An interface to the Flux deep learning models for the [MLJ](https://github.com/alan-turing-institute/MLJ.jl) machine learning framework

Branch	Julia	CPU CI	GPU CI	Coverage
master	v1
dev	v1

Code Snippet

using MLJ, MLJFlux, RDatasets, Plots

Grab some data and split into features and target:

iris = RDatasets.dataset("datasets", "iris");
y, X = unpack(iris, ==(:Species), rng=123);
X = Float32.(X);      # To optmise for GPUs

Load model code and instantiate an MLJFlux model:

NeuralNetworkClassifier = @load NeuralNetworkClassifier pkg=MLJFlux

clf = NeuralNetworkClassifier(
    builder=MLJFlux.MLP(; hidden=(5,4)),
    batch_size=8,
    epochs=50,
    acceleration=CUDALibs()  # for training on a GPU
)

Wrap in "iteration controls":

stop_conditions = [
    Step(1),            # Apply controls every epoch
    NumberLimit(1000),  # Don't train for more than 1000 steps
    Patience(4),        # Stop after 4 iterations of deteriation in validation loss
    NumberSinceBest(5), # Or if the best loss occurred 5 iterations ago
    TimeLimit(30/60),   # Or if 30 minutes has passed
]

validation_losses = []
train_losses = []
callbacks = [
    WithLossDo(loss->push!(validation_losses, loss)),
    WithTrainingLossesDo(losses->push!(train_losses, losses[end])),
]

iterated_model = IteratedModel(
    model=clf,
    resampling=Holdout(fraction_train=0.5); # loss and stopping are based on out-of-sample
    measures=log_loss,
    controls=vcat(stop_conditions, callbacks),
);

Train the wrapped model:

julia> mach = machine(iterated_model, X, y)
julia> fit!(mach)

[ Info: No iteration parameter specified. Using `iteration_parameter=:(epochs)`. 
[ Info: final loss: 0.1284184007796247
[ Info: final training loss: 0.055630706
[ Info: Stop triggered by NumberSinceBest(5) stopping criterion. 
[ Info: Total of 811 iterations. 

Inspect results:

julia> plot(train_losses, label="Training Loss")
julia> plot!(validation_losses, label="Validation Loss", linewidth=2, size=(800,400))