Merge pull request #111 from PumasAI/trainbatcheddocstring

move `train_batched!` docstring to correct function

src/optimize.jl

@@ -496,20 +496,6 @@ end
 @inline view_slice_last(X::AbstractArray{<:Any,3}, r) = view(X, :, :, r)
 @inline view_slice_last(X::AbstractArray{<:Any,4}, r) = view(X, :, :, :, r)
 @inline view_slice_last(X::AbstractArray{<:Any,5}, r) = view(X, :, :, :, :, r)
-"""
-    train_batched!(g::AbstractVecOrMat, p, chn, X, opt, iters; batchsize = nothing)
-
-Train while batching arguments.
-
-Arguments:
-- `g` pre-allocated gradient buffer. Can be allocated with `similar(p)` (if you want to run single threaded), or `alloc_threaded_grad(chn, size(X))` (`size(X)` argument is only necessary if the input dimension was not specified when constructing the chain). If a matrix, the number of columns gives how many threads to use. Do not use more threads than batch size would allow.
-- `p` is the parameter vector. It is updated inplace. It should be pre-initialized, e.g. with `init_params`/`init_params!`. This is to allow calling `train_unbatched!` several times to train in increments.
-- `chn` is the `SimpleChain`. It must include a loss (see `SimpleChains.add_loss`) containing the target information (dependent variables) you're trying to fit.
-- `X` the training data input argument (independent variables).
-- `opt` is the optimizer. Currently, only `SimpleChains.ADAM` is supported.
-- `iters`, how many iterations to train for.
-- `batchsize` keyword argument: the size of the batches to use. If `batchsize = nothing`, it'll try to do a half-decent job of picking the batch size for you. However, this is not well optimized at the moment.
-"""
 function train_batched_core!(
   _chn::Chain,
   pu::Ptr{UInt8},
@@ -593,6 +579,20 @@ function train_batched_core!(
   offset = static_sizeof(T) * aligned_glen * numthreads
   train_batched_core!(c, pu + offset, g, p, pX, opt, iters, leaveofflast, mpt, N_bs)
 end
+"""
+    train_batched!(g::AbstractVecOrMat, p, chn, X, opt, iters; batchsize = nothing)
+
+Train while batching arguments.
+
+Arguments:
+- `g` pre-allocated gradient buffer. Can be allocated with `similar(p)` (if you want to run single threaded), or `alloc_threaded_grad(chn, size(X))` (`size(X)` argument is only necessary if the input dimension was not specified when constructing the chain). If a matrix, the number of columns gives how many threads to use. Do not use more threads than batch size would allow.
+- `p` is the parameter vector. It is updated inplace. It should be pre-initialized, e.g. with `init_params`/`init_params!`. This is to allow calling `train_unbatched!` several times to train in increments.
+- `chn` is the `SimpleChain`. It must include a loss (see `SimpleChains.add_loss`) containing the target information (dependent variables) you're trying to fit.
+- `X` the training data input argument (independent variables).
+- `opt` is the optimizer. Currently, only `SimpleChains.ADAM` is supported.
+- `iters`, how many iterations to train for.
+- `batchsize` keyword argument: the size of the batches to use. If `batchsize = nothing`, it'll try to do a half-decent job of picking the batch size for you. However, this is not well optimized at the moment.
+"""
 function train_batched!(
   g::Union{Nothing,AbstractVector,AbstractMatrix},
   p::AbstractVector,