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-rw-r--r--nmt/nmt-1.js144
-rw-r--r--nmt/nmt.html23
-rw-r--r--nmt/nmt.js218
-rw-r--r--nmt/notes.txt19
4 files changed, 404 insertions, 0 deletions
diff --git a/nmt/nmt-1.js b/nmt/nmt-1.js
new file mode 100644
index 0000000..3d25713
--- /dev/null
+++ b/nmt/nmt-1.js
@@ -0,0 +1,144 @@
+/*var encode_time_step = function (src, model, lh, hidden_sizes, G, x=false)
+{
+ if (x)
+ G = new R.Graph(false);
+ /*else
+ var G = new R.Graph();*/
+ var inp = one_hot($vocab_sz_src, $vocab_src[src]);
+ var out = R.forwardRNN(G, model, hidden_sizes, inp, lh);
+
+ return [G, out];
+}
+
+var encode_update = function (model, G, dw)
+{
+ G.backward();
+ var solver = new R.Solver();
+ solver.step(model, 0.01, 0.0001, 5.0);
+}
+
+
+var decode_time_step = function (src, tgt, model, lh, solver, hidden_sizes, context, G)
+{
+ var inp = cat(one_hot($vocab_sz_tgt, $vocab_tgt[src]), context);
+ var out = R.forwardRNN(G, model, hidden_sizes, inp, lh);
+ var logprobs = out.o;
+ var probs = R.softmax(logprobs);
+ var target = $vocab_tgt[tgt];
+ cost = -Math.log(probs.w[target]);
+ logprobs.dw = probs.w;
+ logprobs.dw[target] -= 1;
+
+ return [model, cost, out, probs, G];
+}
+
+ var hidden_sizes = [20];
+/*var train = function (hidden_sizes)
+{*/
+ var encoder = R.initRNN($vocab_sz_src, hidden_sizes, $vocab_sz_src+100);
+ encoder["name"] = "encoder";
+ var decoder = R.initRNN($vocab_sz_src+100+$vocab_sz_tgt, hidden_sizes, $vocab_sz_tgt);
+ decoder["name"] = "decoder";
+
+ decoder["encoder_Wxh0"] = encoder["Wxh0"];
+ decoder["encoder_Whh0"] = encoder["Whh0"];
+ decoder["encoder_bhh0"] = encoder["bhh0"];
+ decoder["encoder_Whd"] = encoder["Whd"];
+ decoder["encoder_bd"] = encoder["bd"];
+
+ var solver = new R.Solver();
+ var G = new R.Graph();
+
+
+ lh = {};
+ costs = [];
+ var l = 0;
+ while (true)
+ {
+ var cost = 0.0;
+ for (var i=0; i<$data.length; i++) {
+ var last_encode_lh,enc_g;
+ for (var j=0; j<$data[i]["src"].length; j++) {
+ [G, lh] = encode_time_step($data[i]["src"][j], encoder, lh, hidden_sizes, G);
+ }
+ last_encode_lh = lh;
+
+ var context = last_encode_lh.o;
+ lh = {}
+ for (var k=0; k<$data[i]["tgt"].length; k++) {
+ var [decoder, c, lh, probs, G] = decode_time_step($data[i]["tgt"][k], $data[i]["tgt"][k+1], decoder, lh, solver, hidden_sizes, context, G);
+ cost += c;
+
+ G.backward();
+ solver.step(encoder, 0.01, 0.0001, 5.0);
+ solver.step(decoder, 0.01, 0.0001, 5.0);
+ }
+
+ //encode_update(encoder, enc_g, lh);
+
+ }
+ l++;
+ costs.push(cost);
+ if (stopping_criterion(costs[costs.length-2], cost, l))
+ break;
+ }
+
+ //return [encoder, decoder, costs];
+
+/*}*/
+
+
+//var x = ["<bos>", "das", "ist", "<eos>"];
+
+
+
+var k = 3;
+var kbest = [];
+
+for (var q=0; q < k; q++) {
+
+var x = $data[0]["src"];
+var _, cntxt;
+var o = {};
+for (var i=0; i<x.length; i++)
+ [_, o] = encode_time_step(x[i], encoder, o, hidden_sizes, null, true);
+cntxt = o.o;
+
+var w = "<bos>", lh;
+var prev = {};
+var str = "";
+var z = 0;
+var p = 0;
+ while (true) {
+ var G = new R.Graph(false);
+ var inp = cat(one_hot($vocab_sz_tgt, $vocab_tgt[w]), cntxt);
+ var lh = R.forwardRNN(G, decoder, hidden_sizes, inp, prev);
+ prev = lh;
+ var logprobs = lh.o;
+ var probs = R.softmax(logprobs);
+ var x = R.samplei(probs.w);
+ p += probs.w[x];
+ src = $ivocab_tgt[x];
+ str += " "+src;
+ z++;
+ if (src == "<eos>" || z==100)
+ break;
+ }
+ kbest.push( {"transl":str, "score":p } );
+}
+
+
+
+var main = function ()
+{
+ /*var hidden_sizes = [20];
+ var [encoder, decoder, costs] = train($data, hidden_sizes);*/
+
+ return false;
+}
+
+$(document).ready(function()
+{
+ main();
+});*/
+
diff --git a/nmt/nmt.html b/nmt/nmt.html
new file mode 100644
index 0000000..5cf60a5
--- /dev/null
+++ b/nmt/nmt.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="nmt.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/nmt/nmt.js b/nmt/nmt.js
new file mode 100644
index 0000000..6710c36
--- /dev/null
+++ b/nmt/nmt.js
@@ -0,0 +1,218 @@
+/*var $data = [{"src":["<bos>", "das", "ist", "ein", "kleines", "haus", "<eos>"],
+ "tgt":["<bos>", "this", "is", "a", "small", "house", "<eos>"]}
+];*/
+
+var $data = [{"src":["<bos>", "das", "<eos>"],
+ "tgt":["<bos>", "this", "<eos>"]}];//,
+/* {"src":["<bos>", "mein", "<eos>"],
+ "tgt":["<bos>", "my", "<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_src, $ivocab_src,$vocab_sz_src;
+var $vocab_tgt, $ivocab_tgt,$vocab_sz_tgt;
+[$vocab_src,$ivocab_src,$vocab_sz_src] = make_vocab($data.map(function(i){return i["src"]}));
+$vocab_sz_src++;
+[$vocab_tgt,$ivocab_tgt,$vocab_sz_tgt] = make_vocab($data.map(function(i){return i["tgt"]}));
+
+var one_hot = function (n, i)
+{
+ var m = new R.Mat(n,1);
+ m.set(i, 0, 1);
+
+ return m;
+}
+
+var stopping_criterion = function (c, d, iter, margin=0.01, max_iter=100)
+{
+ if (Math.abs(c-d) < margin || iter>=max_iter)
+ return true;
+ return false;
+}
+
+var cat = function (a, b)
+{
+ R.assert(a.d==1 && b.d==1);
+ var m = new R.Mat(a.n+b.n, 1);
+ var i;
+ for (i=0; i<a.n; i++) {
+ m.w[i] = a.w[i];
+ m.dw[i] = a.dw[i];
+ }
+ for (var j=0; j<b.n; j++) {
+ m.w[i+j] = b.w[j];
+ m.dw[i+j] = b.dw[j];
+ }
+
+ return m;
+}
+
+function shuffle(array) {
+ var currentIndex = array.length, temporaryValue, randomIndex;
+
+ // While there remain elements to shuffle...
+ while (0 !== currentIndex) {
+
+ // Pick a remaining element...
+ randomIndex = Math.floor(Math.random() * currentIndex);
+ currentIndex -= 1;
+
+ // And swap it with the current element.
+ temporaryValue = array[currentIndex];
+ array[currentIndex] = array[randomIndex];
+ array[randomIndex] = temporaryValue;
+ }
+
+ return array;
+}
+
+var hidden_sizes = [5];
+var encoder = R.initRNN($vocab_sz_src, hidden_sizes, $vocab_sz_src+100);
+encoder["name"] = "encoder";
+var decoder = R.initRNN($vocab_sz_src+100+$vocab_sz_tgt, hidden_sizes, $vocab_sz_tgt);
+decoder["name"] = "decoder";
+
+var solver = new R.Solver();
+var h = {};
+var inp = null;
+costs = [];
+var epoch = 0;
+
+decoder["encoder_Wxh0"] = encoder["Wxh0"];
+decoder["encoder_Whh0"] = encoder["Whh0"];
+decoder["encoder_bhh0"] = encoder["bhh0"];
+decoder["encoder_Whd"] = encoder["Whd"];
+decoder["encoder_bd"] = encoder["bd"];
+
+var x = encoder.Whh0.w[0]
+var y = decoder.Whh0.w[0]
+
+var enc_hs = [];
+var dec_hs = [];
+var dec_inps = [];
+
+while (true)
+{
+ $data = shuffle($data);
+ var c = 0.0;
+ for (var i=0; i<$data.length; i++) {
+ var last_enc_h;
+ var G = new R.Graph();
+ for (var j=0; j<$data[i]["src"].length; j++) {
+ var src = $data[i]["src"][j];
+ var inp = one_hot($vocab_sz_src, $vocab_src[src]);
+ var h = R.forwardRNN(G, encoder, hidden_sizes, inp, h);
+ last_enc_h = h;
+ enc_hs.push(h);
+ }
+
+ for (var z=0; z<G.backprop.length; z++) {
+ G.backprop[z]["mark"] = "encoder";
+ }
+
+ var context = last_enc_h.o;
+ var h = {}
+ for (var k=0; k<$data[i]["tgt"].length; k++) {
+ var src = $data[i]["tgt"][k],
+ tgt = $data[i]["tgt"][k+1];
+
+ inp = cat(one_hot($vocab_sz_tgt, $vocab_tgt[src]), context);
+ dec_inps.push(inp);
+ h = R.forwardRNN(G, decoder, hidden_sizes, inp, h);
+ dec_hs.push(h);
+
+ var logprobs = h.o;
+ var probs = R.softmax(logprobs);
+ var target = $vocab_tgt[tgt];
+ cost = -Math.log(probs.w[target]);
+ if (!cost || cost==Infinity) cost = 0; // hmmm
+ logprobs.dw = probs.w;
+ logprobs.dw[target] -= 1;
+ c += cost;
+
+ // update weights
+ G.backward();
+ // copy grads? decoder.Wxh0 -> encoder.Whd ?
+ for (var z=0; z<last_enc_h.o.dw.length; z++) {
+ last_enc_h.o.dw[z] = dec_inps[0].dw[z];
+ }
+ G.backward1();
+ //exit();
+ solver.step(decoder, 0.01, 0.000001, 5.0);
+ }
+ }
+
+ epoch++;
+ costs.push(c);
+ if (stopping_criterion(costs[costs.length-2], cost, epoch))
+ break;
+}
+
+
+
+var k = 10;
+var samples = [];
+
+for (var q=0; q < k; q++) {
+
+//var x = ["<bos>", "das", "ist", "ein", "kleines", "haus", "<eos>"];
+var x = ["<bos>", "das", "<eos>"];
+var _, cntxt;
+var o = {};
+var G = new R.Graph(false);
+for (var i=0; i<x.length; i++) {
+ var src = x[i];
+ var inp = one_hot($vocab_sz_src, $vocab_src[src]);
+ var h = R.forwardRNN(G, encoder, hidden_sizes, inp, h);
+}
+
+cntxt = h.o;
+
+var w = "<bos>", lh;
+var prev = {};
+var str = "";
+var z = 0;
+var p = 1;
+ while (true) {
+ var inp = cat(one_hot($vocab_sz_tgt, $vocab_tgt[w]), cntxt);
+ var lh = R.forwardRNN(G, decoder, hidden_sizes, inp, prev);
+ prev = lh;
+ var logprobs = lh.o;
+ var probs = R.softmax(logprobs);
+ var x = R.samplei(probs.w);
+ p *= probs.w[x];
+ src = $ivocab_tgt[x];
+ str += " "+src;
+ z++;
+ if (src == "<eos>" || z==100)
+ break;
+ }
+ samples.push( {"transl":str, "score":p } );
+}
+
+best = "";
+best_score = -999999;
+for (var q = 0; q<samples.length; q++) {
+ if (best_score < samples[q].score) {
+ best = samples[q].transl
+ }
+}
+
+alert(best);
+
diff --git a/nmt/notes.txt b/nmt/notes.txt
new file mode 100644
index 0000000..0bc0b9c
--- /dev/null
+++ b/nmt/notes.txt
@@ -0,0 +1,19 @@
+forward:
+ single tick
+ advance hidden state
+ add functions to G
+
+Graph
+ does backprop
+ sets .dw fields of matrices
+
+Solver
+ updates models
+ step cache doesn't matter
+
+input
+forward
+calc loss,set deriv in last layer
+backward
+solver
+