Comments (4)
@drujensen that's a great idea! Feel free to give it a go in a PR :)
If you don't have time for this, we will add it to our task list and do our best to get to it.
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Ok, I will take a stab when I get a chance. In theory, someone could easily extend SHAInet by creating their own classes that inherit from the base to implement their own Learn/Cost/Activation functions.
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@drujensen Lately @ArtLinkov has been working on cost function as Proc, do you feel this is enough in regard to dynamic needs?
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@bararchy right, I saw that and its a huge improvement. 💯
The thing is that procs is more a functional approach instead of an object oriented one. I took a couple attempts to refactor some of this to try and make it more OO but it is difficult to do it in smaller chunks and will require more changes than I think you would want.
I think procs works just fine so not sure this is worth the effort. If I have the time, I might make another attempt at it but its not on my priority list at the moment.
You can close this if you don't want the ticket hanging around.
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Related Issues (20)
- Network class - Add q-learning as a training option
- Create layer class
- Support HDF5 and JSON to save and load models? HOT 4
- Using Crystal Workbook fails to create SHAINet model HOT 3
- NaN in Total error and MSE HOT 28
- Ability to set activation function per layer HOT 1
- Try and find a way to introduce multi-threading for training HOT 2
- save then load gives less accurate results HOT 9
- overfitting - BatchNormalization and Dropout HOT 5
- Add save/load for the CNN HOT 2
- Autosave option while training HOT 1
- Feedback on design HOT 3
- Add a way to convert the neurons representation to matrix
- Replace ES example? HOT 3
- Are there any benchmarks? HOT 2
- Logo design for shainet HOT 6
- OCR recognise image on image HOT 4
- Help building Cats vs Dogs CNN network HOT 5
- JSON.mapping and Logger are deprecated in Crystal 1.0.0 HOT 1
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