diff --git a/src/networks.jl b/src/networks.jl
new file mode 100644
index 0000000000..b547292c91
--- /dev/null
+++ b/src/networks.jl
@@ -0,0 +1,51 @@
+struct AdaptiveActivation{T}
+  a::T
+  n::T
+end
+
+Flux.@functor AdaptiveActivation
+
+(fn::AdaptiveActivation)(x) = (fn.n * fn.a) .* x # to be worked on (for weight tying)
+
+
+struct NonlinearActivation{T}
+  σ::T
+end
+
+Flux.@functor NonlinearActivation
+
+(a::NonlinearActivation)(x) = (a.σ).(x)
+
+
+function AdaptiveActivationFeedForwardNetwork(N::Integer, in::Integer, out::Integer, σ = Identity, n::Integer; nn_param_init = glorot_uniform)
+  # another parameter would be the type of adaptive fn to be used
+  # N = no. of hidden layers
+
+  a = 1/n # initial a scaled such that n*a=1 ?
+  function slope_recovery_loss_func(phi, θ, p)
+    # calculate the slope_recovery loss function here as a function of the θ parameters that are generated for this
+    # network
+    for i in 1:1:length(θ):
+      # the loss
+      """
+      if adaptive_fn_without_slope_recovery
+        0
+      elseif with_slope_recovery_layerwise
+        ...
+      elseif neuronwise
+        ...
+      else
+        error
+      """
+
+    return regularizer_loss
+  end
+
+  layer = Flux.Chain(
+    Dense(in, out, σ=identity; bias=true, init=nn_param_init),
+    AdaptiveActivation(n, a),
+    NonlinearActivation(nonlinearity),
+  ) # to be stacked for as many hidden layers specified (N)
+
+  return (network=Flux.Chain(...), loss_func=slope_recovery_loss_func)
+end