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Plots.jl/examples/meetup/nnet.ipynb
Thomas Breloff c4020080b3 nnet
2015-10-22 00:40:41 -04:00

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{
"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": 941,
"metadata": {
"collapsed": false
},
"outputs": [],
"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; hiddenActivation = TanhActivation())\n",
"\n",
"# show the network\n",
"viz = visualize(net);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# On to the fun..."
]
},
{
"cell_type": "code",
"execution_count": 942,
"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": [
"# Yikes... that looks tricky to separate..."
]
},
{
"cell_type": "code",
"execution_count": 945,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"activateHidden (generic function with 1 method)"
]
},
"execution_count": 945,
"metadata": {},
"output_type": "execute_result"
}
],
"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",
" for layer in net.layers[1:end-1]\n",
" proj = Array(nrows(x), layer.nout)\n",
" for i in 1:nrows(x)\n",
" OnlineAI.forward!(layer, row(proj,i), false)\n",
" row!(proj, i, layer.a)\n",
" end\n",
" input = proj\n",
" end\n",
" vec(proj[:,1]), vec(proj[:,2])\n",
"end "
]
},
{
"cell_type": "code",
"execution_count": 946,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "LoadError",
"evalue": "LoadError: MethodError: `convert` has no method matching convert(::Type{Array{T,N}}, ::Int64, ::Int64)\nThis may have arisen from a call to the constructor Array{T,N}(...),\nsince type constructors fall back to convert methods.\nClosest candidates are:\n convert{T,N}(::Type{Array{T,N}}, !Matched::DataArrays.DataArray{T,N}, ::Any)\n convert{T,R,N}(::Type{Array{T,N}}, !Matched::DataArrays.PooledDataArray{T,R,N}, ::Any)\n Array{T}(!Matched::Type{T}, ::Integer)\n ...\nwhile loading In[946], in expression starting on line 8",
"output_type": "error",
"traceback": [
"LoadError: MethodError: `convert` has no method matching convert(::Type{Array{T,N}}, ::Int64, ::Int64)\nThis may have arisen from a call to the constructor Array{T,N}(...),\nsince type constructors fall back to convert methods.\nClosest candidates are:\n convert{T,N}(::Type{Array{T,N}}, !Matched::DataArrays.DataArray{T,N}, ::Any)\n convert{T,R,N}(::Type{Array{T,N}}, !Matched::DataArrays.PooledDataArray{T,R,N}, ::Any)\n Array{T}(!Matched::Type{T}, ::Integer)\n ...\nwhile loading In[946], in expression starting on line 8",
"",
" in activateHidden at In[945]:17"
]
}
],
"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": 940,
"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
}
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