From d3ba4dade035c4a5dfa4e39606b2728393683a97 Mon Sep 17 00:00:00 2001 From: autodocs Date: Sun, 15 Oct 2017 22:48:01 +0000 Subject: [PATCH] build based on 9a155ab --- latest/assets/documenter.css | 50 +++++++-- latest/assets/search.js | 178 +++++++++++++++++++++++++++++--- latest/contributing.html | 2 +- latest/data/onehot.html | 2 +- latest/gpu.html | 2 +- latest/index.html | 2 +- latest/models/basics.html | 2 +- latest/models/layers.html | 4 +- latest/models/recurrence.html | 2 +- latest/search.html | 2 +- latest/training/optimisers.html | 2 +- latest/training/training.html | 2 +- 12 files changed, 219 insertions(+), 31 deletions(-) diff --git a/latest/assets/documenter.css b/latest/assets/documenter.css index b8514efd..0aa36a9a 100644 --- a/latest/assets/documenter.css +++ b/latest/assets/documenter.css @@ -20,7 +20,7 @@ body, input { text-rendering: optimizeLegibility; } -pre, code { +pre, code, kbd { font-family: 'Roboto Mono', Monaco, courier, monospace; font-size: 0.90em; } @@ -46,15 +46,37 @@ body { line-height: 1.5; } -h1 { font-size: 1.75em; } -h2 { font-size: 1.50em; } -h3 { font-size: 1.25em; } +h1 { + font-size: 1.75em; +} + +/* Unless the

the is very first thing on the page (i.e. the second element + * in the
, * after the
, we add some additional styling to it + * to make it stand out a bit more. This way we get a reasonable fallback if CSS3 + * selectors are not supported in the browser. + */ +article > h1:not(:nth-child(2)) { + margin: 2.5em 0 0; + padding-bottom: 0.30em; + border-bottom: 1px solid #e5e5e5; +} +h2 { + font-size: 1.50em; + margin: 2.3em 0 0; + padding-bottom: 0.25em; + border-bottom: 1px solid #e5e5e5; +} +h3 { + font-size: 1.25em; + margin: 2.0em 0 0; +} h4 { font-size: 1.15em; } h5 { font-size: 1.10em; } h6 { font-size: 1em; } h4, h5, h6 { - margin: 1em 0; + margin-top: 1.5em; + margin-bottom: 1em; } img { @@ -105,6 +127,20 @@ pre code { background-color: initial; } +kbd { + font-size: 0.70em; + display: inline-block; + padding: 0.1em 0.5em 0.4em 0.5em; + line-height: 1.0em; + color: #444d56; + vertical-align: middle; + background-color: #fafbfc; + border: solid 1px #c6cbd1; + border-bottom-color: #959da5; + border-radius: 3px; + box-shadow: inset 0 -1px 0 #959da5; +} + /* Headers in admonitions and docstrings */ .admonition h1, article section.docstring h1 { @@ -341,8 +377,8 @@ article section.docstring .docstring-category { } article section.docstring a.source-link { - float: left; - font-weight: bold; + display: block; + font-weight: bold; } .nav-anchor, diff --git a/latest/assets/search.js b/latest/assets/search.js index 4e3e9a4a..5eb7fee8 100644 --- a/latest/assets/search.js +++ b/latest/assets/search.js @@ -38,29 +38,180 @@ parseUri.options = { requirejs.config({ paths: { - 'jquery': 'https://code.jquery.com/jquery-3.1.0.js?', - 'lunr': 'https://cdnjs.cloudflare.com/ajax/libs/lunr.js/0.7.1/lunr.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.1.1/jquery.min', + 'lunr': 'https://cdnjs.cloudflare.com/ajax/libs/lunr.js/2.1.3/lunr.min', + 'lodash': 'https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.4/lodash.min', } }); var currentScript = document.currentScript; -require(["jquery", "lunr"], function($, lunr) { +require(["jquery", "lunr", "lodash"], function($, lunr, _) { + $("#search-form").submit(function(e) { + e.preventDefault() + }) + + // list below is the lunr 2.1.3 list minus the intersect with names(Base) + // (all, any, get, in, is, which) and (do, else, for, let, where, while, with) + // ideally we'd just filter the original list but it's not available as a variable + lunr.stopWordFilter = lunr.generateStopWordFilter([ + 'a', + 'able', + 'about', + 'across', + 'after', + 'almost', + 'also', + 'am', + 'among', + 'an', + 'and', + 'are', + 'as', + 'at', + 'be', + 'because', + 'been', + 'but', + 'by', + 'can', + 'cannot', + 'could', + 'dear', + 'did', + 'does', + 'either', + 'ever', + 'every', + 'from', + 'got', + 'had', + 'has', + 'have', + 'he', + 'her', + 'hers', + 'him', + 'his', + 'how', + 'however', + 'i', + 'if', + 'into', + 'it', + 'its', + 'just', + 'least', + 'like', + 'likely', + 'may', + 'me', + 'might', + 'most', + 'must', + 'my', + 'neither', + 'no', + 'nor', + 'not', + 'of', + 'off', + 'often', + 'on', + 'only', + 'or', + 'other', + 'our', + 'own', + 'rather', + 'said', + 'say', + 'says', + 'she', + 'should', + 'since', + 'so', + 'some', + 'than', + 'that', + 'the', + 'their', + 'them', + 'then', + 'there', + 'these', + 'they', + 'this', + 'tis', + 'to', + 'too', + 'twas', + 'us', + 'wants', + 'was', + 'we', + 'were', + 'what', + 'when', + 'who', + 'whom', + 'why', + 'will', + 'would', + 'yet', + 'you', + 'your' + ]) + + // add . as a separator, because otherwise "title": "Documenter.Anchors.add!" + // would not find anything if searching for "add!", only for the entire qualification + lunr.tokenizer.separator = /[\s\-\.]+/ + + // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names + lunr.trimmer = function (token) { + return token.update(function (s) { + return s.replace(/^[^a-zA-Z0-9@!]+/, '').replace(/[^a-zA-Z0-9@!]+$/, '') + }) + } + + lunr.Pipeline.registerFunction(lunr.stopWordFilter, 'juliaStopWordFilter') + lunr.Pipeline.registerFunction(lunr.trimmer, 'juliaTrimmer') + var index = lunr(function () { this.ref('location') - this.field('title', {boost: 10}) + this.field('title') this.field('text') + documenterSearchIndex['docs'].forEach(function(e) { + this.add(e) + }, this) }) var store = {} documenterSearchIndex['docs'].forEach(function(e) { - index.add(e) - store[e.location] = e + store[e.location] = {title: e.title, category: e.category} }) $(function(){ - function update_search(query) { - results = index.search(query) + function update_search(querystring) { + tokens = lunr.tokenizer(querystring) + results = index.query(function (q) { + tokens.forEach(function (t) { + q.term(t.toString(), { + fields: ["title"], + boost: 10, + usePipeline: false, + editDistance: 2, + wildcard: lunr.Query.wildcard.NONE + }) + q.term(t.toString(), { + fields: ["text"], + boost: 1, + usePipeline: true, + editDistance: 2, + wildcard: lunr.Query.wildcard.NONE + }) + }) + }) $('#search-info').text("Number of results: " + results.length) $('#search-results').empty() results.forEach(function(result) { @@ -75,15 +226,16 @@ require(["jquery", "lunr"], function($, lunr) { } function update_search_box() { - query = $('#search-query').val() - update_search(query) + querystring = $('#search-query').val() + update_search(querystring) } - $('#search-query').keyup(update_search_box) + $('#search-query').keyup(_.debounce(update_search_box, 250)) $('#search-query').change(update_search_box) - search_query = parseUri(window.location).queryKey["q"] - if(search_query !== undefined) { + search_query_uri = parseUri(window.location).queryKey["q"] + if(search_query_uri !== undefined) { + search_query = decodeURIComponent(search_query_uri.replace(/\+/g, '%20')) $("#search-query").val(search_query) } update_search_box(); diff --git a/latest/contributing.html b/latest/contributing.html index 71bbb94f..32ca2dde 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 fe3023e9..394dbbad 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 b6c47b12..1c71d7d4 100644
--- a/latest/gpu.html
+++ b/latest/gpu.html
@@ -6,7 +6,7 @@ m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
 
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 ga('send', 'pageview');
-
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
+
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)) # a 2×5 CuArray
 b = cu(rand(2))
diff --git a/latest/index.html b/latest/index.html
index decac085..e6699979 100644
--- a/latest/index.html
+++ b/latest/index.html
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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
index 34b0d47b..9bc6d552 100644
--- 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
index 6cd0b063..c6cd47df 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 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.html b/latest/search.html
index d94fbbc3..a9cd7ed4 100644
--- a/latest/search.html
+++ b/latest/search.html
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-
Search
Search
Number of results: loading...
    
+
    Search
    Search
    Number of results: loading...
      
diff --git a/latest/training/optimisers.html b/latest/training/optimisers.html
index ebb018ea..949cb4f1 100644
--- 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 b8012d4a..9ab44956 100644
--- 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)