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  1. 1D Redmine
  2. REDMINE1D-244

[RM-5497] CFHTLS training tests with high resolution photo-z added

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    • Type: Task
    • Status: Open (View Workflow)
    • Priority: Normal
    • Resolution: Unresolved
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      Description

      Created on 2019-12-18 17:50:49 by Marie Treyer. % Done: 0

      TRAINING SAMPLE:

      Stephane added high resolution photo-z + lower resolution spec-z to the initial SPEC only catalog that Johanna and Jerome previously used for training.
      total = ~250k galaxies with i<25.5 + ~15k galaxies kept aside for testing (randomly picked but with smooth N(z) distribution).

      here's the mag/zspec and zspec distributions ("zspec" refers to the redshifts used for training even if there are zphot) :

      TRAINING TESTS:

      model "x" (Jo&Je settings) :
      learning rate = 0.0001 to iteration 150000
      learning rate = 0.00001 from iteration 150000 to 300000
      the model is saved at iteration 300k

      Given that the loss function and other parameters for the validation samples seem to reach a minimum far sooner than iteration 300k (see fig below), i tried these 2 things:

      model "u":
      learning rate = 0.0001 to iteration 80000
      learning rate = 0.00001 from iteration 80000 to 200000
      models are saved at iterations 100k, 130k, 160k, and 200k

      model "v":
      learning rate = 0.0001 to iteration 50000
      learning rate = 0.00001 from iteration 50000 to 200000
      models are saved at iterations 100k, 130k, 160k, and 200k

      Here's what's happening. There are 5 cross-validations for each model, the averages are shown in black.
      M_square= <(zspec-zcnn_mean)**2.0>
      bias = < (zcnn_mean-zspec)/(1+zspec) > as in our paper (the plot is incomplete because i added it to the code half way through the process).
      I kept zcnn_mean (pdf weighted mean) because it's faster to compute, although the median gives better results.

      INFERENCES:

      The performance at 160k and 200k for "u" and "v" are quasi similar, and only slightly better than at 100k.
      Here's how the models compare for the test sample that was kept aside (ZCNN is the PDF median here) :


      The models aren't significantly different but the "u" (and even "v") trainings run in half the time as "x" (~4h for 1 cross-validation versus ~8h). Also the PDFs are smoother. I wanted to show a random sample of PDFs as well as the distribution of local peaks (above 5%) for "x", "u" and "v" but i seem to have exceeded my quota. Can we change this? Also Jerome is not part of this group and Johanna's address will change soon, we need to do something about that too!

        Attachments

        1. DELTAZ_ZMED_TEST.png
          DELTAZ_ZMED_TEST.png
          32 kB
        2. DENSITY.png
          DENSITY.png
          29 kB
        3. NZ_TEST.png
          NZ_TEST.png
          41 kB
        4. NZ.png
          NZ.png
          10 kB
        5. PIT_TEST.png
          PIT_TEST.png
          23 kB
        6. SIGMA_ZMED_TEST.png
          SIGMA_ZMED_TEST.png
          35 kB
        7. TRAINING_PERFS.png
          TRAINING_PERFS.png
          88 kB
        8. VALIDATION_PERFS.png
          VALIDATION_PERFS.png
          215 kB
        9. ZSPEC_ZCNN_igt235_TEST.png
          ZSPEC_ZCNN_igt235_TEST.png
          80 kB
        10. ZSPEC_ZCNN_ilt235_TEST.png
          ZSPEC_ZCNN_ilt235_TEST.png
          78 kB

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            • Assignee:
              r2j.migrate Redmine-Jira Migtation
              Reporter:
              r2j.migrate Redmine-Jira Migtation
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                Updated: