Over the past 2 days when I did not post a devlog. I managed to get a lot done.
Instead of relying on hiddenLayersDepth and hiddenLayersWidth, the neteork now uses an array of layer sizes, allowing for more flexibility.
I also added training based on Dataset classes, which are much easier for data organization by using ArrayLists (It didnt change the network architecture so I still am using arrays).
I needed to convert 2D weight/bias arrays into ArrayLists, which made a static method in the util class for.
After that, I did some cleanup by removing redundant variables, which made the code much less messy.
Today, I worked on allowing to customize the activation functions used. For now you can only change one function for the whole hidden layer section, but I will add the ability to make an array later. I used an Enum and a bunch of methods from the util class to add relu, leaky relu, tanh, sigmoid, and softmax to the Activation enum.
Also today, I started work on my convolutional neural network, which is the architecture used for object recognition. I finished most of the layers necessary, but I am yet to test its prediction for errors, so I have not pushed it to github yet. I have the filters and the feed forward network already done, so it shouldn’t be that hard.