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mnist.html
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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
<title>ConvNetJS MNIST demo</title>
<meta name="description" content="">
<meta name="author" content="">
<style>
.layer {
border: 1px solid #999;
margin-bottom: 5px;
text-align: left;
padding: 10px;
}
.layer_act {
width: 450px;
float: right;
}
.ltconv {
background-color: #FDD;
}
.ltrelu {
background-color: #FDF;
}
.ltpool {
background-color: #DDF;
}
.ltsoftmax {
background-color: #FFD;
}
.ltfc {
background-color: #DFF;
}
.ltlrn {
background-color: #DFD;
}
.ltdropout {
background-color: #AAA;
}
.ltitle {
color: #333;
font-size: 18px;
}
.actmap {
margin: 1px;
}
#trainstats {
text-align: left;
}
.clear {
clear: both;
}
#wrap {
width: 800px;
margin-left: auto;
margin-right: auto;
}
h1 {
font-size: 16px;
color: #333;
background-color: #DDD;
border-bottom: 1px #999 solid;
text-align: center;
}
.secpart {
width: 400px;
float: left;
}
#lossgraph {
/*border: 1px solid #F0F;*/
width: 100%;
}
.probsdiv canvas {
float: left;
}
.probsdiv {
height: 60px;
width: 180px;
display: inline-block;
font-size: 12px;
box-shadow: 0px 0px 2px 2px #EEE;
margin: 5px;
padding: 5px;
color: black;
}
.pp {
margin: 1px;
padding: 1px;
}
#testset_acc {
margin-bottom: 200px;
}
body {
font-family: Arial, "Helvetica Neue", Helvetica, sans-serif;
}
</style>
<script src="scripts/jquery-2.1.4.min.js"></script>
<script src="scripts/vis.js"></script>
<script src="scripts/util.js"></script>
<script src="scripts/convnet-min.js"></script>
<script src="scripts/tsne.js"></script>
<script src="mnist/mnist_labels.js"></script>
<script>
var layer_defs, net, trainer;
var t = "layer_defs = [];\n\
layer_defs.push({type:'input', out_sx:24, out_sy:24, out_depth:1});\n\
layer_defs.push({type:'conv', sx:5, filters:4, stride:1, pad:2, activation:'relu'});\n\
layer_defs.push({type:'pool', sx:2, stride:2});\n\
layer_defs.push({type:'conv', sx:5, filters:8, stride:1, pad:2, activation:'relu'});\n\
layer_defs.push({type:'pool', sx:3, stride:3});\n\
layer_defs.push({type:'softmax', num_classes:10});\n\
\n\
net = new convnetjs.Net();\n\
net.makeLayers(layer_defs);\n\
\n\
trainer = new convnetjs.SGDTrainer(net, {method:'adadelta', batch_size:20, l2_decay:0.001});\n\
";
// ------------------------
// BEGIN MNIST SPECIFIC STUFF
// ------------------------
classes_txt = ['0','1','2','3','4','5','6','7','8','9'];
var use_validation_data = true;
var sample_training_instance = function() {
// find an unloaded batch
var bi = Math.floor(Math.random()*loaded_train_batches.length);
var b = loaded_train_batches[bi];
var k = Math.floor(Math.random()*3000); // sample within the batch
var n = b*3000+k;
// load more batches over time
if(step_num%5000===0 && step_num>0) {
for(var i=0;i<num_batches;i++) {
if(!loaded[i]) {
// load it
load_data_batch(i);
break; // okay for now
}
}
}
// fetch the appropriate row of the training image and reshape into a Vol
var p = img_data[b].data;
var x = new convnetjs.Vol(28,28,1,0.0);
var W = 28*28;
for(var i=0;i<W;i++) {
var ix = ((W * k) + i) * 4;
x.w[i] = p[ix]/255.0;
}
x = convnetjs.augment(x, 24);
var isval = use_validation_data && n%10===0 ? true : false;
return {x:x, label:labels[n], isval:isval};
}
// sample a random testing instance
var sample_test_instance = function() {
var b = 20;
var k = Math.floor(Math.random()*3000);
var n = b*3000+k;
var p = img_data[b].data;
var x = new convnetjs.Vol(28,28,1,0.0);
var W = 28*28;
for(var i=0;i<W;i++) {
var ix = ((W * k) + i) * 4;
x.w[i] = p[ix]/255.0;
}
var xs = [];
for(var i=0;i<4;i++) {
xs.push(convnetjs.augment(x, 24));
}
// return multiple augmentations, and we will average the network over them
// to increase performance
return {x:xs, label:labels[n]};
}
var num_batches = 21; // 20 training batches, 1 test
var data_img_elts = new Array(num_batches);
var img_data = new Array(num_batches);
var loaded = new Array(num_batches);
var loaded_train_batches = [];
// int main
$(window).load(function() {
$("#newnet").val(t);
eval($("#newnet").val());
update_net_param_display();
for(var k=0;k<loaded.length;k++) { loaded[k] = false; }
load_data_batch(0); // async load train set batch 0 (6 total train batches)
load_data_batch(20); // async load test set (batch 6)
start_fun();
});
var start_fun = function() {
if(loaded[0] && loaded[20]) {
console.log('starting!');
setInterval(load_and_step, 0); // lets go!
}
else { setTimeout(start_fun, 200); } // keep checking
}
var load_data_batch = function(batch_num) {
// Load the dataset with JS in background
data_img_elts[batch_num] = new Image();
var data_img_elt = data_img_elts[batch_num];
data_img_elt.onload = function() {
var data_canvas = document.createElement('canvas');
data_canvas.width = data_img_elt.width;
data_canvas.height = data_img_elt.height;
var data_ctx = data_canvas.getContext("2d");
data_ctx.drawImage(data_img_elt, 0, 0); // copy it over... bit wasteful :(
img_data[batch_num] = data_ctx.getImageData(0, 0, data_canvas.width, data_canvas.height);
loaded[batch_num] = true;
if(batch_num < 20) { loaded_train_batches.push(batch_num); }
console.log('finished loading data batch ' + batch_num);
console.log(img_data);
};
data_img_elt.src = "mnist/mnist_batch_" + batch_num + ".png";
}
// ------------------------
// END MNIST SPECIFIC STUFF
// ------------------------
var maxmin = cnnutil.maxmin;
var f2t = cnnutil.f2t;
// elt is the element to add all the canvas activation drawings into
// A is the Vol() to use
// scale is a multiplier to make the visualizations larger. Make higher for larger pictures
// if grads is true then gradients are used instead
var draw_activations = function(elt, A, scale, grads) {
var s = scale || 2; // scale
var draw_grads = false;
if(typeof(grads) !== 'undefined') draw_grads = grads;
// get max and min activation to scale the maps automatically
var w = draw_grads ? A.dw : A.w;
var mm = maxmin(w);
// create the canvas elements, draw and add to DOM
for(var d=0;d<A.depth;d++) {
var canv = document.createElement('canvas');
canv.className = 'actmap';
var W = A.sx * s;
var H = A.sy * s;
canv.width = W;
canv.height = H;
var ctx = canv.getContext('2d');
var g = ctx.createImageData(W, H);
for(var x=0;x<A.sx;x++) {
for(var y=0;y<A.sy;y++) {
if(draw_grads) {
var dval = Math.floor((A.get_grad(x,y,d)-mm.minv)/mm.dv*255);
} else {
var dval = Math.floor((A.get(x,y,d)-mm.minv)/mm.dv*255);
}
for(var dx=0;dx<s;dx++) {
for(var dy=0;dy<s;dy++) {
var pp = ((W * (y*s+dy)) + (dx + x*s)) * 4;
for(var i=0;i<3;i++) { g.data[pp + i] = dval; } // rgb
g.data[pp+3] = 255; // alpha channel
}
}
}
}
ctx.putImageData(g, 0, 0);
elt.appendChild(canv);
}
}
var visualize_activations = function(net, elt) {
// clear the element
elt.innerHTML = "";
// show activations in each layer
var N = net.layers.length;
//console.log("Number of Layers: "+N);
for(var i=0;i<N;i++) {
var L = net.layers[i];
//console.log("\tLayer "+i+": "+L.layer_type);
var layer_div = document.createElement('div');
// visualize activations
var activations_div = document.createElement('div');
activations_div.appendChild(document.createTextNode('Activations:'));
activations_div.appendChild(document.createElement('br'));
activations_div.className = 'layer_act';
var scale = 2;
if(L.layer_type==='softmax' || L.layer_type==='fc') scale = 10; // for softmax
draw_activations(activations_div, L.out_act, scale);
// visualize data gradients
if(L.layer_type !== 'softmax') {
var grad_div = document.createElement('div');
grad_div.appendChild(document.createTextNode('Activation Gradients:'));
grad_div.appendChild(document.createElement('br'));
grad_div.className = 'layer_grad';
var scale = 2;
if(L.layer_type==='softmax' || L.layer_type==='fc') scale = 10; // for softmax
draw_activations(grad_div, L.out_act, scale, true);
activations_div.appendChild(grad_div);
}
// visualize filters if they are of reasonable size
if(L.layer_type === 'conv') {
var filters_div = document.createElement('div');
if(L.filters[0].sx>3) {
// actual weights
filters_div.appendChild(document.createTextNode('Weights:'));
filters_div.appendChild(document.createElement('br'));
for(var j=0;j<L.filters.length;j++) {
filters_div.appendChild(document.createTextNode('('));
draw_activations(filters_div, L.filters[j], 2);
filters_div.appendChild(document.createTextNode(')'));
}
// gradients
filters_div.appendChild(document.createElement('br'));
filters_div.appendChild(document.createTextNode('Weight Gradients:'));
filters_div.appendChild(document.createElement('br'));
for(var j=0;j<L.filters.length;j++) {
filters_div.appendChild(document.createTextNode('('));
draw_activations(filters_div, L.filters[j], 2, true);
filters_div.appendChild(document.createTextNode(')'));
}
} else {
filters_div.appendChild(document.createTextNode('Weights hidden, too small'));
}
activations_div.appendChild(filters_div);
}
layer_div.appendChild(activations_div);
// print some stats on left of the layer
layer_div.className = 'layer ' + 'lt' + L.layer_type;
var title_div = document.createElement('div');
title_div.className = 'ltitle'
var t = L.layer_type + ' (' + L.out_sx + 'x' + L.out_sy + 'x' + L.out_depth + ')';
title_div.appendChild(document.createTextNode(t));
layer_div.appendChild(title_div);
if(L.layer_type==='conv') {
var t = 'filter size ' + L.filters[0].sx + 'x' + L.filters[0].sy + 'x' + L.filters[0].depth + ', stride ' + L.stride;
layer_div.appendChild(document.createTextNode(t));
layer_div.appendChild(document.createElement('br'));
}
if(L.layer_type==='pool') {
var t = 'pooling size ' + L.sx + 'x' + L.sy + ', stride ' + L.stride;
layer_div.appendChild(document.createTextNode(t));
layer_div.appendChild(document.createElement('br'));
}
// find min, max activations and display them
var mma = maxmin(L.out_act.w);
var t = 'max activation: ' + f2t(mma.maxv) + ', min: ' + f2t(mma.minv);
layer_div.appendChild(document.createTextNode(t));
layer_div.appendChild(document.createElement('br'));
var mma = maxmin(L.out_act.dw);
var t = 'max gradient: ' + f2t(mma.maxv) + ', min: ' + f2t(mma.minv);
layer_div.appendChild(document.createTextNode(t));
layer_div.appendChild(document.createElement('br'));
// number of parameters
if(L.layer_type==='conv') {
var tot_params = L.sx*L.sy*L.in_depth*L.filters.length + L.filters.length;
var t = 'parameters: ' + L.filters.length + 'x' + L.sx + 'x' + L.sy + 'x' + L.in_depth + '+' + L.filters.length + ' = ' + tot_params;
layer_div.appendChild(document.createTextNode(t));
layer_div.appendChild(document.createElement('br'));
}
if(L.layer_type==='fc') {
var tot_params = L.num_inputs*L.filters.length + L.filters.length;
var t = 'parameters: ' + L.filters.length + 'x' + L.num_inputs + '+' + L.filters.length + ' = ' + tot_params;
layer_div.appendChild(document.createTextNode(t));
layer_div.appendChild(document.createElement('br'));
}
// css madness needed here...
var clear = document.createElement('div');
clear.className = 'clear';
layer_div.appendChild(clear);
elt.appendChild(layer_div);
}
}
// loads a training image and trains on it with the network
var paused = false;
var load_and_step = function() {
if(paused) return;
var sample = sample_training_instance();
step(sample); // process this image
}
// evaluate current network on test set
var test_predict = function() {
var num_classes = net.layers[net.layers.length-1].out_depth;
document.getElementById('testset_acc').innerHTML = '';
// grab a random test image
for(num=0;num<50;num++) {
var sample = sample_test_instance();
var y = sample.label; // ground truth label
// forward prop it through the network
var aavg = new convnetjs.Vol(1,1,num_classes,0.0);
// ensures we always have a list, regardless if above returns single item or list
var xs = [].concat(sample.x);
var n = xs.length;
for(var i=0;i<n;i++) {
var a = net.forward(xs[i]);
aavg.addFrom(a);
}
var preds = [];
for(var k=0;k<aavg.w.length;k++) { preds.push({k:k,p:aavg.w[k]}); }
preds.sort(function(a,b){return a.p<b.p ? 1:-1;});
var div = document.createElement('div');
div.className = 'testdiv';
// draw the image into a canvas
draw_activations(div, xs[0], 2); // draw Vol into canv
// add predictions
var probsdiv = document.createElement('div');
div.className = 'probsdiv';
var t = '';
for(var k=0;k<3;k++) {
var col = preds[k].k===y ? 'rgb(85,187,85)' : 'rgb(187,85,85)';
t += '<div class=\"pp\" style=\"width:' + Math.floor(preds[k].p/n*100) + 'px; margin-left: 60px; background-color:' + col + ';\">' + classes_txt[preds[k].k] + '</div>'
}
probsdiv.innerHTML = t;
div.appendChild(probsdiv);
// add it into DOM
$("#testset_acc").append(div).fadeIn(1000);
}
}
var test_predict_coactivate = function() {
var num_classes = net.layers[net.layers.length-1].out_depth;
var layers = net.layers.length;
var activations = [];
var count = 0;
for (var l=0; l<(layers); l++) {
var type = net.layers[l].layer_type;
if (type != "fc" && type != "conv") {
var neurons = net.layers[l].out_act.w.length;
for (var nw=0; nw<(neurons); nw++) {
activations[count] = [];
count = count+1;
}
}
}
console.log(count+" total neurons");
// grab a random test image
for(num=0;num<25;num++) {
var sample = sample_test_instance();
var y = sample.label; // ground truth label
// forward prop it through the network
//var aavg = new convnetjs.Vol(1,1,num_classes,0.0);
// ensures we always have a list, regardless if above returns single item or list
var xs = [].concat(sample.x);
var num_tests = xs.length;
console.log(num+" testing Mnist with "+num_tests+" samples.");
// TODO more than just one
for(var i=0;i<num_tests;i++) {
console.log("\tTest input: "+i);
var a = net.forward(xs[i]);
//aavg.addFrom(a);
layer_data(net, activations);
}
}
console.log("Activation data length: "+activations.length);
console.log(" data length: "+activations[0].length);
console.log(activations);
/*
var preds = [];
for(var k=0;k<aavg.w.length;k++) {
preds.push({k:k,p:aavg.w[k]});
}
preds.sort(function(a,b){return a.p<b.p ? 1:-1;});
*/
//var div = document.createElement('div');
//div.className = 'testdiv';
// draw the image into a canvas
//draw_activations(div, xs[0], 2); // draw Vol into canv
// save to file
return activations;
//var opt = {epsilon: 10, perplexity: 30, dim: 1};
//console.log("getting tsne data");
//var points = get_tsne(opt, activations, 100);
//var tsne_output = JSON.stringify(points);
//console.log("\tgot tsne data");
//return data_to_sankey( net, 'mnist_sankey', points);
}
function layer_data(network, act) {
var layers = network.layers.length;
var count = 0;
for (var l=0; l<(layers); l++) {
var type = network.layers[l].layer_type;
if (type != "fc" && type != "conv") {
//console.log("layer "+l+" "+type);
var neurons = network.layers[l].out_act.w.length;
for (var nw=0; nw<neurons; nw++) {
//console.log("\tweight "+nw);
weight = network.layers[l].out_act.w[nw];
act[count].push(weight);
count = count+1;
}
}
}
}
function get_tsne(options, data, iters) {
var tsne = new tsnejs.tSNE(options); // create a tSNE instance
console.log("tSNE options set");
tsne.initDataDist(data);
console.log("tSNE initialized");
if (iters <= 0) {
return null;
}
for(var k = 0; k < iters; k++) {
if (k % 50 == 0) {
console.log("\t\t"+k+" tSNE iterations out of "+iters);
}
tsne.step(); // every time you call this, solution gets better
}
return tsne.getSolution();
}
var data_to_sankey = function(network, filename, points) {
var data = {"nodes":[], "links":[], "meta":[]};
// for each layer, add nodes for each neuron
var count = 0;
var layers = network.layers.length;
console.log("SAVING TO FILE: "+filename);
for (var l=0; l<(layers-1); l++) {
var L = network.layers[l];
console.log("Layer "+l+": "+L.layer_type);
//if (L.layer_type != "fc" && L.layer_type != "conv") {
var neurons = network.layers[l].out_act.w.length;
var next = network.layers[l+1].out_act.w.length;
console.log("layer: "+l);
for (var n=0; n<neurons; n++) {
var name = "L"+l+"N"+n;
data.nodes.push({"name":name,
"layer":L.layer_type,
"value":points[count+n]});
data.meta.push({"size":Math.abs(network.layers[l].out_act.w[n]),
"pos":points[count+n]});
if (l<(layers-2)) {
for (var n2=0; n2<next; n2++) {
//console.log("\t\tto neuron: "+(count+neurons+n2));
//console.log(count+neurons+n2);
//data.links.push({"source":count+n,
// "target":count+neurons+n2,
// //"value":Math.sqrt(points[count+neurons+n2]^2+points[count+n]^2),
// "value":1-Math.abs(points[count+neurons+n2]-points[count+n]),
// "v1":points[count+n],
// "v2":points[count+neurons+n2]});
}
}
}
//}
count = count + neurons;
}
return data;
}
/*
function to_file(str, filename) {
var fs = require('fs');
fs.writeFile(filename, str, function(err) {
if (err) {
console.log(err);
} else {
console.log("json saved to "+filename);
}
});
}
*/
var lossGraph = new cnnvis.Graph();
var xLossWindow = new cnnutil.Window(100);
var wLossWindow = new cnnutil.Window(100);
var trainAccWindow = new cnnutil.Window(100);
var valAccWindow = new cnnutil.Window(100);
var step_num = 0;
var step = function(sample) {
var x = sample.x;
var y = sample.label;
if(sample.isval) {
// use x to build our estimate of validation error
net.forward(x);
var yhat = net.getPrediction();
var val_acc = yhat === y ? 1.0 : 0.0;
valAccWindow.add(val_acc);
return; // get out
}
// train on it with network
var stats = trainer.train(x, y);
var lossx = stats.cost_loss;
var lossw = stats.l2_decay_loss;
// keep track of stats such as the average training error and loss
var yhat = net.getPrediction();
var train_acc = yhat === y ? 1.0 : 0.0;
xLossWindow.add(lossx);
wLossWindow.add(lossw);
trainAccWindow.add(train_acc);
// visualize training status
var train_elt = document.getElementById("trainstats");
train_elt.innerHTML = '';
var t = 'Forward time per example: ' + stats.fwd_time + 'ms';
train_elt.appendChild(document.createTextNode(t));
train_elt.appendChild(document.createElement('br'));
var t = 'Backprop time per example: ' + stats.bwd_time + 'ms';
train_elt.appendChild(document.createTextNode(t));
train_elt.appendChild(document.createElement('br'));
var t = 'Classification loss: ' + f2t(xLossWindow.get_average());
train_elt.appendChild(document.createTextNode(t));
train_elt.appendChild(document.createElement('br'));
var t = 'L2 Weight decay loss: ' + f2t(wLossWindow.get_average());
train_elt.appendChild(document.createTextNode(t));
train_elt.appendChild(document.createElement('br'));
var t = 'Training accuracy: ' + f2t(trainAccWindow.get_average());
train_elt.appendChild(document.createTextNode(t));
train_elt.appendChild(document.createElement('br'));
var t = 'Validation accuracy: ' + f2t(valAccWindow.get_average());
train_elt.appendChild(document.createTextNode(t));
train_elt.appendChild(document.createElement('br'));
var t = 'Examples seen: ' + step_num;
train_elt.appendChild(document.createTextNode(t));
train_elt.appendChild(document.createElement('br'));
// visualize activations
if(step_num % 100 === 0) {
var vis_elt = document.getElementById("visnet");
visualize_activations(net, vis_elt);
}
// log progress to graph, (full loss)
if(step_num % 200 === 0) {
var xa = xLossWindow.get_average();
var xw = wLossWindow.get_average();
if(xa >= 0 && xw >= 0) { // if they are -1 it means not enough data was accumulated yet for estimates
lossGraph.add(step_num, xa + xw);
lossGraph.drawSelf(document.getElementById("lossgraph"));
}
}
// run prediction on test set
if(step_num % 1000 === 0) {
test_predict();
}
step_num++;
}
// user settings
var change_lr = function() {
trainer.learning_rate = parseFloat(document.getElementById("lr_input").value);
update_net_param_display();
}
var change_momentum = function() {
trainer.momentum = parseFloat(document.getElementById("momentum_input").value);
update_net_param_display();
}
var change_batch_size = function() {
trainer.batch_size = parseFloat(document.getElementById("batch_size_input").value);
update_net_param_display();
}
var change_decay = function() {
trainer.l2_decay = parseFloat(document.getElementById("decay_input").value);
update_net_param_display();
}
var update_net_param_display = function() {
document.getElementById('lr_input').value = trainer.learning_rate;
document.getElementById('momentum_input').value = trainer.momentum;
document.getElementById('batch_size_input').value = trainer.batch_size;
document.getElementById('decay_input').value = trainer.l2_decay;
}
var toggle_pause = function() {
paused = !paused;
var btn = document.getElementById('buttontp');
if(paused) { btn.value = 'resume' }
else { btn.value = 'pause'; }
}
var dump_json = function() {
document.getElementById("dumpjson").value = JSON.stringify(net.toJSON());
}
var dump_coact = function() {
// TODO
// run TSNE gather and output
var tsne_data = test_predict_coactivate();
document.getElementById("dump_coactivations").value = JSON.stringify(tsne_data);
}
var clear_graph = function() {
lossGraph = new cnnvis.Graph(); // reinit graph too
}
var reset_all = function() {
update_net_param_display();
// reinit windows that keep track of val/train accuracies
xLossWindow.reset();
wLossWindow.reset();
trainAccWindow.reset();
valAccWindow.reset();
lossGraph = new cnnvis.Graph(); // reinit graph too
step_num = 0;
}
var load_from_json = function() {
var jsonString = document.getElementById("dumpjson").value;
var json = JSON.parse(jsonString);
net = new convnetjs.Net();
net.fromJSON(json);
reset_all();
}
var change_net = function() {
eval($("#newnet").val());
reset_all();
}
</script>
</head>
<body>
<div id="wrap">
<h2 style="text-align: center;"><a href="http://cs.stanford.edu/people/karpathy/convnetjs/">ConvNetJS</a> MNIST demo</h2>
<h1>Description</h1>
<p>
This demo trains a Convolutional Neural Network on the <a href="http://yann.lecun.com/exdb/mnist/">MNIST digits dataset</a> in your browser, with nothing but Javascript. The dataset is fairly easy and one should expect to get somewhere around 99% accuracy within few minutes. I used <a href="mnist_parse.zip">this python script</a> to parse the <a href="http://deeplearning.net/tutorial/gettingstarted.html">original files</a> into batches of images that can be easily loaded into page DOM with img tags.
</p>
<p>
This network takes a 28x28 MNIST image and crops a random 24x24 window before training on it (this technique is called data augmentation and improves generalization). Similarly to do prediction, 4 random crops are sampled and the probabilities across all crops are averaged to produce final predictions. The network runs at about 5ms for both forward and backward pass on my reasonably decent Ubuntu+Chrome machine.
</p>
<p>
By default, in this demo we're using Adadelta which is one of per-parameter adaptive step size methods, so we don't have to worry about changing learning rates or momentum over time. However, I still included the text fields for changing these if you'd like to play around with SGD+Momentum trainer.
</p>
<p>Report questions/bugs/suggestions to <a href="https://twitter.com/karpathy">@karpathy</a>.</p>
<h1>Training Stats</h1>
<div class="divsec" style="270px;">
<div class="secpart">
<input id="buttontp" type="submit" value="pause" onclick="toggle_pause();" style="width: 100px; height:30px; background-color: #FCC;"/>
<div id="trainstats"></div>
<div id="controls">
Learning rate: <input name="lri" type="text" maxlength="20" id="lr_input"/>
<input id="buttonlr" type="submit" value="change" onclick="change_lr();"/>
<br />
Momentum: <input name="momi" type="text" maxlength="20" id="momentum_input"/>
<input id="buttonmom" type="submit" value="change" onclick="change_momentum();"/>
<br />
Batch size: <input name="bsi" type="text" maxlength="20" id="batch_size_input"/>
<input id="buttonbs" type="submit" value="change" onclick="change_batch_size();"/>
<br />
Weight decay: <input name="wdi" type="text" maxlength="20" id="decay_input"/>
<input id="buttonwd" type="submit" value="change" onclick="change_decay();"/>
</div>
<input id="buttondj" type="submit" value="save network snapshot as JSON" onclick="dump_json();"/><br />
<input id="buttondj" type="submit" value="save coactivations from test" onclick="dump_coact();"/><br />
<input id="buttonlfj" type="submit" value="init network from JSON snapshot" onclick="load_from_json();"/><br />
<textarea id="dumpjson"></textarea>
<textarea id="dump_coactivations"></textarea>
</div>
<div class="secpart">
<div>
Loss:<br />
<canvas id="lossgraph">
</canvas>
<br />
<input id="buttoncg" type="submit" value="clear graph" onclick="clear_graph();"/>
</div>
</div>
<div style="clear:both;"></div>
</div>
<h1>Instantiate a Network and Trainer</h1>
<div>
<textarea id="newnet" style="width:100%; height:200px;"></textarea><br />
<input id="buttonnn" type="submit" value="change network" onclick="change_net();" style="width:200px;height:30px;"/>
</div>
<div class="divsec">
<h1>Network Visualization</h1>
<div id="visnet"></div>
</div>
<div class="divsec">
<h1>Example predictions on Test set</h1>
<div id="testset_acc"></div>
</div>
</div>
</body>
</html>