NeuralNetworkRegressor

mutable struct NeuralNetworkRegressor <: MLJModelInterface.Deterministic

A simple but flexible Feedforward Neural Network, from the Beta Machine Learning Toolkit (BetaML) for regression of a single dimensional target.

Parameters:

layers: Array of layer objects [def: nothing, i.e. basic network]. See subtypes(BetaML.AbstractLayer) for supported layers
loss: Loss (cost) function [def: BetaML.squared_cost]. Should always assume y and ŷ as matrices, even if the regression task is 1-D
Warning
If you change the parameter loss, you need to either provide its derivative on the parameter dloss or use autodiff with dloss=nothing.
dloss: Derivative of the loss function [def: BetaML.dsquared_cost, i.e. use the derivative of the squared cost]. Use nothing for autodiff.
epochs: Number of epochs, i.e. passages trough the whole training sample [def: 200]
batch_size: Size of each individual batch [def: 16]
opt_alg: The optimisation algorithm to update the gradient at each batch [def: BetaML.ADAM()]. See subtypes(BetaML.OptimisationAlgorithm) for supported optimizers
shuffle: Whether to randomly shuffle the data at each iteration (epoch) [def: true]
descr: An optional title and/or description for this model
cb: A call back function to provide information during training [def: fitting_info]
rng: Random Number Generator (see FIXEDSEED) [deafult: Random.GLOBAL_RNG]

Notes:

data must be numerical
the label should be be a n-records vector.

Example:

julia> using MLJ

julia> X, y        = @load_boston;

julia> modelType   = @load NeuralNetworkRegressor pkg = "BetaML" verbosity=0
BetaML.Nn.NeuralNetworkRegressor

julia> layers                      = [BetaML.DenseLayer(12,20,f=BetaML.relu),BetaML.DenseLayer(20,20,f=BetaML.relu),BetaML.DenseLayer(20,1,f=BetaML.relu)];

julia> model       = modelType(layers=layers,opt_alg=BetaML.ADAM());
NeuralNetworkRegressor(
  layers = BetaML.Nn.AbstractLayer[BetaML.Nn.DenseLayer([-0.23249759178069676 -0.4125090172711131 … 0.41401934928739 -0.33017881111237535; -0.27912169279319965 0.270551221249931 … 0.19258414323473344 0.1703002982374256; … ; 0.31186742456482447 0.14776438287394805 … 0.3624993442655036 0.1438885872964824; 0.24363744610286758 -0.3221033024934767 … 0.14886090419299408 0.038411663101909355], [-0.42360286004241765, -0.34355377040029594, 0.11510963232946697, 0.29078650404397893, -0.04940236502546075, 0.05142849152316714, -0.177685375947775, 0.3857630523957018, -0.25454667127064756, -0.1726731848206195, 0.29832456225553444, -0.21138505291162835, -0.15763643112604903, -0.08477044513587562, -0.38436681165349196, 0.20538016429104916, -0.25008157754468335, 0.268681800562054, 0.10600581996650865, 0.4262194464325672], BetaML.Utils.relu, BetaML.Utils.drelu), BetaML.Nn.DenseLayer([-0.08534180387478185 0.19659398307677617 … -0.3413633217504578 -0.0484925247381256; 0.0024419192794883915 -0.14614102508129 … -0.21912059923003044 0.2680725396694708; … ; 0.25151545823147886 -0.27532269951606037 … 0.20739970895058063 0.2891938885916349; -0.1699020711688904 -0.1350423717084296 … 0.16947589410758873 0.3629006047373296], [0.2158116357688406, -0.3255582642532289, -0.057314442103850394, 0.29029696770539953, 0.24994080694366455, 0.3624239027782297, -0.30674318230919984, -0.3854738338935017, 0.10809721838554087, 0.16073511121016176, -0.005923262068960489, 0.3157147976348795, -0.10938918304264739, -0.24521229198853187, -0.307167732178712, 0.0808907777008302, -0.014577497150872254, -0.0011287181458157214, 0.07522282588658086, 0.043366500526073104], BetaML.Utils.relu, BetaML.Utils.drelu), BetaML.Nn.DenseLayer([-0.021367697115938555 -0.28326652172347155 … 0.05346175368370165 -0.26037328415871647], [-0.2313659199724562], BetaML.Utils.relu, BetaML.Utils.drelu)], 
  loss = BetaML.Utils.squared_cost, 
  dloss = BetaML.Utils.dsquared_cost, 
  epochs = 100, 
  batch_size = 32, 
  opt_alg = BetaML.Nn.ADAM(BetaML.Nn.var"#90#93"(), 1.0, 0.9, 0.999, 1.0e-8, BetaML.Nn.Learnable[], BetaML.Nn.Learnable[]), 
  shuffle = true, 
  descr = "", 
  cb = BetaML.Nn.fitting_info, 
  rng = Random._GLOBAL_RNG())

julia> mach        = machine(model, X, y);

julia> fit!(mach);

julia> ŷ    = predict(mach, X);

julia> hcat(y,ŷ)
506×2 Matrix{Float64}:
 24.0  30.7726
 21.6  28.0811
 34.7  31.3194
  ⋮    
 23.9  30.9032
 22.0  29.49
 11.9  27.2438