Model-Building Basics

Taking Gradients

Consider a simple linear regression, which tries to predict an output array y from an input x. (It's a good idea to follow this example in the Julia repl.)

W = rand(2, 5)
b = rand(2)

predict(x) = W*x .+ b
loss(x, y) = sum((predict(x) .- y).^2)

x, y = rand(5), rand(2) # Dummy data
loss(x, y) # ~ 3

To improve the prediction we can take the gradients of W and b with respect to the loss function and perform gradient descent. We could calculate gradients by hand, but Flux will do it for us if we tell it that W and b are trainable parameters.

using Flux.Tracker

W = param(W)
b = param(b)

l = loss(x, y)

back!(l)

loss(x, y) returns the same number, but it's now a tracked value that records gradients as it goes along. Calling back! then calculates the gradient of W and b. We can see what this gradient is, and modify W to train the model.

W.grad

# Update the parameter
W.data .-= 0.1(W.grad)

loss(x, y) # ~ 2.5

The loss has decreased a little, meaning that our prediction x is closer to the target y. If we have some data we can already try training the model.

All deep learning in Flux, however complex, is a simple generalisation of this example. Of course, models can look very different – they might have millions of parameters or complex control flow, and there are ways to manage this complexity. Let's see what that looks like.

Building Layers

It's common to create more complex models than the linear regression above. For example, we might want to have two linear layers with a nonlinearity like sigmoid (σ) in between them. In the above style we could write this as:

W1 = param(rand(3, 5))
b1 = param(rand(3))
layer1(x) = W1 * x .+ b1

W2 = param(rand(2, 3))
b2 = param(rand(2))
layer2(x) = W2 * x .+ b2

model(x) = layer2(σ.(layer1(x)))

model(rand(5)) # => 2-element vector

This works but is fairly unwieldy, with a lot of repetition – especially as we add more layers. One way to factor this out is to create a function that returns linear layers.

function linear(in, out)
  W = param(randn(out, in))
  b = param(randn(out))
  x -> W * x .+ b
end

linear1 = linear(5, 3) # we can access linear1.W etc
linear2 = linear(3, 2)

model(x) = linear2(σ.(linear1(x)))

model(x) # => 2-element vector

Another (equivalent) way is to create a struct that explicitly represents the affine layer.

struct Affine
  W
  b
end

Affine(in::Integer, out::Integer) =
  Affine(param(randn(out, in)), param(randn(out)))

# Overload call, so the object can be used as a function
(m::Affine)(x) = m.W * x .+ m.b

a = Affine(10, 5)

a(rand(10)) # => 5-element vector

Congratulations! You just built the Dense layer that comes with Flux. Flux has many interesting layers available, but they're all things you could have built yourself very easily.

(There is one small difference with Dense – for convenience it also takes an activation function, like Dense(10, 5, σ).)

Stacking It Up

It's pretty common to write models that look something like:

layer1 = Dense(10, 5, σ)
# ...
model(x) = layer3(layer2(layer1(x)))

For long chains, it might be a bit more intuitive to have a list of layers, like this:

using Flux

layers = [Dense(10, 5, σ), Dense(5, 2), softmax]

model(x) = foldl((x, m) -> m(x), x, layers)

model(rand(10)) # => 2-element vector

Handily, this is also provided for in Flux:

model2 = Chain(
  Dense(10, 5, σ),
  Dense(5, 2),
  softmax)

model2(rand(10)) # => 2-element vector

This quickly starts to look like a high-level deep learning library; yet you can see how it falls out of simple abstractions, and we lose none of the power of Julia code.

A nice property of this approach is that because "models" are just functions (possibly with trainable parameters), you can also see this as simple function composition.

m = Dense(5, 2) ∘ Dense(10, 5, σ)

m(rand(10))

Likewise, Chain will happily work with any Julia function.

m = Chain(x -> x^2, x -> x+1)

m(5) # => 26

Layer helpers

Flux provides a set of helpers for custom layers, which you can enable by calling

Flux.treelike(Affine)

This enables a useful extra set of functionality for our Affine layer, such as collecting its parameters or moving it to the GPU.