From c64cc3ff2908e41318375b874fe2bf3670d990aa Mon Sep 17 00:00:00 2001 From: autodocs Date: Wed, 3 May 2017 17:35:58 +0000 Subject: [PATCH] build based on c9dcc81 --- latest/apis/backends.html | 4 ++-- latest/apis/batching.html | 2 +- latest/apis/storage.html | 2 +- latest/contributing.html | 2 +- latest/examples/char-rnn.html | 2 +- latest/examples/logreg.html | 2 +- latest/index.html | 2 +- latest/internals.html | 2 +- latest/models/basics.html | 2 +- latest/models/debugging.html | 2 +- latest/models/recurrent.html | 2 +- latest/models/templates.html | 2 +- latest/search_index.js | 2 +- 13 files changed, 14 insertions(+), 14 deletions(-) diff --git a/latest/apis/backends.html b/latest/apis/backends.html index a3da91d9..1ce58c3d 100644 --- a/latest/apis/backends.html +++ b/latest/apis/backends.html @@ -150,7 +150,7 @@ Backends - + @@ -176,7 +176,7 @@ Currently, Flux's pure-Julia backend has no optimisations. This means that c 

model(rand(10)) #> [0.0650, 0.0655, ...]

-directly won't have great performance. In order to run a computationally intensive training process, we rely on a backend like MXNet or TensorFlow. +directly won't have great performance. In order to run a computationally intensive training process, we need to use a backend like MXNet or TensorFlow.

This is easy to do. Just call either diff --git a/latest/apis/batching.html b/latest/apis/batching.html index 6e81ee37..c3fa3a8d 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 c3aa3f22..5976cc5c 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 364a5230..f4e1b2be 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 95c30c10..8081cb24 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 2e25725d..16775fe3 100644 --- a/latest/examples/logreg.html +++ b/latest/examples/logreg.html @@ -139,7 +139,7 @@ Simple MNIST - + diff --git a/latest/index.html b/latest/index.html index 9c353cff..a8eea678 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 62e74d6e..fa4e9a82 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 c17b56f5..22113ca2 100644 --- a/latest/models/basics.html +++ b/latest/models/basics.html @@ -155,7 +155,7 @@ Model Building Basics - + diff --git a/latest/models/debugging.html b/latest/models/debugging.html index 88959b55..811f6b53 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 828de642..6a493dda 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 23fdf9b3..d6a6e814 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 df807d86..81d62e4e 100644 --- a/latest/search_index.js +++ b/latest/search_index.js @@ -205,7 +205,7 @@ var documenterSearchIndex = {"docs": [ "page": "Backends", "title": "Basic Usage", "category": "section", - "text": "model = Chain(Affine(10, 20), σ, Affine(20, 15), softmax)\nxs = rand(10)Currently, Flux's pure-Julia backend has no optimisations. This means that callingmodel(rand(10)) #> [0.0650, 0.0655, ...]directly won't have great performance. In order to run a computationally intensive training process, we rely on a backend like MXNet or TensorFlow.This is easy to do. Just call either mxnet or tf on a model to convert it to a model of that kind:mxmodel = mxnet(model)\nmxmodel(xs) #> [0.0650, 0.0655, ...]\n# or\ntfmodel = tf(model)\ntfmodel(xs) #> [0.0650, 0.0655, ...]These new models look and feel exactly like every other model in Flux, including returning the same result when you call them, and can be trained as usual using Flux.train!(). The difference is that the computation is being carried out by a backend, which will usually give a large speedup." + "text": "model = Chain(Affine(10, 20), σ, Affine(20, 15), softmax)\nxs = rand(10)Currently, Flux's pure-Julia backend has no optimisations. This means that callingmodel(rand(10)) #> [0.0650, 0.0655, ...]directly won't have great performance. In order to run a computationally intensive training process, we need to use a backend like MXNet or TensorFlow.This is easy to do. Just call either mxnet or tf on a model to convert it to a model of that kind:mxmodel = mxnet(model)\nmxmodel(xs) #> [0.0650, 0.0655, ...]\n# or\ntfmodel = tf(model)\ntfmodel(xs) #> [0.0650, 0.0655, ...]These new models look and feel exactly like every other model in Flux, including returning the same result when you call them, and can be trained as usual using Flux.train!(). The difference is that the computation is being carried out by a backend, which will usually give a large speedup." }, {