Regularisation

Applying regularisation to model parameters is straightforward. We just need to apply an appropriate regulariser to each model parameter and add the result to the overall loss.

For example, say we have a simple regression.

julia> using Flux

julia> using Flux.Losses: logitcrossentropy

julia> m = Dense(10 => 5)
Dense(10 => 5)      # 55 parameters

julia> loss(x, y) = logitcrossentropy(m(x), y);

We can apply L2 regularisation by taking the squared norm of the parameters , m.weight and m.bias.

julia> penalty() = sum(abs2, m.weight) + sum(abs2, m.bias);

julia> loss(x, y) = logitcrossentropy(m(x), y) + penalty();

When working with layers, Flux provides the params function to grab all parameters at once. We can easily penalise everything with sum:

julia> Flux.params(m)
Params([Float32[0.34704182 -0.48532376 … -0.06914271 -0.38398427; 0.5201164 -0.033709668 … -0.36169025 -0.5552353; … ; 0.46534058 0.17114447 … -0.4809643 0.04993277; -0.47049698 -0.6206029 … -0.3092334 -0.47857067], Float32[0.0, 0.0, 0.0, 0.0, 0.0]])

julia> sqnorm(x) = sum(abs2, x);

julia> sum(sqnorm, Flux.params(m))
8.34994f0

Here's a larger example with a multi-layer perceptron.

julia> m = Chain(Dense(28^2 => 128, relu), Dense(128 => 32, relu), Dense(32 => 10))
Chain(
  Dense(784 => 128, relu),              # 100_480 parameters
  Dense(128 => 32, relu),               # 4_128 parameters
  Dense(32 => 10),                      # 330 parameters
)                   # Total: 6 arrays, 104_938 parameters, 410.289 KiB.

julia> sqnorm(x) = sum(abs2, x);

julia> loss(x, y) = logitcrossentropy(m(x), y) + sum(sqnorm, Flux.params(m));

julia> loss(rand(28^2), rand(10))
300.76693683244997

One can also easily add per-layer regularisation via the activations function:

julia> using Flux: activations

julia> c = Chain(Dense(10 => 5, σ), Dense(5 => 2), softmax)
Chain(
  Dense(10 => 5, σ),                    # 55 parameters
  Dense(5 => 2),                        # 12 parameters
  NNlib.softmax,
)                   # Total: 4 arrays, 67 parameters, 524 bytes.

julia> activations(c, rand(10))
([0.3274892431795043, 0.5360197770386552, 0.3447464835514667, 0.5273025865532305, 0.7513168089280781], [-0.3533774181890544, -0.010937055274926138], [0.4152168057978045, 0.5847831942021956])

julia> sum(sqnorm, ans)
1.9953131077618562
Flux.activationsFunction
activations(c::Chain, input)

Calculate the forward results of each layers in Chain c with input as model input.

source