My implementation of CBOW in TensorFlow

CBOW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CBOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "%matplotlib inline\n",
    "import collections\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import tensorflow as tf\n",
    "import urllib\n",
    "import zipfile\n",
    "from matplotlib import pylab\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the data from the source website if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found and verified text8.zip\n"
     ]
    }
   ],
   "source": [
    "url = 'http://mattmahoney.net/dc/'\n",
    "\n",
    "def maybe_download(filename, expected_bytes):\n",
    "  \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
    "  if not os.path.exists(filename):\n",
    "    filename, _ = urllib.urlretrieve(url + filename, filename)\n",
    "  statinfo = os.stat(filename)\n",
    "  if statinfo.st_size == expected_bytes:\n",
    "    print 'Found and verified', filename\n",
    "  else:\n",
    "    print statinfo.st_size\n",
    "    raise Exception(\n",
    "      'Failed to verify ' + filename + '. Can you get to it with a browser?')\n",
    "  return filename\n",
    "\n",
    "filename = maybe_download('text8.zip', 31344016)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the data into a string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size 17005207\n"
     ]
    }
   ],
   "source": [
    "def read_data(filename):\n",
    "  f = zipfile.ZipFile(filename)\n",
    "  for name in f.namelist():\n",
    "    return f.read(name).split()\n",
    "  f.close()\n",
    "  \n",
    "words = read_data(filename)\n",
    "print 'Data size', len(words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the dictionary and replace rare words with UNK token."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common words (+UNK) [['UNK', 418391], ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764)]\n",
      "Sample data [5239, 3084, 12, 6, 195, 2, 3137, 46, 59, 156]\n"
     ]
    }
   ],
   "source": [
    "vocabulary_size = 50000\n",
    "\n",
    "def build_dataset(words):\n",
    "  count = [['UNK', -1]]\n",
    "  count.extend(collections.Counter(words).most_common(vocabulary_size - 1))\n",
    "  dictionary = dict()\n",
    "  for word, _ in count:\n",
    "    dictionary[word] = len(dictionary)\n",
    "  data = list()\n",
    "  unk_count = 0\n",
    "  for word in words:\n",
    "    if word in dictionary:\n",
    "      index = dictionary[word]\n",
    "    else:\n",
    "      index = 0  # dictionary['UNK']\n",
    "      unk_count = unk_count + 1\n",
    "    data.append(index)\n",
    "  count[0][1] = unk_count\n",
    "  reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) \n",
    "  return data, count, dictionary, reverse_dictionary\n",
    "\n",
    "data, count, dictionary, reverse_dictionary = build_dataset(words)\n",
    "print 'Most common words (+UNK)', count[:5]\n",
    "print 'Sample data', data[:10]\n",
    "del words  # Hint to reduce memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function to generate a training batch for the CBOW model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 1)\n",
      "5239 -> 3084\n",
      "anarchism -> originated\n",
      "12 -> 3084\n",
      "as -> originated\n",
      "3084 -> 12\n",
      "originated -> as\n",
      "6 -> 12\n",
      "a -> as\n",
      "12 -> 6\n",
      "as -> a\n",
      "195 -> 6\n",
      "term -> a\n",
      "6 -> 195\n",
      "a -> term\n",
      "2 -> 195\n",
      "of -> term\n"
     ]
    }
   ],
   "source": [
    "data_index = 0\n",
    "\n",
    "def generate_batch_cbow(batch_size, skip_window):\n",
    "  global data_index\n",
    "  context_window = 2 * skip_window # calculate the context_window - this is the total number of words around the target\n",
    "  assert batch_size % context_window == 0 # ensure the context window can be taken from the batch size\n",
    "  num_labels = batch_size / context_window # the number of labels is the how many context windows fit in the batch\n",
    "  batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "  labels = np.ndarray(shape=(num_labels, 1), dtype=np.int32)\n",
    "  span = 2 * skip_window + 1 # [ skip_window target skip_window ]\n",
    "  buffer = collections.deque(maxlen=span)\n",
    "  for _ in range(span):\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "  for i in range(num_labels):\n",
    "    target = skip_window  # target label at the center of the buffer\n",
    "    labels[i, 0] = buffer[target] # set he label\n",
    "    targets_to_avoid = [ skip_window ]\n",
    "    for j in range(context_window):\n",
    "      while target in targets_to_avoid:\n",
    "        target = random.randint(0, span - 1)\n",
    "      targets_to_avoid.append(target)\n",
    "      batch[i * context_window + j] = buffer[target]\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "  return batch, labels\n",
    "\n",
    "skip_window = 1\n",
    "batch, labels = generate_batch_cbow(8, skip_window)\n",
    "print np.shape(labels)\n",
    "for i in range(8):\n",
    "  print batch[i], '->', labels[i/(2*skip_window), 0]\n",
    "  print reverse_dictionary[batch[i]], '->', reverse_dictionary[labels[i/(2*skip_window), 0]]\n",
    "del skip_window # remove skip_window setting used for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  0  1  1  2  2  3  3  4  4  5  5  6  6  7  7  8  8  9  9 10 10 11 11 12\n",
      " 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24\n",
      " 25 25 26 26 27 27 28 28 29 29 30 30 31 31 32 32 33 33 34 34 35 35 36 36 37\n",
      " 37 38 38 39 39 40 40 41 41 42 42 43 43 44 44 45 45 46 46 47 47 48 48 49 49\n",
      " 50 50 51 51 52 52 53 53 54 54 55 55 56 56 57 57 58 58 59 59 60 60 61 61 62\n",
      " 62 63 63]\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "embedding_size = 128 # Dimension of the embedding vector.\n",
    "skip_window = 1 # How many words to consider left and right.\n",
    "# We pick a random validation set to sample nearest neighbors. here we limit the\n",
    "# validation samples to the words that have a low numeric ID, which by\n",
    "# construction are also the most frequent. \n",
    "valid_size = 16 # Random set of words to evaluate similarity on.\n",
    "valid_window = 100 # Only pick dev samples in the head of the distribution.\n",
    "valid_examples = np.array(random.sample(xrange(valid_window), valid_size))\n",
    "num_sampled = 32 # Number of negative examples to sample.\n",
    "\n",
    "## General defines\n",
    "context_window = 2 * skip_window\n",
    "num_labels = batch_size / context_window\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default():\n",
    "\n",
    "  # Input data.\n",
    "  train_dataset = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "  train_labels = tf.placeholder(tf.int32, shape=[num_labels, 1])\n",
    "  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n",
    "  \n",
    "  # Variables.\n",
    "  embeddings = tf.Variable(\n",
    "    tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
    "  softmax_weights = tf.Variable(\n",
    "    tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                         stddev=1.0 / math.sqrt(embedding_size)))\n",
    "  softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "  \n",
    "  # Model.\n",
    "  # Look up embeddings for inputs.\n",
    "  embed = tf.nn.embedding_lookup(embeddings, train_dataset)\n",
    "\n",
    "  # seq_ids only needs to be generated once so do this as a numpy array rather than a tensor.\n",
    "  seq_ids = np.zeros(batch_size, dtype=np.int32)\n",
    "  cur_id = -1\n",
    "  for i in range(batch_size):\n",
    "    if i % context_window == 0:\n",
    "      cur_id = cur_id + 1\n",
    "    seq_ids[i] = cur_id\n",
    "  print seq_ids\n",
    "  \n",
    "  # use segment_sum to add together the related words and reduce the output to be num_labels in size.\n",
    "  final_embed = tf.segment_sum(embed, seq_ids)\n",
    "  \n",
    "  # Compute the softmax loss, using a sample of the negative labels each time.\n",
    "  loss = tf.reduce_mean(\n",
    "    tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases, final_embed,\n",
    "                               train_labels, num_sampled, vocabulary_size))\n",
    "\n",
    "  # Optimizer.\n",
    "  optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)\n",
    "  \n",
    "  # Compute the similarity between minibatch examples and all embeddings.\n",
    "  # We use the cosine distance:\n",
    "  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n",
    "  normalized_embeddings = embeddings / norm\n",
    "  valid_embeddings = tf.nn.embedding_lookup(\n",
    "    normalized_embeddings, valid_dataset)\n",
    "  similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step 0 : 7.0780620575\n",
      "Nearest to some: brewer, bonds, bethel, equivocal, winningest, originated, cetacean, alvar,\n",
      "Nearest to two: nga, pandora, catabolism, secular, quorum, bunch, heinrich, rex,\n",
      "Nearest to while: enhancers, serrano, reefs, shunting, telemann, upload, fervent, bentsen,\n",
      "Nearest to which: mnr, sparingly, assassination, enamoured, ebrd, isostatic, financiers, artifice,\n",
      "Nearest to most: handel, jaguar, jaya, excelling, sideways, functionality, honeys, inquisitions,\n",
      "Nearest to may: paulus, preferences, marginal, panipat, robespierre, functionality, quotes, detail,\n",
      "Nearest to called: untimely, keeshond, sails, ditko, concerts, corning, iterated, office,\n",
      "Nearest to american: andersson, smoothing, weave, bactria, surrounds, notting, aggadic, photography,\n",
      "Nearest to are: subscripts, miracles, nowak, zarqawi, heroines, rages, kick, forgets,\n",
      "Nearest to nine: bulldogs, netbsd, howe, substrates, stagecoach, chanced, slaughterhouse, misconception,\n",
      "Nearest to from: sideband, haitian, raison, surges, enact, aaas, fallout, jfk,\n",
      "Nearest to the: grander, infoplease, paragraphs, regency, disinterested, humans, superficial, mjs,\n",
      "Nearest to between: recollection, pick, inflict, formulaic, haudenosaunee, quartic, succeeding, titicaca,\n",
      "Nearest to can: macrae, vicinity, outsiders, hairy, sunspots, oda, twice, archaeopteryx,\n",
      "Nearest to system: cloak, polynesia, barcelona, seclusion, hinayana, fraction, lipsius, unbiased,\n",
      "Nearest to this: leisure, byrne, zinc, recieved, baptismal, cocoa, seasoning, augmenting,\n",
      "Average loss at step 2000 : 3.68298477945\n",
      "Average loss at step 4000 : 3.13787364481\n",
      "Average loss at step 6000 : 3.03230214143\n",
      "Average loss at step 8000 : 2.88097445367\n",
      "Average loss at step 10000 : 2.79137699497\n",
      "Nearest to some: many, other, honors, envelopes, most, several, guarantee, sven,\n",
      "Nearest to two: six, three, four, nine, five, eight, seven, zero,\n",
      "Nearest to while: through, asanga, elric, flask, marsupial, unfounded, reefs, presbyterianism,\n",
      "Nearest to which: that, this, also, lies, it, who, what, shrews,\n",
      "Nearest to most: castile, more, glutamate, anarchism, many, fourteenth, some, undecidable,\n",
      "Nearest to may: can, could, will, would, must, saskatchewan, might, panipat,\n",
      "Nearest to called: dodgson, keeshond, sanjo, used, secondly, sails, ala, dukakis,\n",
      "Nearest to american: discouraging, english, candidates, vilas, andersson, thierry, jotham, and,\n",
      "Nearest to are: were, is, kaiserliche, have, can, was, plumb, include,\n",
      "Nearest to nine: eight, seven, six, five, zero, four, three, two,\n",
      "Nearest to from: gaborone, after, during, reaffirm, gout, ewe, on, laia,\n",
      "Nearest to the: its, a, this, his, sandhi, myasthenic, syrian, mullin,\n",
      "Nearest to between: sloths, with, braudel, succeeding, kissing, shapeshifting, degenerated, farmhouse,\n",
      "Nearest to can: would, will, may, could, should, must, might, does,\n",
      "Nearest to system: flutes, cloak, huston, chulainn, hominem, barcelona, overtones, coils,\n",
      "Nearest to this: it, the, which, miriam, canto, vittoria, a, wtro,\n",
      "Average loss at step 12000 : 2.77704590178\n",
      "Average loss at step 14000 : 2.7430245108\n",
      "Average loss at step 16000 : 2.54262745874\n",
      "Average loss at step 18000 : 2.56550521304\n",
      "Average loss at step 20000 : 2.65315301831\n",
      "Nearest to some: many, several, most, these, envelopes, all, any, honors,\n",
      "Nearest to two: five, six, four, three, one, eight, seven, zero,\n",
      "Nearest to while: although, during, though, through, when, unfounded, for, evas,\n",
      "Nearest to which: who, what, also, typically, now, chez, these, that,\n",
      "Nearest to most: some, more, glutamate, many, capitolina, fourteenth, less, domini,\n",
      "Nearest to may: can, could, would, will, might, must, should, cannot,\n",
      "Nearest to called: named, psychomotor, used, referred, sails, cheney, known, keeshond,\n",
      "Nearest to american: australian, istria, british, english, discouraging, termination, cerberus, vaults,\n",
      "Nearest to are: were, is, elevates, have, will, classicists, conics, decommissioned,\n",
      "Nearest to nine: eight, seven, six, four, zero, five, three, two,\n",
      "Nearest to from: into, membrane, after, around, fuses, during, in, and,\n",
      "Nearest to the: their, his, abandons, its, a, an, xs, another,\n",
      "Nearest to between: with, braudel, recollection, sloths, haudenosaunee, desk, degenerated, lorenzo,\n",
      "Nearest to can: would, may, could, will, must, should, might, cannot,\n",
      "Nearest to system: systems, coils, soft, bestiary, terminate, schuler, kropotkin, zuse,\n",
      "Nearest to this: it, miriam, another, greenspan, martina, decry, which, any,\n",
      "Average loss at step 22000 : 2.61656622361\n",
      "Average loss at step 24000 : 2.57401862925\n",
      "Average loss at step 26000 : 2.55876488201\n",
      "Average loss at step 28000 : 2.57108395195\n",
      "Average loss at step 30000 : 2.58505210897\n",
      "Nearest to some: many, several, various, these, all, most, any, those,\n",
      "Nearest to two: four, six, three, five, seven, eight, one, nine,\n",
      "Nearest to while: although, but, when, though, with, townshend, kr, spiegel,\n",
      "Nearest to which: this, practises, also, that, it, aru, myrddin, utah,\n",
      "Nearest to most: more, some, castile, among, fourteenth, less, intolerable, use,\n",
      "Nearest to may: would, can, will, could, must, should, might, cannot,\n",
      "Nearest to called: considered, known, referred, named, parisian, munch, conjunctive, used,\n",
      "Nearest to american: italian, australian, british, discouraging, english, russian, canadian, andersson,\n",
      "Nearest to are: were, being, have, is, iu, including, misconduct, elevates,\n",
      "Nearest to nine: eight, seven, six, five, four, zero, three, two,\n",
      "Nearest to from: under, into, after, before, during, wiccan, through, showing,\n",
      "Nearest to the: its, their, his, an, eia, pataki, these, a,\n",
      "Nearest to between: with, recollection, among, betting, benedictines, succeeding, against, degenerated,\n",
      "Nearest to can: could, may, would, will, must, should, might, cannot,\n",
      "Nearest to system: systems, zuse, gagauz, ean, px, canonic, darren, keeps,\n",
      "Nearest to this: it, which, rucker, miriam, another, what, some, saintly,\n",
      "Average loss at step 32000 : 2.581563499\n",
      "Average loss at step 34000 : 2.55444832517\n",
      "Average loss at step 36000 : 2.51041188031\n",
      "Average loss at step 38000 : 2.32207171647\n",
      "Average loss at step 40000 : 2.48453657191\n",
      "Nearest to some: many, these, any, several, different, various, certain, other,\n",
      "Nearest to two: three, four, six, seven, five, eight, one, zero,\n",
      "Nearest to while: although, after, for, when, and, but, unary, through,\n",
      "Nearest to which: that, also, usually, sometimes, this, who, often, what,\n",
      "Nearest to most: more, less, fourteenth, glutamate, devastation, earliest, titan, osip,\n",
      "Nearest to may: can, will, would, must, might, should, could, cannot,\n",
      "Nearest to called: referred, considered, beads, known, rde, travelled, com, there,\n",
      "Nearest to american: russian, british, australian, supernaturally, canadian, scholz, indian, german,\n",
      "Nearest to are: were, have, is, including, aeons, include, be, although,\n",
      "Nearest to nine: eight, seven, four, five, six, zero, three, two,\n",
      "Nearest to from: into, showing, after, through, saab, in, of, illuminate,\n",
      "Nearest to the: their, a, its, his, or, your, another, widowed,\n",
      "Nearest to between: among, ibid, with, unita, recollection, from, siena, sts,\n",
      "Nearest to can: will, may, must, could, would, should, might, cannot,\n",
      "Nearest to system: systems, zuse, charpentier, legislative, product, kurgan, doublespeak, landmasses,\n",
      "Nearest to this: which, that, greenspan, it, psk, some, what, the,\n",
      "Average loss at step 42000 : 2.48600063923\n",
      "Average loss at step 44000 : 2.48801102757\n",
      "Average loss at step 46000 : 2.48157630724\n",
      "Average loss at step 48000 : 2.39659479999\n",
      "Average loss at step 50000 : 2.40559511888\n",
      "Nearest to some: many, several, these, other, various, numerous, those, any,\n",
      "Nearest to two: four, three, five, one, six, zero, eight, nine,\n",
      "Nearest to while: when, although, though, where, including, evelyn, but, however,\n",
      "Nearest to which: that, this, now, also, often, what, who, encrusted,\n",
      "Nearest to most: more, less, devastation, many, fourteenth, among, some, use,\n",
      "Nearest to may: can, must, should, will, might, could, would, cannot,\n",
      "Nearest to called: named, referred, orlando, beads, les, conjunctive, used, abernethy,\n",
      "Nearest to american: indian, scholz, russian, canadian, agglutinative, austrian, italian, australian,\n",
      "Nearest to are: were, is, have, elevates, haredi, tend, do, contain,\n",
      "Nearest to nine: eight, six, three, zero, four, five, seven, two,\n",
      "Nearest to from: into, in, during, on, under, through, showing, before,\n",
      "Nearest to the: their, its, this, a, his, some, larsen, our,\n",
      "Nearest to between: with, among, against, recollection, ibid, hiller, tango, unita,\n",
      "Nearest to can: could, must, may, should, will, would, might, cannot,\n",
      "Nearest to system: systems, moi, recension, koko, zuse, mrna, management, landmasses,\n",
      "Nearest to this: which, the, it, rude, that, he, transcended, kay,\n",
      "Average loss at step 52000 : 2.45496852723\n",
      "Average loss at step 54000 : 2.43438997093\n",
      "Average loss at step 56000 : 2.46343041617\n",
      "Average loss at step 58000 : 2.38097675344\n",
      "Average loss at step 60000 : 2.39415344898\n",
      "Nearest to some: many, several, any, most, numerous, these, this, various,\n",
      "Nearest to two: three, four, one, six, five, eight, seven, nine,\n",
      "Nearest to while: although, when, after, before, despite, though, sclc, during,\n",
      "Nearest to which: that, what, this, derived, typically, still, hogarth, actinidia,\n",
      "Nearest to most: more, some, many, less, devastation, especially, glutamate, crusaders,\n",
      "Nearest to may: can, should, must, could, would, will, might, cannot,\n",
      "Nearest to called: named, considered, used, referred, beads, ala, included, known,\n",
      "Nearest to american: russian, australian, indian, german, british, canadian, scholz, italian,\n",
      "Nearest to are: were, have, including, is, elevates, mackerel, manicheanism, often,\n",
      "Nearest to nine: eight, six, seven, five, four, zero, three, two,\n",
      "Nearest to from: into, through, during, under, phosphors, within, wiccan, subordinates,\n",
      "Nearest to the: our, this, their, a, his, its, abandons, cumbric,\n",
      "Nearest to between: with, recollection, among, over, within, sloths, vaccinium, against,\n",
      "Nearest to can: may, could, would, must, should, cannot, will, might,\n",
      "Nearest to system: systems, network, hardware, economies, loss, management, process, line,\n",
      "Nearest to this: the, which, it, some, intravenous, there, a, that,\n",
      "Average loss at step 62000 : 2.21289241751\n",
      "Average loss at step 64000 : 2.22467358486\n",
      "Average loss at step 66000 : 2.38653177696\n",
      "Average loss at step 68000 : 2.40463131215\n",
      "Average loss at step 70000 : 2.33369058567\n",
      "Nearest to some: many, several, those, any, all, these, the, interpretative,\n",
      "Nearest to two: three, six, four, one, five, seven, eight, zero,\n",
      "Nearest to while: although, though, when, caricatured, isn, after, before, but,\n",
      "Nearest to which: what, that, typically, this, still, also, usually, however,\n",
      "Nearest to most: less, more, particularly, iteration, devastation, examination, especially, dynastic,\n",
      "Nearest to may: can, must, could, will, might, should, would, cannot,\n",
      "Nearest to called: named, known, considered, referred, laval, azhar, see, calvary,\n",
      "Nearest to american: indian, australian, adult, german, european, afghan, commencement, scholz,\n",
      "Nearest to are: were, is, have, be, include, containing, including, contain,\n",
      "Nearest to nine: eight, seven, six, five, four, zero, three, one,\n",
      "Nearest to from: through, into, via, wiccan, walla, by, within, under,\n",
      "Nearest to the: its, their, a, our, any, this, his, cpi,\n",
      "Nearest to between: recollection, among, with, against, observance, myocardial, bel, degenerated,\n",
      "Nearest to can: may, could, cannot, will, must, would, should, might,\n",
      "Nearest to system: systems, rhombic, device, network, plan, project, markers, schizophrenia,\n",
      "Nearest to this: which, it, isotopic, there, the, nesmith, that, some,\n",
      "Average loss at step 72000 : 2.36434135616\n",
      "Average loss at step 74000 : 2.33372141238\n",
      "Average loss at step 76000 : 2.33251439241\n",
      "Average loss at step 78000 : 2.33264574406\n",
      "Average loss at step 80000 : 2.35605267814\n",
      "Nearest to some: many, several, certain, various, numerous, both, all, most,\n",
      "Nearest to two: three, one, four, six, five, seven, eight, zero,\n",
      "Nearest to while: though, although, after, caricatured, before, when, camcorder, however,\n",
      "Nearest to which: that, what, this, usually, aru, mainly, also, actually,\n",
      "Nearest to most: more, many, especially, less, some, glutamate, among, castile,\n",
      "Nearest to may: must, might, can, could, will, should, would, cannot,\n",
      "Nearest to called: referred, considered, named, known, included, azhar, imperator, see,\n",
      "Nearest to american: british, german, australian, indian, french, russian, canadian, commencement,\n",
      "Nearest to are: were, have, is, although, containing, manicheanism, contain, include,\n",
      "Nearest to nine: eight, seven, six, five, four, three, zero, two,\n",
      "Nearest to from: via, into, wiccan, within, through, brundtland, between, penitent,\n",
      "Nearest to the: a, abandons, its, your, their, this, his, cassady,\n",
      "Nearest to between: among, within, throughout, with, under, across, over, in,\n",
      "Nearest to can: may, could, cannot, would, will, might, must, should,\n",
      "Nearest to system: systems, plan, project, device, program, model, service, koko,\n",
      "Nearest to this: which, it, the, that, unilaterally, another, effigy, actinidia,\n",
      "Average loss at step 82000 : 2.39013940632\n",
      "Average loss at step 84000 : 2.39545152342\n",
      "Average loss at step 86000 : 2.3624093712\n",
      "Average loss at step 88000 : 2.31394826466\n",
      "Average loss at step 90000 : 2.32365341792\n",
      "Nearest to some: many, any, most, all, those, certain, several, numerous,\n",
      "Nearest to two: three, six, four, five, seven, eight, zero, one,\n",
      "Nearest to while: before, although, when, during, after, until, despite, though,\n",
      "Nearest to which: that, actually, typically, also, erupt, what, usually, this,\n",
      "Nearest to most: some, more, especially, many, less, castile, glutamate, various,\n",
      "Nearest to may: must, should, could, can, might, will, would, cannot,\n",
      "Nearest to called: referred, named, considered, known, used, described, termed, azhar,\n",
      "Nearest to american: australian, indian, british, german, african, french, italian, european,\n",
      "Nearest to are: were, include, is, remain, have, contain, tend, can,\n",
      "Nearest to nine: eight, seven, six, five, four, zero, three, chapter,\n",
      "Nearest to from: through, into, across, under, brundtland, towards, via, before,\n",
      "Nearest to the: their, this, its, every, his, abandons, a, barre,\n",
      "Nearest to between: with, among, within, through, across, in, from, around,\n",
      "Nearest to can: cannot, could, must, may, should, might, will, would,\n",
      "Nearest to system: systems, plan, mantra, approach, device, structure, ullmann, victim,\n",
      "Nearest to this: the, it, another, which, thebes, some, critical, actinidia,\n",
      "Average loss at step 92000 : 2.36127738935\n",
      "Average loss at step 94000 : 2.20348089258\n",
      "Average loss at step 96000 : 2.32693400991\n",
      "Average loss at step 98000 : 2.2066062343\n",
      "Average loss at step 100000 : 2.31135605289\n",
      "Nearest to some: many, several, any, these, numerous, all, certain, those,\n",
      "Nearest to two: six, four, three, five, seven, eight, one, zero,\n",
      "Nearest to while: although, after, when, until, like, before, including, though,\n",
      "Nearest to which: that, this, what, typically, actually, usually, also, often,\n",
      "Nearest to most: more, less, especially, particularly, glutamate, extremely, castile, some,\n",
      "Nearest to may: should, must, can, could, might, would, will, cannot,\n",
      "Nearest to called: named, termed, referred, used, known, blondes, considered, played,\n",
      "Nearest to american: australian, italian, canadian, british, indian, greatest, agglutinative, leaked,\n",
      "Nearest to are: were, have, elevates, including, tend, remain, be, is,\n",
      "Nearest to nine: eight, seven, six, four, five, three, zero, two,\n",
      "Nearest to from: into, in, via, across, during, through, brundtland, grahame,\n",
      "Nearest to the: its, a, their, our, your, his, this, each,\n",
      "Nearest to between: with, among, into, hiller, across, through, burger, against,\n",
      "Nearest to can: could, may, would, might, should, cannot, will, must,\n",
      "Nearest to system: systems, machine, program, prophylaxis, ullmann, device, project, mrna,\n",
      "Nearest to this: which, what, it, the, another, greenspan, sundance, beyer,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 100001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  tf.initialize_all_variables().run()\n",
    "  print \"Initialized\"\n",
    "  average_loss = 0\n",
    "  for step in xrange(num_steps):\n",
    "    batch_data, batch_labels = generate_batch_cbow(batch_size, skip_window)\n",
    "    feed_dict = {train_dataset : batch_data, train_labels : batch_labels}\n",
    "    _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "    average_loss += l\n",
    "    if step % 2000 == 0:\n",
    "      if step > 0:\n",
    "        average_loss = average_loss / 2000\n",
    "      # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "      print \"Average loss at step\", step, \":\", average_loss\n",
    "      average_loss = 0\n",
    "    # note that this is expensive (~20% slowdown if computed every 500 steps)\n",
    "    if step % 10000 == 0:\n",
    "      sim = similarity.eval()\n",
    "      for i in xrange(valid_size):\n",
    "        valid_word = reverse_dictionary[valid_examples[i]]\n",
    "        top_k = 8 # number of nearest neighbors\n",
    "        nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
    "        log = \"Nearest to %s:\" % valid_word\n",
    "        for k in xrange(top_k):\n",
    "          close_word = reverse_dictionary[nearest[k]]\n",
    "          log = \"%s %s,\" % (log, close_word)\n",
    "        print log\n",
    "  final_embeddings = normalized_embeddings.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_points = 400\n",
    "\n",
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
    "two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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VKVu2LHv27CEhIeGa9w0JCWHKlClWQLd3717OnTt3xX2CgoKYPn26tcYtp+h4amoq9913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FXSeoNhbZ2dnYvn07Jk+erOymMOV4e3vD29u70uMRERFvvaw8/bZ8PU8NjVKF+oZM41HX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12243a7a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(embeddings, labels):\n",
    "  assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'\n",
    "  pylab.figure(figsize=(15,15))  # in inches\n",
    "  for i, label in enumerate(labels):\n",
    "    x, y = embeddings[i,:]\n",
    "    pylab.scatter(x, y)\n",
    "    pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',\n",
    "                   ha='right', va='bottom')\n",
    "  pylab.show()\n",
    "\n",
    "words = [reverse_dictionary[i] for i in xrange(1, num_points+1)]\n",
    "plot(two_d_embeddings, words)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}