Updating Initial Z Parameter Sweep Results #13
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I believe the issue you reference for |
jaclyn-taroni
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May 7, 2018
1.initial-z-sweep/README.md
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| A similar pattern appears where lower dimensionality benefits from increased sparsity. | ||
| ADAGE models are also generally stable, particularly at high dimensions. | ||
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| It appears that `learning rate` is globally optimal at 0.0005; epochs at 100; batch size at 50; sparsity at 0; with decreasing noise for larger z dimensions. | ||
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This figure does not appear to be showing up here: https://github.com/gwaygenomics/interpret-compression/blob/c6bc2e39f80ecdc62078d4c92f77e2b8b86d583a/1.initial-z-sweep/README.md
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ah, yeah, typo. My bad
1.initial-z-sweep/README.md
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| ### Tied Weights | ||
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| By constrianing the compression and decompression networks to contain the same weights (tied weights), ADAGE models had variable performance across models. |
1.initial-z-sweep/README.md
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| **Figure 8.** The loss of validation sets at the end of training for 432 tied weight ADAGE models. | ||
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| It appears the models perform better without any induced sparsity |
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Missing a period at the end of this sentence
1.initial-z-sweep/README.md
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| It appears that `learning rate` is globally optimal at 0.0005; epochs at 100; batch size at 50; and noise and sparsity at 0. | ||
| | 5 | 0 | 0.0 | 100 | 50 | 0.0015 | 0.0042 | | ||
| | 25 | 0 | 0.0 | 100 | 50 | 0.0015 | 0.0029 | | ||
| | 50 | 0.0 | 0 | 100 | 50 | 0.0005 | 0.0023 | |
danich1
approved these changes
May 7, 2018
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After updates to data processing (#9) and a reorganization of the module (#11), I reran the initial Z sweep. The updated results are below.
While there are many files updated in this PR (2,837), the primary updates are found in the updated
README.md. The only script updated is1.initial-z-sweep/scripts/param_sweep_latent_space_viz.R.I also removed the
colorblindrpackage dependency because it is not a recipe in conda (see #13).