diff --git a/latest/apis/backends.html b/latest/apis/backends.html index f7256c1f..ac3d0ecd 100644 --- a/latest/apis/backends.html +++ b/latest/apis/backends.html @@ -150,7 +150,7 @@ Backends - + diff --git a/latest/apis/batching.html b/latest/apis/batching.html index 7227fdb5..fbdf326f 100644 --- a/latest/apis/batching.html +++ b/latest/apis/batching.html @@ -155,7 +155,7 @@ Batching - + diff --git a/latest/apis/storage.html b/latest/apis/storage.html index cda9ed30..3cd598bf 100644 --- a/latest/apis/storage.html +++ b/latest/apis/storage.html @@ -139,7 +139,7 @@ Storing Models - + diff --git a/latest/contributing.html b/latest/contributing.html index ee244b3e..566a8fc1 100644 --- a/latest/contributing.html +++ b/latest/contributing.html @@ -136,7 +136,7 @@ Contributing & Help - + diff --git a/latest/examples/char-rnn.html b/latest/examples/char-rnn.html index 2017d9f5..f7c5df85 100644 --- a/latest/examples/char-rnn.html +++ b/latest/examples/char-rnn.html @@ -139,7 +139,7 @@ Char RNN - + diff --git a/latest/examples/logreg.html b/latest/examples/logreg.html index a4622a4c..44415c82 100644 --- a/latest/examples/logreg.html +++ b/latest/examples/logreg.html @@ -139,7 +139,7 @@ Logistic Regression - + diff --git a/latest/index.html b/latest/index.html index 15c8fb2c..a689655c 100644 --- a/latest/index.html +++ b/latest/index.html @@ -147,7 +147,7 @@ Home - + diff --git a/latest/internals.html b/latest/internals.html index af8af078..49680916 100644 --- a/latest/internals.html +++ b/latest/internals.html @@ -136,7 +136,7 @@ Internals - + diff --git a/latest/models/basics.html b/latest/models/basics.html index a5e2efa5..9f555c2c 100644 --- a/latest/models/basics.html +++ b/latest/models/basics.html @@ -155,7 +155,7 @@ Model Building Basics - + @@ -229,7 +229,7 @@ softmax(affine2(x1)) # [0.125361, 0.246448, 0.21966, 0.124596, 0.283935]<

We just created two separate Affine - layers, and each contains its own version of + layers, and each contains its own (randomly initialised) version of W and b diff --git a/latest/models/debugging.html b/latest/models/debugging.html index bf3d8379..40018272 100644 --- a/latest/models/debugging.html +++ b/latest/models/debugging.html @@ -139,7 +139,7 @@ Debugging - + diff --git a/latest/models/recurrent.html b/latest/models/recurrent.html index ac0f1dd9..0ecda207 100644 --- a/latest/models/recurrent.html +++ b/latest/models/recurrent.html @@ -139,7 +139,7 @@ Recurrence - + diff --git a/latest/models/templates.html b/latest/models/templates.html index 355dfedf..46099c7e 100644 --- a/latest/models/templates.html +++ b/latest/models/templates.html @@ -155,7 +155,7 @@ Model Templates - + diff --git a/latest/search_index.js b/latest/search_index.js index d5bfaf8b..682eadb5 100644 --- a/latest/search_index.js +++ b/latest/search_index.js @@ -53,7 +53,7 @@ var documenterSearchIndex = {"docs": [ "page": "Model Building Basics", "title": "The Model", "category": "section", - "text": "... Initialising Photon Beams ...The core concept in Flux is the model. A model (or \"layer\") is simply a function with parameters. For example, in plain Julia code, we could define the following function to represent a logistic regression (or simple neural network):W = randn(3,5)\nb = randn(3)\naffine(x) = W * x + b\n\nx1 = rand(5) # [0.581466,0.606507,0.981732,0.488618,0.415414]\ny1 = softmax(affine(x1)) # [0.32676,0.0974173,0.575823]affine is simply a function which takes some vector x1 and outputs a new one y1. For example, x1 could be data from an image and y1 could be predictions about the content of that image. However, affine isn't static. It has parameters W and b, and if we tweak those parameters we'll tweak the result – hopefully to make the predictions more accurate.This is all well and good, but we usually want to have more than one affine layer in our network; writing out the above definition to create new sets of parameters every time would quickly become tedious. For that reason, we want to use a template which creates these functions for us:affine1 = Affine(5, 5)\naffine2 = Affine(5, 5)\n\nsoftmax(affine1(x1)) # [0.167952, 0.186325, 0.176683, 0.238571, 0.23047]\nsoftmax(affine2(x1)) # [0.125361, 0.246448, 0.21966, 0.124596, 0.283935]We just created two separate Affine layers, and each contains its own version of W and b, leading to a different result when called with our data. It's easy to define templates like Affine ourselves (see templates), but Flux provides Affine out of the box, so we'll use that for now." + "text": "... Initialising Photon Beams ...The core concept in Flux is the model. A model (or \"layer\") is simply a function with parameters. For example, in plain Julia code, we could define the following function to represent a logistic regression (or simple neural network):W = randn(3,5)\nb = randn(3)\naffine(x) = W * x + b\n\nx1 = rand(5) # [0.581466,0.606507,0.981732,0.488618,0.415414]\ny1 = softmax(affine(x1)) # [0.32676,0.0974173,0.575823]affine is simply a function which takes some vector x1 and outputs a new one y1. For example, x1 could be data from an image and y1 could be predictions about the content of that image. However, affine isn't static. It has parameters W and b, and if we tweak those parameters we'll tweak the result – hopefully to make the predictions more accurate.This is all well and good, but we usually want to have more than one affine layer in our network; writing out the above definition to create new sets of parameters every time would quickly become tedious. For that reason, we want to use a template which creates these functions for us:affine1 = Affine(5, 5)\naffine2 = Affine(5, 5)\n\nsoftmax(affine1(x1)) # [0.167952, 0.186325, 0.176683, 0.238571, 0.23047]\nsoftmax(affine2(x1)) # [0.125361, 0.246448, 0.21966, 0.124596, 0.283935]We just created two separate Affine layers, and each contains its own (randomly initialised) version of W and b, leading to a different result when called with our data. It's easy to define templates like Affine ourselves (see templates), but Flux provides Affine out of the box, so we'll use that for now." }, {