meetup
This commit is contained in:
Thomas Breloff
parent
d4896e1978
commit
949f11a8c9
@ -2,24 +2,13 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"plotgrid (generic function with 1 method)"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"using Plots; qwt()\n",
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"using Plots; immerse()\n",
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"default(size=(500,300), leg=false)\n",
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"\n",
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"# creates x/y vectors which can define a grid in a zig-zag pattern\n",
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@ -27,11 +16,6 @@
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" xs = linspace(lim..., n)\n",
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" xypairs = vec([(x,y) for x in vcat(xs,reverse(xs)), y in xs])\n",
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" Plots.unzip(xypairs)\n",
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"end\n",
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"\n",
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"# plot a grid from x/y vectors\n",
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"function plotgrid(x, y)\n",
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" plot([x y], [y x], c=:black)\n",
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"end"
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]
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},
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@ -44,83 +28,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"f2 (generic function with 1 method)"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"# these are the functions we want to classify\n",
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"f1(x) = 0.6sin(10x) + 0.1\n",
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"scalar = 10 # larger is harder... start with 3\n",
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"f1(x) = 0.6sin(scalar * x) + 0.1\n",
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"f2(x) = f1(x) - 0.2"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Build a neural net"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO: Recompiling stale cache file /home/tom/.julia/lib/v0.4/OnlineStats.ji for module OnlineStats.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" 0.366577 seconds (2.08 M allocations: 156.080 MB, 3.22% gc time)\n",
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"\n",
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"\n",
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"\n",
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"maxabs(β - coef(o)) for\n",
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"\n",
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"glm: 0.005542500269197448\n",
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"sgd: NaN\n",
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"proxgrad: 0.004859362717011262\n",
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"rda: 0.007882387662070361\n"
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]
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}
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],
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"source": [
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"using OnlineAI\n",
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"\n",
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"# first create a neural net to separate the functions\n",
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"numInputs = 2\n",
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"numOutputs = 1\n",
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"hiddenLayerStructure = [3,3,2]\n",
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"net = buildClassificationNet(numInputs, numOutputs, hiddenLayerStructure;\n",
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" hiddenActivation = TanhActivation(),\n",
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" finalActivation = TanhActivation(),\n",
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"\n",
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")\n",
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"\n",
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"# show the network\n",
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"viz = visualize(net);"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@ -138,30 +57,35 @@
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"source": [
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"# pick the plotting limits\n",
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"lim = (-1,1)\n",
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"default(xlim = lim, ylim = lim)\n",
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"\n",
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"# show the grid\n",
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"funcs = [f1, f2]\n",
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"n = 40\n",
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"gridx, gridy = gridxy(lim, n)\n",
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"p = plotgrid(gridx, gridy)\n",
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"default(xlim = lim, ylim = lim)\n",
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"\n",
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"# show the funcs\n",
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"funcs = [f1, f2]\n",
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"plot!(funcs, lim..., w=3)\n",
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"function initialize_plot(funcs, lim, gridx, gridy; kw...)\n",
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" # show the grid\n",
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" plot([gridx gridy], [gridy gridx], c=:black, kw...)\n",
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"\n",
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"# kick off an animation... we can save frames whenever we want, lets save the start\n",
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"anim = Animation()\n",
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"frame(anim)\n",
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" # show the funcs\n",
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" plot!(funcs, lim..., l=(4,[:blue :red]))\n",
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"end\n",
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"\n",
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"# open a gui window\n",
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"gui()"
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"# kick off an animation... we can save frames whenever we want, lets save the starting frame\n",
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"function initialize_animation()\n",
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" anim = Animation()\n",
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" frame(anim)\n",
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" anim\n",
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"end\n",
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"\n",
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"# lets see what we're dealing with...\n",
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"p = initialize_plot(funcs, lim, gridx, gridy)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# That looks tricky..."
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"# That looks tricky... lets build a neural net!"
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]
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},
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{
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@ -172,61 +96,35 @@
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},
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"outputs": [],
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"source": [
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"# function to sample from x's\n",
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"xsample() = rand(Distributions.Uniform(lim...)) \n",
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"\n",
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"# pick one of the functions at random, sample from the x line, then update the\n",
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"# neural net with [x, f(x)] as the inputs\n",
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"function sampleAndUpdate()\n",
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" f = sample(funcs)\n",
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" x = xsample()\n",
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" y = float(f == f1)\n",
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" update!(net, Float64[x, f(x)], [y])\n",
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"end\n",
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"using OnlineAI\n",
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"net = buildTanhClassificationNet(\n",
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" 2, # number of inputs\n",
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" 1, # number of outputs\n",
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" [4,4,2], # hidden layers structure\n",
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"# params = NetParams(gradientModel = SGDModel(η=1e-5))\n",
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")\n",
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"\n",
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"# take x matrix and convert to the first layer's activation\n",
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"function activateHidden(net, x)\n",
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"# input = x\n",
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" @assert net.layers[end].nin == 2\n",
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" proj = Array(nrows(x), 2)\n",
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" proj = zeros(nrows(x), 2)\n",
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" for i in 1:nrows(x)\n",
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" data = row(x,i)\n",
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" for layer in net.layers[1:end-1]\n",
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" #proj = Array(nrows(x), layer.nout)\n",
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" OnlineAI.forward!(layer, row(proj,i), false)\n",
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" OnlineAI.forward!(layer, data, false)\n",
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" data = layer.a\n",
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"# row!(proj, i, layer.a)\n",
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" end\n",
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" row!(proj, i, data)\n",
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"# input = proj\n",
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" end\n",
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" vec(proj[:,1]), vec(proj[:,2])\n",
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"end "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# update net with new samples\n",
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"for i in 1:10000\n",
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" sampleAndUpdate()\n",
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"end\n",
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"\n",
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"# update the plot... project each series to the first hidden layer and reset the data\n",
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"x = linspace(lim..., 100)\n",
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"p[1] = activateHidden(net, hcat(gridx, gridy))\n",
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"p[2] = activateHidden(net, hcat(gridy, gridx))\n",
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"p[3] = activateHidden(net, hcat(x, map(f1,x)))\n",
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"p[4] = activateHidden(net, hcat(x, map(f2,x)))\n",
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"\n",
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"# show/update the plot\n",
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"gui(p)\n",
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"frame(anim);"
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"# Update our model and the visualization"
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]
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},
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{
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@ -237,8 +135,100 @@
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},
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"outputs": [],
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"source": [
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"# build an animated gif\n",
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"gif(anim, fps = 10)"
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"p = initialize_plot(funcs, lim, gridx, gridy)\n",
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"anim = initialize_animation()\n",
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"gui()\n",
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"\n",
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"progressviz = track_progress(net)\n",
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"gui(progressviz.subplt)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"iterations_per_frame = 2000\n",
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"total_frames = 100\n",
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"dist = Distributions.Uniform(lim...)\n",
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"\n",
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"for frm in 1:total_frames\n",
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" # pick one of the functions at random, sample from the x line, then update the\n",
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" # neural net with [x, f(x)] as the inputsn = 1000\n",
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" for i in 1:iterations_per_frame\n",
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" f = sample(funcs)\n",
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" x = rand(dist)\n",
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" y = f == f1 ? 1.0 : -1.0\n",
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" update!(net, Float64[x, f(x)], [y])\n",
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" end\n",
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"\n",
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" # update the plot... project each series to the first hidden layer and reset the data\n",
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" # NOTE: this works because `getindex` and `setindex` are overloaded to get/set the underlying plot series data\n",
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" x = linspace(lim..., 50)\n",
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" p[1] = activateHidden(net, hcat(gridx, gridy))\n",
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" p[2] = activateHidden(net, hcat(gridy, gridx))\n",
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" p[3] = activateHidden(net, hcat(x, map(f1,x)))\n",
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" p[4] = activateHidden(net, hcat(x, map(f2,x)))\n",
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"\n",
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" # show/update the plot\n",
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" gui(p)\n",
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" frame(anim)\n",
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" \n",
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" # update the progress visualization\n",
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" update!(progressviz, true)\n",
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"end"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Animate!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"gif(anim, fps = 20)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# show the network (uses Qwt, visualize isn't available unless you import it)\n",
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"import Qwt\n",
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"viz = visualize(net);"
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]
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},
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{
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@ -296,7 +286,9 @@
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"collapsed": true
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},
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"outputs": [],
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"source": []
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"source": [
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"gui(progressviz.subplt)"
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]
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},
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{
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"cell_type": "code",
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@ -385,6 +377,17 @@
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"gui()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"sp = progressviz.subplt.plts[1].o.widget[:minimumSizeHint]()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -78,7 +78,7 @@ ncols(layout::GridLayout, row::Int) = layout.nc
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# get the plot index given row and column
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Base.getindex(layout::GridLayout, r::Int, c::Int) = (r-1) * layout.nc + c
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Base.getindex(subplt::Subplot, args...) = subplt.layout[args...]
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Base.getindex(subplt::Subplot, args...) = subplt.plts[subplt.layout[args...]]
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# handle "linking" the subplot axes together
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# each backend should implement the handleLinkInner and expandLimits! methods
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