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Contributing & Help

Contributing & Help

If you need help, please ask on the Julia forum, the slack (channel #machine-learning), or Flux's Gitter.

Right now, the best way to help out is to try out the examples and report any issues or missing features as you find them. The second best way is to help us spread the word, perhaps by starring the repo.

If you're interested in hacking on Flux, most of the code is pretty straightforward. Adding new layer definitions or cost functions is simple using the Flux DSL itself, and things like data utilities and training processes are all plain Julia code.

If you get stuck or need anything, let us know!

+
Contributing & Help

Contributing & Help

If you need help, please ask on the Julia forum, the slack (channel #machine-learning), or Flux's Gitter.

Right now, the best way to help out is to try out the examples and report any issues or missing features as you find them. The second best way is to help us spread the word, perhaps by starring the repo.

If you're interested in hacking on Flux, most of the code is pretty straightforward. Adding new layer definitions or cost functions is simple using the Flux DSL itself, and things like data utilities and training processes are all plain Julia code.

If you get stuck or need anything, let us know!

diff --git a/latest/index.html b/latest/index.html index fb4df270..05a41d95 100644 --- a/latest/index.html +++ b/latest/index.html @@ -6,5 +6,5 @@ m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m) ga('create', 'UA-36890222-9', 'auto'); ga('send', 'pageview'); -
Home

Flux: The Julia Machine Learning Library

Flux is a library for machine learning. It comes "batteries-included" with many useful tools built in, but also lets you use the full power of the Julia language where you need it. The whole stack is implemented in clean Julia code (right down to the GPU kernels) and any part can be tweaked to your liking.

Installation

Install Julia 0.6.0 or later, if you haven't already.

Pkg.add("Flux")
+
Home
Flux: The Julia Machine Learning Library
Flux is a library for machine learning. It comes "batteries-included" with many useful tools built in, but also lets you use the full power of the Julia language where you need it. The whole stack is implemented in clean Julia code (right down to the GPU kernels) and any part can be tweaked to your liking.
Installation
Install Julia 0.6.0 or later, if you haven't already.
Pkg.add("Flux")
 Pkg.test("Flux") # Check things installed correctly
Start with the basics. The model zoo is also a good starting point for many common kinds of models.

diff --git a/latest/models/basics.html b/latest/models/basics.html
index 9dd7d3ef..31f2a9b6 100644
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Basics
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)
+
Basics
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
diff --git a/latest/models/layers.html b/latest/models/layers.html
index 51dde6f5..75389ee2 100644
--- a/latest/models/layers.html
+++ b/latest/models/layers.html
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Layer Reference
Model Layers
Flux.Chain — Type.
Chain(layers...)
Chain multiple layers / functions together, so that they are called in sequence on a given input.
m = Chain(x -> x^2, x -> x+1)
+
Layer Reference
Model Layers
Flux.Chain — Type.
Chain(layers...)
Chain multiple layers / functions together, so that they are called in sequence on a given input.
m = Chain(x -> x^2, x -> x+1)
 m(5) == 26
 
 m = Chain(Dense(10, 5), Dense(5, 2))
 x = rand(10)
-m(x) == m[2](m[1](x))
Chain also supports indexing and slicing, e.g. m[2] or m[1:end-1]. m[1:3](x) will calculate the output of the first three layers.
source
Flux.Dense — Type.
Dense(in::Integer, out::Integer, σ = identity)
Creates a traditional Dense layer with parameters W and b.
y = σ.(W * x .+ b)
The input x must be a vector of length in, or a batch of vectors represented as an in × N matrix. The out y will be a vector or batch of length in.
source

+m(x) == m[2](m[1](x))
Chain also supports indexing and slicing, e.g. m[2] or m[1:end-1]. m[1:3](x) will calculate the output of the first three layers.
source
Flux.Dense — Type.
Dense(in::Integer, out::Integer, σ = identity)
Creates a traditional Dense layer with parameters W and b.
y = σ.(W * x .+ b)
The input x must be a vector of length in, or a batch of vectors represented as an in × N matrix. The out y will be a vector or batch of length in.
source

diff --git a/latest/models/recurrence.html b/latest/models/recurrence.html
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Recurrence
Recurrent Cells
In the simple feedforward case, our model m is a simple function from various inputs xᵢ to predictions yᵢ. (For example, each x might be an MNIST digit and each y a digit label.) Each prediction is completely independent of any others, and using the same x will always produce the same y.
y₁ = f(x₁)
+
Recurrence
Recurrent Cells
In the simple feedforward case, our model m is a simple function from various inputs xᵢ to predictions yᵢ. (For example, each x might be an MNIST digit and each y a digit label.) Each prediction is completely independent of any others, and using the same x will always produce the same y.
y₁ = f(x₁)
 y₂ = f(x₂)
 y₃ = f(x₃)
 # ...
Recurrent networks introduce a hidden state that gets carried over each time we run the model. The model now takes the old h as an input, and produces a new h as output, each time we run it.
h = # ... initial state ...
diff --git a/latest/search_index.js b/latest/search_index.js
index 40db56bf..f539f3a5 100644
--- a/latest/search_index.js
+++ b/latest/search_index.js
@@ -157,7 +157,23 @@ var documenterSearchIndex = {"docs": [
     "page": "Training",
     "title": "Training",
     "category": "page",
-    "text": "Flux.train!(loss, repeated((x,y), 1000), SGD(params(m), 0.1),\n            cb = throttle(() -> @show(loss(x, y)), 5))"
+    "text": "To actually train a model we need three things:A loss function, that evaluates how well a model is doing given some input data.\nA collection of data points that will be provided to the loss function.\nAn optimiser that will update the model parameters appropriately.With these we can call Flux.train!:Flux.train!(loss, data, opt)There are plenty of examples in the model zoo."
+},
+
+{
+    "location": "training/training.html#Loss-Functions-1",
+    "page": "Training",
+    "title": "Loss Functions",
+    "category": "section",
+    "text": "The loss that we defined in basics is completely valid for training. We can also define a loss in terms of some model:m = Chain(\n  Dense(784, 32, σ),\n  Dense(32, 10), softmax)\n\nloss(x, y) = Flux.mse(m(x), y)The loss will almost always be defined in terms of some cost function that measures the distance of the prediction m(x) from the target y. Flux has several of these built in, like mse for mean squared error or logloss for cross entropy loss, but you can calculate it however you want."
+},
+
+{
+    "location": "training/training.html#Callbacks-1",
+    "page": "Training",
+    "title": "Callbacks",
+    "category": "section",
+    "text": "train! takes an additional argument, cb, that's used for callbacks so that you can observe the training process. For example:train!(loss, data, opt, cb = () -> println(\"training\"))Callbacks are called for every batch of training data. You can slow this down using Flux.throttle(f, timeout) which prevents f from being called more than once every timeout seconds.A more typical callback might look like this:test_x, test_y = # ... create single batch of test data ...\nevalcb() = @show(loss(test_x, test_y))\n\nFlux.train!(loss, data, opt,\n            cb = throttle(evalcb, 5))"
 },
 
 {
diff --git a/latest/training/optimisers.html b/latest/training/optimisers.html
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Optimisers
Optimisers
Consider a simple linear regression. We create some dummy data, calculate a loss, and backpropagate to calculate gradients for the parameters W and b.
W = param(rand(2, 5))
+
Optimisers
Optimisers
Consider a simple linear regression. We create some dummy data, calculate a loss, and backpropagate to calculate gradients for the parameters W and b.
W = param(rand(2, 5))
 b = param(rand(2))
 
 predict(x) = W*x .+ b
diff --git a/latest/training/training.html b/latest/training/training.html
index 254bfebf..6b218dd2 100644
--- a/latest/training/training.html
+++ b/latest/training/training.html
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-
+
Training
To actually train a model we need three things:
  • A loss function, that evaluates how well a model is doing given some input data.
  • A collection of data points that will be provided to the loss function.
  • An optimiser that will update the model parameters appropriately.
With these we can call Flux.train!:
Flux.train!(loss, data, opt)
There are plenty of examples in the model zoo.
Loss Functions
The loss that we defined in basics is completely valid for training. We can also define a loss in terms of some model:
m = Chain(
+  Dense(784, 32, σ),
+  Dense(32, 10), softmax)
+
+loss(x, y) = Flux.mse(m(x), y)
The loss will almost always be defined in terms of some cost function that measures the distance of the prediction m(x) from the target y. Flux has several of these built in, like mse for mean squared error or logloss for cross entropy loss, but you can calculate it however you want.
Callbacks
train! takes an additional argument, cb, that's used for callbacks so that you can observe the training process. For example:
train!(loss, data, opt, cb = () -> println("training"))
Callbacks are called for every batch of training data. You can slow this down using Flux.throttle(f, timeout) which prevents f from being called more than once every timeout seconds.
A more typical callback might look like this:
test_x, test_y = # ... create single batch of test data ...
+evalcb() = @show(loss(test_x, test_y))
+
+Flux.train!(loss, data, opt,
+            cb = throttle(evalcb, 5))