2016-12-15 22:41:29 +00:00
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# Based on https://arxiv.org/abs/1409.0473
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using Flux
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using Flux: flip
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2016-12-21 13:04:33 +00:00
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# A recurrent model which takes a token and returns a context-dependent
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2016-12-15 22:41:29 +00:00
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# annotation.
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@net type Encoder
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forward
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backward
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token -> hcat(forward(token), backward(token))
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end
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Encoder(in::Integer, out::Integer) =
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Encoder(LSTM(in, out÷2), flip(LSTM(in, out÷2)))
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# A recurrent model which takes a sequence of annotations, attends, and returns
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# a predicted output token.
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@net type Decoder
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attend
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recur
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state; y; N
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function (anns)
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energies = map(ann -> exp(attend(hcat(state{-1}, ann))[1]), seq(anns, N))
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weights = energies./sum(energies)
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ctx = sum(map((α, ann) -> α .* ann, weights, anns))
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(_, state), y = recur((state{-1},y{-1}), ctx)
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y
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end
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end
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Decoder(in::Integer, out::Integer; N = 1) =
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Decoder(Affine(in+out, 1),
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unroll1(LSTM(in, out)),
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param(zeros(1, out)), param(zeros(1, out)), N)
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# The model
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Nalpha = 5 # The size of the input token vector
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Nphrase = 7 # The length of (padded) phrases
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Nhidden = 12 # The size of the hidden state
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encode = Encoder(Nalpha, Nhidden)
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decode = Chain(Decoder(Nhidden, Nhidden, N = Nphrase), Affine(Nhidden, Nalpha), softmax)
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model = Chain(
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unroll(encode, Nphrase, stateful = false),
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unroll(decode, Nphrase, stateful = false, seq = false))
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xs = Batch([Seq(rand(Float32, Nalpha) for _ = 1:Nphrase)])
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