5_word2vec.ipynb

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   "source": [
    "Deep Learning\n",
    "=============\n",
    "\n",
    "Assignment 5\n",
    "------------\n",
    "\n",
    "The goal of this assignment is to train a Word2Vec skip-gram model over [Text8](http://mattmahoney.net/dc/textdata) data."
   ]
  },
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   "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",
    "from __future__ import print_function\n",
    "import collections\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import tensorflow as tf\n",
    "import zipfile\n",
    "from matplotlib import pylab\n",
    "from six.moves import range\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aCjPJE944bkV"
   },
   "source": [
    "Download the data from the source website if necessary."
   ]
  },
  {
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   "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, _ = urlretrieve(url + filename, filename)\n",
    "  statinfo = os.stat(filename)\n",
    "  if statinfo.st_size == expected_bytes:\n",
    "    print('Found and verified %s' % 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)"
   ]
  },
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   "source": [
    "Read the data into a string."
   ]
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size 17005207\n"
     ]
    }
   ],
   "source": [
    "def read_data(filename):\n",
    "  \"\"\"Extract the first file enclosed in a zip file as a list of words\"\"\"\n",
    "  with zipfile.ZipFile(filename) as f:\n",
    "    data = tf.compat.as_str(f.read(f.namelist()[0])).split()\n",
    "  return data\n",
    "  \n",
    "words = read_data(filename)\n",
    "print('Data size %d' % len(words))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "Zdw6i4F8glpp"
   },
   "source": [
    "Build the dictionary and replace rare words with UNK token."
   ]
  },
  {
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   "execution_count": 4,
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   "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": [
    "Let's display the internal variables to better understand their structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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     "text": [
      "[5239, 3084, 12, 6, 195, 2, 3137, 46, 59, 156]\n",
      "[['UNK', 418391], ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764), ('in', 372201), ('a', 325873), ('to', 316376), ('zero', 264975), ('nine', 250430)]\n",
      "[('fawn', 45848), ('homomorphism', 9648), ('nordisk', 39343), ('nunnery', 36075), ('chthonic', 33554), ('sowell', 40562), ('sonja', 38175), ('showa', 32906), ('woods', 6263), ('hsv', 44222)]\n",
      "[(0, 'UNK'), (1, 'the'), (2, 'of'), (3, 'and'), (4, 'one'), (5, 'in'), (6, 'a'), (7, 'to'), (8, 'zero'), (9, 'nine')]\n"
     ]
    }
   ],
   "source": [
    "print(data[:10])\n",
    "print(count[:10])\n",
    "print(dictionary.items()[:10])\n",
    "print(reverse_dictionary.items()[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lFwoyygOmWsL"
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   "source": [
    "Function to generate a training batch for the skip-gram model."
   ]
  },
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against', 'early', 'working', 'class', 'radicals', 'including', 'the', 'diggers', 'of', 'the', 'english', 'revolution', 'and', 'the', 'sans', 'UNK', 'of', 'the', 'french', 'revolution', 'whilst', 'the', 'term']\n",
      "\n",
      "with num_skips = 2 and skip_window = 1:\n",
      "    batch: ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term', 'of', 'of', 'abuse', 'abuse', 'first', 'first', 'used', 'used']\n",
      "    labels: ['as', 'anarchism', 'a', 'originated', 'term', 'as', 'of', 'a', 'abuse', 'term', 'of', 'first', 'abuse', 'used', 'first', 'against']\n",
      "\n",
      "with num_skips = 4 and skip_window = 2:\n",
      "    batch: ['as', 'as', 'as', 'as', 'a', 'a', 'a', 'a', 'term', 'term', 'term', 'term', 'of', 'of', 'of', 'of']\n",
      "    labels: ['a', 'term', 'anarchism', 'originated', 'as', 'of', 'originated', 'term', 'as', 'abuse', 'a', 'of', 'term', 'a', 'first', 'abuse']\n",
      "\n",
      "with num_skips = 2 and skip_window = 1:\n",
      "    batch: ['as', 'as', 'a', 'a', 'term', 'term', 'of', 'of', 'abuse', 'abuse', 'first', 'first', 'used', 'used', 'against', 'against']\n",
      "    labels: ['originated', 'a', 'as', 'term', 'of', 'a', 'abuse', 'term', 'of', 'first', 'used', 'abuse', 'first', 'against', 'early', 'used']\n",
      "\n",
      "with num_skips = 4 and skip_window = 2:\n",
      "    batch: ['a', 'a', 'a', 'a', 'term', 'term', 'term', 'term', 'of', 'of', 'of', 'of', 'abuse', 'abuse', 'abuse', 'abuse']\n",
      "    labels: ['of', 'originated', 'as', 'term', 'a', 'of', 'as', 'abuse', 'abuse', 'a', 'first', 'term', 'first', 'of', 'used', 'term']\n"
     ]
    }
   ],
   "source": [
    "data_index = 0\n",
    "\n",
    "def generate_batch(batch_size, num_skips, skip_window):\n",
    "  global data_index\n",
    "  assert batch_size % num_skips == 0\n",
    "  assert num_skips <= 2 * skip_window\n",
    "  batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "  labels = np.ndarray(shape=(batch_size, 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(batch_size // num_skips):\n",
    "    target = skip_window  # target label at the center of the buffer\n",
    "    targets_to_avoid = [ skip_window ]\n",
    "    for j in range(num_skips):\n",
    "      while target in targets_to_avoid:\n",
    "        target = random.randint(0, span - 1)\n",
    "      targets_to_avoid.append(target)\n",
    "      batch[i * num_skips + j] = buffer[skip_window]\n",
    "      labels[i * num_skips + j, 0] = buffer[target]\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "  return batch, labels\n",
    "\n",
    "print('data:', [reverse_dictionary[di] for di in data[:32]])\n",
    "\n",
    "for num_skips, skip_window in [(2, 1), (4, 2)]:\n",
    "    data_index = 0\n",
    "    batch, labels = generate_batch(batch_size=16, num_skips=num_skips, skip_window=skip_window)\n",
    "    print('\\nwith num_skips = %d and skip_window = %d:' % (num_skips, skip_window))\n",
    "    print('    batch:', [reverse_dictionary[bi] for bi in batch])\n",
    "    print('    labels:', [reverse_dictionary[li] for li in labels.reshape(16)])\n",
    "    \n",
    "for num_skips, skip_window in [(2, 1), (4, 2)]:\n",
    "    data_index = 1\n",
    "    batch, labels = generate_batch(batch_size=16, num_skips=num_skips, skip_window=skip_window)\n",
    "    print('\\nwith num_skips = %d and skip_window = %d:' % (num_skips, skip_window))\n",
    "    print('    batch:', [reverse_dictionary[bi] for bi in batch])\n",
    "    print('    labels:', [reverse_dictionary[li] for li in labels.reshape(16)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the labels is a sliding random value of the word surrounding the words of the batch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is not obvious with the output above, but all the data are based on index, and not the word directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   6    6    6    6  195  195  195  195    2    2    2    2 3137 3137 3137\n",
      " 3137]\n",
      "[[   2]\n",
      " [3084]\n",
      " [  12]\n",
      " [ 195]\n",
      " [   6]\n",
      " [   2]\n",
      " [  12]\n",
      " [3137]\n",
      " [3137]\n",
      " [   6]\n",
      " [  46]\n",
      " [ 195]\n",
      " [  46]\n",
      " [   2]\n",
      " [  59]\n",
      " [ 195]]\n"
     ]
    }
   ],
   "source": [
    "print(batch)\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ofd1MbBuwiva"
   },
   "source": [
    "Train a skip-gram model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "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",
    "num_skips = 2 # How many times to reuse an input to generate a label.\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(range(valid_window), valid_size))\n",
    "num_sampled = 64 # Number of negative examples to sample.\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default(), tf.device('/cpu:0'):\n",
    "\n",
    "  # Input data.\n",
    "  train_dataset = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 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",
    "  # 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, 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))"
   ]
  },
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     "text": [
      "Initialized\n",
      "Average loss at step 0: 7.973985\n",
      "Nearest to people: preserved, shear, cheats, refusing, elca, transcontinental, galician, ppg,\n",
      "Nearest to called: suitability, extracellular, hangzhou, conducts, surrender, roadway, biographies, babylonians,\n",
      "Nearest to there: clicks, pumps, hipc, depictions, streaming, overwritten, conifers, urartu,\n",
      "Nearest to three: inadequately, idiopathic, ancona, epigram, northumbrian, rains, rquez, ldl,\n",
      "Nearest to such: cylindrical, kanal, disposition, cao, denotation, spreads, engraver, outspoken,\n",
      "Nearest to as: breakdancing, konqueror, sealing, eritreans, brubeck, olwen, denotation, marty,\n",
      "Nearest to or: charlestown, gleaming, subsumed, proposal, anisotropic, goodfellas, auvergne, schloss,\n",
      "Nearest to between: sufficed, privacy, amar, equates, peloponnesus, fuji, statuette, endowed,\n",
      "Nearest to into: usr, glucagon, responds, rupture, caeiro, galvani, alexandra, pitfalls,\n",
      "Nearest to an: copycat, darts, polytechnique, sartre, anglicanism, morrison, technologies, cortona,\n",
      "Nearest to with: mansi, amortized, abendana, baal, ifrcs, unpaved, nokia, opposite,\n",
      "Nearest to for: wadis, wide, optimal, accustomed, mezzo, davids, dns, retroviruses,\n",
      "Nearest to i: lorica, conn, guadalajara, feat, krzy, randomization, yat, repression,\n",
      "Nearest to in: transmigration, paralleled, uniquely, ouadda, coding, panama, worshipping, oems,\n",
      "Nearest to the: reflex, marcellinus, anchovies, ecm, parlement, fitzroy, cavers, angels,\n",
      "Nearest to years: eminem, fortunate, progression, signalled, elmore, disallowed, uvular, hotspot,\n",
      "Average loss at step 2000: 4.363197\n",
      "Average loss at step 4000: 3.864227\n",
      "Average loss at step 6000: 3.793599\n",
      "Average loss at step 8000: 3.684167\n",
      "Average loss at step 10000: 3.617109\n",
      "Nearest to people: preserved, transcontinental, mention, addressee, driver, myspace, flower, anarchists,\n",
      "Nearest to called: conducts, shielded, eeg, chapels, extracellular, sisak, hangzhou, wandered,\n",
      "Nearest to there: it, still, he, also, overwritten, not, they, pumps,\n",
      "Nearest to three: five, four, six, eight, two, seven, nine, zero,\n",
      "Nearest to such: kanal, known, processes, cylindrical, demo, dag, annoyed, disposition,\n",
      "Nearest to as: login, sensation, keel, neighboring, capitalization, belong, by, denotation,\n",
      "Nearest to or: and, characterizations, coerce, texans, s, organometallic, acct, subsidised,\n",
      "Nearest to between: forsyth, laminar, moire, fuji, endowed, banana, in, equates,\n",
      "Nearest to into: usr, caeiro, galvani, extradition, gerald, revealing, succeed, from,\n",
      "Nearest to an: grimoires, unsettling, the, copycat, slings, terrible, anglicanism, ectopic,\n",
      "Nearest to with: in, lilly, ohm, by, bayezid, mansi, ebrd, from,\n",
      "Nearest to for: rothbard, towards, over, mamertines, of, cha, dubna, theodor,\n",
      "Nearest to i: guadalajara, conn, juice, it, danner, malay, yetzirah, bra,\n",
      "Nearest to in: of, on, during, at, by, subtype, panama, greatest,\n",
      "Nearest to the: a, its, this, his, their, alamo, lavrenty, an,\n",
      "Nearest to years: eminem, nzenberg, rescued, signalled, disallowed, center, sutras, cesium,\n",
      "Average loss at step 12000: 3.604228\n",
      "Average loss at step 14000: 3.570798\n",
      "Average loss at step 16000: 3.409652\n",
      "Average loss at step 18000: 3.457004\n",
      "Average loss at step 20000: 3.536956\n",
      "Nearest to people: preserved, addressee, transcontinental, mention, anarchists, semicolon, members, myspace,\n",
      "Nearest to called: named, conducts, sisak, universally, spines, olney, eeg, chapels,\n",
      "Nearest to there: it, they, still, he, which, also, overwritten, pumps,\n",
      "Nearest to three: six, four, five, seven, two, eight, zero, one,\n",
      "Nearest to such: known, kanal, demo, well, liege, these, processes, cylindrical,\n",
      "Nearest to as: login, roo, adema, halting, murray, postmodernist, kashmiri, classes,\n",
      "Nearest to or: and, organometallic, hoare, than, stefano, texans, activate, visits,\n",
      "Nearest to between: in, with, forsyth, from, secular, selling, laminar, fuji,\n",
      "Nearest to into: usr, caeiro, galvani, from, extradition, gerald, recites, pitfalls,\n",
      "Nearest to an: the, unsettling, grimoires, slings, xxxix, sartre, expanse, mehr,\n",
      "Nearest to with: at, in, between, artifact, for, duals, bayezid, caa,\n",
      "Nearest to for: rothbard, towards, by, of, in, from, with, alene,\n",
      "Nearest to i: conn, brainstem, malay, guadalajara, brute, designs, danner, program,\n",
      "Nearest to in: at, on, during, from, between, panama, with, for,\n",
      "Nearest to the: its, an, their, a, this, any, nanaimo, his,\n",
      "Nearest to years: eminem, nzenberg, rescued, centuries, circularly, center, disallowed, hours,\n",
      "Average loss at step 22000: 3.499382\n",
      "Average loss at step 24000: 3.491234\n",
      "Average loss at step 26000: 3.482409\n",
      "Average loss at step 28000: 3.477295\n",
      "Average loss at step 30000: 3.501148\n",
      "Nearest to people: members, players, addressee, students, transcontinental, asparagales, carthusian, ammanati,\n",
      "Nearest to called: named, universally, polymorphic, objection, spines, chapels, olney, shielded,\n",
      "Nearest to there: they, it, still, he, jerky, often, pumps, this,\n",
      "Nearest to three: four, five, seven, six, eight, two, nine, zero,\n",
      "Nearest to such: known, well, these, liege, demo, spaceflights, kanal, processes,\n",
      "Nearest to as: became, puritanism, gediminas, halting, ambiguities, capitalization, by, clamp,\n",
      "Nearest to or: and, but, biotechnology, without, widowed, organometallic, than, gloster,\n",
      "Nearest to between: with, from, volunteers, secular, forsyth, drilled, in, swore,\n",
      "Nearest to into: from, galvani, usr, caeiro, extradition, through, over, in,\n",
      "Nearest to an: unsettling, tut, sartre, xxxix, busby, grimoires, desk, disguising,\n",
      "Nearest to with: between, in, by, secular, among, when, guybrush, ohm,\n",
      "Nearest to for: eduardo, of, from, alene, rothbard, in, dubna, gamecube,\n",
      "Nearest to i: ii, you, designs, we, guadalajara, iii, dial, they,\n",
      "Nearest to in: during, at, of, from, on, under, by, with,\n",
      "Nearest to the: their, its, his, smile, some, delson, chromatography, many,\n",
      "Nearest to years: centuries, hours, eminem, circularly, center, year, thence, times,\n",
      "Average loss at step 32000: 3.503519\n",
      "Average loss at step 34000: 3.490309\n",
      "Average loss at step 36000: 3.457498\n",
      "Average loss at step 38000: 3.294679\n",
      "Average loss at step 40000: 3.427992\n",
      "Nearest to people: members, someone, addressee, students, players, codepoint, ver, ammanati,\n",
      "Nearest to called: named, considered, conducts, overhaul, tachi, universally, olney, enver,\n",
      "Nearest to there: they, it, still, often, which, he, now, also,\n",
      "Nearest to three: four, two, five, six, seven, eight, nine, zero,\n",
      "Nearest to such: known, well, many, these, including, kanal, liege, described,\n",
      "Nearest to as: capitalization, gediminas, when, sphinx, bathtub, commonly, expectancies, petition,\n",
      "Nearest to or: and, than, organometallic, a, splash, com, deflecting, coerce,\n",
      "Nearest to between: with, drilled, volunteers, swore, melting, forsyth, secular, jun,\n",
      "Nearest to into: galvani, through, from, during, over, bankhead, iron, usr,\n",
      "Nearest to an: tut, grimoires, probus, unsettling, roamed, busby, sartre, robots,\n",
      "Nearest to with: between, secular, by, when, in, caa, sophist, ruck,\n",
      "Nearest to for: in, dubna, of, without, kilda, to, is, towards,\n",
      "Nearest to i: we, you, they, ii, t, he, it, designs,\n",
      "Nearest to in: during, of, and, from, for, on, without, at,\n",
      "Nearest to the: this, a, its, his, their, shilton, celestines, gulden,\n",
      "Nearest to years: hours, centuries, times, eminem, year, days, anu, thence,\n",
      "Average loss at step 42000: 3.436011\n",
      "Average loss at step 44000: 3.454509\n",
      "Average loss at step 46000: 3.448703\n",
      "Average loss at step 48000: 3.359070\n",
      "Average loss at step 50000: 3.382532\n",
      "Nearest to people: men, members, children, addressee, someone, austronesian, students, leave,\n",
      "Nearest to called: named, overhaul, conducts, tachi, polymorphic, universally, nan, chapels,\n",
      "Nearest to there: they, it, he, still, hattie, jerky, believed, she,\n",
      "Nearest to three: six, four, seven, eight, two, five, nine, zero,\n",
      "Nearest to such: well, known, these, many, follows, spaceflights, liege, regarded,\n",
      "Nearest to as: became, capitalization, equity, francophones, martius, walters, puritanism, stipend,\n",
      "Nearest to or: and, organometallic, than, gloster, hagbard, conjectured, com, whose,\n",
      "Nearest to between: with, from, in, volunteers, melting, forsyth, crabbe, maximilien,\n",
      "Nearest to into: through, from, during, galvani, usr, over, caeiro, afghan,\n",
      "Nearest to an: unsettling, grimoires, probus, rooted, roamed, polytechnique, officio, sartre,\n",
      "Nearest to with: between, among, by, caa, clinician, ruck, mcdonough, when,\n",
      "Nearest to for: dubna, towards, against, glasnost, without, hermeticism, siemens, rhotic,\n",
      "Nearest to i: we, you, ii, they, brasses, t, danner, inbound,\n",
      "Nearest to in: during, on, of, within, since, from, throughout, including,\n",
      "Nearest to the: its, this, their, his, architecturally, a, epilepsy, some,\n",
      "Nearest to years: times, days, centuries, hours, months, year, decades, nattiez,\n",
      "Average loss at step 52000: 3.436867\n",
      "Average loss at step 54000: 3.426783\n",
      "Average loss at step 56000: 3.439991\n",
      "Average loss at step 58000: 3.400027\n",
      "Average loss at step 60000: 3.390829\n",
      "Nearest to people: men, members, children, someone, students, players, semicolon, scientists,\n",
      "Nearest to called: named, considered, used, overhaul, chapels, conducts, universally, prost,\n",
      "Nearest to there: they, it, now, still, today, this, he, often,\n",
      "Nearest to three: four, six, five, eight, two, seven, nine, zero,\n",
      "Nearest to such: known, well, these, regarded, spaceflights, including, described, liege,\n",
      "Nearest to as: puritanism, biomass, halting, em, belong, misdemeanor, capitalization, netsplit,\n",
      "Nearest to or: and, organometallic, but, than, without, gloster, volunteering, whit,\n",
      "Nearest to between: with, in, among, crabbe, within, goodall, volunteers, maximilien,\n",
      "Nearest to into: through, from, over, in, galvani, within, presbyter, goth,\n",
      "Nearest to an: unsettling, grimoires, roamed, probus, xxxix, upheld, cabbage, colophon,\n",
      "Nearest to with: between, when, among, by, duals, caa, in, although,\n",
      "Nearest to for: of, including, alene, against, dubna, coughing, messerschmitt, bureaucrat,\n",
      "Nearest to i: you, we, ii, they, brasses, danner, she, t,\n",
      "Nearest to in: during, within, of, from, on, between, among, since,\n",
      "Nearest to the: a, their, its, each, epilepsy, this, nanaimo, stimulates,\n",
      "Nearest to years: centuries, days, times, hours, months, decades, year, nattiez,\n",
      "Average loss at step 62000: 3.236575\n",
      "Average loss at step 64000: 3.254499\n",
      "Average loss at step 66000: 3.404697\n",
      "Average loss at step 68000: 3.394414\n",
      "Average loss at step 70000: 3.359899\n",
      "Nearest to people: men, children, members, someone, students, players, sides, semicolon,\n",
      "Nearest to called: named, considered, overhaul, known, reflector, used, referred, universally,\n",
      "Nearest to there: they, it, now, still, she, usually, often, he,\n",
      "Nearest to three: six, five, four, seven, two, eight, nine, zero,\n",
      "Nearest to such: well, these, known, regarded, liege, spaceflights, many, described,\n",
      "Nearest to as: capitalization, segmented, equity, puritanism, sphinx, is, corals, when,\n",
      "Nearest to or: and, organometallic, fried, your, than, coupe, objecting, patton,\n",
      "Nearest to between: with, within, among, rebelling, drilled, from, jun, separatist,\n",
      "Nearest to into: through, from, within, toward, attachments, bogota, destabilize, usr,\n",
      "Nearest to an: unsettling, probus, robots, bleaching, upheld, roamed, busby, diffuse,\n",
      "Nearest to with: between, embittered, caa, ruck, when, confidential, jun, duals,\n",
      "Nearest to for: including, without, if, alene, in, towards, gamecube, against,\n",
      "Nearest to i: you, we, ii, she, they, t, brasses, g,\n",
      "Nearest to in: during, within, of, on, including, until, by, for,\n",
      "Nearest to the: their, this, its, a, any, these, your, some,\n",
      "Nearest to years: days, months, hours, centuries, times, decades, year, minutes,\n",
      "Average loss at step 72000: 3.373863\n",
      "Average loss at step 74000: 3.346582\n",
      "Average loss at step 76000: 3.316972\n",
      "Average loss at step 78000: 3.355325\n",
      "Average loss at step 80000: 3.380022\n",
      "Nearest to people: men, children, students, members, someone, players, women, individuals,\n",
      "Nearest to called: named, considered, used, known, universally, overhaul, referred, prost,\n",
      "Nearest to there: it, they, he, often, still, she, now, believed,\n",
      "Nearest to three: four, six, seven, five, eight, two, nine, zero,\n",
      "Nearest to such: well, regarded, known, these, spaceflights, including, follows, described,\n",
      "Nearest to as: massive, hexen, like, puritanism, equity, hao, gne, bugzilla,\n",
      "Nearest to or: and, organometallic, gloster, while, com, coupe, than, objecting,\n",
      "Nearest to between: within, with, jun, among, over, drilled, goodall, through,\n",
      "Nearest to into: through, from, within, attachments, towards, toward, during, using,\n",
      "Nearest to an: unsettling, grimoires, desk, probus, busby, slovakian, dhtml, cabbage,\n",
      "Nearest to with: between, when, duals, barnum, caa, formalization, among, embittered,\n",
      "Nearest to for: kilda, prefaced, towards, alene, after, gamecube, siemens, against,\n",
      "Nearest to i: you, ii, t, we, danner, g, iii, dial,\n",
      "Nearest to in: during, within, on, until, at, throughout, of, since,\n",
      "Nearest to the: a, its, his, tofu, mellitus, their, episcopi, this,\n",
      "Nearest to years: days, times, decades, months, hours, centuries, year, minutes,\n",
      "Average loss at step 82000: 3.404380\n",
      "Average loss at step 84000: 3.408689\n",
      "Average loss at step 86000: 3.388645\n",
      "Average loss at step 88000: 3.351643\n",
      "Average loss at step 90000: 3.363047\n",
      "Nearest to people: men, children, students, women, authors, players, christians, someone,\n",
      "Nearest to called: named, considered, used, referred, known, universally, overhaul, said,\n",
      "Nearest to there: it, they, still, he, we, now, she, believed,\n",
      "Nearest to three: four, two, five, seven, eight, six, zero, nine,\n",
      "Nearest to such: well, known, regarded, spaceflights, these, certain, follows, selves,\n",
      "Nearest to as: nuisance, puritanism, notoc, kampf, sphinx, gernika, biomass, varuna,\n",
      "Nearest to or: and, organometallic, than, houghton, but, polyester, eightfold, lulu,\n",
      "Nearest to between: with, within, among, from, jun, through, under, drilled,\n",
      "Nearest to into: through, from, within, under, attachments, caeiro, between, cowes,\n",
      "Nearest to an: cabbage, probus, unsettling, desk, elysium, kickboxing, slovakian, shemini,\n",
      "Nearest to with: between, by, in, jun, when, prost, among, ruck,\n",
      "Nearest to for: against, including, of, without, after, while, bureaucrat, when,\n",
      "Nearest to i: you, g, we, t, ii, danner, dial, iii,\n",
      "Nearest to in: during, within, of, under, at, and, on, throughout,\n",
      "Nearest to the: its, this, his, a, conti, any, each, their,\n",
      "Nearest to years: days, hours, decades, months, centuries, times, minutes, year,\n",
      "Average loss at step 92000: 3.398833\n",
      "Average loss at step 94000: 3.253199\n",
      "Average loss at step 96000: 3.356412\n",
      "Average loss at step 98000: 3.240982\n",
      "Average loss at step 100000: 3.359134\n",
      "Nearest to people: children, men, students, women, players, authors, speakers, individuals,\n",
      "Nearest to called: named, referred, overhaul, used, considered, www, misplaced, known,\n",
      "Nearest to there: they, it, still, we, he, now, generally, often,\n",
      "Nearest to three: four, two, five, seven, eight, six, zero, nine,\n",
      "Nearest to such: known, well, regarded, these, spaceflights, follows, certain, many,\n",
      "Nearest to as: sphinx, puritanism, notoc, varuna, equity, hotbed, segmented, gru,\n",
      "Nearest to or: and, than, organometallic, coupe, houghton, adding, cafeteria, objecting,\n",
      "Nearest to between: within, among, with, around, drilled, jun, goodall, rebelling,\n",
      "Nearest to into: through, within, from, during, towards, off, across, attachments,\n",
      "Nearest to an: grimoires, probus, unsettling, another, bleaching, copycat, roamed, xxxix,\n",
      "Nearest to with: between, including, when, using, jets, embittered, in, to,\n",
      "Nearest to for: without, when, hgp, while, after, during, if, dubna,\n",
      "Nearest to i: you, we, ii, danner, v, t, they, g,\n",
      "Nearest to in: during, throughout, within, until, on, among, of, from,\n",
      "Nearest to the: a, their, your, lodge, his, its, this, our,\n",
      "Nearest to years: days, hours, decades, centuries, months, times, year, minutes,\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 range(num_steps):\n",
    "    batch_data, batch_labels = generate_batch(\n",
    "      batch_size, num_skips, 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 %d: %f' % (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 range(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 range(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": "markdown",
   "metadata": {},
   "source": [
    "This is what an embedding looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.02051404 -0.01841166 -0.1244709   0.05074964 -0.06100134 -0.00200008\n",
      "  0.11170887 -0.08766419  0.02409515  0.1101989   0.16667137 -0.00165691\n",
      " -0.14681569 -0.11056326 -0.03933677 -0.02154945 -0.10062175 -0.05628699\n",
      "  0.01922635  0.1639808   0.01684226 -0.0540074  -0.03108865  0.01777779\n",
      "  0.04706197 -0.01260165 -0.29304644 -0.0302702  -0.10937209  0.03770837\n",
      "  0.15830405  0.06936062  0.16194052 -0.03061437  0.04542041 -0.07499804\n",
      " -0.00841922  0.01684443  0.07148984 -0.06406043  0.09492613 -0.09933698\n",
      " -0.00687152 -0.08409496  0.01544465 -0.11804878  0.14000055 -0.09653874\n",
      "  0.02137009  0.01145039 -0.06205352  0.02977284 -0.01883831 -0.11986119\n",
      "  0.05494772  0.05486212 -0.00120243 -0.02710927 -0.0569164  -0.11858083\n",
      " -0.01223189  0.09930295  0.09467366 -0.02073516 -0.12372935  0.00244062\n",
      "  0.05924704  0.14948699 -0.1119219  -0.07551062  0.00928106 -0.01831606\n",
      " -0.05067647 -0.10931925  0.0324928   0.00191521 -0.01634123  0.08661727\n",
      "  0.12392872 -0.01820764 -0.16330577 -0.24632891  0.02271111  0.00542232\n",
      "  0.00801045 -0.00690781  0.16911526  0.01407609  0.02365589 -0.07911306\n",
      "  0.07899767  0.16927634  0.07133543  0.05735794  0.08887751  0.19834489\n",
      "  0.17564225 -0.08074389  0.17213371  0.04207939  0.02885242  0.02857619\n",
      " -0.05606408 -0.12006876 -0.07494419 -0.10423107 -0.05341928 -0.09745979\n",
      " -0.13150343  0.05612956 -0.08829316  0.12047683  0.00201026 -0.09098013\n",
      "  0.00637597  0.08963603 -0.02186407  0.01224304 -0.02592809  0.05893643\n",
      " -0.0640655   0.00615067 -0.07741468  0.10470562  0.01330675  0.08730693\n",
      " -0.0361954  -0.05102392]\n"
     ]
    }
   ],
   "source": [
    "print(final_embeddings[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the values are abstract, there is practical meaning of the them. Moreover, the final embeddings are normalized as you can see here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(np.square(final_embeddings[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": false,
    "id": "jjJXYA_XzV79"
   },
   "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": 13,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 4763,
     "status": "ok",
     "timestamp": 1445965465525,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2f1ffade4c9f20de",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "o_e0D_UezcDe",
    "outputId": "df22e4a5-e8ec-4e5e-d384-c6cf37c68c34"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DBzEMg6ioKKxWK56enkyf/jYmUxAeHsG4uITRsmVTHn/8cTp37kxmZqZ9ovD4+HhiY2Pp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\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RF3n77f97roqDL1++nObNm+Ph4cGwYcPIyMjA3t7eODr56aef0qRJE9q0aUOfPn344osv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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12ac5c5d0>"
      ]
     },
     "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 range(1, num_points+1)]\n",
    "plot(two_d_embeddings, words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QB5EFrBnpNnc"
   },
   "source": [
    "---\n",
    "\n",
    "Problem\n",
    "-------\n",
    "\n",
    "An alternative to skip-gram is another Word2Vec model called [CBOW](http://arxiv.org/abs/1301.3781) (Continuous Bag of Words). In the CBOW model, instead of predicting a context word from a word vector, you predict a word from the sum of all the word vectors in its context. Implement and evaluate a CBOW model trained on the text8 dataset.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the continuous bag of words, the train inputs are slightly different from the skip-gram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against', 'early', 'working', 'class', 'radicals', 'including', 'the']\n",
      "\n",
      "with bag_window = 1:\n",
      "    batch: [['anarchism', 'as'], ['originated', 'a'], ['as', 'term'], ['a', 'of']]\n",
      "    labels: ['originated', 'as', 'a', 'term']\n",
      "\n",
      "with bag_window = 2:\n",
      "    batch: [['anarchism', 'originated', 'a', 'term'], ['originated', 'as', 'term', 'of'], ['as', 'a', 'of', 'abuse'], ['a', 'term', 'abuse', 'first']]\n",
      "    labels: ['as', 'a', 'term', 'of']\n"
     ]
    }
   ],
   "source": [
    "data_index = 0\n",
    "\n",
    "def generate_batch(batch_size, bag_window):\n",
    "  global data_index\n",
    "  span = 2 * bag_window + 1 # [ bag_window target bag_window ]\n",
    "  batch = np.ndarray(shape=(batch_size, span - 1), dtype=np.int32)\n",
    "  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)  \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(batch_size):\n",
    "    # just for testing\n",
    "    buffer_list = list(buffer)\n",
    "    labels[i, 0] = buffer_list.pop(bag_window)\n",
    "    batch[i] = buffer_list\n",
    "    # iterate to the next buffer\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "  return batch, labels\n",
    "\n",
    "print('data:', [reverse_dictionary[di] for di in data[:16]])\n",
    "\n",
    "for bag_window in [1, 2]:\n",
    "  data_index = 0\n",
    "  batch, labels = generate_batch(batch_size=4, bag_window=bag_window)\n",
    "  print('\\nwith bag_window = %d:' % (bag_window))  \n",
    "  print('    batch:', [[reverse_dictionary[w] for w in bi] for bi in batch])  \n",
    "  print('    labels:', [reverse_dictionary[li] for li in labels.reshape(4)])  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the instruction change on the loss function, with ``reduce_sum`` to sum the word vectors in the context: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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",
    "###num_skips = 2 # How many times to reuse an input to generate a label.\n",
    "bag_window = 2 # 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(range(valid_window), valid_size))\n",
    "num_sampled = 64 # Number of negative examples to sample.\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default(), tf.device('/cpu:0'):\n",
    "\n",
    "  # Input data.\n",
    "  train_dataset = tf.placeholder(tf.int32, shape=[batch_size, bag_window * 2])\n",
    "  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 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",
    "  embeds = tf.nn.embedding_lookup(embeddings, train_dataset)\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, tf.reduce_sum(embeds, 1),\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": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step 0: 7.983871\n",
      "Nearest to an: ambiguously, tennessee, cats, regents, numerals, inpatient, madre, krak,\n",
      "Nearest to d: woolly, atypical, climatic, textual, caapi, sungorus, collusion, disarmament,\n",
      "Nearest to one: modi, optimizations, williams, overtake, finned, glissando, inciting, gasoline,\n",
      "Nearest to may: fogo, kwanzaa, rivaled, doi, cavour, cante, tansley, incorruptible,\n",
      "Nearest to has: pollock, literatures, existential, evelyn, inspector, ventricle, sweeps, tending,\n",
      "Nearest to while: embouchure, analogue, petomane, etymology, etienne, referencing, starvation, hinds,\n",
      "Nearest to will: waived, introduced, lithography, purge, nss, fabian, torquato, helicopters,\n",
      "Nearest to nine: dore, promise, ailments, forty, boast, tarleton, courtney, apo,\n",
      "Nearest to but: fucked, catalyzed, uart, commemorating, minicomputers, theatrically, sombart, meshech,\n",
      "Nearest to are: bosch, handedly, dummies, bearings, repulsive, allegedly, eph, nk,\n",
      "Nearest to this: shore, smog, dwell, injuries, poet, engraver, wafers, leviathan,\n",
      "Nearest to when: derives, postfix, shimei, warlords, gorbachev, obtain, whale, prophesies,\n",
      "Nearest to see: symbolized, algebra, fanpage, undesirable, jena, exerting, sierpinski, albans,\n",
      "Nearest to up: cumming, thresholds, organic, ullman, manoeuvre, cnt, maguey, philanthropic,\n",
      "Nearest to time: connie, amnesia, photoelectric, archdiocese, tennant, auburn, turbofan, rubicon,\n",
      "Nearest to is: silicon, organisms, honesty, ghola, duckworth, glaze, wealthier, sheldon,\n",
      "Average loss at step 2000: 7.571952\n",
      "Average loss at step 4000: 4.962989\n",
      "Average loss at step 6000: 5.264466\n",
      "Average loss at step 8000: 4.020034\n",
      "Average loss at step 10000: 3.823763\n",
      "Nearest to an: the, a, expense, contends, thumb, stroke, bad, ontario,\n",
      "Nearest to d: b, chicxulub, qf, kotzebue, balls, berenguer, apo, mustelids,\n",
      "Nearest to one: crannog, fmln, accented, vivian, adl, baths, nesting, amedeo,\n",
      "Nearest to may: can, should, could, would, might, must, will, cannot,\n",
      "Nearest to has: had, have, was, is, agilent, finegold, lockyer, schottky,\n",
      "Nearest to while: and, of, creed, than, thus, secundus, width, athanasian,\n",
      "Nearest to will: would, must, creed, fish, at, oneself, thus, to,\n",
      "Nearest to nine: eight, seven, five, six, zero, iga, three, apollo,\n",
      "Nearest to but: however, meso, yamaha, korean, encounter, which, does, injustice,\n",
      "Nearest to are: is, include, fish, creed, of, were, possibly, at,\n",
      "Nearest to this: what, nigeria, stings, physiol, chili, dalek, danielle, husayn,\n",
      "Nearest to when: where, fertilize, relative, sovereignty, because, oxidise, by, awake,\n",
      "Nearest to see: utica, metonic, rimfire, subgroups, declarative, ratsiraka, commoner, bani,\n",
      "Nearest to up: ranges, azores, thresholds, mano, imax, application, down, migrated,\n",
      "Nearest to time: least, prosciutto, oxidant, hellish, place, end, kennel, year,\n",
      "Nearest to is: are, was, of, holds, include, will, at, a,\n",
      "Average loss at step 12000: 3.904979\n",
      "Average loss at step 14000: 3.926719\n",
      "Average loss at step 16000: 3.572611\n",
      "Average loss at step 18000: 3.749069\n",
      "Average loss at step 20000: 3.532388\n",
      "Nearest to an: the, a, this, umlauts, doc, sorel, stad, melaka,\n",
      "Nearest to d: b, balls, artifact, mahavira, broadly, zf, currency, remainders,\n",
      "Nearest to one: sourcewatch, compaq, inflation, glissando, durch, artisan, amendment, duration,\n",
      "Nearest to may: would, can, will, must, could, should, might, latvia,\n",
      "Nearest to has: had, have, having, is, agilent, cordillera, pods, was,\n",
      "Nearest to while: when, among, although, with, and, but, like, willie,\n",
      "Nearest to will: would, must, can, could, should, might, may, cannot,\n",
      "Nearest to nine: zero, eight, five, seven, two, vigor, appellation, complexities,\n",
      "Nearest to but: however, although, and, though, while, paradigmatic, than, asleep,\n",
      "Nearest to are: were, is, include, teleological, have, jfif, was, mutate,\n",
      "Nearest to this: it, what, that, any, an, bismarck, a, beekeeping,\n",
      "Nearest to when: if, where, while, because, during, before, since, tidewater,\n",
      "Nearest to see: declarative, subgroups, lian, rimfire, known, ligase, mosquitia, utica,\n",
      "Nearest to up: down, out, back, intelligibility, him, organic, spotters, azores,\n",
      "Nearest to time: least, end, year, rubicon, period, subpixels, aemilius, unsurprising,\n",
      "Nearest to is: was, are, became, were, has, wired, does, refers,\n",
      "Average loss at step 22000: 3.588760\n",
      "Average loss at step 24000: 4.065557\n",
      "Average loss at step 26000: 3.706526\n",
      "Average loss at step 28000: 3.455289\n",
      "Average loss at step 30000: 3.427290\n",
      "Nearest to an: another, this, ncipe, cryonics, propel, kanagawa, sade, albeit,\n",
      "Nearest to d: zf, r, lysis, intercity, tua, b, unnamed, rubble,\n",
      "Nearest to one: francs, seven, dominus, two, statism, paymaster, addition, toe,\n",
      "Nearest to may: can, would, will, could, must, should, might, cannot,\n",
      "Nearest to has: had, have, having, was, agilent, unionism, smiths, includes,\n",
      "Nearest to while: although, berlin, afc, wall, cow, sse, convertible, mandelbrot,\n",
      "Nearest to will: would, can, could, may, might, should, must, cannot,\n",
      "Nearest to nine: eight, seven, late, five, th, derry, four, six,\n",
      "Nearest to but: however, and, than, see, although, button, afterward, which,\n",
      "Nearest to are: were, is, possibly, johnstone, chad, cow, afc, berlin,\n",
      "Nearest to this: it, what, that, which, piecewise, stoicism, a, an,\n",
      "Nearest to when: because, before, however, where, episcopate, since, after, by,\n",
      "Nearest to see: but, prematurely, scandinavian, rimfire, include, staircase, cartier, deductions,\n",
      "Nearest to up: down, off, out, back, appendix, intelligibility, him, animated,\n",
      "Nearest to time: least, period, year, end, start, beginning, unsurprising, times,\n",
      "Nearest to is: mandelbrot, wall, berlin, finland, are, cow, chad, afc,\n",
      "Average loss at step 32000: 3.131431\n",
      "Average loss at step 34000: 3.298668\n",
      "Average loss at step 36000: 3.281630\n",
      "Average loss at step 38000: 3.314990\n",
      "Average loss at step 40000: 3.389277\n",
      "Nearest to an: another, the, chechen, amway, ascend, sade, vanzetti, a,\n",
      "Nearest to d: b, zf, acorn, intercity, hitters, gentlemen, xilinx, shifter,\n",
      "Nearest to one: inappropriately, headquartered, dominus, granny, irreconcilable, recollections, sweep, smuggle,\n",
      "Nearest to may: can, will, should, would, could, must, might, does,\n",
      "Nearest to has: have, had, having, is, was, includes, contains, agilent,\n",
      "Nearest to while: however, although, though, and, but, when, jakobson, before,\n",
      "Nearest to will: would, could, may, must, should, can, does, might,\n",
      "Nearest to nine: eight, seven, six, september, five, four, late, bb,\n",
      "Nearest to but: and, however, than, though, although, while, see, asks,\n",
      "Nearest to are: were, is, was, include, being, have, exist, mutate,\n",
      "Nearest to this: it, which, the, any, what, piecewise, a, another,\n",
      "Nearest to when: if, where, while, before, since, by, because, laughs,\n",
      "Nearest to see: introduce, but, undamaged, rimfire, include, known, called, cartier,\n",
      "Nearest to up: off, out, down, forward, appendix, intelligibility, veered, migrated,\n",
      "Nearest to time: least, end, beginning, rubicon, base, times, manner, bijection,\n",
      "Nearest to is: was, are, has, became, remains, makes, includes, were,\n",
      "Average loss at step 42000: 3.285913\n",
      "Average loss at step 44000: 3.211095\n",
      "Average loss at step 46000: 3.221205\n",
      "Average loss at step 48000: 3.127906\n",
      "Average loss at step 50000: 3.156376\n",
      "Nearest to an: another, reconsideration, eden, the, stad, any, mohamed, turboprop,\n",
      "Nearest to d: b, asin, pricing, origami, entourage, odie, whispering, peacemaker,\n",
      "Nearest to one: enroll, pumps, thither, honor, favor, papen, taya, batavians,\n",
      "Nearest to may: can, must, will, could, should, would, might, cannot,\n",
      "Nearest to has: had, have, having, contains, is, includes, agilent, agreeable,\n",
      "Nearest to while: although, when, though, and, however, if, where, before,\n",
      "Nearest to will: must, may, should, could, would, can, might, does,\n",
      "Nearest to nine: eight, seven, six, zero, august, january, four, five,\n",
      "Nearest to but: although, though, than, while, include, monies, ucla, formulaic,\n",
      "Nearest to are: were, is, was, include, exist, being, mugabe, awaiting,\n",
      "Nearest to this: which, what, it, piecewise, another, that, epictetus, hypnotic,\n",
      "Nearest to when: while, where, because, if, although, since, before, during,\n",
      "Nearest to see: called, include, includes, rimfire, known, islami, operates, deductions,\n",
      "Nearest to up: off, out, them, back, forward, together, down, spotters,\n",
      "Nearest to time: least, period, times, night, end, subpixels, moment, height,\n",
      "Nearest to is: was, are, remains, were, became, makes, has, contains,\n",
      "Average loss at step 52000: 3.190961\n",
      "Average loss at step 54000: 3.200878\n",
      "Average loss at step 56000: 3.025388\n",
      "Average loss at step 58000: 3.134411\n",
      "Average loss at step 60000: 3.130078\n",
      "Nearest to an: another, this, the, talons, doc, waza, a, sedition,\n",
      "Nearest to d: berlin, cow, school, acid, afc, creed, joseph, dissociation,\n",
      "Nearest to one: two, favor, afghanistan, terms, finlandization, adl, lipi, allein,\n",
      "Nearest to may: can, must, will, might, should, would, could, cannot,\n",
      "Nearest to has: had, have, having, is, includes, contains, agilent, isambard,\n",
      "Nearest to while: although, though, however, when, and, including, if, before,\n",
      "Nearest to will: would, may, must, could, might, can, should, cannot,\n",
      "Nearest to nine: eight, seven, six, five, zero, late, january, september,\n",
      "Nearest to but: farad, examines, dissociation, somewhere, ghanima, afc, sire, perhaps,\n",
      "Nearest to are: were, is, observances, include, use, was, suborders, many,\n",
      "Nearest to this: it, which, another, longwave, lasorda, epictetus, wasp, she,\n",
      "Nearest to when: if, where, though, while, since, before, although, after,\n",
      "Nearest to see: include, rimfire, known, surfing, provides, backers, minnesotans, references,\n",
      "Nearest to up: off, out, back, veered, intelligibility, together, appendix, forward,\n",
      "Nearest to time: least, night, moment, period, start, point, times, end,\n",
      "Nearest to is: was, are, becomes, has, remains, exists, makes, became,\n",
      "Average loss at step 62000: 3.094546\n",
      "Average loss at step 64000: 3.011245\n",
      "Average loss at step 66000: 3.013084\n",
      "Average loss at step 68000: 3.084206\n",
      "Average loss at step 70000: 3.157549\n",
      "Nearest to an: another, a, the, mohamed, ruth, no, reconsideration, disrepair,\n",
      "Nearest to d: b, unobstructed, zf, liza, teborg, intercity, militants, acorn,\n",
      "Nearest to one: recollections, nchen, offside, adl, hultsfred, deakin, dilemmas, heraclius,\n",
      "Nearest to may: can, should, must, might, could, would, will, cannot,\n",
      "Nearest to has: had, have, having, is, includes, was, agilent, contains,\n",
      "Nearest to while: scrip, and, though, montage, although, level, without, thus,\n",
      "Nearest to will: would, must, should, could, can, may, might, cannot,\n",
      "Nearest to nine: eight, th, zero, july, january, april, september, kapitan,\n",
      "Nearest to but: though, and, although, however, which, while, until, that,\n",
      "Nearest to are: were, is, include, exist, have, semmelweis, contain, was,\n",
      "Nearest to this: another, what, some, which, that, aryeh, lasorda, beekeeping,\n",
      "Nearest to when: if, before, because, after, where, although, since, however,\n",
      "Nearest to see: allows, rimfire, include, includes, putnam, provides, gives, enables,\n",
      "Nearest to up: off, down, out, forth, back, veered, forward, gaius,\n",
      "Nearest to time: least, night, speed, summer, moment, beginning, end, period,\n",
      "Nearest to is: was, are, makes, remains, provides, becomes, exists, includes,\n",
      "Average loss at step 72000: 3.039138\n",
      "Average loss at step 74000: 2.935000\n",
      "Average loss at step 76000: 3.065070\n",
      "Average loss at step 78000: 3.059412\n",
      "Average loss at step 80000: 2.909747\n",
      "Nearest to an: another, rhyming, the, durand, doc, ascend, bau, turboprop,\n",
      "Nearest to d: b, c, zf, unobstructed, teborg, newington, blotter, symbolizing,\n",
      "Nearest to one: intrauterine, gentile, involved, spectacularly, ericaceae, underpinnings, sociopathic, two,\n",
      "Nearest to may: can, might, could, should, must, would, will, cannot,\n",
      "Nearest to has: having, had, have, is, includes, contains, maintains, was,\n",
      "Nearest to while: although, though, where, and, however, but, trypanosomiasis, including,\n",
      "Nearest to will: would, must, could, can, should, might, may, cannot,\n",
      "Nearest to nine: eight, seven, oi, agile, august, berlin, ghanima, methodologies,\n",
      "Nearest to but: though, however, although, see, and, or, while, which,\n",
      "Nearest to are: were, is, was, include, exist, have, semmelweis, including,\n",
      "Nearest to this: what, which, it, that, beekeeping, a, the, another,\n",
      "Nearest to when: if, because, though, before, after, although, where, finally,\n",
      "Nearest to see: but, include, provides, rimfire, gives, rada, known, surfing,\n",
      "Nearest to up: off, down, together, quickly, empty, back, me, decisions,\n",
      "Nearest to time: night, least, period, moment, manner, battle, arrival, antidisestablishmentarianism,\n",
      "Nearest to is: was, are, makes, remains, exists, becomes, has, includes,\n",
      "Average loss at step 82000: 3.032796\n",
      "Average loss at step 84000: 2.954726\n",
      "Average loss at step 86000: 2.974028\n",
      "Average loss at step 88000: 3.004254\n",
      "Average loss at step 90000: 2.893942\n",
      "Nearest to an: another, the, reconsideration, doc, xvii, any, a, turboprop,\n",
      "Nearest to d: disrepair, village, h, reckoning, emitting, berlin, dissociation, hindi,\n",
      "Nearest to one: two, racer, harman, shaker, qh, wallach, attenuation, resulting,\n",
      "Nearest to may: can, might, should, could, would, will, must, cannot,\n",
      "Nearest to has: had, have, having, includes, contains, is, was, rb,\n",
      "Nearest to while: although, though, and, including, where, programmes, amongst, trypanosomiasis,\n",
      "Nearest to will: would, can, could, might, should, cannot, may, did,\n",
      "Nearest to nine: eight, seven, mid, late, six, five, four, zero,\n",
      "Nearest to but: although, however, though, see, declarative, and, than, inundated,\n",
      "Nearest to are: were, is, include, exist, was, contain, denote, have,\n",
      "Nearest to this: which, it, another, what, any, lasorda, beekeeping, piecewise,\n",
      "Nearest to when: if, after, because, before, where, though, by, actually,\n",
      "Nearest to see: includes, rimfire, include, provides, ponty, but, schechter, gives,\n",
      "Nearest to up: off, down, together, back, forward, out, forth, spotters,\n",
      "Nearest to time: least, height, odds, moment, beginning, period, corner, look,\n",
      "Nearest to is: was, remains, are, makes, becomes, exists, includes, became,\n",
      "Average loss at step 92000: 2.952135\n",
      "Average loss at step 94000: 2.945493\n",
      "Average loss at step 96000: 2.800004\n",
      "Average loss at step 98000: 2.569032\n",
      "Average loss at step 100000: 2.762196\n",
      "Nearest to an: another, inpatient, doc, stad, xvii, the, reconsideration, csc,\n",
      "Nearest to d: b, pricing, kocher, wheat, deansgate, tightening, berenguer, wiesbaden,\n",
      "Nearest to one: implicated, allein, crannog, excerpted, hoop, heraclius, cocos, irreconcilable,\n",
      "Nearest to may: might, should, can, could, would, will, must, cannot,\n",
      "Nearest to has: had, have, having, contains, includes, is, holds, takes,\n",
      "Nearest to while: although, where, however, but, savory, though, until, when,\n",
      "Nearest to will: would, should, could, might, can, may, cannot, must,\n",
      "Nearest to nine: eight, seven, five, six, zero, july, four, june,\n",
      "Nearest to but: however, although, and, though, than, while, or, nor,\n",
      "Nearest to are: were, is, was, include, exist, contain, semmelweis, have,\n",
      "Nearest to this: which, some, another, every, piecewise, fateful, any, that,\n",
      "Nearest to when: if, where, because, although, though, before, that, while,\n",
      "Nearest to see: include, allow, references, known, allows, say, but, schechter,\n",
      "Nearest to up: off, down, together, out, back, forward, forth, sends,\n",
      "Nearest to time: moment, stage, period, least, start, night, distances, subpixels,\n",
      "Nearest to is: was, are, makes, exists, includes, remains, contains, provides,\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 range(num_steps):\n",
    "    batch_data, batch_labels = generate_batch(\n",
    "      batch_size, bag_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 %d: %f' % (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 range(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 range(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": 17,
   "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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W+Pr62o/liKqVl7pfU6ZM4auvvmLOnDksW7bMXmDlwvP8tn6BFStW0Lx58yLr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\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0lAAMCwtL28HOyL0D1CZK0tx01DyU+sLj8RATEwNNTc0a62QPQlOmTMGUKVMAVD60yjh/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\n5R1LXjRs2BB7e3uOHDmCr68va9asKcc3KIQQtZsM5ES15ezsXCIM527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lC2PGjGHZsmXk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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122527890>"
      ]
     },
     "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 range(1, num_points+1)]\n",
    "plot(two_d_embeddings, words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some clusters are less obvious (like the standalone characters), but it clearly totaly works!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does your CBOW model perform compared to the given Word2Vec model? (to be answered)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the first sight, they look similar. The CBOW is a more compact."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "default_view": {},
   "name": "5_word2vec.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
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