Lightweight Kotlin based library for neural networks I am using this for my personal project for stock trading. Comes with risks and frequent changes. Feel free to fork or to explore the project Uses Kotlin's Multik under the hood
To focus on a CPU based use case for reinforcement learning. One algorithm are supported for optimisation:
- GAT - is a custom implementation of a GA (genetic algorithm)
- PSO - removed
- GA - removed
Input - defines entry for a set of data, multiple inputs can be used in the same model
Dense - the most basic layer, can have an activation function, bias
Activation - wrapper around the activation function
Flatten - turns an {X,Y} layer into a {X*Y, 1} array
Concat - turns a list of layers into one (layers have to be of same height)
... and others
This a set of components developed for genetic algorithms coupled with a deep neural network. As a reference for declaration style tensorflow-keras was used
val input = Input(3)
val d0 = Dense(4, Activations.ReLu) { input }
val d1 = Dense(4, Activations.ReLu) { d0 }
val d2 = Dense(4, Activations.ReLu) { d0 }
val concat = Concat(d1, d2)
val builder = ModelBuilder(input, concat)
builder.build()Models support multi-input and multi-output