run_training.jl

"""
    run_training(loss_change,
                 learn, 
	         x_in, 
	         y_in)

Run training epochs until the Δloss ≤ loss_change.
"""
function run_training(loss_change,
                      learn, 
	              x_in, 
	              y_in)

    # Initialize Flux model
    flux_model = Dense(1 => 1)	

    loss_initial = get_loss(flux_model, x_in, y_in)
    all_losses = [loss_initial]
    flux_model_new = nothing
    num_epochs = 0
	
    while true
		
        flux_model_new = update_model!(learn, flux_model, x_in, y_in)
	loss_new = get_loss(flux_model_new, x_in, y_in)

	num_epochs += 1
	push!(all_losses, loss_new)
		
        if abs(loss_new - loss_initial) ≤ loss_change
	    break
	else
	    loss_initial = loss_new
	    flux_model = flux_model_new
	end

    end

    return flux_model_new, all_losses, num_epochs	

end