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+# Choosing differentiable/gpu parts of the model
+!!! note 
+    This tutorial features somewhat disconnected topics about customizing your
+    models even further. It is advised to be familiar with
+    [`Flux.@layer`](@ref), [`Flux.@functor`](@ref), [`freeze!`](@ref
+    Flux.freeze!) and other basics of Flux.
+
+Flux provides several ways of freezing, excluding from backprop entirely and
+marking custom struct fields not to be moved to the GPU
+([Functors.@functor](@ref)) hence excluded from being trained. The following
+subsections should make it clear which one suits your needs the best.
+
+## On-the-fly freezing per model instance
+Perhaps you'd like to freeze some of the weights of the model (even at
+mid-training), and Flux accomplishes this through [`freeze!`](@ref Flux.freeze!) and `thaw!`.
+
+```julia
+m = Chain(
+      Dense(784 => 64, relu), # freeze this one
+      Dense(64 => 64, relu),
+      Dense(32 => 10)
+    )
+opt_state = Flux.setup(Momentum(), m);
+
+# Freeze some layers right away
+Flux.freeze!(opt_state.layers[1])
+
+for data in train_set
+    input, label = data
+
+    # Some params could be frozen during the training:
+    Flux.freeze!(opt_state.layers[2])
+
+    grads = Flux.gradient(m) do m
+        result = m(input)
+        loss(result, label)
+    end
+    Flux.update!(opt_state, m, grads[1])
+
+    # Optionally unfreeze the params later
+    Flux.thaw!(opt_state.layers[1])
+end
+```
+
+## Static freezing per model definition
+Sometimes some parts of the model ([`Flux.@layer`](@ref)) needn't to be trained at all but these params
+still need to reside on the GPU (these params are still needed in the forward
+and/or backward pass).
+```julia
+struct MaskedLayer{T}
+    chain::Chain
+    mask::T
+end
+Flux.@layer MyLayer trainable=(chain,)
+# mask field will not be updated in the training loop
+
+function (m::MaskedLayer)(x)
+    # mask field will still move to to gpu for efficient operations:
+  return m.chain(x) + x + m.mask
+end
+
+model = MaskedLayer(...) # this model will not have the `mask` field trained
+```
+Note how this method permanently sets some model fields to be excluded from
+training without on-the-fly changing.
+
+## Excluding from model definition
+Sometimes some parameters aren't just "not trainable" but they shouldn't even
+transfer to the GPU (or be part of the functor). All scalar fields are like this
+by default, so things like learning rate multipliers are not trainable nor
+transferred to the GPU by default.
+```julia
+struct CustomLayer{T, F}
+    chain::Chain
+    activation_results::Vector{F}
+    lr_multiplier::Float32
+end
+Flux.@functor CustomLayer (chain, ) # Explicitly leaving out `activation_results`
+
+function (m::CustomLayer)(x)
+    result = m.chain(x) + x
+    
+    # `activation_results` are not part of the GPU loop, hence we could do
+    # things like `push!`
+    push!(m.activation_results, mean(result))
+    return result
+end
+```
+See more about this in [`Flux.@functor`](@ref)
+
+
+## Freezing Layer Parameters (deprecated)
+
+When it is desired to not include all the model parameters (for e.g. transfer learning), we can simply not pass in those layers into our call to `params`.
+
+!!! compat "Flux ≤ 0.14"
+    The mechanism described here is for Flux's old "implicit" training style.
+    When upgrading for Flux 0.15, it should be replaced by [`freeze!`](@ref Flux.freeze!) and `thaw!`.
+
+Consider a simple multi-layer perceptron model where we want to avoid optimising the first two `Dense` layers. We can obtain
+this using the slicing features `Chain` provides:
+
+```julia
+m = Chain(
+      Dense(784 => 64, relu),
+      Dense(64 => 64, relu),
+      Dense(32 => 10)
+    );
+
+ps = Flux.params(m[3:end])
+```
+
+The `Zygote.Params` object `ps` now holds a reference to only the parameters of the layers passed to it.
+
+During training, the gradients will only be computed for (and applied to) the last `Dense` layer, therefore only that would have its parameters changed.
+
+`Flux.params` also takes multiple inputs to make it easy to collect parameters from heterogenous models with a single call. A simple demonstration would be if we wanted to omit optimising the second `Dense` layer in the previous example. It would look something like this:
+
+```julia
+Flux.params(m[1], m[3:end])
+```
+
+Sometimes, a more fine-tuned control is needed.
+We can freeze a specific parameter of a specific layer which already entered a `Params` object `ps`,
+by simply deleting it from `ps`:
+
+```julia
+ps = Flux.params(m)
+delete!(ps, m[2].bias) 
+```
+