Flux's core feature is taking gradients of Julia code. The
gradient function takes another Julia function
f and a set of arguments, and returns the gradient with respect to each argument. (It's a good idea to try pasting these examples in the Julia terminal.)
using Flux.Tracker f(x) = 3x^2 + 2x + 1 # df/dx = 6x + 2 df(x) = Tracker.gradient(f, x) df(2) # 14.0 (tracked) # d²f/dx² = 6 d2f(x) = Tracker.gradient(df, x) d2f(2) # 6.0 (tracked)
(We'll learn more about why these numbers show up as
When a function has many parameters, we can pass them all in explicitly:
f(W, b, x) = W * x + b Tracker.gradient(f, 2, 3, 4) (4.0 (tracked), 1.0, 2.0 (tracked))
But machine learning models can have hundreds of parameters! Flux offers a nice way to handle this. We can tell Flux to treat something as a parameter via
param. Then we can collect these together and tell
gradient to collect the gradients of all of them at once.
W = param(2) # 2.0 (tracked) b = param(3) # 3.0 (tracked) f(x) = W * x + b params = Params([W, b]) grads = Tracker.gradient(() -> f(4), params) grads[W] # 4.0 grads[b] # 1.0
There are a few things to notice here. Firstly,
b now show up as tracked. Tracked things behave like normal numbers or arrays, but keep records of everything you do with them, allowing Flux to calculate their gradients.
gradient takes a zero-argument function; no arguments are necessary because the
Params tell it what to differentiate.
This will come in really handy when dealing with big, complicated models. For now, though, let's start with something simple.
Consider a simple linear regression, which tries to predict an output array
y from an input
W = rand(2, 5) b = rand(2) predict(x) = W*x .+ b function loss(x, y) ŷ = predict(x) sum((y .- ŷ).^2) end x, y = rand(5), rand(2) # Dummy data loss(x, y) # ~ 3
To improve the prediction we can take the gradients of
b with respect to the loss and perform gradient descent. Let's tell Flux that
b are parameters, just like we did above.
using Flux.Tracker W = param(W) b = param(b) gs = Tracker.gradient(() -> loss(x, y), Params([W, b]))
Now that we have gradients, we can pull them out and update
W to train the model. The
update!(W, Δ) function applies
W = W + Δ, which we can use for gradient descent.
using Flux.Tracker: update! Δ = gs[W] # Update the parameter and reset the gradient update!(W, -0.1Δ) 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. Let's see how Flux handles more complex models.
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(rand(5)) # => 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), layers, init = x) 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))
Chain will happily work with any Julia function.
m = Chain(x -> x^2, x -> x+1) m(5) # => 26
Flux provides a set of helpers for custom layers, which you can enable by calling