diff --git a/latest/models/layers.html b/latest/models/layers.html index f7d2e0ff..44233755 100644 --- a/latest/models/layers.html +++ b/latest/models/layers.html @@ -11,4 +11,10 @@ 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.

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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.

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+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.

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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.

julia> d = Dense(5, 2)
+Dense(5, 2)
+
+julia> d(rand(5))
+Tracked 2-element Array{Float64,1}:
+0.00257447
+-0.00449443
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diff --git a/latest/search_index.js b/latest/search_index.js index cce8dd70..6a4795f2 100644 --- a/latest/search_index.js +++ b/latest/search_index.js @@ -133,7 +133,7 @@ var documenterSearchIndex = {"docs": [ "page": "Layer Reference", "title": "Flux.Dense", "category": "Type", - "text": "Dense(in::Integer, out::Integer, σ = identity)\n\nCreates a traditional Dense layer with parameters W and b.\n\ny = σ.(W * x .+ b)\n\nThe 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.\n\n\n\n" + "text": "Dense(in::Integer, out::Integer, σ = identity)\n\nCreates a traditional Dense layer with parameters W and b.\n\ny = σ.(W * x .+ b)\n\nThe 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.\n\njulia> d = Dense(5, 2)\nDense(5, 2)\n\njulia> d(rand(5))\nTracked 2-element Array{Float64,1}:\n0.00257447\n-0.00449443\n\n\n\n" }, { diff --git a/latest/training/optimisers.html b/latest/training/optimisers.html index 3a282e3d..0e9e6b61 100644 --- a/latest/training/optimisers.html +++ b/latest/training/optimisers.html @@ -27,4 +27,4 @@ end

If we call update, the parameters W Dense(10, 5, σ), Dense(5, 2), softmax)

Instead of having to write [m[1].W, m[1].b, ...], Flux provides a params function params(m) that returns a list of all parameters in the model for you.

For the update step, there's nothing whatsoever wrong with writing the loop above – it'll work just fine – but Flux provides various optimisers that make it more convenient.

opt = SGD([W, b], 0.1) # Gradient descent with learning rate 0.1
 
-opt() # Carry out the update, modifying `W` and `b`.

An optimiser takes a parameter list and returns a function that does the same thing as update above. We can pass either opt or update to our training loop, which will then run the optimiser after every mini-batch of data.

Optimiser Reference

All optimisers return a function that, when called, will update the parameters passed to it.

Flux.Optimise.SGD — Function.
SGD(params, η = 1; decay = 0)

Classic gradient descent optimiser. For each parameter p and its gradient δp, this runs p -= η*δp.

Supports decayed learning rate decay if the decay argument is provided.

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Flux.Optimise.Momentum — Function.
Momentum(params, ρ, decay = 0)

SGD with momentum ρ and optional learning rate decay.

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Flux.Optimise.Nesterov — Function.
Nesterov(params, ρ, decay = 0)

SGD with Nesterov momentum ρ and optional learning rate decay.

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Flux.Optimise.RMSProp — Function.
RMSProp(params; η = 0.001, ρ = 0.9, ϵ = 1e-8, decay = 0)

RMSProp optimiser. Parameters other than learning rate don't need tuning. Often a good choice for recurrent networks.

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Flux.Optimise.ADAM — Function.
ADAM(params; η = 0.001, β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0)

ADAM optimiser.

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Flux.Optimise.ADAGrad — Function.
ADAGrad(params; η = 0.01, ϵ = 1e-8, decay = 0)

ADAGrad optimiser. Parameters don't need tuning.

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Flux.Optimise.ADADelta — Function.
ADADelta(params; η = 0.01, ρ = 0.95, ϵ = 1e-8, decay = 0)

ADADelta optimiser. Parameters don't need tuning.

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+opt() # Carry out the update, modifying `W` and `b`.

An optimiser takes a parameter list and returns a function that does the same thing as update above. We can pass either opt or update to our training loop, which will then run the optimiser after every mini-batch of data.

Optimiser Reference

All optimisers return a function that, when called, will update the parameters passed to it.

Flux.Optimise.SGD — Function.
SGD(params, η = 1; decay = 0)

Classic gradient descent optimiser. For each parameter p and its gradient δp, this runs p -= η*δp.

Supports decayed learning rate decay if the decay argument is provided.

source
Flux.Optimise.Momentum — Function.
Momentum(params, ρ, decay = 0)

SGD with momentum ρ and optional learning rate decay.

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Flux.Optimise.Nesterov — Function.
Nesterov(params, ρ, decay = 0)

SGD with Nesterov momentum ρ and optional learning rate decay.

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Flux.Optimise.RMSProp — Function.
RMSProp(params; η = 0.001, ρ = 0.9, ϵ = 1e-8, decay = 0)

RMSProp optimiser. Parameters other than learning rate don't need tuning. Often a good choice for recurrent networks.

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Flux.Optimise.ADAM — Function.
ADAM(params; η = 0.001, β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0)

ADAM optimiser.

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Flux.Optimise.ADAGrad — Function.
ADAGrad(params; η = 0.01, ϵ = 1e-8, decay = 0)

ADAGrad optimiser. Parameters don't need tuning.

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Flux.Optimise.ADADelta — Function.
ADADelta(params; η = 0.01, ρ = 0.95, ϵ = 1e-8, decay = 0)

ADADelta optimiser. Parameters don't need tuning.

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