Related Issues (20)
- May want to try this: glorot_normal https://keras.io/initializers/ for the intiialization
- And you might want to start thinking about monitoring parameters during training with Tensorboard.
- And you can implement early_stopping as well, to make sure that your model doesn't start getting worse on the validation:
- How does number of features relate to number of data points relate to number of parameters?
- Gotta get that test error going down.
- Create notebook for baseline eyes open eyes closed task (baseline_task.py)
- Rather than having a text cell before a code cell, code cells should have titles inline (and then you can hide code): HOT 1
- Wrap your plotting code into a function.
- Should start using logging instead of print, it will help in the long run.
- Normalization should be an option... you are still normalizing before splitting your train/val/test
- Worth trying xgboost on this problem, it is super fast / tree based (descriptive) / and powerful
- Unbalanced classes. Stratified sampling. k-fold stratified cross validation helps HOT 1
- Batch Decisions
- Multilevel Model over subjects
- Brain Vision problem how to solve this?
- Random input
- Stratify
- I have no notion that it should be able to differentiate the two classes.
- Either plot log(loss) or use a log scale...
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