diff --git a/src/layers/basic.jl b/src/layers/basic.jl
index f42a9619f9..2a46520818 100644
--- a/src/layers/basic.jl
+++ b/src/layers/basic.jl
@@ -44,19 +44,23 @@ end
 # it might be replaced in the future for better performance
 # see issue https://github.com/FluxML/Flux.jl/issues/702
 # Johnny Chen -- @johnnychen94
+# only slightly changed to better handle interaction with Zygote @dsweber2
 """
     activations(c::Chain, input)
 Calculate the forward results of each layers in Chain `c` with `input` as model input.
 """
 function activations(c::Chain, input)
-  rst = []
-  for l in c
-    x = get(rst, length(rst), input)
-    push!(rst, l(x))
-  end
-  return rst
+    extraChain(c.layers, input)
 end
 
+function extraChain(fs::Tuple, x)
+    res = first(fs)(x)
+    return (res, extraChain(Base.tail(fs), res)...)
+end
+
+extraChain(::Tuple{}, x) = ()
+
+
 
 """
     Dense(in::Integer, out::Integer, σ = identity)
diff --git a/test/layers/basic.jl b/test/layers/basic.jl
index cbe250fcca..0ff1776db8 100644
--- a/test/layers/basic.jl
+++ b/test/layers/basic.jl
@@ -4,11 +4,13 @@ import Flux: activations
 @testset "basic" begin
   @testset "helpers" begin
     @testset "activations" begin
-      dummy_model = Chain(Dense(10,5,σ),Dense(5,2),softmax)
-      x = rand(10)
-      @test activations(Chain(), x) == []
-      @test activations(dummy_model, x)[1] == dummy_model[1](x)
-      @test activations(dummy_model, x)[2] == x |> dummy_model[1] |> dummy_model[2]
+      dummy_model = Chain(x->x.^2, x->x .- 3, x -> tan.(x))
+      x = randn(10)
+      @test activations(dummy_model, x)[1] == x.^2
+      @test activations(dummy_model, x)[2] == (x.^2 .- 3)
+      @test activations(dummy_model, x)[3] == tan.(x.^2 .- 3)
+
+      @test activations(Chain(), x) == ()
       @test activations(Chain(identity, x->:foo), x)[2] == :foo # results include `Any` type
     end
   end
@@ -19,6 +21,12 @@ import Flux: activations
     # numeric test should be put into testset of corresponding layer
   end
 
+  @testset "Activations" begin
+    c = Chain(Dense(3,5,relu), Dense(5,1,relu))
+    X = Float32.([1.0; 1.0; 1.0])
+    @test_nowarn gradient(()->Flux.activations(c, X)[2][1], params(c))
+  end
+
   @testset "Dense" begin
     @test  length(Dense(10, 5)(randn(10))) == 5
     @test_throws DimensionMismatch Dense(10, 5)(randn(1))