I had been thinking for a while about the most flexible way to add Convolutional layers to my existing API when I read an article about the so-called Deep Residual Learning. Roughly speaking Residual Learning requires a network architecture in which each layer consists of two layers, duplicates of the same amount of neurons, and […]
Since the last AI Fight article I did not make conscious efforts to improve the performance of the algorithms, I have been busy with updating the existing interfaces/application, and with creating new interfaces. I have rewritten all test harnesses from scratch, and I have added a new WebAPI interface. Also I started experimenting with an […]
I have added a new interface for the DeepTrainer library that makes the algorithms accessible through a web interface using queries and which makes it possible to deploy the library in the cloud. The service implements Open Authorization (OAuth) using bearer tokens. Here are the interface functions it implements at the moment: User account registration: […]
I have been reading about the ONNX file format recently that has been created internally by Google, but before I delve myself into protocol buffers I still needed an easily readable (read: debuggable) file format to exchange neural network states. I know that eventually I will have to end up supporting ONNX so I did […]
After I was done with the WinForms test harness update I noticed that the application was leaking memory like the Titanic after meeting an iceberg. I have managed to narrow the issue down to the OpenGL window – then I realized that the best course of action I could take here is to implement both […]
I did a complete overhaul for the algorithmic structure of the library because when I first wrote it I kept extending an existing structure as new ideas came without being concerned about code redundancy. So now I decided to get rid of all redundant code pieces in all my classes. I ended up with a […]
Recently I have been putting a lot of effort into the WinForms test harness application, mainly because it was very user unfriendly. It was missing essential controls even to adjust the number of inputs and outputs of a network, you had to change everything in the code yourself to make it working. With these latest […]
Recently I’ve been reading quite a lot about activation functions and Neural Networks in general and I think I found a good answer to a question that has been bugging me (and others who know what I am working on) ever since I started working on my own deep learning framework. I’ve had conversations with […]
Positive results My marketing department that’s just around in the bedroom (where dreams come t̶r̶u̶e̶ and go) have been bugging me to continue the AI Fight sequel so here it is. When I reach #XVI someone please warn me diplomatically to stop otherwise it will gain consciousness and start its own Netflix pilot. There is […]
After implementing a set of new activation functions in DeepTrainer (see my previous post) I had to come to the conclusion that most modern activation functions require different treatment than the almighty Hyperbolic Tangent. With TanH I could conveniently use the same activation function for all layers and neurons in the network, and it worked […]
