diff --git a/latest/contributing.html b/latest/contributing.html index 6b606b06..2b183014 100644 --- a/latest/contributing.html +++ b/latest/contributing.html @@ -6,4 +6,4 @@ 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'); -

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/data/onehot.html b/latest/data/onehot.html new file mode 100644 index 00000000..15407cce --- /dev/null +++ b/latest/data/onehot.html @@ -0,0 +1,40 @@ + +One-Hot Encoding · Flux

One-Hot Encoding

One-Hot Encoding

It's common to encode categorical variables (like true, false or cat, dog) in "one-of-k" or "one-hot" form. Flux provides the onehot function to make this easy.

julia> using Flux: onehot
+
+julia> onehot(:b, [:a, :b, :c])
+3-element Flux.OneHotVector:
+ false
+  true
+ false
+
+julia> onehot(:c, [:a, :b, :c])
+3-element Flux.OneHotVector:
+ false
+ false
+  true

The inverse is argmax (which can take a general probability distribution, as well as just booleans).

julia> argmax(ans, [:a, :b, :c])
+:c
+
+julia> argmax([true, false, false], [:a, :b, :c])
+:a
+
+julia> argmax([0.3, 0.2, 0.5], [:a, :b, :c])
+:c

Batches

onehotbatch creates a batch (matrix) of one-hot vectors, and argmax treats matrices as batches.

julia> using Flux: onehotbatch
+
+julia> onehotbatch([:b, :a, :b], [:a, :b, :c])
+3×3 Flux.OneHotMatrix:
+ false   true  false
+  true  false   true
+ false  false  false
+
+julia> onecold(ans, [:a, :b, :c])
+3-element Array{Symbol,1}:
+  :b
+  :a
+  :b

Note that these operations returned OneHotVector and OneHotMatrix rather than Arrays. OneHotVectors behave like normal vectors but avoid any unnecessary cost compared to using an integer index directly.. For example, multiplying a matrix with a one-hot vector simply slices out the relevant row of the matrix under the hood.

diff --git a/latest/index.html b/latest/index.html index 3089c6ea..d96451f1 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 eec350c3..9488a9d6 100644 --- a/latest/models/basics.html +++ b/latest/models/basics.html @@ -6,7 +6,7 @@ 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'); -

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 9cd74bae..9d707bba 100644
--- a/latest/models/layers.html
+++ b/latest/models/layers.html
@@ -6,9 +6,9 @@ m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
 
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 ga('send', 'pageview');
-

Layer Reference

Model Layers

Flux.ChainType.
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.ChainType.
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.DenseType.
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.DenseType.
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 index b8110486..ffe7a072 100644 --- a/latest/models/recurrence.html +++ b/latest/models/recurrence.html @@ -6,7 +6,7 @@ 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'); -

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 Models

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.html b/latest/search.html
index 62135ff1..86fc913d 100644
--- a/latest/search.html
+++ b/latest/search.html
@@ -6,4 +6,4 @@ 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');
-

Search

Search

Number of results: loading...

    +

    Search

    Search

    Number of results: loading...

      diff --git a/latest/search_index.js b/latest/search_index.js index f539f3a5..541a78bb 100644 --- a/latest/search_index.js +++ b/latest/search_index.js @@ -72,6 +72,14 @@ var documenterSearchIndex = {"docs": [ "text": "" }, +{ + "location": "models/recurrence.html#Recurrent-Models-1", + "page": "Recurrence", + "title": "Recurrent Models", + "category": "section", + "text": "" +}, + { "location": "models/recurrence.html#Recurrent-Cells-1", "page": "Recurrence", @@ -157,6 +165,14 @@ var documenterSearchIndex = {"docs": [ "page": "Training", "title": "Training", "category": "page", + "text": "" +}, + +{ + "location": "training/training.html#Training-1", + "page": "Training", + "title": "Training", + "category": "section", "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." }, @@ -176,6 +192,30 @@ var documenterSearchIndex = {"docs": [ "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))" }, +{ + "location": "data/onehot.html#", + "page": "One-Hot Encoding", + "title": "One-Hot Encoding", + "category": "page", + "text": "" +}, + +{ + "location": "data/onehot.html#One-Hot-Encoding-1", + "page": "One-Hot Encoding", + "title": "One-Hot Encoding", + "category": "section", + "text": "It's common to encode categorical variables (like true, false or cat, dog) in \"one-of-k\" or \"one-hot\" form. Flux provides the onehot function to make this easy.julia> using Flux: onehot\n\njulia> onehot(:b, [:a, :b, :c])\n3-element Flux.OneHotVector:\n false\n true\n false\n\njulia> onehot(:c, [:a, :b, :c])\n3-element Flux.OneHotVector:\n false\n false\n trueThe inverse is argmax (which can take a general probability distribution, as well as just booleans).julia> argmax(ans, [:a, :b, :c])\n:c\n\njulia> argmax([true, false, false], [:a, :b, :c])\n:a\n\njulia> argmax([0.3, 0.2, 0.5], [:a, :b, :c])\n:c" +}, + +{ + "location": "data/onehot.html#Batches-1", + "page": "One-Hot Encoding", + "title": "Batches", + "category": "section", + "text": "onehotbatch creates a batch (matrix) of one-hot vectors, and argmax treats matrices as batches.julia> using Flux: onehotbatch\n\njulia> onehotbatch([:b, :a, :b], [:a, :b, :c])\n3×3 Flux.OneHotMatrix:\n false true false\n true false true\n false false false\n\njulia> onecold(ans, [:a, :b, :c])\n3-element Array{Symbol,1}:\n :b\n :a\n :bNote that these operations returned OneHotVector and OneHotMatrix rather than Arrays. OneHotVectors behave like normal vectors but avoid any unnecessary cost compared to using an integer index directly.. For example, multiplying a matrix with a one-hot vector simply slices out the relevant row of the matrix under the hood." +}, + { "location": "contributing.html#", "page": "Contributing & Help", diff --git a/latest/training/optimisers.html b/latest/training/optimisers.html index 306e1f1f..c1a6f651 100644 --- a/latest/training/optimisers.html +++ b/latest/training/optimisers.html @@ -6,7 +6,7 @@ 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'); -

      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 e8cec6cc..464725e5 100644
      --- a/latest/training/training.html
      +++ b/latest/training/training.html
      @@ -6,7 +6,7 @@ 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');
      -

      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(
      +

      Training

      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)
       
      @@ -14,4 +14,4 @@ loss(x, y) = Flux.mse(m(x), y)

      The loss will almost always be def evalcb() = @show(loss(test_x, test_y)) Flux.train!(loss, data, opt, - cb = throttle(evalcb, 5))

      + cb = throttle(evalcb, 5))