ML Dive

Diving into some ML... While I have cloned repo's and applied them successfully with transfer learning with models like inceptionv3, and resnet, I wanted to get in a little deeper. Before I could get started, I set out to setup my VR computer (GTX 1070 Graphics) as a development box. This entailed getting dual boot windows/ubuntu setup along with VNC.

Tensorflow Setup
I was surprised because just getting my computer properly setup to use GPU accelerated machine learning was more of a chore then I expected. It took me a solid 3 hours to decipher all the things I had to do in order to get actually training neural nets on the GPU. I'm confident it was worth it for speed, but geez!!! It shouldn't be this hard! Docker, Cuda 10 drivers, linking to the OS so I could save files permanently, etc... Anyway, I documented the entire arduous process on github - a little messy because they were quick notes for myself, but it might help somebody:

[SetupML Github repository]

VNC = dev speed
Can I just mention the ability to log into my VR computer from anywhere in the house is a godsend! I can sit with my laptop and work on it from the couch, from the bed, from my desktop computer or laptop! So much more productive.

Getting started with ML
Once I was setup I verified things were working with these quick tutorials:

* [Basic Classification]
* [Text Classification]


Synthetic Data
Enough with the tutorials! I wanted to branch out on my own more or less scratch built perception model, so I crafted up some semi-automatic 200 synthetic images with truth labels in Unity and set out to work. I spent pretty much this whole weekend putting all this together getting data formatting right, arrays setup with the right datatypes, so I can have an automatically setup training and test data sets.

Complex Model
Unfortunately, I'm trying to get my last layer to be a multi-dimensional regressive output, this is proving to be more challenging than I thought. The few times i successfully trained my rough models, I was getting at best 60% accuracy. I've got to spend a few more days researching keras model architecture, I think its bad. Been messing around with the sigmoid, linear, and softmax activation functions, along with various loss functions. I stumbled onto these interesting links which might help me find the answer:
* [Keras Github Examples]
* [Keras Documentation - with examples]

Whew! Time for sleep!