Add LPNormPool

src/layers/conv.jl

@@ -640,6 +640,8 @@ Global lp norm pooling layer.
 
 Transform (w,h,c,b)-shaped input into (1,1,c,b)-shaped output,
 by performing lp norm pooling on the complete (w,h)-shaped feature maps.
+And expects input `x` to satisfy `all(x .>= 0)` to avoid `function ^(x, y)`
+in Base.Math throw DomainError.
 
 See also [`LPNormPool`](@ref).
 
@@ -793,6 +795,7 @@ also known as LPPool in pytorch.
 
 Expects as input an array with `ndims(x) == N+2`, i.e. channel and
 batch dimensions, after the `N` feature dimensions, where `N = length(window)`.
+Also expects `all(x .>= 0)` to avoid `function ^(x, y)` in Base.Math throw DomainError.
 
 By default the window size is also the stride in each dimension.
 The keyword `pad` accepts the same options as for the `Conv` layer,
@@ -839,6 +842,7 @@ function LPNormPool(k::NTuple{N,Integer}, p::Real; pad = 0, stride = k) where {N
 end
 
 function (l::LPNormPool)(x)
+  all(x .>= 0) || throw(DomainError("LPNormPool requires 'all(x .>= 0)'. Relu before LPNormPool is recommended."))
   pdims = PoolDims(x, l.k; padding=l.pad, stride=l.stride)
   return lpnormpool(x, pdims; p=l.p)
 end