new progress integration
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@ -55,9 +55,9 @@ function Flux.train!(m::SeqModel, Xs, Ys; epoch = 1, η = 0.1,
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Y = placeholder(Float32)
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Loss = loss(Y, output)/batchlen/seqlen
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minimize_op = TensorFlow.train.minimize(opt(), Loss)
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for e in 1:epoch
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@progress "training" for e in 1:epoch
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info("Epoch $e\n")
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@progress for (i, (x, y)) in enumerate(zip(Xs,Ys))
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@progress "epoch" for (i, (x, y)) in enumerate(zip(Xs,Ys))
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out = run(sess, vcat(outstates..., output, Loss, minimize_op),
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merge(Dict(input=>batchone(x), Y=>batchone(y)),
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Dict(zip(instates, state))))
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