How would I implement this New Type of NN




CIO NN stands for Controller Input Output Nerual Network

note due to a typo the "nearon" means "neron"

For this we have to redefine the Nearon

  • 2 Inputs
  • 2 Outputs
  • 4 Weights (each input and output have their own weights)
  • Internal Memory Cell (any byte or bit or block size with variable size)
  • Activation Function (Defines what weights and what inputs activate this Nearon)
  • Memory Storage Function (Defines what and when this cell should store said memory or memory stream)
  • Memory Transpose Function (Once activated any stored memory that the activation function can trigger will be played/pushed into the Nerual Network)
  • Forget Function (Defines when and/or how and/or why these memories can be destroyed/removed based on Activation function with Memory states and any input stateses itself)

How would I implement this in the form of Code? please take note of the spec. (this is non profit/GNU v3)

This would look something like these: Basic CIO Nearon

These would be arranges like this: enter image description here

Which we can build it into this: enter image description here

it can be trained like this:

  • do normal NN training from the inputs to the input outputs like a hidden layer NN

then trained to be controlled like this:

  • then set the known NN dataset (inputs) to be corrected to actual or correct values via the CI (Controller Input) which will be outputed on the "Input + Controller Input" Output

by training like this:

  • you have a normal NN with hidden layers which can be trainned (this can be done with CNNs) with backpropergation, and a cost function (use least amount of nerons) etc
  • now you can allow the CIO NN to retrain / teach itself with supervised or unsupervised learning.
  • you can combine the "Input Output" and the "Input + Controller Input" Output with another NN which can then connect with this NN in a similiar way that a Neron connects to a Neron

Dean Van Greunen

Posted 2018-01-11T22:07:19.627

Reputation: 221

No answers