Going through a deep learning class on udemy a bit, I learned about keras, and thought it’d be great for testing since it has other neural networks set up for training and general usage. However, I’m wondering what would be a good model for training. I’m planning on keeping the model running continuously if I can so I can see how it reacts to different things in real life, and there’d be an output function to look around.
Here’s what I was thinking:
For input, from what I can tell, the retina already takes in information somewhat pyrimidally, so I’ll just pass it all in that way.
For convolution + max pooling, I’ll select for simple features like lines or colors at first, then take the max pool and use that to shrink the result to about 1/(e^(1/2)) the input, which will give some translation/skew/rotation invariance. Later on, I can also pass in that shrunken pyramid, minus the top, as the input to a similar network with a different convolution.
For the spatial pooler, I’ll use a dense network first, and I’m only planning on passing only the smallest part of the pyramid in. That way large, simple features can be inputted alongside small, detailed features.
Then I plan on passing that into an RNN I’ll replace with a temporal pooler.
I think I can modify my image pyramid function for this pretty readily, and it should be both simple and expandable.
What do you guys think? Should I use this model? Some other existing model? Something closer to the neocortex and surrounding systems?