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",
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    "id": "aCjPJE944bkV"
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   "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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     "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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   "source": [
    "Build the dictionary and replace rare words with UNK token."
   ]
  },
  {
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     "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": {
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    "id": "lFwoyygOmWsL"
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   "source": [
    "Function to generate a training batch for the skip-gram model."
   ]
  },
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     "text": [
      "data: ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first']\n",
      "\n",
      "with num_skips = 2 and skip_window = 1:\n",
      "    batch: ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term']\n",
      "    labels: ['anarchism', 'as', 'originated', 'a', 'term', 'as', 'of', 'a']\n",
      "\n",
      "with num_skips = 4 and skip_window = 2:\n",
      "    batch: ['as', 'as', 'as', 'as', 'a', 'a', 'a', 'a']\n",
      "    labels: ['anarchism', 'a', 'originated', 'term', 'as', 'of', 'originated', 'term']\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, 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",
    "def generate_batch_cbow(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[:8]])\n",
    "\n",
    "for num_skips, skip_window in [(2, 1), (4, 2)]:\n",
    "    data_index = 0\n",
    "    batch, labels = generate_batch(batch_size=8, 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(8)])\n",
    "\n",
    "for bag_window in [1, 2]:\n",
    "  data_index = 0\n",
    "  batch, labels = generate_batch_cbow(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": {
    "colab_type": "text",
    "id": "Ofd1MbBuwiva"
   },
   "source": [
    "Train a skip-gram model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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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",
    "  # Note: The optimizer will optimize the softmax_weights AND the embeddings.\n",
    "  # This is because the embeddings are defined as a variable quantity and the\n",
    "  # optimizer's `minimize` method will by default modify all variable quantities \n",
    "  # that contribute to the tensor it is passed.\n",
    "  # See docs on `tf.train.Optimizer.minimize()` for more details.\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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     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step 0: 7.789532\n",
      "Nearest to was: abbreviating, saltpeter, stimulated, merciful, mount, aesthetic, courtesy, flamenco,\n",
      "Nearest to new: lemnos, spectacularly, hobby, canceling, decreed, breastfeeding, arthurian, gordon,\n",
      "Nearest to can: foreigners, miscarriages, haploid, congested, obe, agathocles, easiest, lewiston,\n",
      "Nearest to b: fleetwood, grim, pedersen, dicots, deviant, soundtrack, ioannes, restroom,\n",
      "Nearest to these: potential, closeup, duchy, superimposed, letterboxes, guardians, nepal, qx,\n",
      "Nearest to four: watson, booting, gough, sucrose, coke, culp, ayahuasca, mascot,\n",
      "Nearest to when: onto, jesu, christa, summertime, senussi, watched, violent, gallium,\n",
      "Nearest to zero: repertoire, primates, unionism, accompaniments, takes, ecuador, surpassing, farms,\n",
      "Nearest to into: hypnotherapy, prd, iridium, ire, pocket, wetter, barrios, minucius,\n",
      "Nearest to over: presents, qui, glagolitic, dramatists, romana, rubies, karen, yours,\n",
      "Nearest to had: gallery, prostate, renn, obiang, digs, pogrom, revenge, good,\n",
      "Nearest to one: fairies, impunity, domo, librarians, driving, funchal, frontal, roe,\n",
      "Nearest to its: fret, windward, sesame, rallies, enrollment, appreciably, polished, chifley,\n",
      "Nearest to most: engaged, summand, exhausts, roo, underarm, joss, sculpted, humility,\n",
      "Nearest to i: mcbeal, methuen, coprocessor, configured, brink, prep, ariadne, toolkit,\n",
      "Nearest to american: pathway, geographically, southey, nobi, lignite, metropolitan, velde, vapors,\n",
      "Average loss at step 2000: 4.349237\n",
      "Average loss at step 4000: 3.863916\n",
      "Average loss at step 6000: 3.782121\n",
      "Average loss at step 8000: 3.688278\n",
      "Average loss at step 10000: 3.607068\n",
      "Nearest to was: is, were, had, has, by, be, nut, ammonium,\n",
      "Nearest to new: lemnos, spectacularly, ibanez, congress, gordon, hobby, drunk, pageant,\n",
      "Nearest to can: would, must, may, will, tdi, should, lewiston, fortifications,\n",
      "Nearest to b: d, fleetwood, kelvin, deviant, jimmu, cno, bukhara, claudius,\n",
      "Nearest to these: scrap, potential, superimposed, sauerkraut, several, moseley, many, cosa,\n",
      "Nearest to four: eight, three, six, five, seven, nine, two, zero,\n",
      "Nearest to when: senussi, however, that, does, onto, gravity, honduran, refute,\n",
      "Nearest to zero: five, nine, eight, seven, four, six, three, two,\n",
      "Nearest to into: to, iridium, from, minucius, by, oxfordshire, catastrophe, sorcerers,\n",
      "Nearest to over: euler, archelaus, glagolitic, presents, rubies, older, karen, dramatists,\n",
      "Nearest to had: has, was, have, maecenas, nsw, were, gershom, lucie,\n",
      "Nearest to one: two, three, eight, four, six, five, nine, seven,\n",
      "Nearest to its: his, their, the, each, a, unprepared, chief, ducal,\n",
      "Nearest to most: sergius, richmond, jotham, bennington, councillors, commonwealth, gorilla, aggressor,\n",
      "Nearest to i: formosa, newly, prep, britannia, benoit, he, lied, dcc,\n",
      "Nearest to american: pinned, and, krupp, pathway, malaysian, endian, wikis, aoun,\n",
      "Average loss at step 12000: 3.605397\n",
      "Average loss at step 14000: 3.568304\n",
      "Average loss at step 16000: 3.407166\n",
      "Average loss at step 18000: 3.452152\n",
      "Average loss at step 20000: 3.539939\n",
      "Nearest to was: is, were, became, has, had, nut, makes, does,\n",
      "Nearest to new: ibanez, lemnos, congress, drunk, path, spectacularly, distant, france,\n",
      "Nearest to can: would, may, will, must, could, should, to, cannot,\n",
      "Nearest to b: d, triplet, fleetwood, ideological, recorders, jimmu, and, deviant,\n",
      "Nearest to these: many, several, some, moseley, scrap, such, superimposed, all,\n",
      "Nearest to four: seven, five, eight, six, three, nine, two, zero,\n",
      "Nearest to when: if, but, however, does, after, conjugations, that, since,\n",
      "Nearest to zero: five, six, four, seven, three, eight, nine, two,\n",
      "Nearest to into: from, iridium, catastrophe, by, with, adair, minucius, before,\n",
      "Nearest to over: euler, karen, archelaus, lep, hebrew, older, glagolitic, pokey,\n",
      "Nearest to had: has, have, was, were, been, are, maecenas, nsw,\n",
      "Nearest to one: two, three, seven, four, eight, six, five, nine,\n",
      "Nearest to its: their, his, each, the, emergency, brilliant, convicts, bello,\n",
      "Nearest to most: more, many, councillors, some, alastair, commonwealth, jotham, bennington,\n",
      "Nearest to i: ii, iii, touchstone, you, champaign, they, hmg, rod,\n",
      "Nearest to american: french, german, british, english, wikis, courant, catv, tagore,\n",
      "Average loss at step 22000: 3.495784\n",
      "Average loss at step 24000: 3.489893\n",
      "Average loss at step 26000: 3.482023\n",
      "Average loss at step 28000: 3.475128\n",
      "Average loss at step 30000: 3.496911\n",
      "Nearest to was: is, had, were, has, became, nut, been, be,\n",
      "Nearest to new: path, distant, lemnos, persian, congress, marrying, janissaries, drunk,\n",
      "Nearest to can: may, would, will, could, must, should, cannot, might,\n",
      "Nearest to b: d, hx, elgin, sailboat, jellyfish, abijah, geographers, xvi,\n",
      "Nearest to these: some, many, several, their, such, they, are, moseley,\n",
      "Nearest to four: five, six, seven, eight, three, nine, two, zero,\n",
      "Nearest to when: if, after, where, but, however, before, although, alchemist,\n",
      "Nearest to zero: five, eight, seven, four, nine, three, six, two,\n",
      "Nearest to into: from, under, adair, catastrophe, before, iridium, averse, prd,\n",
      "Nearest to over: lep, euler, archelaus, karen, romana, pokey, contraceptive, older,\n",
      "Nearest to had: has, have, was, were, is, ausgleich, been, became,\n",
      "Nearest to one: two, seven, four, five, nine, three, eight, six,\n",
      "Nearest to its: their, the, his, vocals, anhydrous, sterne, bmp, molotov,\n",
      "Nearest to most: more, some, many, jotham, theological, commonwealth, principle, aggressor,\n",
      "Nearest to i: ii, you, they, iii, formosa, touchstone, snapper, rod,\n",
      "Nearest to american: english, french, british, german, courant, russian, catv, def,\n",
      "Average loss at step 32000: 3.502066\n",
      "Average loss at step 34000: 3.491088\n",
      "Average loss at step 36000: 3.456689\n",
      "Average loss at step 38000: 3.296245\n",
      "Average loss at step 40000: 3.428398\n",
      "Nearest to was: is, became, had, were, has, nut, be, been,\n",
      "Nearest to new: path, marrying, distant, second, persian, lemnos, optimization, henceforth,\n",
      "Nearest to can: may, will, could, would, must, should, might, cannot,\n",
      "Nearest to b: d, c, j, h, yards, footballer, n, ch,\n",
      "Nearest to these: some, many, several, they, both, geodesic, such, moseley,\n",
      "Nearest to four: six, three, eight, five, two, seven, nine, one,\n",
      "Nearest to when: if, after, while, but, envisaged, with, thorndike, before,\n",
      "Nearest to zero: seven, five, nine, eight, six, three, four, two,\n",
      "Nearest to into: from, adair, before, iridium, catastrophe, through, repackaged, averse,\n",
      "Nearest to over: lep, euler, through, scans, melaka, emmy, between, adhered,\n",
      "Nearest to had: has, have, was, were, surged, been, became, ausgleich,\n",
      "Nearest to one: seven, four, two, six, five, nine, three, eight,\n",
      "Nearest to its: their, his, the, brilliant, her, convicts, fret, bello,\n",
      "Nearest to most: more, quite, many, among, commonwealth, inwardly, dung, jotham,\n",
      "Nearest to i: ii, you, they, we, he, iii, t, snapper,\n",
      "Nearest to american: english, french, british, def, russian, australian, german, delany,\n",
      "Average loss at step 42000: 3.437779\n",
      "Average loss at step 44000: 3.449353\n",
      "Average loss at step 46000: 3.444338\n",
      "Average loss at step 48000: 3.353884\n",
      "Average loss at step 50000: 3.382835\n",
      "Nearest to was: is, became, were, has, be, had, efficiently, nut,\n",
      "Nearest to new: second, optimization, vortex, persian, mysia, enlistment, thynne, marrying,\n",
      "Nearest to can: may, could, will, would, must, should, might, cannot,\n",
      "Nearest to b: d, c, h, ch, j, f, yards, footballer,\n",
      "Nearest to these: some, many, several, both, such, geodesic, they, moseley,\n",
      "Nearest to four: six, five, seven, eight, three, nine, two, zero,\n",
      "Nearest to when: if, while, after, although, before, where, however, but,\n",
      "Nearest to zero: five, four, seven, eight, six, nine, two, three,\n",
      "Nearest to into: from, through, adair, before, after, repackaged, iridium, catastrophe,\n",
      "Nearest to over: through, lep, near, scriptures, into, emmy, onset, euler,\n",
      "Nearest to had: has, have, was, were, having, mee, been, won,\n",
      "Nearest to one: eight, two, six, seven, four, three, nine, five,\n",
      "Nearest to its: their, his, the, convicts, brilliant, her, anhydrous, maintaining,\n",
      "Nearest to most: more, many, less, quite, among, gorilla, some, wakes,\n",
      "Nearest to i: ii, you, we, t, formosa, n, iii, they,\n",
      "Nearest to american: english, french, russian, endian, international, delany, african, australian,\n",
      "Average loss at step 52000: 3.435083\n",
      "Average loss at step 54000: 3.428763\n",
      "Average loss at step 56000: 3.440372\n",
      "Average loss at step 58000: 3.395421\n",
      "Average loss at step 60000: 3.391566\n",
      "Nearest to was: is, became, had, were, has, nut, be, librarian,\n",
      "Nearest to new: mysia, distant, enlistment, jahangir, persian, optimization, thynne, second,\n",
      "Nearest to can: may, could, will, would, must, should, might, cannot,\n",
      "Nearest to b: d, c, h, r, j, f, z, ch,\n",
      "Nearest to these: some, several, many, they, those, all, both, geodesic,\n",
      "Nearest to four: six, five, eight, three, seven, nine, two, zero,\n",
      "Nearest to when: if, after, although, before, while, however, where, until,\n",
      "Nearest to zero: five, seven, eight, four, six, nine, three, two,\n",
      "Nearest to into: from, through, adair, matt, iridium, averse, pocket, over,\n",
      "Nearest to over: through, about, between, into, puts, lep, emmy, tolerant,\n",
      "Nearest to had: has, have, was, were, having, been, nsw, could,\n",
      "Nearest to one: nine, two, seven, eight, six, four, three, five,\n",
      "Nearest to its: their, his, the, her, emergency, enhanced, maintaining, une,\n",
      "Nearest to most: more, many, some, wakes, all, inwardly, chewed, among,\n",
      "Nearest to i: ii, you, we, t, they, he, she, iii,\n",
      "Nearest to american: english, british, french, russian, african, australian, endian, international,\n",
      "Average loss at step 62000: 3.238354\n",
      "Average loss at step 64000: 3.255140\n",
      "Average loss at step 66000: 3.405512\n",
      "Average loss at step 68000: 3.390721\n",
      "Average loss at step 70000: 3.360736\n",
      "Nearest to was: is, were, has, became, had, been, sync, be,\n",
      "Nearest to new: optimization, jahangir, eichmann, distant, yeo, vortex, thynne, marrying,\n",
      "Nearest to can: may, could, will, would, must, should, cannot, might,\n",
      "Nearest to b: d, h, f, r, z, j, p, starling,\n",
      "Nearest to these: such, some, many, several, the, are, their, various,\n",
      "Nearest to four: five, six, seven, eight, three, nine, zero, two,\n",
      "Nearest to when: if, after, while, before, where, although, however, though,\n",
      "Nearest to zero: five, eight, four, seven, six, nine, three, two,\n",
      "Nearest to into: from, through, adair, within, repackaged, averse, matt, alms,\n",
      "Nearest to over: through, about, tolerant, euler, goddard, heterosexual, onset, neri,\n",
      "Nearest to had: have, has, was, were, having, fijian, never, ausgleich,\n",
      "Nearest to one: six, two, seven, eight, four, five, nine, three,\n",
      "Nearest to its: their, his, the, her, brilliant, enhanced, disarray, emergency,\n",
      "Nearest to most: more, many, less, particularly, quite, all, several, some,\n",
      "Nearest to i: ii, we, you, nye, they, p, occupies, he,\n",
      "Nearest to american: english, british, australian, international, endian, african, french, designers,\n",
      "Average loss at step 72000: 3.372817\n",
      "Average loss at step 74000: 3.344802\n",
      "Average loss at step 76000: 3.308779\n",
      "Average loss at step 78000: 3.349436\n",
      "Average loss at step 80000: 3.377266\n",
      "Nearest to was: is, became, had, were, has, nut, been, hagia,\n",
      "Nearest to new: distant, yeo, zooming, jahangir, old, analytic, persian, henceforth,\n",
      "Nearest to can: could, may, will, must, would, should, cannot, might,\n",
      "Nearest to b: d, h, z, r, c, nx, baer, f,\n",
      "Nearest to these: several, those, many, some, such, various, both, geodesic,\n",
      "Nearest to four: five, six, seven, three, eight, two, nine, zero,\n",
      "Nearest to when: if, before, after, during, although, though, where, until,\n",
      "Nearest to zero: seven, five, six, eight, four, nine, three, two,\n",
      "Nearest to into: through, from, adair, within, over, under, down, off,\n",
      "Nearest to over: into, about, through, lep, dawn, before, further, trianon,\n",
      "Nearest to had: has, have, was, having, were, never, chose, tried,\n",
      "Nearest to one: two, seven, six, three, four, five, eight, nine,\n",
      "Nearest to its: their, his, her, the, enhanced, our, jermaine, emergency,\n",
      "Nearest to most: more, many, particularly, dung, among, some, quite, best,\n",
      "Nearest to i: ii, you, we, g, they, n, p, rod,\n",
      "Nearest to american: british, international, australian, french, english, designers, russian, mexican,\n",
      "Average loss at step 82000: 3.406586\n",
      "Average loss at step 84000: 3.411442\n",
      "Average loss at step 86000: 3.388926\n",
      "Average loss at step 88000: 3.351327\n",
      "Average loss at step 90000: 3.360607\n",
      "Nearest to was: is, had, became, were, has, nut, been, be,\n",
      "Nearest to new: distant, yeo, old, vortex, different, jahangir, janissaries, recent,\n",
      "Nearest to can: could, will, may, must, would, should, cannot, might,\n",
      "Nearest to b: d, r, z, h, f, starling, ch, j,\n",
      "Nearest to these: several, many, some, those, are, various, such, both,\n",
      "Nearest to four: seven, six, five, eight, three, two, nine, zero,\n",
      "Nearest to when: if, after, before, while, where, during, although, until,\n",
      "Nearest to zero: five, seven, eight, six, four, nine, three, two,\n",
      "Nearest to into: through, from, under, repackaged, off, adair, down, matt,\n",
      "Nearest to over: lep, about, sowing, between, through, lac, gurps, off,\n",
      "Nearest to had: has, have, was, were, having, chose, since, never,\n",
      "Nearest to one: seven, four, five, eight, six, three, two, nine,\n",
      "Nearest to its: their, his, her, the, relinquishing, enhanced, whose, your,\n",
      "Nearest to most: more, many, less, particularly, some, chihuahua, all, several,\n",
      "Nearest to i: ii, we, you, t, n, g, mathrm, starling,\n",
      "Nearest to american: british, australian, french, russian, english, mexican, international, designers,\n",
      "Average loss at step 92000: 3.401245\n",
      "Average loss at step 94000: 3.252300\n",
      "Average loss at step 96000: 3.357634\n",
      "Average loss at step 98000: 3.235324\n",
      "Average loss at step 100000: 3.357605\n",
      "Nearest to was: is, became, had, has, were, been, nut, after,\n",
      "Nearest to new: distant, different, jahangir, vortex, abaci, mysia, janissaries, rewards,\n",
      "Nearest to can: could, may, would, will, must, should, cannot, might,\n",
      "Nearest to b: d, galahad, hx, sailboat, kenyon, z, manz, menzies,\n",
      "Nearest to these: several, many, some, those, various, different, their, certain,\n",
      "Nearest to four: six, seven, five, eight, three, nine, two, zero,\n",
      "Nearest to when: if, before, although, while, after, where, though, during,\n",
      "Nearest to zero: five, eight, seven, six, four, nine, three, two,\n",
      "Nearest to into: through, from, off, in, within, repackaged, adair, under,\n",
      "Nearest to over: between, lep, about, within, through, off, gurps, oktoberfest,\n",
      "Nearest to had: has, have, was, having, were, chose, is, mee,\n",
      "Nearest to one: seven, two, four, eight, six, three, five, nine,\n",
      "Nearest to its: their, his, the, her, whose, our, fret, relinquishing,\n",
      "Nearest to most: more, less, dung, many, especially, soloist, particularly, wakes,\n",
      "Nearest to i: we, you, ii, they, mathrm, t, he, n,\n",
      "Nearest to american: british, australian, hh, french, international, canadian, solidifying, delany,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 100001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  session.run(tf.initialize_all_variables())\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": "code",
   "execution_count": 11,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "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": 12,
   "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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J7Nu3zy7XExERuRwlciIiUmtYrVYSEsaSn7+BvLx08vM3kJAwttwVJS/WFqdBgwZER0fz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\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yBEKhEJaWlkhOTsbgwYPRq1cv/Pzzz8w+161bB0tLSwgEAiYn4uLFi5GRkYG1a3+Ar+84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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6994fca2d0>"
      ]
     },
     "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": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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",
    "# 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",
    "  # Note: The optimizer will optimize the softmax_weights AND the embeddings.\n",
    "  # This is because the embeddings are defined as a variable quantity and the\n",
    "  # optimizer's `minimize` method will by default modify all variable quantities \n",
    "  # that contribute to the tensor it is passed.\n",
    "  # See docs on `tf.train.Optimizer.minimize()` for more details.\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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_steps = 100001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  session.run(tf.initialize_all_variables())\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": "code",
   "execution_count": null,
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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)"
   ]
  }
 ],
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