doc fix for FMIFlux.train! (#127)

src/neural.jl

@@ -1682,23 +1682,23 @@ end
 
 """
 
-    train!(loss, params::Union{Flux.Params, Zygote.Params}, data, optim::Flux.Optimise.AbstractOptimiser; gradient::Symbol=:Zygote, cb=nothing, chunk_size::Integer=64, printStep::Bool=false)
+    train!(loss, neuralFMU::Union{ME_NeuralFMU, CS_NeuralFMU}, data, optim; gradient::Symbol=:ReverseDiff, kwargs...)
 
 A function analogous to Flux.train! but with additional features and explicit parameters (faster).
 
 # Arguments
 - `loss` a loss function in the format `loss(p)`
-- `params` a object holding the parameters
+- `neuralFMU` a object holding the neuralFMU with its parameters
 - `data` the training data (or often an iterator)
 - `optim` the optimizer used for training 
 
 # Keywords 
 - `gradient` a symbol determining the AD-library for gradient computation, available are `:ForwardDiff`, `:Zygote` and :ReverseDiff (default)
-- `cb` a custom callback function that is called after every training step
-- `chunk_size` the chunk size for AD using ForwardDiff (ignored for other AD-methods)
-- `printStep` a boolean determining wheater the gradient min/max is printed after every step (for gradient debugging)
-- `proceed_on_assert` a boolean that determins wheater to throw an ecxeption on error or proceed training and just print the error
-- `numThreads` [WIP]: an integer determining how many threads are used for training (how many gradients are generated in parallel)
+- `cb` a custom callback function that is called after every training step (default `nothing`)
+- `chunk_size` the chunk size for AD using ForwardDiff (ignored for other AD-methods) (default `:auto_fmiflux`)
+- `printStep` a boolean determining wheater the gradient min/max is printed after every step (for gradient debugging) (default `false`)
+- `proceed_on_assert` a boolean that determins wheater to throw an ecxeption on error or proceed training and just print the error (default `false`)
+- `multiThreading`: a boolean that determins if multiple gradients are generated in parallel (default `false`)
 - `multiObjective`: set this if the loss function returns multiple values (multi objective optimization), currently gradients are fired to the optimizer one after another (default `false`)
 """
 function train!(loss, neuralFMU::Union{ME_NeuralFMU, CS_NeuralFMU}, data, optim; gradient::Symbol=:ReverseDiff, kwargs...)