From 7ef9733c608889b99335cb9e7db86262a6e7e528 Mon Sep 17 00:00:00 2001
From: Patrick Simianer <p@simianer.de>
Date: Fri, 4 Aug 2017 16:18:12 +0200
Subject: nmt wip

---
 nmt/nmt-1.js  | 144 ++++++++++++++++++++++++++++++++++++++
 nmt/nmt.html  |  23 +++++++
 nmt/nmt.js    | 218 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 nmt/notes.txt |  19 +++++
 4 files changed, 404 insertions(+)
 create mode 100644 nmt/nmt-1.js
 create mode 100644 nmt/nmt.html
 create mode 100644 nmt/nmt.js
 create mode 100644 nmt/notes.txt

(limited to 'nmt')

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
+
-- 
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