TastyPancakes/Flux.jl
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Mike J Innes 2017-02-17 15:55:02 +00:00
parent ba22a5cedc
commit 088438aaa1
1 changed files with 7 additions and 6 deletions
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docs/src/models/templates.md
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@ -38,11 +38,11 @@ This is much better: we can now make as many affine layers as we want. This is a
@net type MyAffine @net type MyAffine
W W
b b
x -> W * x + b x -> x * W + b
end end
``` ```
The function provided, `x -> W * x + b`, will be used when `MyAffine` is used as a model; it's just a shorter way of defining the `(::MyAffine)(x)` method above. The function provided, `x -> x * W + b`, will be used when `MyAffine` is used as a model; it's just a shorter way of defining the `(::MyAffine)(x)` method above. (You may notice that `W` and `x` have swapped order in the model; this is due to the way batching works, which will be covered in more detail later on.)
However, `@net` does not simply save us some keystrokes; it's the secret sauce that makes everything else in Flux go. For example, it analyses the code for the forward function so that it can differentiate it or convert it to a TensorFlow graph. However, `@net` does not simply save us some keystrokes; it's the secret sauce that makes everything else in Flux go. For example, it analyses the code for the forward function so that it can differentiate it or convert it to a TensorFlow graph.
@ -77,7 +77,7 @@ function (self::TLP)(x)
end end
``` ```
Clearly, the `first` and `second` parameters are not arrays here, but should be models themselves, and produce a result when called with an input array `x`. The `Affine` layer fits the bill so we can instantiate `TLP` with two of them: Clearly, the `first` and `second` parameters are not arrays here, but should be models themselves, and produce a result when called with an input array `x`. The `Affine` layer fits the bill, so we can instantiate `TLP` with two of them:
```julia ```julia
model = TLP(Affine(10, 20), model = TLP(Affine(10, 20),
@ -113,10 +113,11 @@ a = Affine(10, 20)
This is easy to implement using the usual Julia syntax for constructors: This is easy to implement using the usual Julia syntax for constructors:
```julia ```julia
Affine(in::Integer, out::Integer) = Affine(randn(in, out), randn(1, out)) Affine(in::Integer, out::Integer) =
Affine(randn(in, out), randn(1, out))
``` ```
In practice, these constructors tend to take the parameter initialisation function as an argument so that it's more easily customisable, and use `Flux.initn` by default (which is equivalent to `randn()/100`). So `Affine`'s constructor really looks like this: In practice, these constructors tend to take the parameter initialisation function as an argument so that it's more easily customisable, and use `Flux.initn` by default (which is equivalent to `randn(...)/100`). So `Affine`'s constructor really looks like this:
```julia ```julia
Affine(in::Integer, out::Integer; init = initn) = Affine(in::Integer, out::Integer; init = initn) =
@ -125,4 +126,4 @@ Affine(in::Integer, out::Integer; init = initn) =
## Supported syntax ## Supported syntax
The syntax used to define a forward pass like `x -> W*x + b` behaves exactly like Julia code for the most part. However, it's important to remember that it's defining a dataflow graph, not a general Julia expression. In practice this means that anything side-effectful, or things like control flow and `println`s, won't work as expected. In future we'll continue expand support for Julia syntax and features. The syntax used to define a forward pass like `x -> x*W + b` behaves exactly like Julia code for the most part. However, it's important to remember that it's defining a dataflow graph, not a general Julia expression. In practice this means that anything side-effectful, or things like control flow and `println`s, won't work as expected. In future we'll continue to expand support for Julia syntax and features.