5_word2vec.ipynb

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    "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. Word2vec is an efficient predictive model that directly predicts a word from context (the word' neighbors) in terms of learning small embedding vectors that represent that word. Word2vec has two variations: the skip-gram model predicts the neighbor words from the target words and the continuous bag-of-words model (CBOW) that predicts the word (e.g. 'coffee') from context ('I start each day with a cup of')."
   ]
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    "# 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"
   ]
  },
  {
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   "source": [
    "Download the data from the source website if necessary."
   ]
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     "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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    "Read the data into a string."
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     "text": [
      "Data size 17005207\n",
      "['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against']\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))\n",
    "print(words[:10])"
   ]
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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",
    "  \n",
    "  dictionary = dict()\n",
    "  for word, _ in count:\n",
    "    dictionary[word] = len(dictionary)\n",
    "    \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."
   ]
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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', 'a', 'of']\n",
      "\n",
      "with num_skips = 4 and skip_window = 2:\n",
      "    batch: ['as', 'as', 'as', 'as', 'a', 'a', 'a', 'a']\n",
      "    labels: ['originated', 'anarchism', 'term', 'a', 'originated', 'term', 'of', 'as']\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[: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)])"
   ]
  },
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    "deletable": true,
    "editable": true,
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   },
   "source": [
    "Train a skip-gram model."
   ]
  },
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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(weights=softmax_weights, biases=softmax_biases, inputs=embed,\n",
    "                               labels=train_labels, num_sampled=num_sampled, num_classes=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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     "text": [
      "Initialized\n",
      "Average loss at step 0: 8.422637\n",
      "Nearest to or: capoeiristas, wharf, iterate, bliss, satisfiability, chromatin, vatican, md,\n",
      "Nearest to most: helmholtz, allahabad, oddly, lcds, opencyc, achievements, botswana, eastwards,\n",
      "Nearest to called: unanimously, benghazi, fountain, significance, smoot, unconcerned, sunga, hemispheric,\n",
      "Nearest to other: cotangent, salamander, tights, breakaway, scrape, dialectical, migrating, absentia,\n",
      "Nearest to if: alexandrina, dnow, sammy, adolphus, dogmas, sven, unnatural, natured,\n",
      "Nearest to at: rosen, gratis, schneider, jorge, rhombic, mahayana, amistad, lucia,\n",
      "Nearest to years: logograms, presocratic, zombie, countess, amendment, coarse, bodied, oliver,\n",
      "Nearest to used: martha, diametrically, trophy, bregovi, compulsive, gotovina, wilhelm, arndt,\n",
      "Nearest to american: abeda, eventual, waters, littoral, remarkable, sitting, gladly, papyri,\n",
      "Nearest to three: midline, vendetta, celled, apprehended, turnips, waxes, backtracking, sefirot,\n",
      "Nearest to people: shamash, danio, newport, weights, jute, warwick, parishes, slippery,\n",
      "Nearest to than: genesee, kart, steeply, bakunin, violation, installation, moldovan, basques,\n",
      "Nearest to he: za, arched, dread, audi, inputs, convened, ventilated, opined,\n",
      "Nearest to when: earthworm, inns, translators, cegep, ritz, freising, thousands, euroscepticism,\n",
      "Nearest to of: style, coerced, competitors, schafer, hamster, factum, wreckin, tully,\n",
      "Nearest to a: support, falsification, bluffs, impedes, releases, weldon, nacional, translational,\n",
      "Average loss at step 2000: 4.359671\n",
      "Average loss at step 4000: 3.870129\n",
      "Average loss at step 6000: 3.793683\n",
      "Average loss at step 8000: 3.690235\n",
      "Average loss at step 10000: 3.613627\n",
      "Nearest to or: and, than, pri, with, openoffice, talmudic, zhukov, milli,\n",
      "Nearest to most: oddly, scholar, eastwards, lcds, jordanian, suharto, phosphors, cas,\n",
      "Nearest to called: gow, alternated, unanimously, benghazi, balochistan, solar, soliciting, calculated,\n",
      "Nearest to other: salamander, ziv, tights, steinberg, jameson, correspondence, ratio, migrating,\n",
      "Nearest to if: flemish, sammy, rossetti, socially, cabinet, thy, moynihan, azimuth,\n",
      "Nearest to at: in, atrocity, infects, for, lucia, of, over, teleport,\n",
      "Nearest to years: logograms, zombie, presocratic, coarse, heh, countess, bodied, mullet,\n",
      "Nearest to used: diametrically, found, czar, captivity, polskiej, endlessly, brim, considered,\n",
      "Nearest to american: and, persians, remarkable, earlier, navy, perpendicular, sitting, birger,\n",
      "Nearest to three: six, seven, four, eight, five, two, nine, zero,\n",
      "Nearest to people: walkway, slippery, shamash, scrabble, danio, covered, italo, contained,\n",
      "Nearest to than: or, willfully, bakunin, genesee, rebellion, ontogeny, subfields, pune,\n",
      "Nearest to he: it, they, she, who, customers, blaze, pyrotechnics, polyethylene,\n",
      "Nearest to when: earthworm, priority, crusade, freising, secretes, thousands, confident, because,\n",
      "Nearest to of: in, at, oyster, compel, for, and, on, dysentery,\n",
      "Nearest to a: the, this, impedes, recessions, thibetanus, soles, raman, shan,\n",
      "Average loss at step 12000: 3.600587\n",
      "Average loss at step 14000: 3.568634\n",
      "Average loss at step 16000: 3.406506\n",
      "Average loss at step 18000: 3.456099\n",
      "Average loss at step 20000: 3.535268\n",
      "Nearest to or: than, and, introducing, pasiphae, pollen, renounced, crossovers, with,\n",
      "Nearest to most: more, eastwards, oddly, allahabad, many, lineages, all, very,\n",
      "Nearest to called: gow, ishaq, unanimously, albertus, ethiopians, known, calculated, alternated,\n",
      "Nearest to other: many, different, some, briefing, ziv, ratio, alen, jameson,\n",
      "Nearest to if: rossetti, flemish, but, devonian, pursuant, though, socially, sammy,\n",
      "Nearest to at: in, on, infects, from, for, atrocity, between, apparently,\n",
      "Nearest to years: zombie, days, logograms, unsolvable, presocratic, coarse, census, quicktime,\n",
      "Nearest to used: found, diametrically, known, considered, read, polskiej, monopoly, killed,\n",
      "Nearest to american: australian, british, remarkable, northrop, english, french, presumably, sitting,\n",
      "Nearest to three: four, five, six, two, seven, eight, zero, one,\n",
      "Nearest to people: slippery, shamash, scrabble, walkway, tasked, mobilization, satrap, covered,\n",
      "Nearest to than: or, genesee, bakunin, alamein, bashing, ontogeny, rebellion, stalinist,\n",
      "Nearest to he: it, they, she, who, there, we, drastically, flirted,\n",
      "Nearest to when: because, was, while, where, hence, earthworm, crusade, hover,\n",
      "Nearest to of: for, oyster, berthe, main, in, progeny, ged, rafter,\n",
      "Nearest to a: bahadur, the, this, recessions, impedes, milling, capone, topics,\n",
      "Average loss at step 22000: 3.502178\n",
      "Average loss at step 24000: 3.495142\n",
      "Average loss at step 26000: 3.479392\n",
      "Average loss at step 28000: 3.481042\n",
      "Average loss at step 30000: 3.501025\n",
      "Nearest to or: and, than, but, talmudic, brainiac, confessional, heightened, afield,\n",
      "Nearest to most: more, many, some, use, solvents, allahabad, phosphors, oddly,\n",
      "Nearest to called: albertus, milius, ishaq, island, known, nuances, gow, unanimously,\n",
      "Nearest to other: different, many, various, some, these, yarn, steinberg, ratio,\n",
      "Nearest to if: when, rossetti, flemish, though, but, where, would, since,\n",
      "Nearest to at: in, flanks, on, mossad, whilst, chu, apparently, after,\n",
      "Nearest to years: days, zombie, unsolvable, quicktime, census, advert, weeks, dormant,\n",
      "Nearest to used: known, found, written, considered, mentioned, parodying, diametrically, read,\n",
      "Nearest to american: british, australian, french, canadian, english, sitting, extremity, birger,\n",
      "Nearest to three: five, four, two, eight, seven, six, nine, zero,\n",
      "Nearest to people: slippery, americans, satrap, mobilization, tasked, facets, walkway, nanometers,\n",
      "Nearest to than: or, bashing, carbamazepine, genesee, bakunin, psychiatrists, penfield, alamein,\n",
      "Nearest to he: it, she, they, who, there, never, firearm, ls,\n",
      "Nearest to when: if, because, where, while, but, after, before, was,\n",
      "Nearest to of: berthe, in, ged, and, jewish, from, incongruous, early,\n",
      "Nearest to a: any, topics, recessions, the, liszt, soles, impedes, capone,\n",
      "Average loss at step 32000: 3.499140\n",
      "Average loss at step 34000: 3.490796\n",
      "Average loss at step 36000: 3.457253\n",
      "Average loss at step 38000: 3.307387\n",
      "Average loss at step 40000: 3.428176\n",
      "Nearest to or: and, than, beckham, samoan, embassy, antoninianus, negativity, tewahedo,\n",
      "Nearest to most: more, many, some, solvents, less, smashed, oddly, protagoras,\n",
      "Nearest to called: unanimously, albertus, ishaq, milius, tumors, imperialism, nuances, considered,\n",
      "Nearest to other: different, nellis, various, many, ratio, some, endangered, salamander,\n",
      "Nearest to if: when, where, rossetti, though, exiles, frankenchrist, is, nephropathy,\n",
      "Nearest to at: on, in, during, infects, for, chu, whilst, affording,\n",
      "Nearest to years: days, weeks, zombie, year, months, unsolvable, dormant, geographer,\n",
      "Nearest to used: known, found, considered, available, cited, referred, preserved, written,\n",
      "Nearest to american: british, australian, french, english, japanese, slot, italian, canadian,\n",
      "Nearest to three: five, four, two, seven, six, eight, nine, zero,\n",
      "Nearest to people: those, tribes, tasked, facets, men, satrap, avengers, americans,\n",
      "Nearest to than: or, much, bashing, penfield, bakunin, inform, willfully, psychiatrists,\n",
      "Nearest to he: she, it, they, who, there, then, eventually, flirted,\n",
      "Nearest to when: if, while, before, where, because, for, vikrant, after,\n",
      "Nearest to of: in, for, honeywell, allergies, berthe, original, forecasts, from,\n",
      "Nearest to a: the, soles, any, no, assures, drip, concerti, impedes,\n",
      "Average loss at step 42000: 3.432759\n",
      "Average loss at step 44000: 3.450980\n",
      "Average loss at step 46000: 3.451206\n",
      "Average loss at step 48000: 3.350260\n",
      "Average loss at step 50000: 3.379777\n",
      "Nearest to or: and, than, confessional, armament, scrolling, though, unmistakable, heist,\n",
      "Nearest to most: more, less, use, many, some, smashed, solvents, highly,\n",
      "Nearest to called: ishaq, albertus, known, unanimously, termed, alboin, referred, milius,\n",
      "Nearest to other: different, various, nellis, many, ratio, these, some, liberalization,\n",
      "Nearest to if: when, where, though, rossetti, although, before, frankenchrist, since,\n",
      "Nearest to at: during, affording, on, in, deseret, chu, overflowing, from,\n",
      "Nearest to years: days, weeks, zombie, year, unsolvable, months, dormant, presocratic,\n",
      "Nearest to used: known, found, written, available, seen, considered, referred, regarded,\n",
      "Nearest to american: australian, english, british, french, italian, irish, scottish, larvae,\n",
      "Nearest to three: four, eight, six, seven, five, nine, two, zero,\n",
      "Nearest to people: men, children, satrap, cenotaph, facets, others, avengers, jews,\n",
      "Nearest to than: penfield, much, or, bashing, carbamazepine, hashimoto, psychiatrists, pip,\n",
      "Nearest to he: it, she, they, there, who, then, eventually, this,\n",
      "Nearest to when: while, if, before, after, where, though, but, because,\n",
      "Nearest to of: in, including, welcomes, and, allergies, crossover, ged, permanent,\n",
      "Nearest to a: recessions, the, impedes, chimney, assures, any, no, another,\n",
      "Average loss at step 52000: 3.434035\n",
      "Average loss at step 54000: 3.425607\n",
      "Average loss at step 56000: 3.437865\n",
      "Average loss at step 58000: 3.399613\n",
      "Average loss at step 60000: 3.389836\n",
      "Nearest to or: and, than, borland, nolan, but, bottled, crossovers, bu,\n",
      "Nearest to most: more, many, use, some, less, smashed, summer, branches,\n",
      "Nearest to called: ishaq, referred, used, known, alboin, albertus, tumors, gabonese,\n",
      "Nearest to other: different, various, many, including, nellis, yarn, salamander, these,\n",
      "Nearest to if: when, where, though, since, before, rossetti, although, could,\n",
      "Nearest to at: during, in, apparently, affording, spivak, chu, infects, sdlp,\n",
      "Nearest to years: days, weeks, year, months, minutes, decades, bc, zombie,\n",
      "Nearest to used: known, written, found, available, held, seen, cited, employed,\n",
      "Nearest to american: australian, british, english, italian, french, indian, irish, european,\n",
      "Nearest to three: five, four, six, eight, seven, two, nine, zero,\n",
      "Nearest to people: men, those, children, satrap, others, socony, scientists, americans,\n",
      "Nearest to than: or, much, penfield, inform, stalinist, and, slow, sunnis,\n",
      "Nearest to he: she, it, they, there, who, flirted, eventually, i,\n",
      "Nearest to when: if, while, before, although, though, after, during, because,\n",
      "Nearest to of: in, ged, for, salads, including, belushi, same, and,\n",
      "Nearest to a: any, the, another, impedes, recessions, borneo, assures, this,\n",
      "Average loss at step 62000: 3.244874\n",
      "Average loss at step 64000: 3.249464\n",
      "Average loss at step 66000: 3.400080\n",
      "Average loss at step 68000: 3.396127\n",
      "Average loss at step 70000: 3.357756\n",
      "Nearest to or: and, than, distinctly, any, confessional, while, the, when,\n",
      "Nearest to most: more, less, some, many, use, summer, highly, throughout,\n",
      "Nearest to called: referred, known, tumors, alboin, termed, cited, gabonese, ishaq,\n",
      "Nearest to other: different, various, many, some, older, others, nellis, adhesives,\n",
      "Nearest to if: when, though, where, before, rossetti, since, could, while,\n",
      "Nearest to at: during, apparently, spivak, infects, around, chu, after, essentially,\n",
      "Nearest to years: days, weeks, months, year, minutes, decades, bc, centuries,\n",
      "Nearest to used: known, found, available, written, seen, responsible, described, employed,\n",
      "Nearest to american: british, italian, australian, english, international, canadian, intrinsic, irish,\n",
      "Nearest to three: four, six, five, two, seven, eight, nine, zero,\n",
      "Nearest to people: men, children, americans, those, others, students, members, scientists,\n",
      "Nearest to than: penfield, or, much, bashing, inform, profess, but, carbamazepine,\n",
      "Nearest to he: she, it, they, there, drastically, we, who, then,\n",
      "Nearest to when: if, while, before, where, because, though, however, after,\n",
      "Nearest to of: including, murad, include, and, ged, morin, ama, in,\n",
      "Nearest to a: the, another, recessions, assures, any, chickasaw, chimney, spiked,\n",
      "Average loss at step 72000: 3.375982\n",
      "Average loss at step 74000: 3.347105\n",
      "Average loss at step 76000: 3.310341\n",
      "Average loss at step 78000: 3.351762\n",
      "Average loss at step 80000: 3.378946\n",
      "Nearest to or: and, than, while, exporters, but, judson, poodle, confessional,\n",
      "Nearest to most: more, less, many, some, summer, throughout, among, especially,\n",
      "Nearest to called: referred, tumors, known, ishaq, considered, termed, used, alboin,\n",
      "Nearest to other: various, different, guanine, others, many, liberalization, nellis, canonical,\n",
      "Nearest to if: when, though, where, before, although, rossetti, until, then,\n",
      "Nearest to at: in, on, during, apparently, sdlp, around, chu, infects,\n",
      "Nearest to years: days, weeks, months, decades, minutes, year, centuries, bc,\n",
      "Nearest to used: known, available, written, seen, responsible, referred, found, cited,\n",
      "Nearest to american: british, australian, indian, italian, irish, scottish, french, canadian,\n",
      "Nearest to three: six, five, four, seven, two, eight, nine, zero,\n",
      "Nearest to people: men, children, students, americans, women, those, others, someone,\n",
      "Nearest to than: or, much, penfield, bashing, cheaper, kio, inform, carbamazepine,\n",
      "Nearest to he: she, it, they, there, who, we, never, fk,\n",
      "Nearest to when: if, before, while, after, though, where, during, although,\n",
      "Nearest to of: in, including, alcmene, daimyo, although, original, benito, foremost,\n",
      "Nearest to a: another, recessions, the, liszt, impedes, borneo, chimney, que,\n",
      "Average loss at step 82000: 3.409147\n",
      "Average loss at step 84000: 3.409493\n",
      "Average loss at step 86000: 3.388412\n",
      "Average loss at step 88000: 3.350290\n",
      "Average loss at step 90000: 3.364509\n",
      "Nearest to or: and, than, fullerene, nolan, though, lz, encoders, while,\n",
      "Nearest to most: more, less, many, some, summer, among, use, throughout,\n",
      "Nearest to called: referred, known, considered, used, termed, named, alboin, ishaq,\n",
      "Nearest to other: various, different, obvious, endangered, including, benefactors, liberalization, bujold,\n",
      "Nearest to if: when, though, where, since, although, rossetti, before, until,\n",
      "Nearest to at: during, near, around, affording, chu, in, sata, ducati,\n",
      "Nearest to years: days, weeks, minutes, months, decades, year, hours, centuries,\n",
      "Nearest to used: known, referred, seen, available, cited, found, required, employed,\n",
      "Nearest to american: british, italian, australian, indian, irish, french, scottish, african,\n",
      "Nearest to three: four, five, eight, two, seven, six, nine, zero,\n",
      "Nearest to people: men, children, students, americans, women, god, those, players,\n",
      "Nearest to than: much, penfield, or, inform, nov, somewhat, stratigraphy, elective,\n",
      "Nearest to he: she, they, it, there, who, later, never, we,\n",
      "Nearest to when: while, if, before, though, where, after, although, because,\n",
      "Nearest to of: including, and, in, morin, veiled, include, dawkins, alcmene,\n",
      "Nearest to a: recessions, the, any, contraband, impedes, liszt, every, soles,\n",
      "Average loss at step 92000: 3.394753\n",
      "Average loss at step 94000: 3.259539\n",
      "Average loss at step 96000: 3.358413\n",
      "Average loss at step 98000: 3.242811\n",
      "Average loss at step 100000: 3.351652\n",
      "Nearest to or: and, than, while, gisela, nottingham, angolan, confessional, exporters,\n",
      "Nearest to most: more, less, especially, throughout, some, many, among, all,\n",
      "Nearest to called: referred, named, used, termed, considered, known, spartacus, ishaq,\n",
      "Nearest to other: various, different, bujold, endangered, individual, others, specific, captives,\n",
      "Nearest to if: when, where, though, while, before, whether, although, since,\n",
      "Nearest to at: during, on, deseret, after, infects, whilst, benthic, under,\n",
      "Nearest to years: days, year, weeks, minutes, months, decades, centuries, hours,\n",
      "Nearest to used: available, designed, known, found, seen, referred, written, required,\n",
      "Nearest to american: british, italian, australian, irish, scottish, indian, canadian, international,\n",
      "Nearest to three: four, five, seven, two, six, eight, nine, zero,\n",
      "Nearest to people: men, students, children, americans, players, tribes, women, those,\n",
      "Nearest to than: or, much, penfield, bashing, considerably, promiscuity, psychiatrists, inform,\n",
      "Nearest to he: she, it, they, there, who, we, never, eventually,\n",
      "Nearest to when: while, if, although, before, where, though, during, after,\n",
      "Nearest to of: including, erasmus, in, welling, and, ces, sukarnoputri, nssdc,\n",
      "Nearest to a: another, the, recessions, any, jal, concerti, borneo, shan,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 100001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  tf.global_variables_initializer().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": "code",
   "execution_count": 8,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "deletable": true,
    "editable": 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": 9,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "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",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Aw4YNGT16NI8++igeHh60adOG1NRr7501ceJExo8fj4eHBz///DOOjo7X7X/ixIm4u7vj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zaenp64ubnRsWNHHjx4wKhRo3BwcCA3NxddXV2io6NLvGZhNWk52tranDhxAhUVFc6dO0dERISg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rp06VKq/ezZomeFBW5lSXHFU6oqRr17A3D/6+XkJCWhY2pK/WkfSu2vypo1a/j11185duwY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\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hKysrvv32W3x9fenatWu599Z1gQjkarjs2NtMmfwRRy6cZKvvMrJjb4u55XWUZh1cZWet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\nAVjHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a9a4e90>"
      ]
     },
     "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",
    "deletable": true,
    "editable": true,
    "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": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size 17005207\n",
      "['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against']\n",
      "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": [
    "# CBOW model\n",
    "# predict a word from the sum of all the word vectors in its context\n",
    "# data: ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first']\n",
    "# batch : [['anarchism', 'as'], ['originated', 'a'], ['as', term'], ['a', 'of'], ['term', 'abuse'], ['of', 'first']]\n",
    "# labels: ['originated', 'as', 'a', 'term', 'of', 'abuse']\n",
    "\n",
    "words = read_data(filename)\n",
    "print('Data size %d' % len(words))\n",
    "print(words[:10])\n",
    "\n",
    "# vocabulary_size = 50000\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": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first']\n",
      "\n",
      "with num_skips = 2 and skip_window = 1:\n",
      "    batch: ['anarchism', 'as', 'originated', 'a', 'as', 'term', 'a', 'of', 'term', 'abuse', 'of', 'first']\n",
      "    labels: ['originated', 'as', 'a', 'term', 'of', 'abuse']\n",
      "\n",
      "with num_skips = 4 and skip_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": [
    "from itertools import compress\n",
    "\n",
    "data_index = 0\n",
    "\n",
    "def generate_batch_cbow(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, num_skips), 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",
    "\n",
    "  # span = 3\n",
    "  for _ in range(span):\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "    \n",
    "  # batch_size = 8\n",
    "  # num_skips = 2\n",
    "  # skip_window = 1\n",
    "  # data: ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first']\n",
    "  # batch: [['anarchism', 'as'], ['originated','a'],['as','term'], ['a','of'], ['term','abuse'],['of','first']]\n",
    "  # labels: ['originated','as','a','term','of','abuse']\n",
    "\n",
    "  # num_skips = 4\n",
    "  # skip_window = 2\n",
    "  # batch: [['anarchism', 'originated','a','term'], ['originated','as', 'term', 'of']]\n",
    "  # labels: ['as','a', 'term', 'of']\n",
    "\n",
    "  for i in range(batch_size ):\n",
    "    mask = [1] * span\n",
    "    target = skip_window  # target label at the center of the buffer\n",
    "    mask[target] = 0\n",
    "    labels[i,0] = buffer[target]\n",
    "    batch[i,:] = list(compress(buffer, mask))\n",
    "    buffer.append(data[data_index])  \n",
    "#     print('buffer  :', [reverse_dictionary[bi] for bi in buffer])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "#     print(\"batch data shape: {}\".format(batch.shape))\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_cbow(batch_size=8-2*skip_window, 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.reshape(num_skips * (8-2*skip_window) )])\n",
    "    print('    labels:', [reverse_dictionary[li] for li in labels.reshape(8-2*skip_window)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train a CBOW model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data shape: Tensor(\"Placeholder:0\", shape=(126, 2), dtype=int32, device=/device:CPU:0)\n"
     ]
    }
   ],
   "source": [
    "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",
    "\n",
    "batch_size = 128 - 2*skip_window\n",
    "embedding_size = 128 - 2*skip_window # Dimension of the embedding vector.\n",
    "\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, num_skips])\n",
    "    \n",
    "  print(\"train data shape: {}\".format(train_dataset))\n",
    "\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",
    "    \n",
    "  embed = tf.zeros([batch_size, embedding_size])\n",
    "  for i in range(num_skips):\n",
    "      embed += tf.nn.embedding_lookup(embeddings, train_dataset[:,i])\n",
    "\n",
    "# Compute the softmax loss, using a sample of the negative labels each time.\n",
    "  loss = tf.reduce_mean(\n",
    "    tf.nn.sampled_softmax_loss(weights=softmax_weights, biases=softmax_biases, inputs=embed,\n",
    "                               labels=train_labels, num_sampled=num_sampled, num_classes=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": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step 0: 7.396968\n",
      "Nearest to this: tamil, redeveloped, jfk, deployable, sae, difficulty, jointly, mexicans,\n",
      "Nearest to six: macrinus, telegrams, dat, hawaiian, ellery, frideric, longbow, ddd,\n",
      "Nearest to into: ej, narayan, backup, biggest, conservation, vieux, stagnant, ready,\n",
      "Nearest to not: chera, blocking, ninlil, manuscripts, mjt, toni, boiling, onyx,\n",
      "Nearest to world: attacks, reeves, curtain, iteration, nadh, transceiver, hexameter, mauser,\n",
      "Nearest to see: outcasts, reminded, immigrants, vizier, complete, litigants, courtney, properly,\n",
      "Nearest to or: inquisitions, apartment, mundi, leonov, wavelet, lambda, traffic, rouse,\n",
      "Nearest to s: gwp, cnd, enzymatic, borrowings, odense, caricatures, novel, rolls,\n",
      "Nearest to can: citizenry, palaestina, estate, rumour, nurturing, kill, collated, ateneo,\n",
      "Nearest to used: dancehall, thiele, distributist, wynne, whimsical, spline, observatory, pendants,\n",
      "Nearest to were: edgar, migrated, savonarola, utamaro, zechariah, strunk, battleships, krak,\n",
      "Nearest to nine: interventions, multiparty, edible, batmobile, blossomed, ambushes, pervading, cnet,\n",
      "Nearest to if: scolded, seditious, remit, immigrate, fer, vil, cooh, manifesto,\n",
      "Nearest to on: quatre, mummified, fractional, incarnations, watcher, schl, publica, snooker,\n",
      "Nearest to been: persisted, pillow, dinneen, chairmanship, ogilvie, ventris, nicaraguan, disfavor,\n",
      "Nearest to he: cimmerian, alef, subversion, riemann, slapped, selig, rallying, neutrons,\n",
      "Average loss at step 2000: 4.102248\n",
      "Average loss at step 4000: 3.572389\n",
      "Average loss at step 6000: 3.373559\n",
      "Average loss at step 8000: 3.255334\n",
      "Average loss at step 10000: 3.185099\n",
      "Nearest to this: it, which, the, heron, his, palgrave, a, another,\n",
      "Nearest to six: eight, seven, nine, five, four, three, one, two,\n",
      "Nearest to into: from, stagnant, towering, cs, biggest, narayan, redacted, in,\n",
      "Nearest to not: also, poking, onyx, shoals, generally, mainly, lesbos, often,\n",
      "Nearest to world: barricade, miko, harem, attacks, rcn, jamil, reeves, emulsion,\n",
      "Nearest to see: numeration, neustadt, stampede, trill, hetzel, but, cheats, lutheranism,\n",
      "Nearest to or: and, than, justice, prejudiced, but, cf, whatever, cottage,\n",
      "Nearest to s: zel, nongovernmental, his, aloe, fake, delta, smiley, transfinite,\n",
      "Nearest to can: may, would, could, must, will, should, might, deeper,\n",
      "Nearest to used: collaborate, distributist, dancehall, whimsical, regarded, envisioned, cellini, rhode,\n",
      "Nearest to were: are, was, had, been, nucleosynthesis, saucer, have, be,\n",
      "Nearest to nine: eight, six, seven, five, four, zero, three, one,\n",
      "Nearest to if: remit, when, seditious, visually, ubangi, pair, scolded, heritage,\n",
      "Nearest to on: in, upon, at, incarnations, against, succeeding, salieri, schleswig,\n",
      "Nearest to been: were, was, be, waring, tremolo, jaguars, hangs, dinneen,\n",
      "Nearest to he: she, it, they, who, panzer, delicious, there, sexy,\n",
      "Average loss at step 12000: 3.189879\n",
      "Average loss at step 14000: 3.157357\n",
      "Average loss at step 16000: 3.155931\n",
      "Average loss at step 18000: 3.125268\n",
      "Average loss at step 20000: 2.988748\n",
      "Nearest to this: it, which, that, another, heron, antisense, doren, orderly,\n",
      "Nearest to six: seven, eight, five, four, nine, three, zero, two,\n",
      "Nearest to into: from, through, back, sexy, biggest, propelling, around, backup,\n",
      "Nearest to not: often, t, generally, always, also, they, lesbos, poking,\n",
      "Nearest to world: vietnam, harem, barricade, outages, miko, deshima, vaishnavism, believing,\n",
      "Nearest to see: winn, but, concludes, known, opposes, stampede, trill, lutheranism,\n",
      "Nearest to or: and, than, bundaberg, birthdate, reassured, tapering, mashed, airbases,\n",
      "Nearest to s: zel, his, dentist, bromide, essendon, superluminal, whose, philologists,\n",
      "Nearest to can: will, may, could, would, must, might, should, cannot,\n",
      "Nearest to used: seen, regarded, available, found, spoken, known, written, considered,\n",
      "Nearest to were: are, was, have, had, suse, poisoning, been, nucleosynthesis,\n",
      "Nearest to nine: eight, seven, six, four, zero, five, three, one,\n",
      "Nearest to if: when, where, seditious, remit, injector, promenade, actinidia, ambients,\n",
      "Nearest to on: upon, in, at, succeeding, atat, celibate, weighting, about,\n",
      "Nearest to been: become, be, was, were, suzy, jaguars, hypocrisy, waring,\n",
      "Nearest to he: she, it, they, who, there, panzer, portrayals, delicious,\n",
      "Average loss at step 22000: 3.073546\n",
      "Average loss at step 24000: 3.066871\n",
      "Average loss at step 26000: 2.982339\n",
      "Average loss at step 28000: 3.039740\n",
      "Average loss at step 30000: 3.005519\n",
      "Nearest to this: it, which, that, what, the, antisense, graceland, heron,\n",
      "Nearest to six: eight, four, five, seven, nine, three, two, zero,\n",
      "Nearest to into: through, from, around, back, within, redacted, over, across,\n",
      "Nearest to not: still, t, onyx, always, usually, now, also, never,\n",
      "Nearest to world: vietnam, miko, deshima, harem, mauser, sexism, barricade, fallopian,\n",
      "Nearest to see: matthews, winn, called, but, concludes, larps, opengl, include,\n",
      "Nearest to or: and, than, cf, itanium, but, slavonic, arleigh, feasts,\n",
      "Nearest to s: whose, panacea, zel, airing, thabit, lambeau, his, warranted,\n",
      "Nearest to can: may, could, will, would, must, should, cannot, might,\n",
      "Nearest to used: known, seen, found, written, called, available, considered, regarded,\n",
      "Nearest to were: are, have, was, been, had, poisoning, being, confess,\n",
      "Nearest to nine: eight, seven, six, four, five, zero, three, two,\n",
      "Nearest to if: when, though, where, pimlico, exe, symbiotic, excite, nativist,\n",
      "Nearest to on: upon, in, about, through, at, anastasia, usc, until,\n",
      "Nearest to been: become, be, were, was, jaguars, bananas, hypocrisy, nasional,\n",
      "Nearest to he: she, it, they, there, who, panzer, i, fahd,\n",
      "Average loss at step 32000: 2.914516\n",
      "Average loss at step 34000: 2.861723\n",
      "Average loss at step 36000: 2.945595\n",
      "Average loss at step 38000: 2.951791\n",
      "Average loss at step 40000: 2.919545\n",
      "Nearest to this: which, it, the, another, what, some, orderly, heron,\n",
      "Nearest to six: seven, five, eight, four, nine, three, two, zero,\n",
      "Nearest to into: through, from, across, within, around, back, vilayet, andrzej,\n",
      "Nearest to not: still, generally, now, also, usually, onyx, almost, always,\n",
      "Nearest to world: vietnam, deshima, barricade, albans, internet, miko, fallopian, jamil,\n",
      "Nearest to see: winn, matthews, include, concludes, opengl, velvet, annoyance, surges,\n",
      "Nearest to or: and, than, crewe, birthdate, bundaberg, kitty, inscribed, cf,\n",
      "Nearest to s: whose, his, bromide, dumbarton, zel, lepidoptera, essendon, aloe,\n",
      "Nearest to can: may, could, would, will, must, should, might, cannot,\n",
      "Nearest to used: known, seen, referred, found, available, considered, regarded, written,\n",
      "Nearest to were: are, have, was, had, been, when, be, cranberries,\n",
      "Nearest to nine: eight, seven, six, four, five, zero, three, two,\n",
      "Nearest to if: when, though, then, where, before, so, quantitatively, since,\n",
      "Nearest to on: upon, in, through, celibate, at, mitsubishi, about, theropod,\n",
      "Nearest to been: become, be, was, were, grown, jaguars, introns, hypocrisy,\n",
      "Nearest to he: she, it, they, there, who, we, alem, bitterly,\n",
      "Average loss at step 42000: 2.935788\n",
      "Average loss at step 44000: 2.979344\n",
      "Average loss at step 46000: 2.919608\n",
      "Average loss at step 48000: 2.920402\n",
      "Average loss at step 50000: 2.845908\n",
      "Nearest to this: it, which, the, another, what, some, heron, palgrave,\n",
      "Nearest to six: seven, eight, five, nine, four, three, zero, one,\n",
      "Nearest to into: through, from, back, around, within, obliquely, across, racecourse,\n",
      "Nearest to not: still, never, mainly, now, onyx, actually, you, generally,\n",
      "Nearest to world: vietnam, deshima, barricade, jamil, miko, u, season, progesterone,\n",
      "Nearest to see: matthews, include, winn, refer, hada, karenga, links, coulter,\n",
      "Nearest to or: and, than, while, but, feasts, arleigh, though, non,\n",
      "Nearest to s: whose, zel, isbn, your, brisingamen, essendon, delta, dentist,\n",
      "Nearest to can: may, could, will, must, would, should, might, cannot,\n",
      "Nearest to used: known, referred, seen, available, found, written, designed, sold,\n",
      "Nearest to were: are, was, have, had, be, phocas, been, while,\n",
      "Nearest to nine: eight, seven, six, five, four, three, zero, one,\n",
      "Nearest to if: when, though, where, before, then, excite, pimlico, protectors,\n",
      "Nearest to on: upon, succeeding, through, regarding, xm, in, rotates, about,\n",
      "Nearest to been: become, be, was, grown, recently, already, jaguars, were,\n",
      "Nearest to he: she, it, they, there, who, we, panzer, russell,\n",
      "Average loss at step 52000: 2.846265\n",
      "Average loss at step 54000: 2.864364\n",
      "Average loss at step 56000: 2.865659\n",
      "Average loss at step 58000: 2.740914\n",
      "Average loss at step 60000: 2.825445\n",
      "Nearest to this: it, which, what, the, another, uscis, guerrero, some,\n",
      "Nearest to six: seven, four, five, eight, nine, three, zero, two,\n",
      "Nearest to into: through, within, around, from, across, between, over, sexy,\n",
      "Nearest to not: never, still, always, actually, now, almost, better, formally,\n",
      "Nearest to world: vietnam, binet, deshima, jamil, convergence, season, arterial, albans,\n",
      "Nearest to see: include, reduce, matthews, winn, cabins, privately, lg, refer,\n",
      "Nearest to or: and, than, rabban, unwilling, though, while, arleigh, mahindra,\n",
      "Nearest to s: whose, zel, studs, sharia, dumbarton, isbn, alamos, arcs,\n",
      "Nearest to can: could, may, will, must, should, would, might, cannot,\n",
      "Nearest to used: designed, written, referred, available, considered, seen, sold, applied,\n",
      "Nearest to were: are, have, had, was, including, been, be, poisoning,\n",
      "Nearest to nine: eight, seven, six, five, four, zero, three, two,\n",
      "Nearest to if: when, though, before, then, where, rogues, cannot, although,\n",
      "Nearest to on: upon, in, through, within, regarding, at, hellbender, around,\n",
      "Nearest to been: become, be, grown, were, jaguars, fallen, was, hypocrisy,\n",
      "Nearest to he: she, it, they, there, who, panzer, eventually, soon,\n",
      "Average loss at step 62000: 2.840291\n",
      "Average loss at step 64000: 2.830208\n",
      "Average loss at step 66000: 2.709616\n",
      "Average loss at step 68000: 2.730703\n",
      "Average loss at step 70000: 2.762144\n",
      "Nearest to this: which, it, what, palgrave, the, there, another, hovering,\n",
      "Nearest to six: five, eight, four, seven, nine, three, two, zero,\n",
      "Nearest to into: through, within, around, from, toward, under, over, towards,\n",
      "Nearest to not: never, now, still, always, actually, almost, officially, thickness,\n",
      "Nearest to world: vietnam, hominy, binet, convergence, eucharist, progesterone, season, jethro,\n",
      "Nearest to see: karenga, references, refer, reduce, matthews, applies, links, but,\n",
      "Nearest to or: and, than, formant, remaking, edu, argo, airbases, citing,\n",
      "Nearest to s: whose, zel, isbn, aloe, sharia, truss, bromide, larps,\n",
      "Nearest to can: must, may, could, will, should, cannot, might, would,\n",
      "Nearest to used: seen, sold, found, available, referred, applied, designed, defined,\n",
      "Nearest to were: are, have, was, include, including, had, although, is,\n",
      "Nearest to nine: eight, seven, five, six, four, zero, three, two,\n",
      "Nearest to if: when, though, where, before, although, since, after, today,\n",
      "Nearest to on: upon, rotates, through, in, regarding, succeeding, anastasia, nl,\n",
      "Nearest to been: become, be, grown, fallen, come, recently, jaguars, already,\n",
      "Nearest to he: she, it, they, who, there, we, eventually, then,\n",
      "Average loss at step 72000: 2.812717\n",
      "Average loss at step 74000: 2.774024\n",
      "Average loss at step 76000: 2.652563\n",
      "Average loss at step 78000: 2.801954\n",
      "Average loss at step 80000: 2.818815\n",
      "Nearest to this: which, it, what, uscis, becquerel, antisense, some, palgrave,\n",
      "Nearest to six: seven, five, eight, four, three, nine, zero, two,\n",
      "Nearest to into: through, within, around, from, under, ditko, across, meade,\n",
      "Nearest to not: still, never, actually, almost, always, t, now, generally,\n",
      "Nearest to world: vietnam, memorabilia, buddhism, binet, deshima, wtc, u, hominy,\n",
      "Nearest to see: include, winn, karenga, cabins, note, matthews, external, mishap,\n",
      "Nearest to or: and, than, remaking, weathered, airbases, uncontrollably, und, absalon,\n",
      "Nearest to s: whose, dumbarton, isbn, antipsychotic, essendon, larps, kevin, celebrates,\n",
      "Nearest to can: could, must, will, may, should, would, might, cannot,\n",
      "Nearest to used: sold, referred, found, seen, described, known, applied, designed,\n",
      "Nearest to were: are, was, been, have, had, be, those, include,\n",
      "Nearest to nine: eight, seven, six, five, zero, three, four, two,\n",
      "Nearest to if: when, though, where, before, since, whether, without, excite,\n",
      "Nearest to on: upon, in, regarding, at, through, during, under, against,\n",
      "Nearest to been: become, be, were, grown, was, come, recently, already,\n",
      "Nearest to he: she, it, they, there, who, we, willow, minefields,\n",
      "Average loss at step 82000: 2.692566\n",
      "Average loss at step 84000: 2.749761\n",
      "Average loss at step 86000: 2.731515\n",
      "Average loss at step 88000: 2.744036\n",
      "Average loss at step 90000: 2.760078\n",
      "Nearest to this: which, it, what, some, the, that, palgrave, heron,\n",
      "Nearest to six: eight, seven, five, nine, four, three, two, zero,\n",
      "Nearest to into: through, from, within, around, across, over, towards, beyond,\n",
      "Nearest to not: never, now, actually, t, always, onyx, generally, still,\n",
      "Nearest to world: vietnam, memorabilia, eucharist, outages, wtc, internet, convergence, kylix,\n",
      "Nearest to see: include, refer, includes, cabins, note, references, matthews, consider,\n",
      "Nearest to or: and, than, remaking, while, though, magenta, definiteness, although,\n",
      "Nearest to s: isbn, whose, his, zel, dumbarton, celebrates, opal, zhang,\n",
      "Nearest to can: could, must, will, should, would, may, cannot, might,\n",
      "Nearest to used: applied, referred, sold, known, cited, described, seen, needed,\n",
      "Nearest to were: are, was, had, have, those, include, including, persons,\n",
      "Nearest to nine: eight, seven, six, five, four, zero, three, two,\n",
      "Nearest to if: when, though, where, before, since, then, whether, without,\n",
      "Nearest to on: upon, through, regarding, anastasia, in, at, within, around,\n",
      "Nearest to been: become, grown, be, fallen, hypocrisy, vampyre, recently, come,\n",
      "Nearest to he: she, they, it, there, we, who, panzer, alem,\n",
      "Average loss at step 92000: 2.658724\n",
      "Average loss at step 94000: 2.759931\n",
      "Average loss at step 96000: 2.712250\n",
      "Average loss at step 98000: 2.690971\n",
      "Average loss at step 100000: 2.261757\n",
      "Nearest to this: which, it, another, what, palgrave, some, ethelred, achaea,\n",
      "Nearest to six: seven, four, five, eight, nine, three, two, zero,\n",
      "Nearest to into: through, within, across, beyond, under, around, toward, onto,\n",
      "Nearest to not: never, actually, always, occasionally, generally, t, still, now,\n",
      "Nearest to world: vietnam, internet, kylix, hindustani, jamil, relinquishing, progesterone, wtc,\n",
      "Nearest to see: includes, include, saw, external, karenga, can, note, cabins,\n",
      "Nearest to or: and, than, airbases, remaking, though, slavonic, prejudiced, while,\n",
      "Nearest to s: whose, his, isbn, dumbarton, loony, american, your, zel,\n",
      "Nearest to can: could, must, should, will, cannot, might, would, may,\n",
      "Nearest to used: sold, referred, applied, cited, described, introduced, defined, needed,\n",
      "Nearest to were: are, was, have, include, had, be, while, lymphedema,\n",
      "Nearest to nine: eight, seven, six, five, four, three, zero, two,\n",
      "Nearest to if: when, though, before, where, excite, without, follows, after,\n",
      "Nearest to on: upon, regarding, through, carducci, under, celibate, around, rotates,\n",
      "Nearest to been: become, be, grown, fallen, come, was, recently, remained,\n",
      "Nearest to he: she, they, it, there, who, we, panzer, ultimately,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 100001\n",
    "# num_steps = 1\n",
    "with tf.Session(graph=graph) as session:\n",
    "  tf.global_variables_initializer().run()\n",
    "  print('Initialized')\n",
    "  average_loss = 0\n",
    "  for step in range(num_steps):    \n",
    "    batch_data, batch_labels = generate_batch_cbow(\n",
    "      batch_size, num_skips, skip_window) \n",
    "    \n",
    "    feed_dict = {train_dataset : batch_data, train_labels : batch_labels}\n",
    "    _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "    \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": false
   },
   "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, :])\n",
    "words = [reverse_dictionary[i] for i in range(1, num_points+1)]\n",
    "plot(two_d_embeddings, words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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