File:One-neuron recurrent network bifurcation diagram.png
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| Description |
English: Reproduces Figure 3 of
Pascanu, Razvan; Mikolov, Tomas; Bengio, Yoshua (21 November 2012). "On the difficulty of training Recurrent Neural Networks". arXiv:1211.5063 [cs.LG]. Plotted in Julia. ```julia using Makie using StatsFuns width, height = 800, 800 scene = Figure(resolution = (width, height)) Axis(scene[1,1], backgroundcolor = "white") xs = LinRange(-3, -2, 40) ys = LinRange(0, 1, 40) xlims!(-3, -2) ylims!(0, 1) vectors = df.(xs, ys) us = [0 for x in xs, y in ys] vs = [-y + logistic(5y + x) for x in xs, y in ys] strength = vec(sqrt.(us .^ 2 .+ vs .^ 2)) strength /= maximum(strength)/20 arrows!(xs, ys, us, vs, arrowsize = strength, lengthscale = 0.3, arrowcolor = strength, linecolor = strength)
ys = LinRange(0, 1, 400) xs = [log(y/(1-y))-5y for y in ys] lines!(xs, ys, linewidth = 5, color="black")
save("one-neuron recurrent network bifurcation diagram.png", scene) ``` |
| Date | |
| Source | Own work |
| Author | Cosmia Nebula |
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 19:32, 28 June 2022 | | 800 × 800 (209 KB) | Cosmia Nebula (talk | contribs) | more visible |
| 19:28, 28 June 2022 |  | 1,600 × 1,600 (241 KB) | Cosmia Nebula (talk | contribs) | Uploaded while editing "Vanishing gradient problem" on en.wikipedia.org |
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