This repository contains a new type of deep neural network which are parameterized by B-spline basis functions, named SpliNet. The interpertation of ResNet as an numerical discretization of a continuous optimal control problem allows us to decouple the parameterization from the numerical scheme.
- src
- unified_spline_network.jl
- train.jl
- hyperopt.jl
- examples
- sine.jl
- peaks.jl
- indianpines.jl
- cifar10.jl
-
unified_spline_network.jl
A unifided neural network struct which can deal with either vector or 2/3D tensor inputs. For vector input, the network uses a dense matrix as the linear transformation while for 2/3D inputs, it uses a convolution filter. -
train.jl A customized function for training SpliNet allowing users to choose learning rate, batch size, epoch, regularization scale, target accuracy/error and so on.
-
hyperopt.jl A hyper-parameter sampling and tuning function.
To run the code, Julia (v1.0 or later) needs to be installed (https://julialang.org/downloads/).
To construct the network and run back-propagation, Flux and Zygote are needed, which can be installed by running
] add Flux/Zygote in Julia's REPL
To run the examples, change parameters in the desiring task under examples folder and run include("*.jl").
For the "sine" examples, you probably obtain the following visualization:
