https:\/\/github.com\/mrchaos\/JuliaDB.jl<\/a><\/p>\n\n\n\n\uc704 \ubc84\uc804\uc744 \uc124\uce58 \ud558\ub824\uba74<\/p>\n\n\n\n
Pkg>add https:\/\/github.com\/mrchaos\/JuliaDB.jl<\/p>\n\n\n\n
Flux \ub97c \ucd5c\uc2e0 \ubc84\uc804\uc73c\ub85c \uc124\uce58 \ud588\uae30 \ub54c\ubb38\uc5d0<\/p>\n\n\n\n
\uae30\uc874\uc758 JuliaDB.ML \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c Flux\uc0ac\uc6a9 \ubd80\ubd84\uc5d0 \uc788\uc5b4 \ucd5c\uc2e0 \ubc84\uc804\uc5d0 \ub9de\uac8c \uc218\uc815 \ud558\uba74\ub41c\ub2e4.<\/p>\n\n\n\n
model = Chain(\r\n Dense(ML.width(input_sch),32,relu),\r\n Dense(32,ML.width(output_sch)),\r\n softmax\r\n )\r\nloss(x,y) = Flux.mse(model(x),y)\r\n#Flux 0.8.2\r\n#opt = Flux.ADAM(Flux.params(model))\r\n\r\n#Flux 0.16.x\r\nopt = Flux.ADAM()\r\n\r\nevalcb = Flux.throttle(()->@show(loss(first(data)...)),2)\n\nfor i = 1:1000\r\n # Flux 0.8.2\r\n # Flux.train!(loss,data,opt,cb=evalcb)\r\n\r\n # Flux 0.16.x\r\n Flux.train!(loss,Flux.params(model),data,opt, cb = evalcb)\r\nend<\/code><\/pre>\n\n\n\n Flux\ub97c \ucd5c\uc2e0\ubc84\uc804\uc73c\ub85c \uc124\uce58\uc804\uc5d0 JuliaDB\uc5d0\uc11c CSV\ub370\uc774\ud130\ub97c \ubc14\uc774\ub108\ub9ac\ub85c \uc800\uc7a5\ud55c\uac83\uc744 \ucd5c\uc2e0\ubc84\uc804 \uc124\uce58 \ud6c4 \ub85c\ub529 \ud558\ub294 \uacbd\uc6b0 \uc624\ub958\uac00 \ubc1c\uc0dd\ud558\ub2c8 \ub370\uc774\ud130\ub97c \ub2e4\uc2dc \ubc14\uc774\ub108\ub9ac\ub85c \uc800\uc7a5 \ud6c4 \ub85c\ub529\ud574\uc57c \ud55c\ub2e4. <\/p>\n\n\n\n
\uc608\uc2dc)<\/p>\n\n\n\n
train_table = loadtable(\"data\/titanic\/train.csv\")\r\nsave(train_table,\"data\/titanic\/train_bin\")\r\ntrain_table = dropmissing(load(\"data\/titanic\/train_bin\"))<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"JuliaDB\uc640 \ucd5c\uc2e0 Flux\uc640 Turing\uc744 \uc0ac\uc6a9\ud558\uace0 \uc2f6\uc740\ub370 JuliaDB \uc124\uce58\uc2dc\uc5d0 Flux\ub294 0.8.3, Turing\uc740 0.8.2 \ubc84\uc804\uc73c\ub85c \uace0\uc815\ub418\uc5b4 \ucd5c\uc2e0 \ubc84\uc804\uc758 Flux(v0.12.4), Turing(v0.16.5)\uc744 \uc124\uce58 \ud560 \uc218 \uc5c6\ub2e4. \uc774\ub294 StatsBase\uc758 \ubc84\uc804\uc774 Project.toml \uc5d0\ub294 0.33 \uae4c\uc9c0 \uc788\ub294\ub370 \uc5ec\uae30\uc5d0 \ud604\uc7ac \uae4c\uc9c0 \ub098\uc628 \ubc84\uc804 0.33.8\uc744 \ucd94\uac00\ud558\uace0 JuliaDB\ub97c \uc124\uce58 \ud558\uba74\ub41c\ub2e4. \uba3c\uc800 Flux, Turing \uc124\uce58 \ud6c4 JuliaDB\ub97c \uc124\uce58 \ud558\uba74\ub41c\ub2e4. 2021.6\uc6d4\uc5d0 JuliaDB (v0.13.1)\ub97c \ud3ec\ud06c \ubc1b\uc544 \uc218\uc815 \ud588\ub2e4 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center 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<\/figure>\n\n\n\n