diff --git a/src/optimise/train.jl b/src/optimise/train.jl
index 54b7f53a..79ebcc06 100644
--- a/src/optimise/train.jl
+++ b/src/optimise/train.jl
@@ -61,7 +61,7 @@ end
 For each datapoint `d` in `data` computes the gradient of `loss(d...)` through
 backpropagation and calls the optimizer `opt`.
 
-In case datapoints `d` are of array type, assumes no splatting is needed 
+In case datapoints `d` are of numeric array type, assumes no splatting is needed 
 and computes the gradient of `loss(d)`.
 
 Takes a callback as keyword argument `cb`. For example, this will print "training"