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/*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();
});*/
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