fixup rebase

src/deprecations.jl

@@ -12,22 +12,3 @@ struct Zeros
   end
 end
 Zeros(args...) = Zeros()
-
-# v0.11 deprecations
-@deprecate poisson poisson_loss false
-@deprecate hinge hinge_loss false
-@deprecate squared_hinge squared_hinge_loss false
-@deprecate normalise(x) normalise(x, dims=1) false
-
-@deprecate binarycrossentropy(ŷ, y) Losses.binarycrossentropy(ŷ, y, agg=identity) false
-@deprecate logitbinarycrossentropy(ŷ, y) Losses.logitbinarycrossentropy(ŷ, y, agg=identity) false
-
-function Broadcast.broadcasted(::typeof(binarycrossentropy), ŷ, y)
-    @warn "binarycrossentropy.(ŷ, y) is deprecated, use Losses.binarycrossentropy(ŷ, y, agg=identity) instead"
-    Losses.binarycrossentropy(ŷ, y, agg=identity)
-end
-
-function Broadcast.broadcasted(::typeof(logitbinarycrossentropy), ŷ, y)
-    @warn "logitbinarycrossentropy.(ŷ, y) is deprecated, use Losses.logitbinarycrossentropy(ŷ, y, agg=identity) instead"
-    Losses.logitbinarycrossentropy(ŷ, y, agg=identity)
-end

src/layers/basic.jl

@@ -94,13 +94,10 @@ The input `x` must be a vector of length `in`, or batch of vectors represented
 as an `in × N` matrix, or any array with `size(x,1) == in`.
 The out `y` will be a vector  of length `out`, or a batch with `size(y) == (out, size(x)[2:end]...)`
 
-Setting `bias` to `false` will switch the bias  off for the layer.
-
-
 Setting `bias=false` creates a layer without bias parameters.
 
-Keywords `initW=glorot_uniform` and `initb=Flux.zeros` control the initialisation of
-parameters, when using the first constructor.
+Two additional keywords `initW=glorot_uniform` and `initb=Flux.zeros` control the
+initialisation of parameters, when using the first constructor.
 
 # Examples
 ```jldoctest
@@ -139,7 +136,7 @@ end
 
 @functor Dense
 
-function (a::Dense)(x::AbstractVecOrMat)
+function (a::Dense)(x::AbstractArray)
   W, b, σ = a.W, a.b, a.σ
   # reshape to handle dims > 1 as batch dimensions
   sz = size(x)