diff --git a/Project.toml b/Project.toml
index 5f7b61b17..08a6e6de6 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "Lux"
 uuid = "b2108857-7c20-44ae-9111-449ecde12c47"
 authors = ["Avik Pal <avikpal@mit.edu> and contributors"]
-version = "1.2.1"
+version = "1.2.2"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
diff --git a/src/extended_ops.jl b/src/extended_ops.jl
index ce9f66215..fafe000f2 100644
--- a/src/extended_ops.jl
+++ b/src/extended_ops.jl
@@ -118,6 +118,7 @@ function ∇eachslice(Δ′, x::AbstractArray, ::Val{dims}) where {dims}
     idx = findfirst(Base.Fix2(isa, AbstractArray), Δs)
     idx === nothing && return zero.(x)
     Δ = similar(x)
+    fill!(Δ, false)
     for i in axes(x, dims)
         Δᵢ = selectdim(Δ, dims, i)
         copyto!(Δᵢ, Δs[i])
diff --git a/src/helpers/losses.jl b/src/helpers/losses.jl
index 1af021e4c..493e1b357 100644
--- a/src/helpers/losses.jl
+++ b/src/helpers/losses.jl
@@ -317,8 +317,10 @@ function unsafe_apply_loss(loss::BinaryFocalLoss, ŷ, y)
 end
 
 @doc doc"""
-    CrossEntropyLoss(; agg=mean, epsilon=nothing, dims=1,
-        label_smoothing::Union{Nothing, Real}=nothing)
+    CrossEntropyLoss(;
+        agg=mean, epsilon=nothing, dims=1, logits::Union{Bool, Val}=Val(false),
+        label_smoothing::Union{Nothing, Real}=nothing
+    )
 
 Return the cross entropy loss which is used in multi-class classification tasks. The input,
 $\hat{y}$, is expected to be normalized (i.e. `softmax` output) if `logits` is `false` or