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Week 3 into GSoC

Today (June 21, 2020) marks the ending of week 3 of GSoC coding period. Let me summarize what I did for this week. Following up the work done in week 2, I went on exploring ArviZ InferenceData and tfp.vi module to learn how to deal with shapes, convergence checks and optimizers.

  • Adding a new axis to samples resolved the shape issues. By this, number of chains parameter is set to 1 and all the priors are handled perfectly.
  • The convergence checks did not seem to work properly. (As pointed out by my mentor, we need to adjust window size).
  • The default tensorflow optimizers also did not lead to convergence really well. Trying out the default values taken from PyMC3, I got really good results. This motivated me to write updates module for Variational Inference Interface.
  • I applied inverse of bijectors to transformed parameters to match support in bounded space. But this approach is wrong. I need to handle this using deterministics callback which I will do in coming week.
  • I had also written tests during this interval.
  • From last week, I did not get quite good results while experimenting with Mean Field ADVI in TFP, PyMC3 and PyMC4. As suggested by my mentor, setting a common random seed and same optimizer leads to very good results . (gist)

Experiments - Source

Whatever experiments I perform, I polish them out and share through GitHub gists. I do not why but I started loving to share code through GitHub gists rather than Colab or GitHub repo.

Here is the notebook -

Tasks for week 4

Phase 1 Evaluations are coming up. So, I need to sync work with my proposed timeline and spend time summarizing all the results in a single notebook. My tasks for week 4 -

  • Write tests for conjugate normal models with known mean/variance.
  • Configure atol argument for np.testing.assert_allclose. (I misunderstood how this parameter works)
  • Complete docs for optimizers by adding **kwargs option and writing corresponding maths equations.
  • Properly configure convergence checks and add an example to quickstart notebook.
  • Configure autobatching. (I need to understand how this works for mcmc)
  • Integrate Deterministics callbacks.
  • Complete Full Rank/Low Rank Approximation. (This will take time)
  • Configure Minibatches.
  • Update quick_start notebook with respect to all changes above.

Extra

If I will be able to complete above mentioned tasks in time, I would love to -

  • Have another implementation of Mean Field ADVI using tfd.MultivariateNormalDiag.
  • Play around with Mean Field ADVI on Eight Schools notebook.
  • Resolve a warning from tfp.vi module regarding repetitive use of tf.function in a loop. Maybe using tf.while_loop will solve this but I am not sure.
  • Experiment with MeanField/ FullRank/ LowRank ADVI in TFP, PyMC3, PyMC4 inspired from ColCarroll's notebook. I am already getting excited to play around with this after having all APIs set correctly.

For Evaluations

I look forward to learn how Variational AutoEncoders are implemented in PyMC3 and try implementing that in PyMC4.

I am thankful to my mentor for his constant guidance and pymc-devs for being such a supportive community.

Thank you for reading!

With ❤, Sayam


Last update: June 21, 2020

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