414 lines
8.4 KiB
Plaintext
414 lines
8.4 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"plotgrid (generic function with 1 method)"
|
|
]
|
|
},
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"using Plots; qwt()\n",
|
|
"default(size=(500,300), leg=false)\n",
|
|
"\n",
|
|
"# creates x/y vectors which can define a grid in a zig-zag pattern\n",
|
|
"function gridxy(lim, n::Int)\n",
|
|
" xs = linspace(lim..., n)\n",
|
|
" xypairs = vec([(x,y) for x in vcat(xs,reverse(xs)), y in xs])\n",
|
|
" Plots.unzip(xypairs)\n",
|
|
"end\n",
|
|
"\n",
|
|
"# plot a grid from x/y vectors\n",
|
|
"function plotgrid(x, y)\n",
|
|
" plot([x y], [y x], c=:black)\n",
|
|
"end"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# The problem... can we classify the functions?"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"f2 (generic function with 1 method)"
|
|
]
|
|
},
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# these are the functions we want to classify\n",
|
|
"f1(x) = 0.6sin(10x) + 0.1\n",
|
|
"f2(x) = f1(x) - 0.2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Build a neural net"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"INFO: Recompiling stale cache file /home/tom/.julia/lib/v0.4/OnlineStats.ji for module OnlineStats.\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" 0.366577 seconds (2.08 M allocations: 156.080 MB, 3.22% gc time)\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"maxabs(β - coef(o)) for\n",
|
|
"\n",
|
|
"glm: 0.005542500269197448\n",
|
|
"sgd: NaN\n",
|
|
"proxgrad: 0.004859362717011262\n",
|
|
"rda: 0.007882387662070361\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"using OnlineAI\n",
|
|
"\n",
|
|
"# first create a neural net to separate the functions\n",
|
|
"numInputs = 2\n",
|
|
"numOutputs = 1\n",
|
|
"hiddenLayerStructure = [3,3,2]\n",
|
|
"net = buildClassificationNet(numInputs, numOutputs, hiddenLayerStructure;\n",
|
|
" hiddenActivation = TanhActivation(),\n",
|
|
" finalActivation = TanhActivation(),\n",
|
|
"\n",
|
|
")\n",
|
|
"\n",
|
|
"# show the network\n",
|
|
"viz = visualize(net);"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# On to the fun..."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# pick the plotting limits\n",
|
|
"lim = (-1,1)\n",
|
|
"default(xlim = lim, ylim = lim)\n",
|
|
"\n",
|
|
"# show the grid\n",
|
|
"n = 40\n",
|
|
"gridx, gridy = gridxy(lim, n)\n",
|
|
"p = plotgrid(gridx, gridy)\n",
|
|
"\n",
|
|
"# show the funcs\n",
|
|
"funcs = [f1, f2]\n",
|
|
"plot!(funcs, lim..., w=3)\n",
|
|
"\n",
|
|
"# kick off an animation... we can save frames whenever we want, lets save the start\n",
|
|
"anim = Animation()\n",
|
|
"frame(anim)\n",
|
|
"\n",
|
|
"# open a gui window\n",
|
|
"gui()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# That looks tricky..."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# function to sample from x's\n",
|
|
"xsample() = rand(Distributions.Uniform(lim...)) \n",
|
|
"\n",
|
|
"# pick one of the functions at random, sample from the x line, then update the\n",
|
|
"# neural net with [x, f(x)] as the inputs\n",
|
|
"function sampleAndUpdate()\n",
|
|
" f = sample(funcs)\n",
|
|
" x = xsample()\n",
|
|
" y = float(f == f1)\n",
|
|
" update!(net, Float64[x, f(x)], [y])\n",
|
|
"end\n",
|
|
"\n",
|
|
"# take x matrix and convert to the first layer's activation\n",
|
|
"function activateHidden(net, x)\n",
|
|
"# input = x\n",
|
|
" @assert net.layers[end].nin == 2\n",
|
|
" proj = Array(nrows(x), 2)\n",
|
|
" for i in 1:nrows(x)\n",
|
|
" data = row(x,i)\n",
|
|
" for layer in net.layers[1:end-1]\n",
|
|
" #proj = Array(nrows(x), layer.nout)\n",
|
|
" OnlineAI.forward!(layer, row(proj,i), false)\n",
|
|
" data = layer.a\n",
|
|
"# row!(proj, i, layer.a)\n",
|
|
" end\n",
|
|
" row!(proj, i, data)\n",
|
|
"# input = proj\n",
|
|
" end\n",
|
|
" vec(proj[:,1]), vec(proj[:,2])\n",
|
|
"end "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# update net with new samples\n",
|
|
"for i in 1:10000\n",
|
|
" sampleAndUpdate()\n",
|
|
"end\n",
|
|
"\n",
|
|
"# update the plot... project each series to the first hidden layer and reset the data\n",
|
|
"x = linspace(lim..., 100)\n",
|
|
"p[1] = activateHidden(net, hcat(gridx, gridy))\n",
|
|
"p[2] = activateHidden(net, hcat(gridy, gridx))\n",
|
|
"p[3] = activateHidden(net, hcat(x, map(f1,x)))\n",
|
|
"p[4] = activateHidden(net, hcat(x, map(f2,x)))\n",
|
|
"\n",
|
|
"# show/update the plot\n",
|
|
"gui(p)\n",
|
|
"frame(anim);"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# build an animated gif\n",
|
|
"gif(anim, fps = 10)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# update the net representation with weights, etc\n",
|
|
"update!(viz)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"p1 = plot(rand(20))\n",
|
|
"p2 = plot(rand(10))\n",
|
|
"p3 = scatter(rand(100))\n",
|
|
"p4 = plot(rand(1000))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"subplot(p1,p2,p3,p4, nr=1, leg=false)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"immerse()\n",
|
|
"p = plot(rand(10))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"gui()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"append!(p,1,rand(10))\n",
|
|
"gui()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Julia 0.4.0",
|
|
"language": "julia",
|
|
"name": "julia-0.4"
|
|
},
|
|
"language_info": {
|
|
"file_extension": ".jl",
|
|
"mimetype": "application/julia",
|
|
"name": "julia",
|
|
"version": "0.4.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|