WordEmbeddings_Word2Vec_NCE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\n",
      "Requirement already satisfied: scipy in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\n",
      "Requirement already satisfied: numpy>=1.8.2 in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages (from scipy)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages (from matplotlib)\n",
      "Requirement already satisfied: pytz in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages (from matplotlib)\n",
      "Requirement already satisfied: numpy>=1.7.1 in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages (from matplotlib)\n",
      "Requirement already satisfied: six>=1.10 in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages (from matplotlib)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages (from matplotlib)\n",
      "Requirement already satisfied: python-dateutil>=2.0 in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages (from matplotlib)\n",
      "Requirement already satisfied: pandas in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\n",
      "Requirement already satisfied: numpy>=1.9.0 in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages (from pandas)\n",
      "Requirement already satisfied: python-dateutil>=2 in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages (from pandas)\n",
      "Requirement already satisfied: pytz>=2011k in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages (from pandas)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages (from python-dateutil>=2->pandas)\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy\n",
    "!pip install scipy\n",
    "!pip install matplotlib\n",
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import math\n",
    "import os\n",
    "import random\n",
    "import zipfile\n",
    "from six.moves import urllib\n",
    "from six.moves import xrange\n",
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "DOWNLOADED_FILENAME = 'SampleText.zip'\n",
    "\n",
    "def maybe_download(url_path, expected_bytes):\n",
    "    if not os.path.exists(DOWNLOADED_FILENAME):\n",
    "        filename, _ = urllib.request.urlretrieve(url_path, DOWNLOADED_FILENAME)\n",
    "    statinfo = os.stat(DOWNLOADED_FILENAME)\n",
    "    if statinfo.st_size == expected_bytes:\n",
    "        print('Found and verified file from this path: ',  url_path)\n",
    "        print('Downloaded file: ', DOWNLOADED_FILENAME)\n",
    "    else:\n",
    "        print(statinfo.st_size)\n",
    "        raise Exception('Failed to verify file from: ' + url_path + '. Can you get to it with a browser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_words():\n",
    "    with zipfile.ZipFile(DOWNLOADED_FILENAME) as f:\n",
    "        firstfile = f.namelist()[0]\n",
    "        filestring = tf.compat.as_str(f.read(firstfile))\n",
    "        words = filestring.split()\n",
    "        \n",
    "        return words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found and verified file from this path:  http://mattmahoney.net/dc/text8.zip\n",
      "Downloaded file:  SampleText.zip\n"
     ]
    }
   ],
   "source": [
    "URL_PATH = 'http://mattmahoney.net/dc/text8.zip'\n",
    "FILESIZE = 31344016\n",
    "maybe_download(URL_PATH, FILESIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocabulary = read_words()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17005207"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocabulary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anarchism',\n",
       " 'originated',\n",
       " 'as',\n",
       " 'a',\n",
       " 'term',\n",
       " 'of',\n",
       " 'abuse',\n",
       " 'first',\n",
       " 'used',\n",
       " 'against',\n",
       " 'early',\n",
       " 'working',\n",
       " 'class',\n",
       " 'radicals',\n",
       " 'including',\n",
       " 'the',\n",
       " 'diggers',\n",
       " 'of',\n",
       " 'the',\n",
       " 'english',\n",
       " 'revolution',\n",
       " 'and',\n",
       " 'the',\n",
       " 'sans',\n",
       " 'culottes']"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocabulary[:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_dataset(words, n_words):\n",
    "    word_counts = [['UNKNOWN', -1]]\n",
    "    \n",
    "    counter = collections.Counter(words)\n",
    "    word_counts.extend(counter.most_common(n_words -  1))\n",
    "    \n",
    "    dictionary = dict()\n",
    "    \n",
    "    for word, _ in word_counts:\n",
    "        dictionary[word] = len(dictionary)\n",
    "        \n",
    "    word_indexes = list()\n",
    "        \n",
    "    unknown_count = 0\n",
    "    for word in words:\n",
    "        if word in dictionary:\n",
    "            index = dictionary[word]\n",
    "        else:\n",
    "            index = 0 # dictionary['UNKNOWN']\n",
    "            unknown_count += 1\n",
    "            \n",
    "        word_indexes.append(index)\n",
    "        \n",
    "    word_counts[0][1] = unknown_count\n",
    "    \n",
    "    reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n",
    "    \n",
    "    return word_counts, word_indexes, dictionary, reversed_dictionary\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "VOCABULARY_SIZE = 10000\n",
    "word_counts, word_indexes, dictionary, reversed_dictionary = build_dataset(vocabulary, VOCABULARY_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['UNKNOWN', 1737307],\n",
       " ('the', 1061396),\n",
       " ('of', 593677),\n",
       " ('and', 416629),\n",
       " ('one', 411764),\n",
       " ('in', 372201),\n",
       " ('a', 325873),\n",
       " ('to', 316376),\n",
       " ('zero', 264975),\n",
       " ('nine', 250430)]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5234,\n",
       " 3081,\n",
       " 12,\n",
       " 6,\n",
       " 195,\n",
       " 2,\n",
       " 3134,\n",
       " 46,\n",
       " 59,\n",
       " 156,\n",
       " 128,\n",
       " 742,\n",
       " 477,\n",
       " 0,\n",
       " 134,\n",
       " 1,\n",
       " 0,\n",
       " 2,\n",
       " 1,\n",
       " 103,\n",
       " 855,\n",
       " 3,\n",
       " 1,\n",
       " 0,\n",
       " 0]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_indexes[:25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minerals : 3950\n",
      "slave : 3256\n",
      "seeking : 3627\n",
      "turing : 3742\n",
      "proposition : 7352\n",
      "amongst : 2816\n",
      "born : 267\n",
      "smith : 1011\n",
      "moment : 2724\n",
      "jesuit : 7403\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "for key in random.sample(list(dictionary), 10):\n",
    "    print(key, ':', dictionary[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8058 : explorers\n",
      "2594 : consumption\n",
      "9413 : blame\n",
      "8854 : refusal\n",
      "8344 : intelsat\n",
      "4705 : lasting\n",
      "5732 : incomplete\n",
      "6340 : challenges\n",
      "3667 : legendary\n",
      "5441 : fruits\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "for key in random.sample(list(reversed_dictionary), 10):\n",
    "    print(key, ':', reversed_dictionary[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "del vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Global index into words maintained across batches\n",
    "global_index = 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_batch(word_indexes, batch_size, num_skips, skip_window):\n",
    "    global global_index\n",
    "\n",
    "    # For every input we find num_skips context words within a window\n",
    "    assert batch_size % num_skips == 0\n",
    "    assert num_skips <= 2 * skip_window\n",
    "    \n",
    "    # batch = [1, 2, 3, .... batch_size]\n",
    "    # labels = [[1], [2], [3], ..., [batch_size]]\n",
    "    batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "    \n",
    "    # The span of a window includes the skip_window elements on each side\n",
    "    # of the input word plus the word itself\n",
    "    span = 2 * skip_window + 1  # [ skip_window input_word skip_window ]\n",
    "\n",
    "    # A deque is double-ended queue which supports memory efficient appends\n",
    "    # and pops from each side\n",
    "    buffer = collections.deque(maxlen=span)\n",
    "\n",
    "    # Initialize the deque with the first words in the deque\n",
    "    for _ in range(span):\n",
    "        buffer.append(word_indexes[global_index])\n",
    "        global_index = (global_index + 1) % len(word_indexes)\n",
    "    \n",
    "    for i in range(batch_size // num_skips):\n",
    "        target = skip_window  # input word at the center of the buffer\n",
    "        targets_to_avoid = [skip_window]\n",
    "\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",
    "            \n",
    "            batch[i * num_skips + j] = buffer[skip_window]  # this is the input word\n",
    "            labels[i * num_skips + j, 0] = buffer[target]  # these are the context words\n",
    "        \n",
    "        # The first word from the buffer is removed automatically when a new word\n",
    "        # is added in at the end\n",
    "        buffer.append(word_indexes[global_index])\n",
    "        global_index = (global_index + 1) % len(word_indexes)\n",
    "    \n",
    "    # Backtrack a little bit to avoid skipping words in the end of a batch, these\n",
    "    # words will be captured in the next batch\n",
    "    global_index = (global_index + len(word_indexes) - span) % len(word_indexes)\n",
    "\n",
    "    return batch, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch, labels = generate_batch(word_indexes, 10, 2, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   2,    2, 3134, 3134,   46,   46,   59,   59,  156,  156])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  59],\n",
       "       [  12],\n",
       "       [  12],\n",
       "       [ 742],\n",
       "       [   2],\n",
       "       [ 128],\n",
       "       [ 742],\n",
       "       [3134],\n",
       "       [   2],\n",
       "       [   0]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "of :  used\n",
      "of :  as\n",
      "abuse :  as\n",
      "abuse :  working\n",
      "first :  of\n",
      "first :  early\n",
      "used :  working\n",
      "used :  abuse\n",
      "against :  of\n"
     ]
    }
   ],
   "source": [
    "for i in range(9):\n",
    "    print(reversed_dictionary[batch[i]], ': ', reversed_dictionary[labels[i][0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "global_index = 0\n",
    "valid_size = 16\n",
    "valid_window = 100\n",
    "\n",
    "valid_examples = np.random.choice(valid_window, valid_size, replace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples = 64 # number of corrupted sample pairs to feed into the NCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "embedding_size = 50\n",
    "skip_window = 2\n",
    "num_skips = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "train_inputs = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_dataset = tf.constant(valid_examples, dtype=tf.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = tf.Variable(\n",
    "    tf.random_uniform([VOCABULARY_SIZE, embedding_size], -1.0, 1.0))\n",
    "embed = tf.nn.embedding_lookup(embeddings, train_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'Variable:0' shape=(10000, 50) dtype=float32_ref>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'embedding_lookup:0' shape=(128, 50) dtype=float32>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "nce_weights = tf.Variable(tf.truncated_normal([VOCABULARY_SIZE, embedding_size], stddev=1.0 / math.sqrt(embedding_size)))\n",
    "nce_biases = tf.Variable(tf.zeros([VOCABULARY_SIZE]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = tf.reduce_mean(\n",
    "    tf.nn.nce_loss(\n",
    "        weights=nce_weights,\n",
    "        biases=nce_biases,\n",
    "        labels=train_labels,\n",
    "        inputs=embed,\n",
    "        num_sampled=num_samples,\n",
    "        num_classes=VOCABULARY_SIZE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.train.GradientDescentOptimizer(0.1).minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "l2_norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n",
    "\n",
    "normalized_embeddings = embeddings / l2_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'embedding_lookup_1:0' shape=(16, 50) dtype=float32>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'truediv:0' shape=(10000, 50) dtype=float32>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_steps = 200001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  0 :  211.421691895\n",
      "Nearest to their extreme, algebra, empress, within, ic, ko, ticket, kent,\n",
      "Nearest to is union, concern, annexed, proposals, home, natives, architects, jedi,\n",
      "Nearest to more highways, carey, one, colts, croatian, helps, potter, pursued,\n",
      "Nearest to than down, programme, teaching, locally, chlorine, correctly, producers, lights,\n",
      "Nearest to some thinkers, bismarck, crusade, quartet, sam, directing, diminished, linguistics,\n",
      "Nearest to other addiction, spectacular, legally, optimal, billion, oxidation, herbert, buried,\n",
      "Nearest to zero kane, martial, driven, offices, copies, literacy, expensive, generalized,\n",
      "Nearest to by famine, vi, presidency, connections, genesis, chains, iceland, fossils,\n",
      "Nearest to it fleming, shooter, finance, plates, armoured, physically, reasoning, fe,\n",
      "Nearest to with aided, introductory, puerto, hung, subsequent, away, aging, holmes,\n",
      "Nearest to had rendering, specified, diary, twins, mus, mirror, promised, downtown,\n",
      "Nearest to that khazar, absorbed, hawaiian, advantage, treated, torture, parts, leibniz,\n",
      "Nearest to between pronunciation, calendar, odd, buttons, clapton, sports, opera, bob,\n",
      "Nearest to eight met, policies, ellis, ranks, principal, grants, afghanistan, secured,\n",
      "Nearest to such instability, gb, bankruptcy, spoken, deities, victorious, slightly, packed,\n",
      "Nearest to been freedoms, twin, bart, servers, oswald, homogeneous, finger, proto,\n",
      "\n",
      "\n",
      "Average loss at step  2000 :  101.095782207\n",
      "Average loss at step  4000 :  45.9591473567\n",
      "Average loss at step  6000 :  30.7741036198\n",
      "Average loss at step  8000 :  23.2816496036\n",
      "Average loss at step  10000 :  19.5140974748\n",
      "Nearest to their f, on, which, because, four, nine, it, also,\n",
      "Nearest to is union, an, states, he, more, eight, general, e,\n",
      "Nearest to more one, was, x, eight, nine, in, four, as,\n",
      "Nearest to than each, states, book, other, part, even, not, down,\n",
      "Nearest to some people, only, at, not, often, other, zero, n,\n",
      "Nearest to other to, most, than, there, often, from, an, have,\n",
      "Nearest to zero with, four, which, nine, six, when, see, first,\n",
      "Nearest to by time, from, seven, five, there, their, has, three,\n",
      "Nearest to it most, four, zero, if, from, their, at, nine,\n",
      "Nearest to with zero, film, only, which, language, its, work, who,\n",
      "Nearest to had two, five, nine, french, f, can, eight, all,\n",
      "Nearest to that were, american, but, have, not, it, such, u,\n",
      "Nearest to between as, eight, four, can, nine, roosevelt, seven, force,\n",
      "Nearest to eight seven, if, four, two, three, an, five, e,\n",
      "Nearest to such american, film, from, d, early, there, two, called,\n",
      "Nearest to been for, a, four, right, five, or, final, world,\n",
      "\n",
      "\n",
      "Average loss at step  12000 :  16.4217142606\n",
      "Average loss at step  14000 :  13.8449496561\n",
      "Average loss at step  16000 :  12.3074943068\n",
      "Average loss at step  18000 :  11.2120859544\n",
      "Average loss at step  20000 :  9.73222854114\n",
      "Nearest to their because, within, f, list, foreign, federal, we, usually,\n",
      "Nearest to is union, home, concern, general, states, results, an, import,\n",
      "Nearest to more x, way, each, years, one, final, following, used,\n",
      "Nearest to than down, book, each, part, even, states, n, years,\n",
      "Nearest to some people, often, only, word, n, evidence, still, born,\n",
      "Nearest to other book, often, n, page, african, different, left, george,\n",
      "Nearest to zero four, see, could, six, brother, traditional, when, with,\n",
      "Nearest to by country, nuclear, study, before, time, we, rather, though,\n",
      "Nearest to it we, her, left, if, most, common, roman, known,\n",
      "Nearest to with away, effect, series, classical, work, something, language, film,\n",
      "Nearest to had french, f, films, teams, u, during, t, can,\n",
      "Nearest to that central, already, american, parties, were, parts, story, but,\n",
      "Nearest to between roosevelt, force, how, calendar, eight, b, if, can,\n",
      "Nearest to eight if, seven, e, frankfurt, francis, usually, links, p,\n",
      "Nearest to such american, film, early, due, called, d, richard, few,\n",
      "Nearest to been right, rights, st, final, place, believed, y, world,\n",
      "\n",
      "\n",
      "Average loss at step  22000 :  8.91881237173\n",
      "Average loss at step  24000 :  8.56620731807\n",
      "Average loss at step  26000 :  8.01872634137\n",
      "Average loss at step  28000 :  7.50661569369\n",
      "Average loss at step  30000 :  7.17169857907\n",
      "Nearest to their within, because, prince, foreign, f, federal, list, algebra,\n",
      "Nearest to is union, concern, home, results, general, an, wanted, early,\n",
      "Nearest to more way, writers, x, following, one, each, similar, cases,\n",
      "Nearest to than down, book, part, each, states, even, n, took,\n",
      "Nearest to some people, word, evidence, often, n, fans, born, still,\n",
      "Nearest to other african, billion, george, herbert, n, book, page, duplicate,\n",
      "Nearest to zero four, brother, blood, traditional, six, two, development, could,\n",
      "Nearest to by country, brief, jesus, nuclear, we, study, rather, solution,\n",
      "Nearest to it we, areas, ethnic, powers, her, roman, questions, left,\n",
      "Nearest to with away, classical, effect, series, brother, something, work, re,\n",
      "Nearest to had teams, french, f, films, seemed, u, t, code,\n",
      "Nearest to that central, parties, already, story, leibniz, parts, possible, american,\n",
      "Nearest to between roosevelt, calendar, force, how, area, sports, b, books,\n",
      "Nearest to eight frankfurt, seven, francis, e, links, if, wine, met,\n",
      "Nearest to such american, early, water, few, richard, due, spoken, slightly,\n",
      "Nearest to been rights, right, st, believed, y, physicist, surface, final,\n",
      "\n",
      "\n",
      "Average loss at step  32000 :  6.88828203094\n",
      "Average loss at step  34000 :  6.51288042903\n",
      "Average loss at step  36000 :  6.34945336819\n",
      "Average loss at step  38000 :  6.27503065729\n",
      "Average loss at step  40000 :  6.02166442955\n",
      "Nearest to their within, because, prince, algebra, fbi, extreme, foreign, greece,\n",
      "Nearest to is union, concern, home, results, an, general, in, wanted,\n",
      "Nearest to more writers, highways, way, wanted, one, x, following, cases,\n",
      "Nearest to than down, book, part, each, took, n, even, states,\n",
      "Nearest to some people, evidence, fans, word, n, born, danish, harry,\n",
      "Nearest to other billion, african, george, herbert, duplicate, n, page, book,\n",
      "Nearest to zero four, brother, blood, two, literacy, traditional, one, six,\n",
      "Nearest to by country, brief, jesus, assembled, nuclear, implement, we, native,\n",
      "Nearest to it we, questions, ethnic, areas, powers, hits, cup, reasoning,\n",
      "Nearest to with away, classical, brother, series, effect, something, re, implies,\n",
      "Nearest to had seemed, teams, french, films, f, capacity, phone, u,\n",
      "Nearest to that central, parties, already, incorrect, to, story, wanted, possible,\n",
      "Nearest to between calendar, roosevelt, sports, pronunciation, force, how, area, opera,\n",
      "Nearest to eight frankfurt, wine, regard, seven, francis, links, met, four,\n",
      "Nearest to such american, spoken, slightly, water, few, richard, early, total,\n",
      "Nearest to been rights, believed, surface, st, physicist, y, right, forth,\n",
      "\n",
      "\n",
      "Average loss at step  42000 :  5.89246825254\n",
      "Average loss at step  44000 :  5.86576004386\n",
      "Average loss at step  46000 :  5.7388670944\n",
      "Average loss at step  48000 :  5.63779939258\n",
      "Average loss at step  50000 :  5.62787168884\n",
      "Nearest to their within, prince, because, fbi, algebra, extreme, greece, frank,\n",
      "Nearest to is union, concern, an, in, home, results, general, wanted,\n",
      "Nearest to more writers, highways, wanted, way, arabic, server, greenland, cases,\n",
      "Nearest to than down, book, took, part, recently, each, n, might,\n",
      "Nearest to some people, fans, evidence, harry, word, danish, n, load,\n",
      "Nearest to other billion, african, herbert, george, duplicate, n, page, done,\n",
      "Nearest to zero four, two, literacy, blood, brother, five, one, six,\n",
      "Nearest to by brief, country, assembled, jesus, and, nuclear, implement, native,\n",
      "Nearest to it questions, ethnic, we, hits, areas, powers, reasoning, cup,\n",
      "Nearest to with classical, away, brother, series, something, implies, effect, decimal,\n",
      "Nearest to had seemed, teams, phone, films, capacity, french, f, mexico,\n",
      "Nearest to that central, parties, to, incorrect, already, story, wanted, possible,\n",
      "Nearest to between calendar, roosevelt, pronunciation, sports, opera, area, force, bob,\n",
      "Nearest to eight wine, frankfurt, regard, seven, four, francis, two, maps,\n",
      "Nearest to such slightly, spoken, american, few, water, richard, document, instability,\n",
      "Nearest to been surface, believed, rights, physicist, y, st, freedoms, servers,\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  52000 :  5.47094173741\n",
      "Average loss at step  54000 :  5.30654977369\n",
      "Average loss at step  56000 :  5.37181170273\n",
      "Average loss at step  58000 :  5.35968945634\n",
      "Average loss at step  60000 :  5.1492719543\n",
      "Nearest to their prince, within, fbi, because, algebra, issue, greece, frank,\n",
      "Nearest to is union, concern, an, was, home, in, results, are,\n",
      "Nearest to more highways, writers, wanted, arabic, server, way, greenland, berlin,\n",
      "Nearest to than down, book, recently, took, part, each, caused, might,\n",
      "Nearest to some people, fans, load, ieee, harry, evidence, danish, thinkers,\n",
      "Nearest to other billion, african, herbert, george, duplicate, n, legally, page,\n",
      "Nearest to zero four, two, literacy, five, six, blood, one, nine,\n",
      "Nearest to by brief, country, assembled, and, jesus, implement, nuclear, native,\n",
      "Nearest to it questions, ethnic, hits, we, powers, reasoning, areas, dividing,\n",
      "Nearest to with classical, brother, away, implies, something, series, decimal, effect,\n",
      "Nearest to had seemed, teams, phone, films, capacity, mexico, subsequent, specified,\n",
      "Nearest to that central, to, parties, incorrect, establishments, story, already, wanted,\n",
      "Nearest to between calendar, pronunciation, roosevelt, sports, opera, bob, warm, force,\n",
      "Nearest to eight wine, frankfurt, regard, seven, four, two, three, five,\n",
      "Nearest to such slightly, spoken, few, water, instability, american, document, richard,\n",
      "Nearest to been servers, surface, freedoms, believed, physicist, rights, proto, y,\n",
      "\n",
      "\n",
      "Average loss at step  62000 :  5.17688908851\n",
      "Average loss at step  64000 :  5.23571161509\n",
      "Average loss at step  66000 :  5.20064215446\n",
      "Average loss at step  68000 :  4.96451871324\n",
      "Average loss at step  70000 :  4.7710554862\n",
      "Nearest to their prince, fbi, because, within, algebra, issue, greece, frank,\n",
      "Nearest to is union, concern, was, an, are, results, home, in,\n",
      "Nearest to more highways, writers, wanted, arabic, server, greenland, way, berlin,\n",
      "Nearest to than down, recently, book, took, plains, caused, nelson, might,\n",
      "Nearest to some people, fans, load, harry, ieee, thinkers, danish, evidence,\n",
      "Nearest to other billion, african, herbert, george, duplicate, speaks, legally, buried,\n",
      "Nearest to zero four, two, literacy, six, five, blood, nine, one,\n",
      "Nearest to by brief, country, assembled, and, jesus, implement, nuclear, from,\n",
      "Nearest to it questions, ethnic, hits, we, dividing, blues, reasoning, powers,\n",
      "Nearest to with classical, brother, implies, away, something, decimal, series, bias,\n",
      "Nearest to had seemed, phone, teams, mexico, capacity, films, specified, subsequent,\n",
      "Nearest to that central, to, parties, establishments, incorrect, story, already, wanted,\n",
      "Nearest to between pronunciation, calendar, roosevelt, sports, opera, bob, warm, thomas,\n",
      "Nearest to eight wine, seven, frankfurt, four, regard, two, three, five,\n",
      "Nearest to such slightly, spoken, few, instability, water, document, richard, total,\n",
      "Nearest to been servers, surface, freedoms, believed, physicist, proto, rights, y,\n",
      "\n",
      "\n",
      "Average loss at step  72000 :  4.88048671639\n",
      "Average loss at step  74000 :  4.87461630499\n",
      "Average loss at step  76000 :  4.95218024063\n",
      "Average loss at step  78000 :  4.96757116151\n",
      "Average loss at step  80000 :  4.91564486742\n",
      "Nearest to their prince, fbi, issue, because, algebra, moses, within, greece,\n",
      "Nearest to is union, was, concern, an, are, results, home, eighth,\n",
      "Nearest to more highways, writers, wanted, arabic, server, greenland, berlin, companies,\n",
      "Nearest to than down, recently, book, took, plains, nelson, caused, eastern,\n",
      "Nearest to some load, people, thinkers, fans, harry, ieee, expensive, danish,\n",
      "Nearest to other billion, african, herbert, speaks, duplicate, george, legally, buried,\n",
      "Nearest to zero four, two, six, literacy, five, nine, blood, seven,\n",
      "Nearest to by brief, country, and, assembled, implement, jesus, from, nuclear,\n",
      "Nearest to it questions, ethnic, hits, we, dividing, that, blues, fe,\n",
      "Nearest to with classical, implies, brother, away, something, decimal, bias, series,\n",
      "Nearest to had seemed, phone, teams, mexico, capacity, films, subsequent, specified,\n",
      "Nearest to that central, parties, establishments, to, incorrect, it, story, already,\n",
      "Nearest to between pronunciation, roosevelt, calendar, opera, sports, bob, warm, thomas,\n",
      "Nearest to eight seven, wine, four, frankfurt, regard, three, two, five,\n",
      "Nearest to such slightly, spoken, instability, few, document, water, richard, total,\n",
      "Nearest to been servers, freedoms, surface, believed, proto, physicist, gibraltar, y,\n",
      "\n",
      "\n",
      "Average loss at step  82000 :  4.98638243032\n",
      "Average loss at step  84000 :  4.94129114854\n",
      "Average loss at step  86000 :  4.94401347589\n",
      "Average loss at step  88000 :  4.8977302891\n",
      "Average loss at step  90000 :  4.98932049608\n",
      "Nearest to their prince, fbi, issue, moses, because, algebra, greece, frank,\n",
      "Nearest to is was, union, concern, are, an, results, home, eighth,\n",
      "Nearest to more highways, writers, wanted, arabic, server, greenland, embassy, berlin,\n",
      "Nearest to than down, recently, plains, took, book, nelson, eastern, programme,\n",
      "Nearest to some load, people, harry, thinkers, fans, ieee, expensive, conjunction,\n",
      "Nearest to other billion, herbert, african, speaks, george, duplicate, legally, buried,\n",
      "Nearest to zero four, two, six, literacy, five, nine, seven, blood,\n",
      "Nearest to by brief, country, and, assembled, implement, jesus, from, nuclear,\n",
      "Nearest to it questions, ethnic, hits, that, dividing, we, blues, fe,\n",
      "Nearest to with classical, implies, brother, bias, decimal, away, something, asked,\n",
      "Nearest to had seemed, phone, teams, mexico, capacity, films, specified, subsequent,\n",
      "Nearest to that central, establishments, parties, incorrect, to, it, story, plains,\n",
      "Nearest to between pronunciation, opera, roosevelt, calendar, sports, bob, warm, nominal,\n",
      "Nearest to eight seven, four, wine, regard, frankfurt, three, five, two,\n",
      "Nearest to such slightly, spoken, instability, few, document, water, richard, honduras,\n",
      "Nearest to been servers, freedoms, surface, believed, proto, physicist, gibraltar, printing,\n",
      "\n",
      "\n",
      "Average loss at step  92000 :  4.9536936661\n",
      "Average loss at step  94000 :  4.85921576393\n",
      "Average loss at step  96000 :  4.91735957432\n",
      "Average loss at step  98000 :  4.89754741275\n",
      "Average loss at step  100000 :  4.8255928961\n",
      "Nearest to their prince, fbi, moses, issue, greece, because, algebra, frank,\n",
      "Nearest to is was, union, are, concern, results, an, home, eighth,\n",
      "Nearest to more highways, writers, wanted, arabic, server, embassy, greenland, potter,\n",
      "Nearest to than down, recently, plains, took, nelson, programme, eastern, book,\n",
      "Nearest to some load, people, thinkers, harry, fans, conjunction, expensive, ieee,\n",
      "Nearest to other billion, speaks, herbert, african, duplicate, george, legally, buried,\n",
      "Nearest to zero four, two, six, five, literacy, nine, seven, one,\n",
      "Nearest to by brief, and, country, assembled, from, implement, jesus, nuclear,\n",
      "Nearest to it questions, that, hits, ethnic, dividing, we, blues, fe,\n",
      "Nearest to with classical, implies, brother, bias, decimal, something, away, asked,\n",
      "Nearest to had seemed, phone, teams, mexico, capacity, films, specified, subsequent,\n",
      "Nearest to that central, establishments, parties, incorrect, it, to, but, story,\n",
      "Nearest to between pronunciation, opera, roosevelt, sports, calendar, bob, warm, nominal,\n",
      "Nearest to eight seven, four, wine, three, regard, two, five, frankfurt,\n",
      "Nearest to such slightly, spoken, instability, few, document, water, richard, initial,\n",
      "Nearest to been servers, freedoms, surface, proto, believed, gibraltar, physicist, printing,\n",
      "\n",
      "\n",
      "Average loss at step  102000 :  4.85334782529\n",
      "Average loss at step  104000 :  4.89167041874\n",
      "Average loss at step  106000 :  4.89629791009\n",
      "Average loss at step  108000 :  4.88666311133\n",
      "Average loss at step  110000 :  4.81906627393\n",
      "Nearest to their prince, fbi, moses, issue, because, greece, algebra, frank,\n",
      "Nearest to is was, union, are, concern, results, home, an, eighth,\n",
      "Nearest to more highways, writers, wanted, arabic, server, embassy, greenland, potter,\n",
      "Nearest to than down, recently, plains, took, nelson, programme, eastern, producers,\n",
      "Nearest to some load, people, thinkers, harry, conjunction, fans, expensive, ieee,\n",
      "Nearest to other billion, speaks, herbert, african, legally, george, duplicate, markets,\n",
      "Nearest to zero four, two, six, five, literacy, nine, seven, one,\n",
      "Nearest to by brief, and, country, assembled, from, implement, jesus, nuclear,\n",
      "Nearest to it questions, that, hits, dividing, ethnic, we, blues, fe,\n",
      "Nearest to with classical, implies, bias, brother, decimal, something, away, asked,\n",
      "Nearest to had seemed, mus, phone, mexico, teams, capacity, accompanying, films,\n",
      "Nearest to that establishments, central, it, parties, incorrect, but, to, refer,\n",
      "Nearest to between pronunciation, opera, roosevelt, sports, calendar, bob, warm, nominal,\n",
      "Nearest to eight seven, four, wine, three, five, two, regard, frankfurt,\n",
      "Nearest to such slightly, instability, spoken, document, few, water, richard, initial,\n",
      "Nearest to been servers, freedoms, proto, surface, gibraltar, believed, printing, physicist,\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  112000 :  4.76791553092\n",
      "Average loss at step  114000 :  4.79294438803\n",
      "Average loss at step  116000 :  4.88700121474\n",
      "Average loss at step  118000 :  4.72684439385\n",
      "Average loss at step  120000 :  4.82928825521\n",
      "Nearest to their moses, fbi, prince, issue, because, greece, frank, algebra,\n",
      "Nearest to is was, union, are, concern, results, home, eighth, an,\n",
      "Nearest to more highways, writers, wanted, arabic, embassy, server, walls, potter,\n",
      "Nearest to than down, recently, plains, nelson, programme, took, producers, eastern,\n",
      "Nearest to some load, people, thinkers, harry, expensive, conjunction, fans, ieee,\n",
      "Nearest to other billion, speaks, herbert, african, legally, george, duplicate, markets,\n",
      "Nearest to zero four, two, six, five, literacy, seven, nine, eight,\n",
      "Nearest to by brief, and, assembled, country, from, implement, jesus, stop,\n",
      "Nearest to it questions, that, dividing, hits, ethnic, we, fe, blues,\n",
      "Nearest to with implies, classical, bias, brother, decimal, something, asked, away,\n",
      "Nearest to had seemed, mus, phone, mexico, teams, capacity, accompanying, films,\n",
      "Nearest to that establishments, central, it, parties, incorrect, but, refer, plains,\n",
      "Nearest to between pronunciation, opera, roosevelt, sports, bob, calendar, warm, nominal,\n",
      "Nearest to eight seven, four, wine, five, three, two, six, regard,\n",
      "Nearest to such slightly, instability, spoken, document, few, water, richard, initial,\n",
      "Nearest to been servers, freedoms, proto, surface, gibraltar, believed, printing, sisters,\n",
      "\n",
      "\n",
      "Average loss at step  122000 :  4.85929284036\n",
      "Average loss at step  124000 :  4.8322750771\n",
      "Average loss at step  126000 :  4.75071253741\n",
      "Average loss at step  128000 :  4.72149627697\n",
      "Average loss at step  130000 :  4.76270981574\n",
      "Nearest to their moses, fbi, prince, issue, because, greece, frank, algebra,\n",
      "Nearest to is was, union, are, concern, results, home, eighth, garbage,\n",
      "Nearest to more highways, writers, wanted, arabic, embassy, server, buddha, walls,\n",
      "Nearest to than down, recently, plains, programme, nelson, took, producers, eastern,\n",
      "Nearest to some load, people, thinkers, harry, expensive, conjunction, fans, legs,\n",
      "Nearest to other billion, speaks, herbert, african, legally, addiction, george, markets,\n",
      "Nearest to zero four, two, six, five, literacy, seven, eight, nine,\n",
      "Nearest to by brief, assembled, country, and, from, implement, jesus, stop,\n",
      "Nearest to it questions, that, dividing, hits, ethnic, we, fe, blues,\n",
      "Nearest to with classical, implies, bias, brother, decimal, something, asked, away,\n",
      "Nearest to had seemed, mus, phone, mexico, teams, capacity, accompanying, films,\n",
      "Nearest to that establishments, it, central, parties, incorrect, but, refer, abilities,\n",
      "Nearest to between pronunciation, opera, roosevelt, bob, sports, warm, calendar, nominal,\n",
      "Nearest to eight seven, four, three, five, wine, two, six, nine,\n",
      "Nearest to such instability, slightly, document, few, spoken, water, richard, initial,\n",
      "Nearest to been servers, freedoms, proto, gibraltar, surface, believed, sisters, printing,\n",
      "\n",
      "\n",
      "Average loss at step  132000 :  4.81882813847\n",
      "Average loss at step  134000 :  4.82267982185\n",
      "Average loss at step  136000 :  4.85272883976\n",
      "Average loss at step  138000 :  4.8005764569\n",
      "Average loss at step  140000 :  4.82573066485\n",
      "Nearest to their moses, fbi, issue, prince, because, greece, frank, algebra,\n",
      "Nearest to is was, are, union, results, concern, home, eighth, garbage,\n",
      "Nearest to more highways, writers, wanted, embassy, buddha, server, arabic, walls,\n",
      "Nearest to than down, recently, plains, programme, nelson, producers, took, eastern,\n",
      "Nearest to some load, people, thinkers, harry, expensive, conjunction, legs, calculate,\n",
      "Nearest to other billion, speaks, herbert, african, legally, addiction, markets, george,\n",
      "Nearest to zero four, two, six, five, literacy, seven, eight, nine,\n",
      "Nearest to by brief, assembled, country, and, from, implement, jesus, stop,\n",
      "Nearest to it questions, that, dividing, hits, we, ethnic, fe, blues,\n",
      "Nearest to with classical, implies, bias, brother, decimal, something, asked, series,\n",
      "Nearest to had seemed, mus, phone, mexico, accompanying, capacity, teams, rendering,\n",
      "Nearest to that establishments, it, central, parties, incorrect, but, refer, abilities,\n",
      "Nearest to between pronunciation, opera, roosevelt, sports, bob, warm, nominal, calendar,\n",
      "Nearest to eight seven, four, three, five, six, wine, two, nine,\n",
      "Nearest to such instability, slightly, document, few, spoken, water, unfortunately, initial,\n",
      "Nearest to been freedoms, servers, proto, gibraltar, sisters, believed, surface, printing,\n",
      "\n",
      "\n",
      "Average loss at step  142000 :  4.8240235424\n",
      "Average loss at step  144000 :  4.7125931201\n",
      "Average loss at step  146000 :  4.7919253056\n",
      "Average loss at step  148000 :  4.74175543296\n",
      "Average loss at step  150000 :  4.63488449097\n",
      "Nearest to their moses, issue, fbi, prince, because, greece, frank, algebra,\n",
      "Nearest to is was, are, union, results, concern, home, enacted, garbage,\n",
      "Nearest to more highways, writers, wanted, buddha, embassy, arabic, walls, server,\n",
      "Nearest to than down, recently, plains, programme, nelson, producers, took, influx,\n",
      "Nearest to some load, people, harry, thinkers, expensive, legs, conjunction, improvement,\n",
      "Nearest to other billion, speaks, addiction, african, legally, herbert, markets, george,\n",
      "Nearest to zero four, two, six, five, seven, literacy, eight, three,\n",
      "Nearest to by brief, assembled, country, and, from, implement, jesus, gnome,\n",
      "Nearest to it questions, that, dividing, we, hits, ethnic, fe, blues,\n",
      "Nearest to with classical, implies, bias, decimal, brother, asked, something, series,\n",
      "Nearest to had seemed, mus, phone, mexico, accompanying, capacity, teams, penguin,\n",
      "Nearest to that establishments, it, central, parties, incorrect, but, refer, abilities,\n",
      "Nearest to between pronunciation, opera, roosevelt, bob, nominal, sports, warm, calendar,\n",
      "Nearest to eight seven, four, five, three, six, two, wine, nine,\n",
      "Nearest to such instability, document, few, slightly, spoken, water, unfortunately, initial,\n",
      "Nearest to been freedoms, servers, proto, gibraltar, sisters, surface, believed, printing,\n",
      "\n",
      "\n",
      "Average loss at step  152000 :  4.65833401501\n",
      "Average loss at step  154000 :  4.74652535546\n",
      "Average loss at step  156000 :  4.78472289622\n",
      "Average loss at step  158000 :  4.74993290257\n",
      "Average loss at step  160000 :  4.74089781916\n",
      "Nearest to their moses, issue, fbi, prince, because, greece, frank, firms,\n",
      "Nearest to is was, are, union, results, concern, home, enacted, garbage,\n",
      "Nearest to more highways, writers, buddha, wanted, embassy, potter, walls, server,\n",
      "Nearest to than down, recently, plains, programme, influx, producers, nelson, took,\n",
      "Nearest to some load, people, harry, thinkers, expensive, legs, improvement, conjunction,\n",
      "Nearest to other speaks, billion, addiction, legally, african, herbert, markets, george,\n",
      "Nearest to zero four, two, six, five, seven, literacy, eight, three,\n",
      "Nearest to by brief, assembled, country, and, from, implement, jesus, chains,\n",
      "Nearest to it questions, that, we, dividing, fe, hits, ethnic, this,\n",
      "Nearest to with classical, bias, implies, decimal, brother, asked, something, series,\n",
      "Nearest to had seemed, mus, phone, mexico, accompanying, capacity, teams, rendering,\n",
      "Nearest to that establishments, it, central, parties, incorrect, refer, but, abilities,\n",
      "Nearest to between pronunciation, opera, nominal, bob, roosevelt, sports, warm, drawings,\n",
      "Nearest to eight seven, four, five, three, six, two, wine, nine,\n",
      "Nearest to such instability, document, few, slightly, spoken, water, unfortunately, bankruptcy,\n",
      "Nearest to been freedoms, servers, gibraltar, proto, sisters, believed, printing, surface,\n",
      "\n",
      "\n",
      "Average loss at step  162000 :  4.75405560791\n",
      "Average loss at step  164000 :  4.69656135511\n",
      "Average loss at step  166000 :  4.79165714312\n",
      "Average loss at step  168000 :  4.77210371101\n",
      "Average loss at step  170000 :  4.77785310125\n",
      "Nearest to their moses, issue, fbi, prince, because, greece, firms, frank,\n",
      "Nearest to is was, are, union, results, home, concern, enacted, garbage,\n",
      "Nearest to more highways, writers, buddha, wanted, embassy, potter, walls, arabic,\n",
      "Nearest to than down, recently, plains, programme, influx, producers, nelson, took,\n",
      "Nearest to some load, people, harry, expensive, thinkers, improvement, legs, mediation,\n",
      "Nearest to other speaks, billion, addiction, legally, african, herbert, markets, chat,\n",
      "Nearest to zero four, two, six, five, seven, literacy, eight, three,\n",
      "Nearest to by brief, assembled, country, and, from, implement, jesus, gnome,\n",
      "Nearest to it questions, that, we, dividing, fe, ethnic, hits, this,\n",
      "Nearest to with classical, bias, implies, decimal, brother, asked, something, series,\n",
      "Nearest to had seemed, mus, phone, mexico, accompanying, capacity, teams, penguin,\n",
      "Nearest to that establishments, it, incorrect, central, parties, refer, but, abilities,\n",
      "Nearest to between pronunciation, opera, nominal, roosevelt, bob, warm, sports, drawings,\n",
      "Nearest to eight seven, four, five, three, six, two, nine, wine,\n",
      "Nearest to such instability, document, few, slightly, spoken, unfortunately, bankruptcy, water,\n",
      "Nearest to been freedoms, servers, gibraltar, proto, sisters, believed, printing, surface,\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  172000 :  4.79859703588\n",
      "Average loss at step  174000 :  4.71843234694\n",
      "Average loss at step  176000 :  4.76785620451\n",
      "Average loss at step  178000 :  4.7904667896\n",
      "Average loss at step  180000 :  4.7807831074\n",
      "Nearest to their moses, issue, fbi, prince, firms, because, greece, frank,\n",
      "Nearest to is was, are, union, results, home, concern, enacted, garbage,\n",
      "Nearest to more highways, writers, buddha, wanted, embassy, potter, server, walls,\n",
      "Nearest to than down, recently, plains, programme, influx, producers, nelson, latvian,\n",
      "Nearest to some load, people, harry, expensive, improvement, mediation, legs, thinkers,\n",
      "Nearest to other speaks, addiction, billion, legally, african, herbert, markets, chat,\n",
      "Nearest to zero four, six, two, five, seven, eight, literacy, three,\n",
      "Nearest to by assembled, brief, country, from, implement, and, gnome, jesus,\n",
      "Nearest to it questions, that, we, dividing, fe, this, hits, ethnic,\n",
      "Nearest to with classical, bias, implies, batting, decimal, brother, asked, series,\n",
      "Nearest to had seemed, mus, phone, mexico, accompanying, capacity, penguin, rendering,\n",
      "Nearest to that establishments, it, incorrect, central, parties, but, refer, abilities,\n",
      "Nearest to between pronunciation, opera, nominal, roosevelt, bob, warm, sports, drawings,\n",
      "Nearest to eight seven, four, five, six, three, nine, two, wine,\n",
      "Nearest to such instability, document, few, slightly, spoken, unfortunately, bankruptcy, initial,\n",
      "Nearest to been freedoms, servers, gibraltar, proto, sisters, believed, printing, bart,\n",
      "\n",
      "\n",
      "Average loss at step  182000 :  4.76783630192\n",
      "Average loss at step  184000 :  4.77435924017\n",
      "Average loss at step  186000 :  4.70829463291\n",
      "Average loss at step  188000 :  4.71878337896\n",
      "Average loss at step  190000 :  4.77009598982\n",
      "Nearest to their moses, issue, fbi, prince, firms, because, greece, uniforms,\n",
      "Nearest to is was, are, union, home, results, concern, enacted, garbage,\n",
      "Nearest to more highways, writers, buddha, wanted, embassy, potter, walls, server,\n",
      "Nearest to than down, recently, plains, influx, programme, producers, nelson, took,\n",
      "Nearest to some load, people, harry, expensive, improvement, mediation, legs, calculate,\n",
      "Nearest to other speaks, addiction, billion, legally, african, markets, herbert, chat,\n",
      "Nearest to zero four, six, two, five, seven, eight, three, literacy,\n",
      "Nearest to by assembled, brief, country, from, implement, and, gnome, jesus,\n",
      "Nearest to it questions, that, this, we, dividing, fe, ethnic, hits,\n",
      "Nearest to with classical, bias, implies, batting, decimal, brother, asked, series,\n",
      "Nearest to had mus, seemed, phone, mexico, accompanying, capacity, penguin, rendering,\n",
      "Nearest to that establishments, it, incorrect, central, parties, but, refer, abilities,\n",
      "Nearest to between pronunciation, opera, nominal, warm, roosevelt, bob, sports, odd,\n",
      "Nearest to eight seven, four, five, six, three, nine, two, wine,\n",
      "Nearest to such document, instability, few, slightly, bankruptcy, unfortunately, spoken, initial,\n",
      "Nearest to been freedoms, servers, gibraltar, proto, bart, sisters, believed, printing,\n",
      "\n",
      "\n",
      "Average loss at step  192000 :  4.76221167374\n",
      "Average loss at step  194000 :  4.78164549136\n",
      "Average loss at step  196000 :  4.81010240471\n",
      "Average loss at step  198000 :  4.78831258619\n",
      "Average loss at step  200000 :  4.64857485402\n",
      "Nearest to their moses, issue, fbi, firms, prince, uniforms, greece, because,\n",
      "Nearest to is was, are, union, home, results, concern, enacted, garbage,\n",
      "Nearest to more highways, writers, buddha, wanted, embassy, potter, server, arabic,\n",
      "Nearest to than down, recently, plains, influx, programme, producers, nelson, latvian,\n",
      "Nearest to some load, people, harry, expensive, mediation, improvement, legs, calculate,\n",
      "Nearest to other speaks, addiction, legally, billion, african, markets, herbert, chat,\n",
      "Nearest to zero four, six, two, five, seven, eight, three, literacy,\n",
      "Nearest to by assembled, brief, country, from, implement, gnome, and, stop,\n",
      "Nearest to it questions, that, this, we, fe, dividing, ethnic, hits,\n",
      "Nearest to with classical, bias, batting, implies, decimal, brother, asked, series,\n",
      "Nearest to had mus, seemed, phone, accompanying, mexico, capacity, penguin, build,\n",
      "Nearest to that establishments, it, incorrect, but, central, refer, parties, abilities,\n",
      "Nearest to between pronunciation, opera, nominal, warm, bob, roosevelt, odd, sports,\n",
      "Nearest to eight seven, four, five, six, three, nine, two, wine,\n",
      "Nearest to such document, instability, few, slightly, bankruptcy, unfortunately, initial, spoken,\n",
      "Nearest to been freedoms, servers, gibraltar, sisters, proto, bart, believed, printing,\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as session:\n",
    "    init.run()\n",
    "    \n",
    "    average_loss = 0\n",
    "    for step in xrange(num_steps):\n",
    "        batch_inputs, batch_labels = generate_batch(\n",
    "            word_indexes, batch_size, num_skips, skip_window)\n",
    "        \n",
    "        feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}\n",
    "        \n",
    "        _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "        \n",
    "        average_loss += loss_val\n",
    "        \n",
    "        # average loss at every step\n",
    "        if step % 2000 == 0:\n",
    "            if step > 0:\n",
    "                average_loss /= 2000\n",
    "                \n",
    "            print('Average loss at step ', step, ': ', average_loss)\n",
    "            average_loss = 0\n",
    "        \n",
    "        # note that this is expensive (compute heavy)\n",
    "        if step % 10000 == 0:\n",
    "            sim = similarity.eval()\n",
    "            \n",
    "            for i in xrange(valid_size):\n",
    "                valid_word = reversed_dictionary[valid_examples[i]]\n",
    "                top_k = 8 # number of nearest neighbors\n",
    "                \n",
    "                # argsort sorts from highest to lowest, flip the sign so the largest distance away is the smallest number\n",
    "                nearest = (-sim[i, :]).argsort()[1:top_k + 1]\n",
    "                \n",
    "                log_str = 'Nearest to %s' % valid_word\n",
    "                \n",
    "                for k in range(top_k):\n",
    "                    close_word = reversed_dictionary[nearest[k]]\n",
    "                    log_str = '%s %s,' % (log_str, close_word)\n",
    "                print(log_str)\n",
    "                \n",
    "            print('\\n')\n",
    "                    \n",
    "    final_embeddings = normalized_embeddings.eval()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_word_embeddings(embeddings, word_counts):\n",
    "    np.save('embeddings.npy', embeddings)\n",
    "    \n",
    "    words = [x[0] for x in word_counts]\n",
    "    np.save('words.npy', words)\n",
    "save_word_embeddings(final_embeddings, word_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_with_labels(low_dim_embs, labels, filename='tsne.jpg'):\n",
    "    assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'\n",
    "    \n",
    "    plt.figure(figsize=(18, 18))\n",
    "    \n",
    "    for i, label in enumerate(labels):\n",
    "        x, y = low_dim_embs[i, :]\n",
    "        \n",
    "        plt.scatter(x, y)\n",
    "        \n",
    "        plt.annotate(\n",
    "            label,\n",
    "            xy=(x, y),\n",
    "            xytext=(5, 2),\n",
    "            textcoords='offset points',\n",
    "            ha='right',\n",
    "            va='bottom')\n",
    "    plt.savefig(filename)\n",
    "    plt.show(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sklearn in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\jenglish\\appdata\\local\\programs\\python\\python36\\lib\\site-packages (from sklearn)\n"
     ]
    }
   ],
   "source": [
    "!pip install sklearn\n",
    "from sklearn.manifold import TSNE\n",
    "NUM_PLOT_POINTS = 500\n",
    "\n",
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')\n",
    "\n",
    "low_dims_embs = tsne.fit_transform(final_embeddings[:NUM_PLOT_POINTS, :])\n",
    "\n",
    "labels = [reversed_dictionary[i] for i in xrange(NUM_PLOT_POINTS)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x190b1b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_with_labels(low_dims_embs, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}