58 lines
950 B
Julia
58 lines
950 B
Julia
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# Simple Perceptron Layer
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@flux type Simple
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weight
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bias
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x -> σ( weight*x + bias )
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end
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Simple(nx::Integer, ny::Integer; init = randn) =
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Simple(init(nx, ny), init(ny))
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# Time Delay Node
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type Delay
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n::Int
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next
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end
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# feed(l::Delay, x) = ...
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# back(l::Delay, y) = ...
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# Simple Recurrent
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@flux type RecurrentU
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Wxh; Whh; Bh
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Wxy; Why; By
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function feed(x, hidden)
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hidden′ = σ( Wxh*x + Whh*hidden + Bh )
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y = σ( Wxy*x + Why*hidden′ + By )
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y, hidden′
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end
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end
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Recurrent(nx, ny, nh; init = randn) =
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Recurrent(init(nx, nh), init(nh, nh), init(nh),
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init(nx, ny), init(nh, ny), init(ny))
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@flux type Looped{T}
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delay::Delay
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layer::T
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function (x)
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y, hidden = layer(x, delay(hidden))
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return y
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end
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end
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type Recurrent
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layer::Looped{RecurrentU}
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end
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Recurrent(nx, ny, nh; init = randn, delay = 10) =
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Looped(Delay(delay, init(nh)), RecurrentU(nx, ny, nh))
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@forward Recurrent.layer feed
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