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| author | Patrick Simianer <p@simianer.de> | 2016-05-21 21:25:39 +0200 |
|---|---|---|
| committer | Patrick Simianer <p@simianer.de> | 2016-05-21 21:25:39 +0200 |
| commit | 500b99183517af88788098281237b70ad63453b9 (patch) | |
| tree | 1e3156a46d768844a2c6c442f5d19f4db6795e39 | |
| parent | 84d69349c5412ddebd2b1cbc9b29861ddf3a8169 (diff) | |
rnnlm
| -rw-r--r-- | rnnlm/rnnlm.html | 23 | ||||
| -rw-r--r-- | rnnlm/rnnlm.js | 151 |
2 files changed, 174 insertions, 0 deletions
diff --git a/rnnlm/rnnlm.html b/rnnlm/rnnlm.html new file mode 100644 index 0000000..8e137d7 --- /dev/null +++ b/rnnlm/rnnlm.html @@ -0,0 +1,23 @@ +<html> + <head> + <meta charset="utf-8"> + <script type="text/javascript" src="../external/jquery-1.8.3.min.js"></script> + <script type="text/javascript" src="../src/recurrent.js"></script> + <script type="text/javascript" src="rnnlm.js"> </script> + </head> + <body> + +<p>Training costs</p> +<ol id="costs"> +</ol> + +<p>Samples</p> +<ul id="samples"> +</ul> + +<p>Predict (context: "welcome to my")</p> +<p id="predict"></p> + + </body> +</html> + diff --git a/rnnlm/rnnlm.js b/rnnlm/rnnlm.js new file mode 100644 index 0000000..a8d2dc7 --- /dev/null +++ b/rnnlm/rnnlm.js @@ -0,0 +1,151 @@ +var $data = [["<bos>", "this", "is", "my", "house", "<eos>"], + ["<bos>", "welcome", "to", "my", "house", "<eos>"], + ["<bos>", "welcome", "to", "my", "tiny", "house", "<eos>"], + ["<bos>", "welcome", "to", "my", "little", "house", "<eos>"]]; + +var make_vocab = function ($data) +{ + var k = 0; + var vocab = {}, ivocab = []; + for (var i=0; i<$data.length; i++) { + for (var j=0; j<$data[i].length; j++) { + var w = $data[i][j]; + if (vocab[w]==undefined) { + vocab[w] = k; + ivocab[k] = w; + k++; + } + } + } + + return [vocab,ivocab,ivocab.length]; +} + +var $vocab, $ivocab,$vocab_sz; +[$vocab,$ivocab,$vocab_sz] = make_vocab($data); + +var one_hot = function (n, i) +{ + var m = new R.Mat(n,1); + m.set(i, 0, 1); + + return m; +} + +var time_step = function (src, tgt, model, lh, solver, hidden_sizes) +{ + var G = new R.Graph(); + var inp = one_hot($vocab_sz, $vocab[src]); + var out = R.forwardRNN(G, model, hidden_sizes, inp, lh); + var logprobs = out.o; + var probs = R.softmax(logprobs); + var target = $vocab[tgt]; + cost = -Math.log(probs.w[target]); + logprobs.dw = probs.w; + logprobs.dw[target] -= 1; + G.backward(); + solver.step(model, 0.01, 0.0001, 5.0); + + return [model, cost, out, probs]; +} + +var stopping_criterion = function (c, d, iter, margin=0.01, max_iter=100) +{ + if (Math.abs(c-d) < margin || iter>=max_iter) + return true; + return false; +} + +var train = function ($data, hidden_sizes) +{ + var model = R.initRNN($vocab_sz, hidden_sizes, $vocab_sz); + var solver = new R.Solver(); + lh = {}; + costs = []; + var k = 0; + while (true) + { + var cost = 0.0; + for (var i=0; i<$data.length; i++) { + for (var j=0; j<$data[i].length-1; j++) { + [model, c, lh, probs] = time_step($data[i][j], $data[i][j+1], model, lh, solver, hidden_sizes); + cost += c; + } + } + k++; + costs.push(cost); + if (stopping_criterion(costs[costs.length-2], cost, k)) + break; + } + + return [model, costs]; +} + +var generate = function (model, hidden_sizes) +{ + var prev = {}; + var str = "<bos>"; + var src = "<bos>"; + while (true) { + var G = new R.Graph(false); + var inp = one_hot($vocab_sz, $vocab[src]); + var lh = R.forwardRNN(G, model, hidden_sizes, inp, prev); + prev = lh; + var logprobs = lh.o; + var probs = R.softmax(logprobs); + var x = R.samplei(probs.w); + src = $ivocab[x]; + str += " "+src; + if (src == "<eos>") + break; + } + + return str; +} + +var predict = function (model, hidden_sizes) +{ + context = ["<bos>", "welcome", "to", "my"]; + var prev = {}, lh; + var logprobs, probs; + for (var i=0; i<context.length; i++) { + var G = new R.Graph(false); + var inp = one_hot($vocab_sz, $vocab[context[i]]); + lh = R.forwardRNN(G, model, hidden_sizes, inp, prev); + prev = lh; + logprobs = lh.o; + probs = R.softmax(logprobs); + } + + var maxi = R.maxi(probs.w); + + return [$ivocab[maxi], probs.w[maxi], probs.w]; +} + +var main = function () +{ + var hidden_sizes = [20]; + var [model, costs] = train($data, hidden_sizes); + + for (var i=0; i<costs.length; i++) + $("#costs").append('<li>'+costs[i]+'</li>'); + + var i=0; + while (1) { + var s = generate(model, hidden_sizes); + $("#samples").append("<li>"+s+"</li>"); + i++; + if (i==13) break; + } + + var [t,tp,dist] = predict(model, hidden_sizes); + $("#predict").html(t+" ("+tp+")"); + + return false; +} + +$(document).ready(function() +{ + main(); +}); + |