From David's Wiki
Jump to navigation Jump to search
\( \newcommand{\P}[]{\unicode{xB6}} \newcommand{\AA}[]{\unicode{x212B}} \newcommand{\empty}[]{\emptyset} \newcommand{\O}[]{\emptyset} \newcommand{\Alpha}[]{Α} \newcommand{\Beta}[]{Β} \newcommand{\Epsilon}[]{Ε} \newcommand{\Iota}[]{Ι} \newcommand{\Kappa}[]{Κ} \newcommand{\Rho}[]{Ρ} \newcommand{\Tau}[]{Τ} \newcommand{\Zeta}[]{Ζ} \newcommand{\Mu}[]{\unicode{x039C}} \newcommand{\Chi}[]{Χ} \newcommand{\Eta}[]{\unicode{x0397}} \newcommand{\Nu}[]{\unicode{x039D}} \newcommand{\Omicron}[]{\unicode{x039F}} \DeclareMathOperator{\sgn}{sgn} \def\oiint{\mathop{\vcenter{\mathchoice{\huge\unicode{x222F}\,}{\unicode{x222F}}{\unicode{x222F}}{\unicode{x222F}}}\,}\nolimits} \def\oiiint{\mathop{\vcenter{\mathchoice{\huge\unicode{x2230}\,}{\unicode{x2230}}{\unicode{x2230}}{\unicode{x2230}}}\,}\nolimits} \)

Flux is a machine learning library for Julia


Basic Usage

using Flux;
using Flux.Tracker: update!;

// Create layers like this
inputSize = 10;
outputSize = 20;
myLayer = Dense(inputSize, outputSize, relu);
// You can get the weights using
// You can call the model to make a prediction
// Equivalent to 
relu(model.W * myData + model.b);

// Create Networks like this
model = Chain(
  Dense(10, 20, relu),
  Dense(20, 2)

// Calling the model will pass data through each layer.

// Get the parameters for the whole model with
p = params(model);

// Calculate the gradient with
gs = Tracker.gradient(function()
  predicted = model(myData);
  loss = sum((predicted - myLabels).^2)'
  return loss;
end, params(model));
// Gradient of layer 1 weights
// Update! will update the weights and clear the gradient
// Make sure to update all layers.
update!(model[1].W, -0.1 * gs[model[1].W]);

// You can also define an optimizer and update using the optimizer
opt = Adam()
update!(opt, model[1].W, gs[model[1].W])