diff --git a/latest/contributing.html b/latest/contributing.html index d044ebb0..21a65472 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 index fce487c5..041bf1b1 100644 --- a/latest/data/onehot.html +++ b/latest/data/onehot.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'); -
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
+
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:
diff --git a/latest/gpu.html b/latest/gpu.html
index 55bd7113..08872a41 100644
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GPU Support
GPU Support
Support for array operations on other hardware backends, like GPUs, is provided by external packages like CuArrays and CLArrays. Flux doesn't care what array type you use, so we can just plug these in without any other changes.
For example, we can use CuArrays (with the cu array converter) to run our basic example on an NVIDIA GPU.
using CuArrays
+
GPU Support
GPU Support
Support for array operations on other hardware backends, like GPUs, is provided by external packages like CuArrays and CLArrays. Flux doesn't care what array type you use, so we can just plug these in without any other changes.
For example, we can use CuArrays (with the cu converter) to run our basic example on an NVIDIA GPU.
using CuArrays
 
-W = cu(rand(2, 5))
+W = cu(rand(2, 5)) # a 2×5 CuArray
 b = cu(rand(2))
 
 predict(x) = W*x .+ b
@@ -22,4 +22,4 @@ d(cu(rand(10))) # CuArray output
 
 m = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)
 m = mapparams(cu, m)
-d(cu(rand(10)))

+d(cu(rand(10)))
The mnist example contains the code needed to run the model on the GPU; just uncomment the lines after using CuArrays.

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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
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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
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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 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)
+
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_index.js b/latest/search_index.js
index 98f41091..9b0f0acc 100644
--- a/latest/search_index.js
+++ b/latest/search_index.js
@@ -229,7 +229,7 @@ var documenterSearchIndex = {"docs": [
     "page": "GPU Support",
     "title": "GPU Support",
     "category": "section",
-    "text": "Support for array operations on other hardware backends, like GPUs, is provided by external packages like CuArrays and CLArrays. Flux doesn't care what array type you use, so we can just plug these in without any other changes.For example, we can use CuArrays (with the cu array converter) to run our basic example on an NVIDIA GPU.using CuArrays\n\nW = cu(rand(2, 5))\nb = cu(rand(2))\n\npredict(x) = W*x .+ b\nloss(x, y) = sum((predict(x) .- y).^2)\n\nx, y = cu(rand(5)), cu(rand(2)) # Dummy data\nloss(x, y) # ~ 3Note that we convert both the parameters (W, b) and the data set (x, y) to cuda arrays. Taking derivatives and training works exactly as before.If you define a structured model, like a Dense layer or Chain, you just need to convert the internal parameters. Flux provides mapparams, which allows you to alter all parameters of a model at once.d = Dense(10, 5, σ)\nd = mapparams(cu, d)\nd.W # Tracked CuArray\nd(cu(rand(10))) # CuArray output\n\nm = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)\nm = mapparams(cu, m)\nd(cu(rand(10)))"
+    "text": "Support for array operations on other hardware backends, like GPUs, is provided by external packages like CuArrays and CLArrays. Flux doesn't care what array type you use, so we can just plug these in without any other changes.For example, we can use CuArrays (with the cu converter) to run our basic example on an NVIDIA GPU.using CuArrays\n\nW = cu(rand(2, 5)) # a 2×5 CuArray\nb = cu(rand(2))\n\npredict(x) = W*x .+ b\nloss(x, y) = sum((predict(x) .- y).^2)\n\nx, y = cu(rand(5)), cu(rand(2)) # Dummy data\nloss(x, y) # ~ 3Note that we convert both the parameters (W, b) and the data set (x, y) to cuda arrays. Taking derivatives and training works exactly as before.If you define a structured model, like a Dense layer or Chain, you just need to convert the internal parameters. Flux provides mapparams, which allows you to alter all parameters of a model at once.d = Dense(10, 5, σ)\nd = mapparams(cu, d)\nd.W # Tracked CuArray\nd(cu(rand(10))) # CuArray output\n\nm = Chain(Dense(10, 5, σ), Dense(5, 2), softmax)\nm = mapparams(cu, m)\nd(cu(rand(10)))The mnist example contains the code needed to run the model on the GPU; just uncomment the lines after using CuArrays."
 },
 
 {
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
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Training
Training
To actually train a model we need three things:
  • A model 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!(modelLoss, 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 model 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!(modelLoss, 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)