From a1b4188877e036548034a4e27ef100e5e50b3144 Mon Sep 17 00:00:00 2001 From: autodocs Date: Tue, 10 Oct 2017 11:35:39 +0000 Subject: [PATCH] build based on 58f4f15 --- latest/contributing.html | 2 +- latest/data/onehot.html | 2 +- latest/gpu.html | 2 +- latest/index.html | 2 +- latest/models/basics.html | 3 ++- latest/models/layers.html | 4 ++-- latest/models/recurrence.html | 2 +- latest/search_index.js | 10 +++++++++- latest/training/optimisers.html | 2 +- latest/training/training.html | 10 ++++++++-- 10 files changed, 27 insertions(+), 12 deletions(-) diff --git a/latest/contributing.html b/latest/contributing.html index a7b2c1bb..8ac8089a 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 9547073d..3f048dd4 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 b9aa78a1..3194cbc0 100644
--- a/latest/gpu.html
+++ b/latest/gpu.html
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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 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 8a4d3dfc..982d654e 100644
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+++ 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 167f3d4d..6200d4c9 100644
--- a/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
@@ -22,6 +22,7 @@ l = loss(x, y)
 
 back!(l)
loss(x, y) returns the same number, but it's now a tracked value that records gradients as it goes along. Calling back! then calculates the gradient of W and b. We can see what this gradient is, and modify W to train the model.
grad(W)
 
+# Update the parameter
 W.data .-= 0.1grad(W)
 
 loss(x, y) # ~ 2.5
The loss has decreased a little, meaning that our prediction x is closer to the target y. If we have some data we can already try training the model.
All deep learning in Flux, however complex, is a simple generalisation of this example. Of course, models can look very different – they might have millions of parameters or complex control flow, and there are ways to manage this complexity. Let's see what that looks like.
Building Layers
It's common to create more complex models than the linear regression above. For example, we might want to have two linear layers with a nonlinearity like sigmoid (σ) in between them. In the above style we could write this as:
W1 = param(rand(3, 5))
diff --git a/latest/models/layers.html b/latest/models/layers.html
index 64715899..e4597bc5 100644
--- a/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
index 9c7667d3..d1777e74 100644
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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 74ca7ab8..a5ad9f4a 100644
--- a/latest/search_index.js
+++ b/latest/search_index.js
@@ -45,7 +45,7 @@ var documenterSearchIndex = {"docs": [
     "page": "Basics",
     "title": "Taking Gradients",
     "category": "section",
-    "text": "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)\nb = rand(2)\n\npredict(x) = W*x .+ b\nloss(x, y) = sum((predict(x) .- y).^2)\n\nx, y = rand(5), rand(2) # Dummy data\nloss(x, y) # ~ 3To improve the prediction we can take the gradients of W and b with respect to the loss function and perform gradient descent. We could calculate gradients by hand, but Flux will do it for us if we tell it that W and b are trainable parameters.using Flux.Tracker: param, back!, data, grad\n\nW = param(W)\nb = param(b)\n\nl = loss(x, y)\n\nback!(l)loss(x, y) returns the same number, but it's now a tracked value that records gradients as it goes along. Calling back! then calculates the gradient of W and b. We can see what this gradient is, and modify W to train the model.grad(W)\n\nW.data .-= 0.1grad(W)\n\nloss(x, y) # ~ 2.5The loss has decreased a little, meaning that our prediction x is closer to the target y. If we have some data we can already try training the model.All deep learning in Flux, however complex, is a simple generalisation of this example. Of course, models can look very different – they might have millions of parameters or complex control flow, and there are ways to manage this complexity. Let's see what that looks like."
+    "text": "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)\nb = rand(2)\n\npredict(x) = W*x .+ b\nloss(x, y) = sum((predict(x) .- y).^2)\n\nx, y = rand(5), rand(2) # Dummy data\nloss(x, y) # ~ 3To improve the prediction we can take the gradients of W and b with respect to the loss function and perform gradient descent. We could calculate gradients by hand, but Flux will do it for us if we tell it that W and b are trainable parameters.using Flux.Tracker: param, back!, data, grad\n\nW = param(W)\nb = param(b)\n\nl = loss(x, y)\n\nback!(l)loss(x, y) returns the same number, but it's now a tracked value that records gradients as it goes along. Calling back! then calculates the gradient of W and b. We can see what this gradient is, and modify W to train the model.grad(W)\n\n# Update the parameter\nW.data .-= 0.1grad(W)\n\nloss(x, y) # ~ 2.5The loss has decreased a little, meaning that our prediction x is closer to the target y. If we have some data we can already try training the model.All deep learning in Flux, however complex, is a simple generalisation of this example. Of course, models can look very different – they might have millions of parameters or complex control flow, and there are ways to manage this complexity. Let's see what that looks like."
 },
 
 {
@@ -184,6 +184,14 @@ var documenterSearchIndex = {"docs": [
     "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\n# Model loss function\nloss(x, y) = Flux.mse(m(x), y)\n\n# later\nFlux.train!(loss, data, opt)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#Datasets-1",
+    "page": "Training",
+    "title": "Datasets",
+    "category": "section",
+    "text": "The data argument provides a collection of data to train with (usually a set of inputs x and a target outputs y). For example, here's a dummy data set with only one data point:x = rand(784)\ny = rand(10)\ndata = [(x, y)]Flux.train! will call loss(x, y), calculate gradients, update the weights and then move on to the next data point if there is one. We can train the model on the same data three times:data = [(x, y), (x, y), (x, y)]\n# Or equivalently\ndata = Iterators.repeated((x, y), 3)It's common to load the xs and ys separately. In this case you can use zip:xs = [rand(784), rand(784), rand(784)]\nys = [rand( 10), rand( 10), rand( 10)]\ndata = zip(xs, ys)"
+},
+
 {
     "location": "training/training.html#Callbacks-1",
     "page": "Training",
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 544bd1b4..dc018f52 100644
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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)
 
@@ -14,7 +14,13 @@ ga('send', 'pageview');
 loss(x, y) = Flux.mse(m(x), y)
 
 # later
-Flux.train!(loss, data, opt)
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 ...
+Flux.train!(loss, data, opt)
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.
Datasets
The data argument provides a collection of data to train with (usually a set of inputs x and a target outputs y). For example, here's a dummy data set with only one data point:
x = rand(784)
+y = rand(10)
+data = [(x, y)]
Flux.train! will call loss(x, y), calculate gradients, update the weights and then move on to the next data point if there is one. We can train the model on the same data three times:
data = [(x, y), (x, y), (x, y)]
+# Or equivalently
+data = Iterators.repeated((x, y), 3)
It's common to load the xs and ys separately. In this case you can use zip:
xs = [rand(784), rand(784), rand(784)]
+ys = [rand( 10), rand( 10), rand( 10)]
+data = zip(xs, ys)
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,