoring the code to match the DIPY design patterns and also with the TensorFlow implementation itself, because the output shape of the Encoder and the input shape of the Decoder were not matching.
After investigating what caused this issue, I discovered that `tf.shape` was not giving me the usual (and expected) shape of the tensor, conveniently stored in a `tuple`. I found this behavior strange, but I solved the problem just calling a `.shape` method on the Tensor, which does give me the shape tuple I needed.