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Stanford ML Project CS229'18
Currently we publish only loss or cv loss values. Needs an accuracy percentage value .
Add tf saver and associated boiler plate code to save and load old models (possibly only neural net)
Gets confusing with CV.
Port code and make sure it runs on a GCP/Azure/AWS instance.
Once done, take an input json of experiment configs and execute them. We will have to arrive on an experiment config to use.
Off the top of my head it should have:
This coupled with model saving / serialization. Should hold us in good stead - come December.
Currently the only method of comparison is cross-validation scores. Once accuracy is added, that will be another method.
Cross-entropy loss = Sum ( y * log yhat)
Create a table accuracy and result of the 10 fold cross entropy loss.
https://www.openml.org/a/estimation-procedures/1
https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html#cross-entropy
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