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alexalemi/golconv.ipynb

Created September 30, 2015 20:44
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Game of Life Convnet
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golconv.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Game of Life Convolutional Network"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try to train a single layer convolutional network that predicts the center square of a 3x3 convolutional region.\n",
"\n",
"This is similar to [this blog post](http://danielrapp.github.io/cnn-gol/) except we are going to do things differently.\n",
"\n",
"I'll be using [torch](http://torch.ch/) for this."
]
},
{
"cell_type": "code",
"execution_count": 1262,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"require 'nn';\n",
"require 'optim';"
]
},
{
"cell_type": "code",
"execution_count": 1263,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hidden = 2\n",
"steps = 0\n",
"coefL2 = 1e-5\n",
"\n",
"net = nn.Sequential()\n",
"convlayer = nn.SpatialConvolution(1,hidden,3,3,1,1,0,0)\n",
"net:add(convlayer)\n",
"net:add(nn.Reshape(hidden))\n",
"net:add(nn.ReLU())\n",
"net:add(nn.Linear(hidden,1))\n",
"net:add(nn.Sigmoid())\n",
"net:float()\n",
"\n",
"criterion = nn.BCECriterion():float()"
]
},
{
"cell_type": "code",
"execution_count": 1264,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"allinputs = torch.FloatTensor(512,1,3,3):zero()\n",
"\n",
"for i = 1,512 do\n",
" local target = i-1\n",
" for j = 1,3 do\n",
" for k = 1,3 do\n",
" local pk = (3-j)*3 + (3-k)\n",
" local pow = 2^pk\n",
" if (target >= pow) then\n",
" allinputs[{i,1,j,k}] = 1\n",
" target = target - pow\n",
" end\n",
" end\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 1265,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"function golforward(allinputs)\n",
" -- assume this is nx1x3x3\n",
" local guy = allinputs[{{},1,2,2}]\n",
" local neighbors = allinputs:sum(3):sum(4):squeeze() - guy\n",
" return neighbors:eq(3):float() + neighbors:eq(2):float():cmul(guy)\n",
"end\n",
"\n",
"targets = golforward(allinputs)"
]
},
{
"cell_type": "code",
"execution_count": 1266,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"parameters, gradParameters = net:getParameters()\n",
"\n",
"function feval(x)\n",
" steps = steps or 1\n",
" \n",
" gradParameters:zero()\n",
" \n",
" if x ~= parameters then\n",
" parameters:copy(x)\n",
" end\n",
" \n",
" outputs = net:forward(allinputs)\n",
" f = criterion:forward(outputs, targets)\n",
" df_do = criterion:backward(outputs, targets)\n",
" net:backward(allinputs, df_do)\n",
" \n",
" -- add L2 weight decay\n",
" f = f + coefL2 * torch.norm(parameters, 2)^2/2\n",
" gradParameters:add( parameters:clone():mul(coefL2) )\n",
" \n",
" return f, gradParameters\n",
"end\n",
"\n",
"function errs(x)\n",
" if x ~= parameters then\n",
" parameters:copy(x)\n",
" end\n",
" outputs = net:forward(allinputs)\n",
" return (512 - outputs:gt(0.5):float():eq(targets):sum())\n",
"end\n",
"\n",
"adamConfig = {learningRate=0.05}\n",
"adamState = {}\n",
"\n",
"function onestep()\n",
" local x, fxt = optim.adam(feval, parameters, adamConfig, adamState)\n",
" steps = steps + 1\n",
" return fxt[1]\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 1267,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"140\t\n"
]
},
"execution_count": 1267,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"errs(parameters)"
]
},
{
"cell_type": "code",
"execution_count": 1278,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"config = {learningRate = 0.1}\n",
"state = {}"
]
},
{
"cell_type": "code",
"execution_count": 1269,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"-- newp, fhist, current_func_val = optim.lbfgs(feval, parameters, config, state)"
]
},
{
"cell_type": "code",
"execution_count": 1274,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vals = {}\n",
"errors = {}"
]
},
{
"cell_type": "code",
"execution_count": 1279,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0\t1.2092050600831\t\n"
]
},
"execution_count": 1279,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i=1,1000 do\n",
" local p, fxt = optim.sgd(feval, parameters, config, state)\n",
" table.insert(vals, fxt[1]*100)\n",
" table.insert(errors, errs(p))\n",
"end\n",
"\n",
"print(errors[#errors], vals[#vals])"
]
},
{
"cell_type": "code",
"execution_count": 1280,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Plot = require 'itorch.Plot';"
]
},
{
"cell_type": "code",
"execution_count": 1285,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<script type=\"text/javascript\">\n",
"$(function() {\n",
" if (typeof (window._bokeh_onload_callbacks) === \"undefined\"){\n",
" window._bokeh_onload_callbacks = [];\n",
" }\n",
" function load_lib(url, callback){\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading){\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", new Date());\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", new Date());\n",
" window._bokeh_is_loading = true;\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = true;\n",
" s.onreadystatechange = s.onload = function(){\n",
" Bokeh.embed.inject_css(\"http://cdn.pydata.org/bokeh-0.7.0.min.css\");\n",
" window._bokeh_onload_callbacks.forEach(function(callback){callback()});\n",
" };\n",
" s.onerror = function(){\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
"\n",
" bokehjs_url = \"http://cdn.pydata.org/bokeh-0.7.0.min.js\"\n",
"\n",
" var elt = document.getElementById(\"0e888dc7-1c95-4b0d-cbc8-d2351e06e286\");\n",
" if(elt==null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid '0e888dc7-1c95-4b0d-cbc8-d2351e06e286'\"\n",
" + \"but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
"\n",
" if(typeof(Bokeh) !== \"undefined\") {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" var modelid = \"ffab8f3d-666f-49d4-c928-a2b5c03e0f32\";\n",
" var modeltype = \"Plot\";\n",
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" Bokeh.load_models(all_models);\n",
" var model = Bokeh.Collections(modeltype).get(modelid);\n",
" $(\"#0e888dc7-1c95-4b0d-cbc8-d2351e06e286\").html(''); // clear any previous plot in window_id\n",
" var view = new model.default_view({model: model, el: \"#0e888dc7-1c95-4b0d-cbc8-d2351e06e286\"});\n",
" } else {\n",
" load_lib(bokehjs_url, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", new Date())\n",
" var modelid = \"ffab8f3d-666f-49d4-c928-a2b5c03e0f32\";\n",
" var modeltype = \"Plot\";\n",
" var all_models = 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" Bokeh.load_models(all_models);\n",
" var model = Bokeh.Collections(modeltype).get(modelid);\n",
" $(\"#0e888dc7-1c95-4b0d-cbc8-d2351e06e286\").html(''); // clear any previous plot in window_id\n",
" var view = new model.default_view({model: model, el: \"#0e888dc7-1c95-4b0d-cbc8-d2351e06e286\"});\n",
" });\n",
" }\n",
"});\n",
"</script>\n",
"<div class=\"plotdiv\" id=\"0e888dc7-1c95-4b0d-cbc8-d2351e06e286\"></div>\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot = Plot():line(torch.range(1,#vals), vals, 'blue'):line(torch.range(1,#errors), errors, 'red'):title(\"GOL\"):draw()"
]
},
{
"cell_type": "code",
"execution_count": 1282,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAGgAAAA0CAAAAABDOyLxAAAAVklEQVRYhe3YsQoAIAhFUQ3/O/ry2muIRITkvlHQAy5SOiUnJv25Z3ig5mkCAgICAgIKjsWM0b1wHO56qwMCAgIC+hEKOnz3B2q91QEBAQEBBUazvjoXfAYFamDx9ocAAAAASUVORK5CYII=",
"text/plain": [
"Console does not support images"
]
},
"metadata": {
"image/png": {
"height": 52,
"width": 104
}
},
"output_type": "display_data"
}
],
"source": [
"W = 50\n",
"local N = (#convlayer.weight)[1]\n",
"bigfilts = torch.FloatTensor(N, W, W)\n",
"\n",
"for i = 1, N do\n",
" bigfilts[i] = image.scale(convlayer.weight[{i,1}], W, 'simple')\n",
"end\n",
"\n",
"itorch.image(bigfilts)"
]
},
{
"cell_type": "code",
"execution_count": 1283,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1,1,.,.) = \n",
" 1.3616 1.3616 1.3616\n",
" 1.3616 1.3623 1.3616\n",
" 1.3616 1.3616 1.3616\n",
"\n",
"(2,1,.,.) = \n",
" 2.0221 2.0221 2.0221\n",
" 2.0221 0.7083 2.0221\n",
" 2.0221 2.0221 2.0221\n",
"[torch.FloatTensor of size 2x1x3x3]\n",
"\n"
]
},
"execution_count": 1283,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"convlayer.weight"
]
},
{
"cell_type": "code",
"execution_count": 1284,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\n",
" 23\n",
"[torch.LongStorage of size 1]\n",
"\n"
]
},
"execution_count": 1284,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameters:size()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "iTorch",
"language": "lua",
"name": "itorch"
},
"language_info": {
"name": "lua",
"version": "20100"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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