8. Embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Embeddings\n",
    "\n",
    "(Some parts of this lecture are adapted material from http://cs224d.stanford.edu/)\n",
    "\n",
    "Deep Learning algorithms require the input to be represented as (sequences of) fixed-length feature vectors. \n",
    "\n",
    "Words in documents and other categorical features such as user/product ids in recommeders, names of places, visited URLs, etc. are usually represented by using a one-of-K scheme (**one-hot encoding**). Phrases are represented by bag-of-words or bag-of-ngrams features, loosing the ordering of words and ignoring semantics. \n",
    "\n",
    "Are these good representations for deep learning?\n",
    "\n",
    "Let's see how to represent **words**, but this line of reasoning can be extended to other items.\n",
    "\n",
    "There are an estimated 13 million tokens for the English language. \n",
    "\n",
    "One possible strategy is to encode word tokens each into some vector that represents a point in some sort of *word space* that *represents* language semantics. \n",
    "\n",
    "The most intuitive reason is that perhaps there actually exists some $N$-dimensional space (such that $N << 13$ million) that is sufficient to encode all semantics of our language. \n",
    "\n",
    "Each dimension would encode some meaning that we transfer using speech."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-hot encoding\n",
    "\n",
    "If we represent every word as an $\\mathbb{R}^{|V|\\times 1}$ vector with all $0$s and one $1$ at the index of that word in the sorted english language, word vectors in this type of encoding would appear as the following:\n",
    "\n",
    "<centering>\n",
    "$$w^{aardvark} = \\left[ \\begin{array}{c} 1 \\\\ 0 \\\\ 0 \\\\ \\vdots \\\\ 0 \\end{array} \\right], w^{a} = \\left[ \\begin{array}{c} 0 \\\\ 1 \\\\ 0 \\\\ \\vdots \\\\ 0 \\end{array} \\right] , w^{at} = \\left[ \\begin{array}{c} 0 \\\\ 0 \\\\ 1 \\\\ \\vdots \\\\ 0 \\end{array} \\right] , \\cdot w^{zebra} = \\left[ \\begin{array}{c} 0 \\\\ 0 \\\\ 0 \\\\ \\vdots \\\\ 1 \\end{array} \\right] $$\n",
    "</centering>\n",
    "\n",
    "\n",
    "We represent each word as a completely independent entity:\n",
    "\n",
    "$$(w^{hotel})^Tw^{motel} = (w^{hotel})^Tw^{cat} = 0$$\n",
    "\n",
    "What other alternatives are there?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Semantics from word-document matrix\n",
    "\n",
    "As our first attempt, we make the bold conjecture that words that are related will often appear in the same documents. \n",
    "\n",
    "For instance, \"banks\", \"bonds\", \"stocks\", \"money\", etc. are probably likely to appear together. But \"banks\", \"octopus\", \"banana\", and \"hockey\" would probably not consistently appear together. \n",
    "\n",
    "We use this fact to build a word-document matrix, $X$ in the following manner: \n",
    "\n",
    "+ Loop over billions of documents and for each time word $i$ appears in document $j$, we add one to entry $X_{ij}$. \n",
    "\n",
    "This is obviously a very large matrix ($\\mathbb{R}^{|V|\\times M}$) and it scales with the number of documents ($M$). \n",
    "\n",
    "So perhaps we can try something better, such as building a window based co-occurrence matrix.\n",
    "\n",
    "In this method we count the number of times each word appears inside a window of a particular size around the word of interest. We calculate this count for all the words in corpus. \n",
    "\n",
    "Let our corpus contain just three sentences and the window size be 1:\n",
    "\n",
    "+ ``I enjoy flying``.\n",
    "+ ``I like NLP``.\n",
    "+ ``I like deep learning``.\n",
    "\n",
    "The resulting counts matrix will then be:\n",
    "\n",
    "$$X=\\left[ \\begin{array}{cccccccc}\n",
    "        & I & like & enjoy & deep & learning & NLP & flying & . \\\\\n",
    "     I & 0 & 2 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
    "    like & 2 & 0 & 0 & 1 & 0 & 1 & 0 & 0\\\\\n",
    "    enjoy & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\\\\n",
    "    deep & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 \\\\\n",
    "    learning & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1\\\\\n",
    "    NLP & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1\\\\\n",
    "    flying & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1\\\\\n",
    "    . & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 0 \\\\\n",
    "  \\end{array} \\right]$$\n",
    "\n",
    "Once the $|V|\\times|V|$ co-occurrence matrix $X$ has been generated, we can apply SVD on $X$ to get $X = USV^T$ and select the first $k$ columns of $U$ to get a $k$-dimensional word vectors. $\\frac{\\sum_{i = 1}^{k}\\sigma_i}{\\sum_{i = 1}^{|V|}\\sigma_i}$ indicates the amount of variance captured by the first $k$ dimensions.\n",
    "\n",
    "These vectors encode some kind of semantics but they have some problems:\n",
    "\n",
    "+ The dimensions of the matrix can change very often (new words are added very frequently and corpus changes in size).\n",
    "+ SVD based methods do not scale well for big matrices and it is hard to incorporate new words or documents. \n",
    "+ The matrix is extremely sparse since most words do not co-occur.\n",
    "+ The matrix is very high dimensional in general ($\\approx 10^6 \\times 10^6$)\n",
    "+ Quadratic cost to train (i.e. to perform SVD)\n",
    "+ Requires the incorporation of some hacks on $X$ to account for the drastic imbalance in word frequency\n",
    "\n",
    "Some solutions to exist to resolve some of the issues discussed above:\n",
    "+ Ignore function words such as \"the\", \"he\", \"has\", etc.\n",
    "+ Apply a ramp window -- i.e. weight the co-occurrence count based on distance between the words in the document. \n",
    "+ Use Pearson correlation and set negative counts to 0 instead of using just raw count.\n",
    "\n",
    "But a NN method can solve many of these issues in a far more elegant manner....\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``word2vec``\n",
    "\n",
    "Instead of computing and storing global information about some huge dataset (which might be billions of sentences), we can try to create a model that will be able to learn one iteration at a time and eventually be able to encode the probability of a word given its context. \n",
    "\n",
    "> The **context of a word** is the set of $m$ surrounding words. For instance, the $m = 2$ context of the word ``fox`` in the sentence ``The quick brown fox jumped over the lazy dog`` is \\{``quick``, ``brown``, ``jumped``, ``over``\\}.\n",
    "\n",
    "The idea is to design a model whose parameters are the word vectors. Then, train the model on a certain objective. \n",
    "\n",
    "At every iteration we run our model, evaluate the errors, and follow an update rule that has some notion of penalizing the model parameters that caused the error. Thus, we learn our word vectors. \n",
    "\n",
    "Mikolov presented a simple, probabilistic model in 2013 that is known as ``word2vec``. In fact, ``word2vec`` includes 2 algorithms (CBOW and skip-gram) and 2 training methods (negative sampling and hierarchical softmax).\n",
    "\n",
    "> This model relies on a very important hypothesis in linguistics, *distributional similarity*, the idea that similar words have similar context."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to create such a model that will assign a probability to a sequence of tokens. Let us start with an example: ``The cat jumped over the puddle``. \n",
    "\n",
    "A good language model will give this sentence a high probability because this is a completely valid sentence, syntactically and semantically. Similarly, the sentence ``stock boil fish is toy`` should have a very low probability because it makes no sense. \n",
    "\n",
    "Mathematically, we can call this probability on any given sequence of $n$ words:\n",
    "\n",
    "$$P(w_{1}, w_{2}, \\cdots, w_{n})$$\n",
    "\n",
    "We can take the unary language model approach and break apart this probability by assuming the word occurrences are completely independent:\n",
    "\n",
    "$$P(w_{1}, w_{2}, \\cdots, w_{n}) = \\prod_{i=1}^n P(w_{i})$$\n",
    "\n",
    "However, we know the next word is highly contingent upon the previous sequence of words. So perhaps we let the probability of the sequence depend on the pairwise probability of a word in the sequence and the word next to it. We call this the bigram model and represent it as:\n",
    "\n",
    "$$P(w_{1}, w_{2}, \\cdots, w_{n}) = \\prod_{i=2}^n P(w_{i} | w_{i-1})$$\n",
    "\n",
    "Again this is certainly a bit naive since we are only concerning ourselves with pairs of neighboring words rather than evaluating a whole sentence, but as we will see, this representation gets us pretty far along. Note in the Word-Word Matrix with a context of size 1, we basically can learn these pairwise probabilities. But again, this would require computing and storing global information about a massive dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuous Bag of Words Model (CBOW)\n",
    "\n",
    "The approach is to treat {``The``, ``cat``, ``over``, ``the``, ``puddle``} as a context and from these words, be able to predict or generate the center word ``jumped``. \n",
    "\n",
    "First, we set up our known parameters. Let the known parameters in our model be the sentence represented by one-hot word vectors. \n",
    "\n",
    "The input one hot vectors or context will represented with an $x^{(c)}$. And the output as $y$ which is the one hot vector of the known center word. \n",
    "\n",
    "Now let's define our unknowns in our model. \n",
    "\n",
    "We create two matrices, $\\mathcal{V} \\in \\mathbb{R}^{n\\times|V|}$ and $\\mathcal{U} \\in \\mathbb{R}^{|V|\\times n}$, where $n$ is an arbitrary size which defines the size of our embedding space. \n",
    "\n",
    "$\\mathcal{V}$ is the input word matrix such that the $i$-th column of $\\mathcal{V}$ is the $n$-dimensional **embedded vector** for word $w_{i}$ when it is an input to this model. We denote this $n\\times1$ vector as $v_{i}$. \n",
    "\n",
    "Similarly, $\\mathcal{U}$ is the output word matrix. The $j$-th row of $\\mathcal{U}$ is an $n$-dimensional embedded vector for word $w_{j}$ when it is an output of the model. We denote this row of $\\mathcal{U}$ as $u_{j}$. Note that we do in fact learn two vectors for every word $w_{i}$ (i.e. input word vector $v_{i}$ and output word vector $u_{i}$).\n",
    "\n",
    "We breakdown the way this model works in these steps:\n",
    "+ We generate our one hot word vectors  for the input context of size $m : (x^{(c-m)}, \\dots, x^{(c-1)}, x^{(c+1)},\\dots, x^{(c+m)} \\in \\mathbb{R}^{|V|}$).\n",
    "\n",
    "+ We get our embedded word vectors for the context ($v_{c-m}=\\mathcal{V}x^{(c-m)}, v_{c-m+1}=\\mathcal{V}x^{(c-m+1)}, \\dots, v_{c+m}=\\mathcal{V}x^{(c+m)} \\in \\mathbb{R}^n$) \n",
    "\n",
    "<center>\n",
    "<img src=\"images/w2v1.png\" alt=\"\" style=\"width: 400px;\"/> \n",
    "</center>\n",
    "\n",
    "\n",
    "+ Average these vectors to get $h = \\frac{v_{c-m}+v_{c-m+1}+... + v_{c+m}}{2m} \\in \\mathbb{R}^n$\n",
    "+ Generate a score vector $z=\\mathcal{U}h \\in \\mathbb{R}^{|V|}$. As the dot product of similar vectors is higher, it will push similar words close to each other in order to achieve a high score.\n",
    "+ Turn the scores into probabilities $\\hat{y}=\\operatorname{softmax}(z) \\in \\mathbb{R}^{|V|}$. \n",
    "+ We desire our probabilities generated, $\\hat{y} \\in \\mathbb{R}^{|V|}$, to match the true probabilities, $y \\in \\mathbb{R}^{|V|}$, which also happens to be the one hot vector of the actual word.\n",
    "\n",
    "How can we learn these two matrices? Well, we need to create an objective function. \n",
    "\n",
    "Here, we use a popular choice of distance/loss measure, cross entropy $H(\\hat{y},y)$. \n",
    "\n",
    "We thus formulate our optimization objective as:\n",
    "\n",
    "$$\n",
    "\\mbox{minimize } J = -\\log P(w_{c}|w_{c-m}, \\dots, w_{c-1}, w_{c+1}, \\dots, w_{c+m}) \\\\\n",
    "= -\\log P(u_{c}|h) \\\\\n",
    "= -\\log \\frac{\\exp(u_{c}^{T}h)}{\\sum_{j=1}^{|V|}\\exp(u_{j}^{T}h)} \\\\\n",
    "= -u_{c}^{T}h+\\log \\sum_{j=1}^{|V|}\\exp(u_{j}^{T}h) \\\\\n",
    "$$\n",
    "\n",
    "We use stochastic gradient descent to update all relevant word vectors $u_{c}$ and $v_{j}$.\n",
    "\n",
    "\n",
    "<center>\n",
    "<img src=\"images/word2vec-cbow.png\" alt=\"\" style=\"width: 400px;\"/> \n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Skip-gram model\n",
    "\n",
    "Another approach is to create a model such that given the center word ``jumped``, the model will be able to predict or generate the surrounding words ``The``, ``cat``, ``over``, ``the``, ``puddle``. Here we call the word ``jumped`` the context. We call this type of model a Skip-Gram model.\n",
    "\n",
    "\n",
    "<center>\n",
    "<img src=\"images/fword2vec-sg.png\" alt=\"\" style=\"width: 400px;\"/> \n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A ``word2vec`` implementation\n",
    "\n",
    "A word embedding layer is usually regarded as a mapping from a discrete set of objects (words) to a real valued vector, i.e. \n",
    "\n",
    "$$k\\in\\{1..|V|\\} \\rightarrow \\mathbb{R}^{n}$$\n",
    "\n",
    "Thus, we can represent the *Embedding layer* as $|V|\\times n$ matrix, or just a table/dictionary.\n",
    "\n",
    "$$\n",
    "\\begin{matrix}\n",
    "word_1: \\\\\n",
    "word_2:\\\\\n",
    "\\vdots\\\\\n",
    "word_{|V|}: \\\\\n",
    "\\end{matrix}\n",
    "\\left[\n",
    "\\begin{matrix}\n",
    "x_{1,1}&x_{1,2}& \\dots &x_{1,n}\\\\\n",
    "x_{2,1}&x_{2,2}& \\dots &x_{2,n}\\\\\n",
    "\\vdots&&\\\\\n",
    "x_{{|V|},1}&x_{{|V|},2}& \\dots &x_{{|V|},n}\\\\\n",
    "\\end{matrix}\n",
    "\\right]\n",
    "$$\n",
    "\n",
    "In this sense, the basic operation that an embedding layer has to accomplish is that given a certain word it returns the assigned code. And the goal in learning is to learn the values in the matrix.\n",
    "\n",
    "Let's see how to code a skip-gram model (This code is based on the word2vec example from Udacity)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function\n",
    "import collections\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import tensorflow as tf\n",
    "import zipfile\n",
    "from matplotlib import pylab\n",
    "from six.moves import range\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found and verified text8.zip\n"
     ]
    }
   ],
   "source": [
    "url = 'http://mattmahoney.net/dc/'\n",
    "\n",
    "def maybe_download(filename, expected_bytes):\n",
    "    \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
    "    if not os.path.exists(filename):\n",
    "        filename, _ = 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('Failed to verify ' + filename + '. Can you get to it with a browser?')\n",
    "    return filename\n",
    "\n",
    "filename = maybe_download('text8.zip', 31344016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size 17005207\n"
     ]
    }
   ],
   "source": [
    "def read_data(filename):\n",
    "    \"\"\"Extract the first file enclosed in a zip file as a list of words\"\"\"\n",
    "    with zipfile.ZipFile(filename) as f:\n",
    "        data = tf.compat.as_str(f.read(f.namelist()[0])).split()\n",
    "    return data\n",
    "  \n",
    "words = read_data(filename)\n",
    "print('Data size %d' % len(words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common words (+UNK) [['UNK', 418391], ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764)]\n",
      "Sample data [5239, 3084, 12, 6, 195, 2, 3137, 46, 59, 156]\n"
     ]
    }
   ],
   "source": [
    "vocabulary_size = 50000\n",
    "\n",
    "def build_dataset(words):\n",
    "    count = [['UNK', -1]]\n",
    "    count.extend(collections.Counter(words).most_common(vocabulary_size - 1))\n",
    "    dictionary = dict()\n",
    "    for word, _ in count:\n",
    "        dictionary[word] = len(dictionary)\n",
    "    data = list()\n",
    "    unk_count = 0\n",
    "    for word in words:\n",
    "        if word in dictionary:\n",
    "            index = dictionary[word]\n",
    "        else:\n",
    "            index = 0  # dictionary['UNK']\n",
    "            unk_count = unk_count + 1\n",
    "        data.append(index)\n",
    "    count[0][1] = unk_count\n",
    "    reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) \n",
    "    return data, count, dictionary, reverse_dictionary\n",
    "\n",
    "data, count, dictionary, reverse_dictionary = build_dataset(words)\n",
    "print('Most common words (+UNK)', count[:5])\n",
    "print('Sample data', data[:10])\n",
    "del words  # Hint to reduce memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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: ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term']\n",
      "    labels: ['as', 'anarchism', 'originated', 'a', 'term', 'as', 'of', 'a']\n",
      "\n",
      "with num_skips = 4 and skip_window = 2:\n",
      "    batch: ['as', 'as', 'as', 'as', 'a', 'a', 'a', 'a']\n",
      "    labels: ['originated', 'a', 'term', 'anarchism', 'term', 'of', 'as', 'originated']\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)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "embedding_size = 64 # 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(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
    "    softmax_weights = tf.Variable(tf.truncated_normal([vocabulary_size, embedding_size],stddev=1.0 / math.sqrt(embedding_size)))\n",
    "    softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "  \n",
    "    # Model.\n",
    "    # Look up embeddings for inputs.\n",
    "    embed = tf.nn.embedding_lookup(embeddings, train_dataset)\n",
    "    # Compute the softmax loss, using a sample of the negative labels each time.\n",
    "    loss = tf.reduce_mean(\n",
    "        tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases, embed,\n",
    "                               train_labels, num_sampled, vocabulary_size))\n",
    "\n",
    "    # Optimizer.\n",
    "    # Note: The optimizer will optimize the softmax_weights AND the embeddings.\n",
    "    # This is because the embeddings are defined as a variable quantity and the\n",
    "    # optimizer's `minimize` method will by default modify all variable quantities \n",
    "    # that contribute to the tensor it is passed.\n",
    "    # See docs on `tf.train.Optimizer.minimize()` for more details.\n",
    "    optimizer = tf.train.AdagradOptimizer(0.1).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(normalized_embeddings, valid_dataset)\n",
    "    similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step 2500: 5.618160\n",
      "Average loss at step 5000: 4.695621\n",
      "Average loss at step 7500: 4.540945\n",
      "Average loss at step 10000: 4.263867\n",
      "Average loss at step 12500: 4.239196\n",
      "Average loss at step 15000: 4.165106\n",
      "Average loss at step 17500: 4.043751\n",
      "Average loss at step 20000: 3.620300\n",
      "Average loss at step 22500: 3.796506\n",
      "Average loss at step 25000: 3.833825\n",
      "Average loss at step 27500: 3.911500\n",
      "Average loss at step 30000: 3.890516\n",
      "Average loss at step 32500: 3.883303\n",
      "Average loss at step 35000: 3.895955\n",
      "Average loss at step 37500: 3.858333\n",
      "Average loss at step 40000: 3.927999\n",
      "Average loss at step 42500: 3.785215\n",
      "Average loss at step 45000: 3.858367\n",
      "Average loss at step 47500: 3.849061\n",
      "Average loss at step 50000: 3.794835\n",
      "Nearest to no: lines, stretch, aroma, chd, nadh, restricting, can, spinal,\n",
      "Nearest to they: we, not, abstraction, who, there, communion, can, often,\n",
      "Nearest to of: and, in, from, for, with, on, at, coordinated,\n",
      "Nearest to nine: eight, five, four, shipowner, seven, six, june, p,\n",
      "Nearest to often: this, phylogeny, govt, that, has, it, they, dvb,\n",
      "Nearest to the: a, his, this, an, its, some, their, pandemonium,\n",
      "Nearest to as: by, for, or, had, is, was, at, became,\n",
      "Nearest to war: faithless, silverman, duffy, protects, throughout, quintet, elba, norwegians,\n",
      "Nearest to UNK: vane, surges, and, polycarp, divorcing, agreements, boeotia, de,\n",
      "Nearest to use: same, resurrection, persons, ten, nasals, airline, barking, maturation,\n",
      "Nearest to their: his, this, a, its, her, the, shmuel, long,\n",
      "Nearest to time: contains, extraction, boats, dogma, motorola, jacopo, malfunction, excel,\n",
      "Nearest to more: coaches, as, afc, a, scsi, not, curve, oath,\n",
      "Nearest to also: which, columns, longest, conclude, it, never, there, generally,\n",
      "Nearest to who: many, they, mentha, ailing, also, lumber, bumper, government,\n",
      "Nearest to two: one, five, three, six, four, eight, seven, x,\n",
      "Average loss at step 52500: 3.854381\n",
      "Average loss at step 55000: 3.830963\n",
      "Average loss at step 57500: 3.790832\n",
      "Average loss at step 60000: 3.632633\n",
      "Average loss at step 62500: 3.692237\n",
      "Average loss at step 65000: 3.695202\n",
      "Average loss at step 67500: 3.714251\n",
      "Average loss at step 70000: 3.786122\n",
      "Average loss at step 72500: 3.697259\n",
      "Average loss at step 75000: 3.659678\n",
      "Average loss at step 77500: 3.715915\n",
      "Average loss at step 80000: 3.762874\n",
      "Average loss at step 82500: 3.751905\n",
      "Average loss at step 85000: 3.701120\n",
      "Average loss at step 87500: 3.726815\n",
      "Average loss at step 90000: 3.653459\n",
      "Average loss at step 92500: 3.661096\n",
      "Average loss at step 95000: 3.672899\n",
      "Average loss at step 97500: 3.496379\n",
      "Average loss at step 100000: 3.632762\n",
      "Nearest to no: lines, ever, chd, public, stretch, restricting, traffic, they,\n",
      "Nearest to they: we, there, who, not, often, she, abstraction, communion,\n",
      "Nearest to of: in, and, for, from, at, with, adobe, on,\n",
      "Nearest to nine: eight, four, five, six, seven, three, june, shipowner,\n",
      "Nearest to often: now, they, sometimes, it, phylogeny, not, that, govt,\n",
      "Nearest to the: its, his, this, a, their, some, an, her,\n",
      "Nearest to as: by, became, for, or, had, after, is, slurred,\n",
      "Nearest to war: throughout, faithless, duffy, protects, quintet, silverman, modern, elba,\n",
      "Nearest to UNK: de, surges, divorcing, vane, music, semaphore, agreements, boeotia,\n",
      "Nearest to use: same, resurrection, persons, noticed, slot, superiority, nasals, walsingham,\n",
      "Nearest to their: his, its, her, the, this, some, a, these,\n",
      "Nearest to time: contains, just, motorola, third, extraction, until, boats, blasted,\n",
      "Nearest to more: very, coaches, scsi, afc, not, less, curve, rather,\n",
      "Nearest to also: which, never, generally, she, longest, who, columns, conclude,\n",
      "Nearest to who: they, subsequently, he, also, many, ailing, bumper, generally,\n",
      "Nearest to two: three, one, five, four, six, eight, seven, zero,\n",
      "Average loss at step 102500: 3.656036\n",
      "Average loss at step 105000: 3.649205\n",
      "Average loss at step 107500: 3.655052\n",
      "Average loss at step 110000: 3.643182\n",
      "Average loss at step 112500: 3.691658\n",
      "Average loss at step 115000: 3.676606\n",
      "Average loss at step 117500: 3.644660\n",
      "Average loss at step 120000: 3.598847\n",
      "Average loss at step 122500: 3.661544\n",
      "Average loss at step 125000: 3.675376\n",
      "Average loss at step 127500: 3.643842\n",
      "Average loss at step 130000: 3.640816\n",
      "Average loss at step 132500: 3.619692\n",
      "Average loss at step 135000: 3.657332\n",
      "Average loss at step 137500: 3.640347\n",
      "Average loss at step 140000: 3.660355\n",
      "Average loss at step 142500: 3.619280\n",
      "Average loss at step 145000: 3.504077\n",
      "Average loss at step 147500: 3.664374\n",
      "Average loss at step 150000: 3.615538\n",
      "Nearest to no: lines, ever, chd, traffic, public, restricting, often, diligently,\n",
      "Nearest to they: we, there, she, you, who, not, abstraction, he,\n",
      "Nearest to of: in, and, for, from, adobe, multifinder, include, at,\n",
      "Nearest to nine: eight, four, seven, six, five, three, june, september,\n",
      "Nearest to often: now, sometimes, generally, also, not, they, phylogeny, still,\n",
      "Nearest to the: its, their, this, his, a, some, any, her,\n",
      "Nearest to as: became, by, for, after, slurred, through, educator, or,\n",
      "Nearest to war: throughout, faithless, duffy, quintet, protects, silverman, elba, modern,\n",
      "Nearest to UNK: de, surges, divorcing, boeotia, vane, music, semaphore, consensual,\n",
      "Nearest to use: same, noticed, superiority, resurrection, slot, irbms, persons, walsingham,\n",
      "Nearest to their: its, his, her, the, some, this, these, remembers,\n",
      "Nearest to time: contains, position, just, way, point, third, blasted, motorola,\n",
      "Nearest to more: very, less, coaches, rather, scsi, accumulator, rides, little,\n",
      "Nearest to also: which, never, generally, still, often, she, who, now,\n",
      "Nearest to who: subsequently, they, he, also, generally, ailing, bumper, there,\n",
      "Nearest to two: three, one, four, five, six, eight, seven, x,\n",
      "Average loss at step 152500: 3.652853\n",
      "Average loss at step 155000: 3.635882\n",
      "Average loss at step 157500: 3.627491\n",
      "Average loss at step 160000: 3.625133\n",
      "Average loss at step 162500: 3.639715\n",
      "Average loss at step 165000: 3.650510\n",
      "Average loss at step 167500: 3.623952\n",
      "Average loss at step 170000: 3.613857\n",
      "Average loss at step 172500: 3.641512\n",
      "Average loss at step 175000: 3.565536\n",
      "Average loss at step 177500: 3.596904\n",
      "Average loss at step 180000: 3.533303\n",
      "Average loss at step 182500: 3.606286\n",
      "Average loss at step 185000: 3.587696\n",
      "Average loss at step 187500: 3.590522\n",
      "Average loss at step 190000: 3.598032\n",
      "Average loss at step 192500: 3.503763\n",
      "Average loss at step 195000: 3.604935\n",
      "Average loss at step 197500: 3.572635\n",
      "Average loss at step 200000: 3.594940\n",
      "Nearest to no: ever, lines, chd, traffic, restricting, often, diligently, there,\n",
      "Nearest to they: we, there, she, you, he, who, itself, not,\n",
      "Nearest to of: in, multifinder, including, for, include, adobe, and, from,\n",
      "Nearest to nine: eight, seven, six, four, five, three, june, september,\n",
      "Nearest to often: sometimes, now, generally, usually, also, still, widely, commonly,\n",
      "Nearest to the: its, their, this, his, any, a, some, each,\n",
      "Nearest to as: became, by, maximized, educator, single, through, slurred, after,\n",
      "Nearest to war: throughout, faithless, duffy, quintet, protects, longships, silverman, elba,\n",
      "Nearest to UNK: de, surges, divorcing, vane, boeotia, george, semaphore, com,\n",
      "Nearest to use: same, noticed, superiority, slot, irbms, persons, walsingham, make,\n",
      "Nearest to their: its, his, her, the, some, this, these, whose,\n",
      "Nearest to time: contains, way, position, point, third, just, blasted, faith,\n",
      "Nearest to more: very, less, rather, coaches, scsi, accumulator, most, too,\n",
      "Nearest to also: never, generally, which, still, often, now, sometimes, variously,\n",
      "Nearest to who: subsequently, he, they, also, generally, she, ailing, never,\n",
      "Nearest to two: three, four, one, five, six, seven, eight, meters,\n",
      "Average loss at step 202500: 3.558359\n",
      "Average loss at step 205000: 3.579161\n",
      "Average loss at step 207500: 3.508187\n",
      "Average loss at step 210000: 3.586982\n",
      "Average loss at step 212500: 3.481638\n",
      "Average loss at step 215000: 3.521411\n",
      "Average loss at step 217500: 3.533665\n",
      "Average loss at step 220000: 3.606407\n",
      "Average loss at step 222500: 3.546392\n",
      "Average loss at step 225000: 3.494799\n",
      "Average loss at step 227500: 3.525150\n",
      "Average loss at step 230000: 3.565238\n",
      "Average loss at step 232500: 3.575878\n",
      "Average loss at step 235000: 3.582258\n",
      "Average loss at step 237500: 3.544350\n",
      "Average loss at step 240000: 3.475993\n",
      "Average loss at step 242500: 3.558477\n",
      "Average loss at step 245000: 3.527850\n",
      "Average loss at step 247500: 3.515312\n",
      "Average loss at step 250000: 3.544951\n",
      "Nearest to no: lines, ever, traffic, chd, diligently, wing, any, restricting,\n",
      "Nearest to they: we, there, she, you, he, who, itself, hardin,\n",
      "Nearest to of: multifinder, in, including, adobe, regarding, include, for, whose,\n",
      "Nearest to nine: eight, six, seven, four, five, three, june, ft,\n",
      "Nearest to often: sometimes, now, usually, generally, commonly, widely, still, typically,\n",
      "Nearest to the: its, their, any, his, this, some, a, each,\n",
      "Nearest to as: became, maximized, by, educator, naval, single, ellipse, after,\n",
      "Nearest to war: throughout, longships, duffy, quintet, faithless, protects, bretons, elaboration,\n",
      "Nearest to UNK: de, surges, divorcing, boeotia, vane, george, semaphore, david,\n",
      "Nearest to use: same, noticed, make, superiority, irbms, form, result, persons,\n",
      "Nearest to their: its, his, her, the, some, whose, these, this,\n",
      "Nearest to time: contains, way, position, point, year, third, blasted, bible,\n",
      "Nearest to more: very, less, most, rather, too, scsi, accumulator, coaches,\n",
      "Nearest to also: never, generally, which, often, still, now, sometimes, variously,\n",
      "Nearest to who: subsequently, he, they, she, also, never, umami, generally,\n",
      "Nearest to two: three, four, five, one, six, seven, eight, meters,\n",
      "Average loss at step 252500: 3.524985\n",
      "Average loss at step 255000: 3.561411\n",
      "Average loss at step 257500: 3.492368\n",
      "Average loss at step 260000: 3.496768\n",
      "Average loss at step 262500: 3.533744\n",
      "Average loss at step 265000: 3.450255\n",
      "Average loss at step 267500: 3.521760\n",
      "Average loss at step 270000: 3.485674\n",
      "Average loss at step 272500: 3.175003\n",
      "Average loss at step 275000: 3.242081\n",
      "Average loss at step 277500: 3.263982\n",
      "Average loss at step 280000: 3.433504\n",
      "Average loss at step 282500: 3.449870\n",
      "Average loss at step 285000: 3.452071\n",
      "Average loss at step 287500: 3.493918\n",
      "Average loss at step 290000: 3.524574\n",
      "Average loss at step 292500: 3.484503\n",
      "Average loss at step 295000: 3.494033\n",
      "Average loss at step 297500: 3.395717\n",
      "Average loss at step 300000: 3.510319\n",
      "Nearest to no: traffic, chd, ever, lines, diligently, any, wing, webzine,\n",
      "Nearest to they: we, there, she, you, he, who, itself, hardin,\n",
      "Nearest to of: multifinder, including, regarding, include, in, whose, adobe, gait,\n",
      "Nearest to nine: eight, six, seven, four, five, three, ft, june,\n",
      "Nearest to often: sometimes, usually, now, generally, commonly, widely, still, typically,\n",
      "Nearest to the: its, their, any, his, this, some, each, a,\n",
      "Nearest to as: became, maximized, educator, by, naval, single, after, ellipse,\n",
      "Nearest to war: throughout, longships, duffy, quintet, bretons, elaboration, faithless, protects,\n",
      "Nearest to UNK: de, divorcing, surges, boeotia, vane, david, semaphore, george,\n",
      "Nearest to use: same, superiority, noticed, make, irbms, form, persons, story,\n",
      "Nearest to their: its, his, her, the, some, whose, my, any,\n",
      "Nearest to time: way, contains, position, point, year, success, third, bible,\n",
      "Nearest to more: less, very, most, rather, too, accumulator, scsi, higher,\n",
      "Nearest to also: never, generally, which, often, still, now, sometimes, variously,\n",
      "Nearest to who: subsequently, he, she, they, never, umami, also, weighing,\n",
      "Nearest to two: three, four, five, one, six, seven, eight, meters,\n",
      "Average loss at step 302500: 3.499659\n",
      "Average loss at step 305000: 3.508238\n",
      "Average loss at step 307500: 3.541372\n",
      "Average loss at step 310000: 3.490294\n",
      "Average loss at step 312500: 3.377665\n",
      "Average loss at step 315000: 3.332164\n",
      "Average loss at step 317500: 3.463564\n",
      "Average loss at step 320000: 3.362650\n",
      "Average loss at step 322500: 3.476604\n",
      "Average loss at step 325000: 3.503295\n",
      "Average loss at step 327500: 3.388288\n",
      "Average loss at step 330000: 3.458251\n",
      "Average loss at step 332500: 3.467531\n",
      "Average loss at step 335000: 3.522474\n",
      "Average loss at step 337500: 3.501907\n",
      "Average loss at step 340000: 3.454138\n",
      "Average loss at step 342500: 3.493211\n",
      "Average loss at step 345000: 3.396590\n",
      "Average loss at step 347500: 3.457870\n",
      "Average loss at step 350000: 3.390653\n",
      "Nearest to no: traffic, chd, ever, lines, any, webzine, wing, diligently,\n",
      "Nearest to they: we, there, she, you, he, who, hardin, enki,\n",
      "Nearest to of: multifinder, regarding, including, include, in, whose, adobe, marrakesh,\n",
      "Nearest to nine: eight, seven, six, five, four, three, ft, zero,\n",
      "Nearest to often: sometimes, usually, generally, now, commonly, widely, typically, still,\n",
      "Nearest to the: its, their, any, his, this, some, each, a,\n",
      "Nearest to as: became, maximized, educator, by, naval, ellipse, rudyard, after,\n",
      "Nearest to war: throughout, longships, duffy, quintet, elaboration, bretons, perception, protects,\n",
      "Nearest to UNK: de, divorcing, surges, lintel, vane, semaphore, david, boeotia,\n",
      "Nearest to use: superiority, same, form, make, noticed, irbms, scaled, persons,\n",
      "Nearest to their: its, his, her, the, some, my, whose, our,\n",
      "Nearest to time: way, contains, position, success, year, point, third, period,\n",
      "Nearest to more: less, very, most, rather, too, accumulator, scsi, solicitors,\n",
      "Nearest to also: never, generally, now, which, often, sometimes, still, variously,\n",
      "Nearest to who: subsequently, she, he, never, they, umami, generally, weighing,\n",
      "Nearest to two: three, four, five, six, one, seven, eight, meters,\n",
      "Average loss at step 352500: 3.354873\n",
      "Average loss at step 355000: 3.444439\n",
      "Average loss at step 357500: 3.461843\n",
      "Average loss at step 360000: 3.427427\n",
      "Average loss at step 362500: 3.463851\n",
      "Average loss at step 365000: 3.463431\n",
      "Average loss at step 367500: 3.490092\n",
      "Average loss at step 370000: 3.482010\n",
      "Average loss at step 372500: 3.355190\n",
      "Average loss at step 375000: 3.498577\n",
      "Average loss at step 377500: 3.482984\n",
      "Average loss at step 380000: 3.489941\n",
      "Average loss at step 382500: 3.470699\n",
      "Average loss at step 385000: 3.442203\n",
      "Average loss at step 387500: 3.488015\n",
      "Average loss at step 390000: 3.474925\n",
      "Average loss at step 392500: 3.452571\n",
      "Average loss at step 395000: 3.486298\n",
      "Average loss at step 397500: 3.343082\n",
      "Average loss at step 400000: 3.449855\n",
      "Nearest to no: traffic, chd, any, ever, lines, webzine, wing, diligently,\n",
      "Nearest to they: we, there, she, you, he, who, enki, hardin,\n",
      "Nearest to of: including, multifinder, regarding, include, whose, in, adobe, current,\n",
      "Nearest to nine: eight, seven, six, four, five, three, ft, samuel,\n",
      "Nearest to often: sometimes, usually, generally, now, commonly, widely, typically, still,\n",
      "Nearest to the: its, their, any, this, his, each, some, a,\n",
      "Nearest to as: became, maximized, educator, naval, ensue, ellipse, rudyard, by,\n",
      "Nearest to war: longships, throughout, duffy, elaboration, quintet, bretons, perception, protects,\n",
      "Nearest to UNK: de, divorcing, surges, boeotia, david, com, vane, semaphore,\n",
      "Nearest to use: form, start, superiority, make, irbms, same, support, scaled,\n",
      "Nearest to their: its, his, her, the, some, my, our, whose,\n",
      "Nearest to time: way, contains, position, success, period, year, point, third,\n",
      "Nearest to more: less, very, most, too, rather, accumulator, scsi, solicitors,\n",
      "Nearest to also: never, generally, now, still, often, sometimes, which, variously,\n",
      "Nearest to who: subsequently, she, he, never, they, umami, generally, weighing,\n",
      "Nearest to two: three, four, five, one, six, seven, eight, meters,\n",
      "Average loss at step 402500: 3.457451\n",
      "Average loss at step 405000: 3.478443\n",
      "Average loss at step 407500: 3.464410\n",
      "Average loss at step 410000: 3.486637\n",
      "Average loss at step 412500: 3.455777\n",
      "Average loss at step 415000: 3.478262\n",
      "Average loss at step 417500: 3.501303\n",
      "Average loss at step 420000: 3.486617\n",
      "Average loss at step 422500: 3.460065\n",
      "Average loss at step 425000: 3.478673\n",
      "Average loss at step 427500: 3.483079\n",
      "Average loss at step 430000: 3.391431\n",
      "Average loss at step 432500: 3.410245\n",
      "Average loss at step 435000: 3.464128\n",
      "Average loss at step 437500: 3.444982\n",
      "Average loss at step 440000: 3.431032\n",
      "Average loss at step 442500: 3.489076\n",
      "Average loss at step 445000: 3.407704\n",
      "Average loss at step 447500: 3.411130\n",
      "Average loss at step 450000: 3.444254\n",
      "Nearest to no: traffic, chd, any, webzine, ever, lines, wing, thwarted,\n",
      "Nearest to they: we, there, she, you, he, enki, hardin, who,\n",
      "Nearest to of: regarding, including, multifinder, whose, include, in, like, original,\n",
      "Nearest to nine: eight, seven, six, five, four, three, vol, kg,\n",
      "Nearest to often: sometimes, usually, generally, now, commonly, widely, typically, still,\n",
      "Nearest to the: its, their, any, each, this, his, some, a,\n",
      "Nearest to as: maximized, became, educator, naval, output, ellipse, unless, ensue,\n",
      "Nearest to war: longships, duffy, elaboration, quintet, throughout, perception, bretons, protects,\n",
      "Nearest to UNK: de, divorcing, surges, david, boeotia, vane, j, com,\n",
      "Nearest to use: start, support, make, scaled, form, superiority, irbms, persons,\n",
      "Nearest to their: its, his, her, the, some, my, our, whose,\n",
      "Nearest to time: way, position, contains, success, period, year, vigil, point,\n",
      "Nearest to more: less, very, most, too, rather, accumulator, scsi, solicitors,\n",
      "Nearest to also: never, generally, now, sometimes, still, often, which, variously,\n",
      "Nearest to who: subsequently, she, he, never, umami, they, generally, weighing,\n",
      "Nearest to two: three, four, five, one, six, seven, eight, meters,\n",
      "Average loss at step 452500: 3.460103\n",
      "Average loss at step 455000: 3.454094\n",
      "Average loss at step 457500: 3.460501\n",
      "Average loss at step 460000: 3.410250\n",
      "Average loss at step 462500: 3.402239\n",
      "Average loss at step 465000: 3.374752\n",
      "Average loss at step 467500: 3.385505\n",
      "Average loss at step 470000: 3.403415\n",
      "Average loss at step 472500: 3.449436\n",
      "Average loss at step 475000: 3.463197\n",
      "Average loss at step 477500: 3.434468\n",
      "Average loss at step 480000: 3.323285\n",
      "Average loss at step 482500: 3.444764\n",
      "Average loss at step 485000: 3.463504\n",
      "Average loss at step 487500: 3.457978\n",
      "Average loss at step 490000: 3.449919\n",
      "Average loss at step 492500: 3.399589\n",
      "Average loss at step 495000: 3.388354\n",
      "Average loss at step 497500: 3.455041\n",
      "Average loss at step 500000: 3.394582\n",
      "Nearest to no: traffic, chd, webzine, any, wing, thwarted, another, lines,\n",
      "Nearest to they: we, there, she, you, he, enki, hardin, who,\n",
      "Nearest to of: regarding, multifinder, including, whose, in, include, marrakesh, automobiles,\n",
      "Nearest to nine: eight, seven, six, five, four, three, rfc, births,\n",
      "Nearest to often: sometimes, usually, generally, commonly, now, typically, widely, still,\n",
      "Nearest to the: its, their, any, his, each, this, some, clamp,\n",
      "Nearest to as: maximized, became, educator, unless, naval, ellipse, rudyard, ensue,\n",
      "Nearest to war: longships, elaboration, duffy, perception, quintet, bretons, throughout, protects,\n",
      "Nearest to UNK: divorcing, de, surges, vane, boeotia, david, shlomo, lintel,\n",
      "Nearest to use: start, support, make, scaled, form, irbms, superiority, thermodynamic,\n",
      "Nearest to their: its, his, her, the, our, some, my, whose,\n",
      "Nearest to time: way, position, contains, period, success, year, vigil, blasted,\n",
      "Nearest to more: less, very, most, too, accumulator, rather, scsi, larger,\n",
      "Nearest to also: never, generally, now, sometimes, still, often, variously, below,\n",
      "Nearest to who: subsequently, she, never, he, umami, they, generally, weighing,\n",
      "Nearest to two: three, four, five, six, one, seven, eight, meters,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 500001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    tf.initialize_all_variables().run()\n",
    "    print('Initialized')\n",
    "    average_loss = 0\n",
    "    for step in range(1,num_steps):\n",
    "        batch_data, batch_labels = generate_batch(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 % 2500 == 0:\n",
    "            if step > 0:\n",
    "                average_loss = average_loss / 2500\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 % 50000 == 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": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##### DUMP\n",
    "import pickle\n",
    "f = open('myembedding.pkl','wb')\n",
    "pickle.dump([final_embeddings,dictionary,reverse_dictionary],f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_points = 400\n",
    "\n",
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
    "two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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9FBgYKEny8fGxnb97924FBwcrODhYkngBAAA4GEIyAAAAAJVaYWGhFi5cpClT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\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ovC+npKQgKysL/fr1Q7du3TBv3jwYGhoCqLwWq6goofKz9+xabGZmWq/H3VSKtzAM04y8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JRCI6deoUFRUVUUBAACUkJFBmZibt2LGDJBIJ7dmzh4iIiouLSSaT0axZs+jGjRt07do1CgkJ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AMGDOC3j46OJnV1dYqMjKTs7Gxavnw56ejoCL7nU6ZMoTFjxvDvXVxcKCgoiIiIzp49S0pK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\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44Ndff0VWVpY09vbbb2PPnj1aY0TNQUlJCWxtbbF8+XJMmjRJ18shIiKiB8jMzKxq5jNQ\nCJH5oPPvh+mWREREBkKX6ZaDBw/Wuu/p6Qm1Ws0C52Twvv/+e2zfvh25ubnIzMxEUFAQZDIZ/vGP\nf+h6aURERNTEmG5JRERERC1WXl4e5s6diytXrqBVq1YYOHAgjhw5gvbt2+t6aURERNTEGCQjIiKi\nB6reiRAA0tPT4ejoyOLmZLAKCwsRFBSCpCSlNDZs2AgkJibAyspKhysjIiIiXWG6JRERET3Q5cuX\nERUVBZVKhcTERKxduxZvvPGGrpdF9MiCgkKQnHwcQAKAfAAJSE4+jvHjJ+h4ZURERKQrDJIRERHd\nZdq0aejQoQMUCgXOnj2r6+XonEwmQ2hoKG7fvg0PDw+Eh4cjMjISU6ZM0fXSiB6JSqVCUpISGs1q\nAMEAugMIhkazCklJSqjVah2vkIiIiHSB6ZZERETV7Nu3D/Hx8UhNTUWvXr3QsWNHXS9J51JSUuDj\n4wM3NzfcuHFD18shqrecnJw7t7zuOjICAJCdnQ1HR8cmXRMRERHpHneSERERVZOdnQ0bGxs8+eST\n6NSpE+Ry/ldJ1NzY29vfuXX4riOpAAAHB4cmXQ8RERHpB/7mT0REdMekSZMQERGB/Px8yOVy9O7d\nW9dL0gsqlQqFhYUoKirS9VKIGoSTkxP8/AKhUESgsibZZQAJUChmwc8vkLvIiIiIWiimWxIREd2x\nevVq2NvbY926dTh16pTe7SL766+/YGFh0ejXKSkpwYwZM7Br1y6Ul5fjr7/+AgCcPXsWV64UsPsf\nNQuJiQkYP34CkpJCpDFf30AkJibocFVERESkS/r12z8REZEOWVpawtLSEgqFAtbW1ujQoYOulwQA\n0Gg0OH/+PNLT09GvX79Gv15UVBTS0tLg4vIYyspMAbgBsADgz+5/1GxYWVlh375voFKpoFQqoVKp\nsG/fNwwAExERtWAMkhEREekZlUqFvXv3Sh32srKyMGjQIPTv3x8zZsxo1GsXFxdj48aNmDNnDr77\n7gQqKv4N4ACACgB92f2Pmh1HR0cEBAQwxZKIiIgYJCMiIsNx69YtBAcHw8LCAra2tli5ciV8fHww\ne/ZsXS+tQRQWFsLf/1k4OzsjMDAQTk5O8Pd/Fj169EBxcTG++uortG3btlHXkJOTg7KysmrX8QJg\nBcD5zv2/u/8RERERETUnDJIREZHBiIyMRHp6Or7++mt8++23SEtLQ2ZmZpNd38fHBxEREYiMjET7\n9u3RpUsXbNiwASUlJZg8eTLatGkDR0dH7Nu375HmDwoKQXLycVQWEs8HkNDk6Y1CCABAz54974yw\n+x8RERERtQwMkhERkUG4desW4uPjsWzZMnh7e8PFxQWbNm2CRqNp0nXEx8fD2toa3333HSIiIjBj\nxgy8/PLLGDp0KE6fPo2RI0ciNDQUf/7550PNq1KpkJSkhEazGkAwgO4Agps8vdHBwQFGRkYoKCio\n1v3vUwA/ArjA7n9ERERE1GwxSEZERAYhNzcX5eXlGDRokDTWpk0bODs73+dRDW/AgAF45513YG9v\nj7feegutWrWCtbU1wsLCYG9vj5iYGFy/fh1nz559qHlzcnLu3PK660jTpjeam5sjLCwM0dHReO21\n6XjyyX4AZgAoAbAPvr6D2f2PiIiIiJolBsmIiMggVKUBymSyWscbyqxZs5Cbm3vP466urtJtuVyO\nDh06oH///tJY586dAQAFBQUPdV17e/s7t3Sf3rh06VIMHz4cwcHBuHQpG2+++SZcXV0xceLEBun+\n15zqyBERERFR88EgGRERGQR7e3sYGRnh5MmT0tjNmzfrnYZ4dyfJBzE2Nta6L5PJaowBQEVFxUOt\nw8nJqVp6YwKAywASdJLeaG5ujri4OPzxxx/45ZdfEBsbizNnzmDTpk1NtgYiIiIioqbGIBkRERkE\nCwsLvPLKK4iKisKhQ4fwww8/ICwsDAqFosbusrq4VyfJoqKiRlh93SQmJsDXdzCAEAA9AIQ0eXrj\nwwYNiYiIiIiaCwbJiIjIYKxYsQJDhgzBqFGjMHLkSAwbNgx9+vRBq1atHnoufegkeTcrKyvs2/cN\nVCoVlEolVCpVg6Q31kVTBw0rKirw5ptvokOHDrCxscH777/fKNchIiIiIqorBsmIiMhgmJubY8uW\nLfjjjz/w888/Y+rUqfjxxx8ful7Xo3aSrG3HWl3HHoajoyMCAgKaNMWyqYOGcXFxsLCwwMmTJ7Fk\nyRIsWLAABw4caJRrERERERHVhZGuF0BERFRX33//PS5evAgPDw/cuHEDCxYsgEwmwz/+8Y+Hmqcu\nnSRrC1ClpKTUGKutyL9Go3mo9ehaVdCwMkAWfGc0GBqNQFJSCNRqdYMH7FxdXTF37lwAlfXm1q5d\niwMHDuDpp59u0OsQEREREdUVd5IREZHByMvLw9y5c+Hq6oqRI0fi9u3bOHLkCNq3b/9Q8+hTJ0l9\nUJegYUOr3iUUAGxsbB66IygRERERUUPiTjIiItJ7hYWFCAoKubPbqdKwYSOQmJjwSPW6qjpJJidH\nQKMRqAwGpUKhmAVf37p3klSpVMjJyYGDg0OTpkY2NO2gYXC1I40XNKytS+jDdgQlIiIiImpI3ElG\nRER6rzHqZdWnk6Q+dsasj6qgoUIRgcrX+DKABCgUs+DnV/egIRERERGRIWOQjIiI9NqjFtl/kPp0\nktTHzpj1VZ+gIRERERFRc8B0SyIi0muPWmS/rhwdHR/q8booct8UqoKGarUa2dnZjZpCWt/un0RE\nREREjYFBMiIi0mu6qJd1P40dtNO1hw0aPorauoR+8cUXjXpNIiIiIqIHYbolERHpNX2rl8XOmERE\nREREzRODZEREpPf0qV6WvgXtDI1KpcLevXsfuZYcEREREVFjYbolERHpvaasl1UXiYkJGD9+ApKS\nQqQxX99AFrm/j8LCQgQFhdyp51bJz6/yNatLswQiIiIiosbGnWRERGQwHB0dERAQoPPdWvXpjGkI\nSktLERERgc6dO6N169YYPnw4Tp06Va85m2NHUCIiIiJqXhgkIyIiekT6ErRraNHR0fjiiy+wZcsW\nnD59Gg4ODvDz88ONGzceab6qjqAazWpUNl/ojsqOoKuQlKRk6iURERER6QUGyYiIqEXy8fFBeHg4\nwsPD0a5dO1hbWyMmJkbXy9K5kpIS/Oc//8FHH32EkSNHok+fPli3bh1at26NDRs2PNKcdekISkRE\nRESkawySERFRixUfHw9jY2N89913WL16NZYvX/7IgaDmIicnB+Xl5RgyZIg0ZmRkBA8PD1y4cOGR\n5mRHUCIiIiIyBCzcT0RELVb37t2xfPlyAJWpk2fPnsWKFSsQFham45XpjhACACCTyWqM3z1WV1Ud\nQZOTI6DRCFTuIEuFQjELvr7sCEpERERE+oE7yYiIqMUaPHiw1n1PT0+o1WopUNQSOTg4wNjYGEeO\nHJHGysvLcerUKfTt2/eR501MTICv72AAIQB6AAiBr+9gdgQlIiIiIr3BnWREREQkMTMzw6uvvoro\n6GhYWVmhe/fuWLJkCW7fvl2vHXZVHUHVajWys7Ph4ODAHWREREREpFcYJCMiohbr+PHjWvfT09Ph\n6Oj4yGmFzUVsbCyEEAgNDcUff/yBJ554Avv370fbtm3rPbejoyODY0RERESklxgkIyKiFuvy5cuI\niorCtGnTkJGRgbVr12LFihW6XpbOmZqaYuXKlVi5cqWul0JERERE1GQYJCMiohYrNDQUt2/fhoeH\nB4yMjNChQwecP39e18siIiIiIiIdYOF+IiJqsYyNjfHxxx/jxo0buH79Onr16qXrJemcSqXC3r17\noVardb0UIiIiIqImxSAZERHpvevXr8PGxgaxsbHSWHp6OkxNTXHw4EEdrqz5KCwshL//s3B2dkZg\nYCCcnJzg7/8sioqKHnqusrKyRlghEREREVHjYpCMiIj0XseOHbFx40bMmzcPmZmZKC4uRkhICCIi\nIuDj4/NIc96+fRuXLl3ijqk7goJCkJx8HEACgHwACUhOPo7x4yc88LE+Pj4IDw9HZGQkrK2t4e/v\n39jLJSIiIiJqcKxJRkREBiEgIADTpk1DUFAQnnjiCVhYWGDRokUPPU9hYSGCgkJw4sQJAMCXX34J\nP79AJCYmNPSSDYZKpUJSkhKVAbLgO6PB0GgEkpJCoFarH9iRMj4+Hq+++iqOHTvW2MslIiIiImoU\nDJIREZHBWLp0KR577DHs3LkTmZmZMDY2fug5tHdMeQE4jOTkiDrtmGqucnJy7tzyuuvICABAdnb2\nA4NkDg4OWumwRERERESGhumWRERkMHJycvDLL7+goqICly5deujHV+2Y0mhWo3LHVHdU7phahaQk\nJW7fvt3QS24yQggsXrwYvXv3hpmZGdzc3PDf//63To+1t7e/c+vwXUdSAVQGwB7kiSeeeIjVEhER\nERHpH+4kIyIig1BWVoYJEyZg3LhxcHZ2xuTJk5GVlQVra+s6z/GgHVOGHCRbtGgRtm3bhs8++wwO\nDg44fPgwQkJC0KlTJwwfPvy+j3VycoKfXyCSkyOg0QhUvh6pUChmwdc38IG7yADA3Ny8YZ4IERER\nEZGOMEhGREQG4Z133sHNmzexZs0amJmZQalUYvLkydizZ0+d59DeMRVc7UjljqnWrVs32HqbUmlp\nKRYvXowDBw7gySefBAD07NkTaWlp+PTTTx8YJAOAxMQEjB8/AUlJIdKYr2/LrtVGRERERC0Lg2RE\nRKT3UlNTsXr1ahw6dEjasRQfH4/HH38cn376KaZPn16neR60Y+qvv0oa70k0ouzsbJSUlOCZZ56B\nEEIaLy4uRufOnes0h5WVFfbt+wZqtRrZ2dlwcHCo0w4yIiIiIqLmgkEyIiLSeyNGjMBff/2lNWZn\nZ4eioqKHnut+O6ZefPHFeq9VF27dugUAUCqV6Nq1qzQeFBQEV1fXh5rL0dHxoYNjMpnsoc4nIiIi\nItJHDJIREVGLcr8dUykpKTpe3aNxcXGBqakp8vLyMGzYMGm8devWsLCwaPTrG+rrRkRERERUHbtb\nEhGR3lOpVNi7dy/UanWDzVk9LdHQWVhYICoqCpGRkYiPj0dubi5Onz6Nn3/+GefPn5fO++abb9C2\nbVskJibW63qN8e9BRERERKRr3ElGRER6q7CwEEFBIUhKUkpjfn6VqZFWVlZ6M6c+WLhwITp37ozY\n2Fjk5uaiXbt2KCsrg5ubGwBg27ZteO2115CYmIjAwMBHukZzfe2IiIiIiADuJCMiIj0WFBSC5OTj\nABIA5ANIQHLycYwfP0Gv5tQXr7/+Os6fP48///wTV69ehaurK2xtbfHvf/8br7/+Ovbs2fPIATKg\neb92RERERETcSUZERHpJpVLd2bGUACD4zmgwNBqBpKQQqNXqhy4w3xhz6rudO3eioKAAR48excCB\nAx95npb42hERERFRy8KdZEREpJdycnLu3PK668gIAEB2drZezKkP7lcjzM3NDdbW1tiwYUO9rtFc\nXzsiIiIioioMkhERkV6yt7e/c+vwXUdSAQAODg56MacuFRYWwt//WTg7OyMwMBBOTk7w938WRUVF\n0jn29vY4ePAgdu/ejfDw8Ee+VnN77YiIiIiI7sYgGRER6SUnJyf4+QVCoYhAZYrfZQAJUChmwc8v\n8JFS+xpjTl2qa40wBwcHHDx4ELt27UJkZOQjXau5vXZERERERHdjkIyIiPRWYmICfH0HAwgB0ANA\nCHx9ByMxMUGv5tSFqhphGs1qVNYI647KGmGrkJSkhFqthkwmk853cnLCgQMHsH37dkRHRz/SNZvL\na0dEREREVBsW7iciIr1lZWWFffu+gVqtRnZ2NhwcHOq9Y6kx5tSFutQIS0lJ0TrSp08fXLly5ZGv\n2VxeOyIiIiKi2jBIRkREes/R0bHBgzGNMWdT0q4RFlztSOPXCDP0146IiIiIqDZMtyQiIjJAD6oR\nJoS4Z8dLIiIiIiKqiUEyIiLCZ599hm7dutUYHz16NKZOnaqDFVFd1FYjzMvLHWVlZffteElERET/\nv737j7KzrvMD/v4QEsIeBWNFwO5u0fyy9pizBduychYObGpCWLqtHtsdkrjV6jF71sJhq2ja8qOs\nLZpal1Klu1bcLRszLV1LfxwiQyMuuFaWEtTuYaMzCawaEUSIqKwrOHz7x72TToYAieTOnczzep2T\nM3O/z3Pv88mc+5k7932/z/MFeCYhGQB585vfnEcffTSf/exn949997vfzW233ZYNGzY8xz0Zpqlr\nhI2Pj2f79u0ZHx/PokWLcscdO/N8K14CAAAHEpIBkCVLlmTNmjXZtm3b/rGbbropJ510Us4555wh\nVsahWL58ec4///y01p53xUsAAODghGQAJEnWr1+fT33qU3nqqaeSJNu2bcvIyMiQq+JwHMqKlwAA\nwMEJyQBIklx44YWZnJzMLbfckr179+Zzn/ucUy2PMgeueDnd4Fe8BACAo92xwy4AgLlh8eLFeeMb\n35itW7dmYmIir371q7Nq1aphl8VhmFrxcseOizM52dKbQXZHFiy4JKtXr8vy5cuHXSIAAMxZQjIA\n9lu/fn0uvPDC3HfffXnLW94y7HL4CYyObs3IyIaMjW3cP7Z69bqMjm4dYlUAADD3CckA2O+8887L\nS1/60kxMTOSiiy4adjn8BKZWvJyYmMju3buzbNkyM8gAAOAQCMkA2O+YY47JN7/5zWGXwRGwfPly\n4RgAABwGF+4HAAAAoPPMJAMg4+Pj2bNnj1PzAACAzjKTDKDDHnvssaxde0FWrlyZdevWZcWKFVm7\n9oLs27dv2KUBAADMKiEZQIdddNHG7NhxV5KtSb6eZGt27LgrIyMbhlwZAADA7HK6JUBHjY+PZ2xs\ne3oB2fr+6PpMTraMjW3MxMREZ0+9PPfcc7Nq1aosXrw4H//4x7No0aJs2rQpV1555bBLAwAABsRM\nMoCO2rNnT/+7s2dsOSdJsnv37lmtZ6658cYb86IXvSh33313tmzZkquvvjqf+cxnhl0WAAAwIEIy\ngI5aunRp/7s7Z2y5I0mybNmyWa1nrlm1alUuv/zyLF26NBs3bszrXve6WQnJWmvZsmVLli9fnsWL\nF+e0007LNddcM/DjAgBA1zndEqCjVqxYkTVr1mXHjoszOdnSm0F2RxYsuCSrV6/r7KmWU1atWnXA\n7VNPPTXf/va3B37c973vfbnhhhty7bXX5qyzzsq3vvWtfOUrXxn4cQEAoOuEZAAdNjq6NSMjGzI2\ntnH/2OrV6zI6unWIVc0NCxcuPOB2VeXpp58e6DF/8IMf5Lrrrsv111+fDRt6iye88pWvzOtf//qB\nHhcAABCSAXTakiVLcuutt2RiYiK7d+/OsmXLOj+DbJh27dqVJ598Muedd96wSwEAgM4RkgGQ5cuX\nC8fmgOOPP37YJQAAQGe5cD8AzFBVQznu1MX6raIJAACzz0wyAJjh9ttvf8bYzTffPPDjHnfccXnv\ne9+byy67LAsXLsxZZ52VRx55JPfdd1/e9ra3Dfz4AADQZUIyAJhDrrjiiixcuDBXXnllHnzwwZx6\n6qnZtGnTsMsCAIB5T0gGANOMj49nz549Q13EYPPmzdm8efNQjg0AAF3lmmQAkOSxxx7L2rUXZOXK\nlVm3bl1WrFiRtWsvyL59+4ZdGgAAMAsGHpJV1aKq+lJVPV1Vq2ZsW1VVd1bVD6vqa1X1nkHXAwAH\nc9FFG7Njx11Jtib5epKt2bHjroyMbJiV44+Pj+fTn/50JiYmZuV4AADAgWZjJtmWJHuTtOmDVfXi\nJGNJHkhyepL3JLmqqt4+CzUBwH7j4+MZG9ueycnrkqxP8jNJ1mdy8t9mbGz7QIMrM9gAAGBuGGhI\nVlXnJ/nbSd6dpGZs3pBkYZJ/1Frb1Vq7Kcl1SX5jkDUBwEx79uzpf3f2jC3nJEl27949sGMPewYb\nAADQM7CQrKpOTvKx9MKwHx5klzOT3Nla+/G0sbEkK6vqxEHVBQAzLV26tP/dnTO23JEkWbZs2UCO\nO8wZbAAAwIEGOZPsd5Nc31r74rNsPyXJwzPGHp62DQBmxYoVK7JmzbosWHBxejO6vpFkaxYsuCRr\n1qwb2CqXw5zBBgAAHOiwQrKquqZ/Af5n+zdZVSuq6uIkL07ywam7Huoh+l/bc+4FAEfY6OjWrF59\nZpKNSX42ycasXn1mRke3DuyYw5rBBgAAPNOxh7n/h9KbIfZcHkhybnqnU/6o6oB87J6q+mRr7a1J\nHkpy8oz7vrz/deYMs2e49NJLc+KJB56VOTIykpGRkee7KwA8w5IlS3LrrbdkYmIiu3fvzrJlywY2\ng2zK1Ay2HTsuzuRkS28G2R1ZsOCSrF49uBlsAABwNBodHc3o6OgBY48//vgRe/xq7chP2qqqn05y\nwrShV6R3vbE3Jbm7tfZgVW1K8v4kJ7fWJvv3+1dJ/m5r7TXP8dinJ9m5c+fOnH766Ue8dgCYTfv2\n7cvIyIaMjW3fP7ZmzbqMjm7NkiVLhlgZAADMfffee2/OOOOMJDmjtXbvC3msw51Jdkhaa3un366q\nJ9I7lfL+1tqD/eFtSa5I8omq+mCS1ya5OMklg6gJAOaiYcxgAwAAnmkgIdmzOGDKWmvte1W1JslH\nktyT5DtJrmqt3TCLNQHAnLB8+XLhGAAADNGshGStta8lWXCQ8T/J1BJeAAAAADAkh7W6JQAAAADM\nR0IyAAAAADpPSAYAAABA5wnJAAAAAOg8IRkAAAAAnSckAwAAAKDzhGQAAAAAdJ6QDAAAAIDOE5IB\nAAAA0HlCMgAAAAA6T0gGAAAAQOcJyQAAAADoPCEZAAAAAJ0nJAMAAACg84RkAAAAAHSekAwAAACA\nzhOSAQAAANB5QjIAAAAAOk9IBgAAAEDnCckAAAAA6DwhGQAAAACdJyQDAAAAoPOEZAAAAAB0npAM\nAAAAgM4TkgEAAADQeUIyAAAAADpPSAYAAABA5wnJAAAAAOg894qYOgAACvZJREFUIRkAAAAAnSck\nAwAAAKDzhGQAAAAAdJ6QDAAAAIDOE5IBAAAA0HlCMgAAAAA6T0gGAAAAQOcJyQAAAADoPCEZAAAA\nAJ0nJAMAAACg84RkAAAAAHSekAwAAACAzhOSAQAAANB5QjIAAAAAOk9IBgAAAEDnCckAAAAA6Dwh\nGQAAAACdJyQDAAAAoPOEZAAAAAB0npAMAAAAgM4TkgEAAADQeUIyAAAAADpPSAYAAABA5wnJAAAA\nAOg8IRkAAAAAnSckAwAAAKDzhGQAAAAAdJ6QDAAAAIDOE5IBAAAA0HlCMgAAAAA6T0gGAAAAQOcJ\nyQAAAADoPCEZAAAAAJ0nJAMAAACg84RkAAAAAHSekAwAAACAzhOSAQAAANB5QjIAAAAAOk9IBgAA\nAEDnCckAAAAA6DwhGQAAAACdJyQDAAAAoPOEZAAAAAB0npAMAAAAgM4TkgEAAADQeUIyAAAAADpP\nSAYAAABA5wnJAAAAAOg8IRkAAAAAnSckAwAAAKDzhGQAAAAAdJ6QDAAAAIDOE5IBAAAA0HlCMgAA\nAAA6T0gGAAAAQOcJyQAAAADoPCEZAAAAAJ0nJAMAAACg84RkAAAAAHSekAwAAACAzhOSAQAAANB5\nQjIAAAAAOk9IBgAAAEDnCckAAAAA6DwhGQAAAACdJyQDAAAAoPOEZAAAAAB0npAMAAAAgM4TkgEA\nAADQeUIyAAAAADpPSAYAAABA5wnJAAAAAOg8IRkAAAAAnSckAwAAAKDzhGQAAAAAdJ6QDAAAAIDO\nE5IBAAAA0HlCMjgKjY6ODrsEGDp9APoAEn0AegCOHCEZHIW8EII+gEQfQKIPQA/AkSMkAwAAAKDz\nhGQAAAAAdJ6QDAAAAIDOO3bYBfwEFifJrl27hl0HDM3jjz+ee++9d9hlwFDpA9AHkOgD0AN03bR8\naPELfaxqrb3Qx5hVVXVRkk8Ouw4AAAAA5oz1rbVtL+QBjsaQ7C8lWZPkz5L8xXCrAQAAAGCIFic5\nLclYa+3RF/JAR11IBgAAAABHmgv3AwAAANB5QjIAAAAAOk9IBgAAAEDnCckAAAAA6LyjLiSrqguq\n6q6q+vOqeqyq/uuM7T9TVbdU1RNV9VBVbamqo+7/Cc+lqhZV1Zeq6umqWjVj26qqurOqflhVX6uq\n9wyrTjjSquqvVNXHq+r+/uvARFVdVVULZ+ynD5jXqurXq+qB/nP8rqr6G8OuCQalqjZX1d1V9b2q\neriqbq6qFTP2Oa6qPlpV36mq71fVH1TVy4dVMwxSvyeerqoPTxvTA8x7VfWKqvr9/vP8z6vqy1V1\n+ox9rq6qB/vb/1dVLTucYxxV4VFVvSnJjUluSPLaJK9Psm3a9mOSbE9ybJIzk/xqkn+Y5OrZrhUG\nbEuSvUkOWJ62ql6cZCzJA0lOT/KeJFdV1dtnvUIYjFcnqSTvSPKaJJcm2ZTkX07toA+Y76rqHyT5\nN0muTPLXk3w5yVhVvWyohcHg/EKSf5fkbyVZnWRhktuq6vhp+1yb5IIkb0pydpJXJPnULNcJA9f/\nUOQd6f3un04PMK9V1UuSfD7Jj5KsSfJXk/yTJPum7fPeJO9K8s4kfzPJE+n9jbTokI/TWnv+veaA\nqlqQ5M+SXN5a+71n2ef8JP8jyamtte/0x96Z5ANJTmqt/Xh2qoXB6T/PP5TeC+CfJvm51tr/7W/7\ntSS/meSUqed7VV2T5Jdba68ZUskwUFX17iSbWmvL+rf1AfNaVd2V5I9ba5f0b1eSbyS5rrW2ZajF\nwSzoB8LfTnJ2a+2PquqEJI8k+ZXW2s39fVYm2ZXkzNba3cOrFo6cqnpRkp1Jfi3J5Um+2Fr7DT1A\nF1TVB5L8fGvtnOfY58Ek/7q19lv92yckeTjJr7bWbjqU4xxNM8lOTy8NT1Xd258+t72qpr/hOTPJ\nn0wFZH1jSU5M8tdmr1QYjKo6OcnHkmxI8sOD7HJmkjtnBMJjSVZW1YmzUCIMw0uSPDbttj5g3uqf\nWnxGks9MjbXeJ547kvz8sOqCWfaS9GbTT/3uPyO9M0mm98VXk3w9+oL55aNJ/mdr7fYZ46+LHmD+\nuzDJPVV1U//U+3unnylSVa9MckoO7IPvJfnjHEYfHE0h2avSO8XmyvROn7wgvWl1d/Sn3SW9H8jD\nM+738LRtcLT73STXt9a++Czb9QCd0r/GwLuS/Pa0YX3AfPayJAty8Oe45zfzXn/m5LVJ/qi19qf9\n4VOSPNl/MzSdvmDeqKpfSfJzSTYfZPPJ0QPMf69KbxblV5O8Ib2//6+rqg397aek9wHKC/obaegh\nWVVd07/o4LP9m+xfmHOq1ve31v5bPyR4a3o/hDcfwqGOjvNK6ZxD7YGqujjJi5N8cOquh3qI/lc9\nwJx1GK8F0+/zl5N8Osl/bq194vkO0f+qD5ivKp7fdMP16V2TcuQQ9tUXzAtV9dPphcMbWmtPHc5d\noweYP45JsrO1dnlr7cuttY8l+Q/pBWfP5bD64NgXUOCR8qH0Zsc8l/vTP9UyvfOqkySttSer6v4k\nP9sfeijJzNWdTu5/nZkmwlxxKD3wQJJz0zuN7Ee9D1H3u6eqPtlae2t6PXDyjPtOrWqjB5jLDvW1\nIElvZZskt6c3k+CdM/bTB8xn30kymYM/xz2/mdeq6iNJ1iX5hdbag9M2PZRkUVWdMGMmjb5gvjgj\nyUlJdtb/fyOwIMnZVfWuJGuTHKcHmOe+lWl5UN+uJG/sf/9QeoHYyTnwef/yJM92JtYzDD0ka609\nmuTR59uvqnamt4rByiT/uz+2MMlpSb7W3+0LSf5pVb1s2nXJ3pDk8fQucA5zzmH0wD9O8s+mDb0i\nvess/f0kUxfj/EKS91fVgtbaZH/sDUm+2lp7/MhVDUfWofZBsn8G2e1J/k+Stx1kF33AvNVae6r/\nN9EvprdY0dTpZ7+Y5Lph1gaD1A/IfjnJOa21r8/YvDPJj9Prg6mLlq9I74P0L8xmnTAgO5K8dsbY\n76UXEHwgyTeTPBU9wPz2+fTyoOlWpp8HtdYeqKqH0uuDqYXtTkhvZeSPHupBhh6SHarW2ver6reT\n/Iuq2pveD+Ky9KbN/Zf+brelF4b9fn/pz1PTW+HsI4c5LRXmnNba3um3q+qJ9JLy+6d9mrotyRVJ\nPlFVH0zvxfTiJJfMZq0wKFV1apI/TG+148uSvHzqA9XW2tQnRvqA+e7DSf5jPyy7O8mlSX4qvTdM\nMO9U1fXpnV75d5I80V/IKEkeb639RWvte1V1Q5IPV9W+JN9PLzT+vFX9mA9aa09kxqSP/nuBR1tr\nu/q39QDz3W8l+XxVbU5yU3rh19uTvGPaPtcm+edVtTu99wu/mWRvkv9+qAc5akKyvnenl5DfmOT4\n9FYpOG9qZkBr7emq+qUk/z692WZPpPcH45VDqRYG74Bzq/t/JK5J8pEk96R3Ws5VrbUbhlEcDMAb\n0rto56uSfKM/NnWdgQWJPmD+a63dVFUvS28ho5OTfCnJmtbaI8OtDAZmU3q/5/9wxvhb03tfkPTC\n4skkf5DkuCS3Jvn1WaoPhmHmNZb0APNaa+2eqvp76c2evDy9SxJd0lr7T9P22VJVP5Xkd9JbCflz\nSc5vrT15qMep3qrhAAAAANBdQ1/dEgAAAACGTUgGAAAAQOcJyQAAAADoPCEZAAAAAJ0nJAMAAACg\n84RkAAAAAHSekAwAAACAzhOSAQAAANB5QjIAAAAAOk9IBgAAAEDnCckAAAAA6DwhGQAAAACd9/8A\nEOnsCpUyth8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe208aafd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(embeddings, labels):\n",
    "    assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'\n",
    "    pylab.figure(figsize=(15,15))  # in inches\n",
    "    for i, label in enumerate(labels):\n",
    "        x, y = embeddings[i,:]\n",
    "        pylab.scatter(x, y)\n",
    "        pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',\n",
    "                   ha='right', va='bottom')\n",
    "    pylab.show()\n",
    "\n",
    "words = [reverse_dictionary[i] for i in range(1, num_points+1)]\n",
    "plot(two_d_embeddings, words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the former code we have the `dictionary` that converts from the word to an index, and the `reverse_dictionary` that given an index returns the corresponding word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "f = open('myembedding.pkl','rb')\n",
    "fe,dic,rdic=pickle.load(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1014"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic['woman']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'but'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdic[42]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The embedding tries to put together words with similar meaning. A good embedding allows to semantically operate. Let us check some simple semantical operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = (fe[dic['two'],:]+ fe[dic['one'],:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "two\n",
      "one\n",
      "three\n",
      "four\n",
      "six\n"
     ]
    }
   ],
   "source": [
    "from scipy.spatial import distance\n",
    "candidates=np.argsort(distance.cdist(fe,result[np.newaxis,:],metric=\"seuclidean\"),axis=0)\n",
    "\n",
    "for i in xrange(5):\n",
    "    idx=candidates[i][0]\n",
    "    print(rdic[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also define word analogies: `football is to ? as foot is to hand`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "football\n",
      "hand\n",
      "batista\n",
      "fujian\n",
      "stayed\n"
     ]
    }
   ],
   "source": [
    "result = (fe[dic['football'],:] - fe[dic['foot'],:] + fe[dic['hand'],:])\n",
    "\n",
    "from scipy.spatial import distance\n",
    "candidates=np.argsort(distance.cdist(fe,result[np.newaxis,:],metric=\"seuclidean\"),axis=0)\n",
    "\n",
    "for i in xrange(5):\n",
    "    idx=candidates[i][0]\n",
    "    print(rdic[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "barcelona\n",
      "germany\n",
      "tackle\n",
      "sonja\n",
      "collected\n"
     ]
    }
   ],
   "source": [
    "result = (fe[dic['barcelona'],:] - fe[dic['spain'],:] + fe[dic['germany'],:])\n",
    "\n",
    "from scipy.spatial import distance\n",
    "candidates=np.argsort(distance.cdist(fe,result[np.newaxis,:],metric=\"seuclidean\"),axis=0)\n",
    "\n",
    "for i in xrange(5):\n",
    "    idx=candidates[i][0]\n",
    "    print(rdic[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us used a pretrained embedding. We will use a simple embedding detailed in `Improving Word Representations via Global Context and Multiple Word Prototypes`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import zipfile \n",
    "\n",
    "zfile = zipfile.ZipFile('data/wordVectors.txt.zip')\n",
    "zfile.extractall(\"data/\")\n",
    "\n",
    "df = pd.read_table(\"data/wordVectors.txt\",delimiter=\" \",header=None)\n",
    "\n",
    "embedding=df.values[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f = open(\"data/vocab.txt\",'r')\n",
    "dictionary=dict()\n",
    "for word in f.readlines():\n",
    "    dictionary[word] = len(dictionary)\n",
    "    \n",
    "reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prince\n",
      "\n",
      "king\n",
      "\n",
      "princess\n",
      "\n",
      "queen\n",
      "\n",
      "bride\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result = embedding[dictionary['king\\n'],:]-embedding[dictionary['man\\n'],:]+embedding[dictionary['girl\\n'],:]\n",
    "import numpy as np\n",
    "from scipy.spatial import distance\n",
    "candidates=np.argsort(distance.cdist(embedding,result[np.newaxis,:],metric=\"seuclidean\"),axis=0)\n",
    "\n",
    "for i in xrange(0,5):\n",
    "    idx=candidates[i][0]\n",
    "    print(reverse_dictionary[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spain\n",
      "germany\n",
      "schmitz\n",
      "chronometer\n",
      "malik\n"
     ]
    }
   ],
   "source": [
    "result = (fe[dic['spain'],:] - fe[dic['barcelona'],:] + fe[dic['germany'],:])\n",
    "\n",
    "from scipy.spatial import distance\n",
    "candidates=np.argsort(distance.cdist(fe,result[np.newaxis,:],metric=\"seuclidean\"),axis=0)\n",
    "\n",
    "for i in xrange(5):\n",
    "    idx=candidates[i][0]\n",
    "    print(rdic[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``par2vec``\n",
    "\n",
    "What about a vector representation for phrases/paragraphs/documents?\n",
    "\n",
    "The ``par2vec`` approach for learning paragraph vectors is inspired by the methods for learning the word vectors. The inspiration is that the word vectors are asked to contribute to a prediction task about the next word in the sentence.\n",
    "\n",
    "We will consider a *paragraph* vector. The paragraph vectors are also\n",
    "asked to contribute to the prediction task of the next word\n",
    "given many contexts sampled from the paragraph.\n",
    "\n",
    "In ``par2vec`` framework, every paragraph is mapped to a unique vector, represented by a column in matrix D and every word is also mapped to a unique vector, represented by a column in matrix W. The paragraph vector and word vectors are averaged or concatenated to predict the next word in a context.\n",
    "\n",
    "<center>\n",
    "<img src=\"images/par2vec.png\" alt=\"\" style=\"width: 500px;\"/> \n",
    "(Source: https://cs.stanford.edu/~quocle/paragraph_vector.pdf)\n",
    "</center>\n",
    "\n",
    "The paragraph token can be thought of as another word. It acts as a memory that remembers what is missing from the\n",
    "current context – or the topic of the paragraph. For this reason, we often call this model the Distributed Memory\n",
    "Model of Paragraph Vectors (PV-DM).\n",
    "\n",
    "The contexts are fixed-length and sampled from a sliding window over the paragraph. \n",
    "\n",
    "The paragraph vector is shared across all contexts generated from the same paragraph but not across paragraphs. \n",
    "\n",
    "The word vector matrix W, however, is shared across paragraphs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At prediction time, one needs to perform an inference step to compute the paragraph vector for a new paragraph. This\n",
    "is also obtained by gradient descent. In this step, the parameters for the rest of the model, the word vectors and\n",
    "the softmax weights, are fixed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using GloVe pre-trained word embeddings for classification.\n",
    "\n",
    "GloVe (https://nlp.stanford.edu/pubs/glove.pdf) consists of a weighted least squares model that trains on global word-word co-occurrence counts and thus makes efficient use of statistics. The model produces a word vector space with meaningful sub-structure. It shows state-of-the-art performance on the word analogy task, and outperforms other current methods on several word similarity tasks.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''This script loads pre-trained word embeddings (GloVe embeddings)\n",
    "into a frozen Keras Embedding layer, and uses it to\n",
    "train a text classification model on the 20 Newsgroup dataset\n",
    "(classication of newsgroup messages into 20 different categories).\n",
    "GloVe embedding data can be found at:\n",
    "http://nlp.stanford.edu/data/glove.6B.zip (822MB)\n",
    "(source page: http://nlp.stanford.edu/projects/glove/)\n",
    "20 Newsgroup data can be found at:\n",
    "http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20.html\n",
    "'''\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.layers import Dense, Input, Flatten\n",
    "from keras.layers import Conv1D, MaxPooling1D, Embedding\n",
    "from keras.models import Model\n",
    "\n",
    "def to_categorical(y, num_classes=None):\n",
    "    \"\"\"Converts a class vector (integers) to binary class matrix.\n",
    "    E.g. for use with categorical_crossentropy.\n",
    "    # Arguments\n",
    "        y: class vector to be converted into a matrix\n",
    "            (integers from 0 to num_classes).\n",
    "        num_classes: total number of classes.\n",
    "    # Returns\n",
    "        A binary matrix representation of the input.\n",
    "    \"\"\"\n",
    "    y = np.array(y, dtype='int').ravel()\n",
    "    if not num_classes:\n",
    "        num_classes = np.max(y) + 1\n",
    "    n = y.shape[0]\n",
    "    categorical = np.zeros((n, num_classes))\n",
    "    categorical[np.arange(n), y] = 1\n",
    "    return categorical\n",
    "\n",
    "BASE_DIR = ''\n",
    "GLOVE_DIR = BASE_DIR + '/glove.6B/'\n",
    "TEXT_DATA_DIR = BASE_DIR + '/20_newsgroup/'\n",
    "MAX_SEQUENCE_LENGTH = 1000\n",
    "MAX_NB_WORDS = 20000\n",
    "EMBEDDING_DIM = 100\n",
    "VALIDATION_SPLIT = 0.2\n",
    "\n",
    "# first, build index mapping words in the embeddings set\n",
    "# to their embedding vector\n",
    "\n",
    "print('Indexing word vectors.')\n",
    "\n",
    "embeddings_index = {}\n",
    "f = open(os.path.join(GLOVE_DIR, 'glove.6B.100d.txt'))\n",
    "for line in f:\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    coefs = np.asarray(values[1:], dtype='float32')\n",
    "    embeddings_index[word] = coefs\n",
    "f.close()\n",
    "\n",
    "print('Found %s word vectors.' % len(embeddings_index))\n",
    "\n",
    "# second, prepare text samples and their labels\n",
    "print('Processing text dataset')\n",
    "\n",
    "texts = []  # list of text samples\n",
    "labels_index = {}  # dictionary mapping label name to numeric id\n",
    "labels = []  # list of label ids\n",
    "for name in sorted(os.listdir(TEXT_DATA_DIR)):\n",
    "    path = os.path.join(TEXT_DATA_DIR, name)\n",
    "    if os.path.isdir(path):\n",
    "        label_id = len(labels_index)\n",
    "        labels_index[name] = label_id\n",
    "        for fname in sorted(os.listdir(path)):\n",
    "            if fname.isdigit():\n",
    "                fpath = os.path.join(path, fname)\n",
    "                if sys.version_info < (3,):\n",
    "                    f = open(fpath)\n",
    "                else:\n",
    "                    f = open(fpath, encoding='latin-1')\n",
    "                t = f.read()\n",
    "                i = t.find('\\n\\n')  # skip header\n",
    "                if 0 < i:\n",
    "                    t = t[i:]\n",
    "                texts.append(t)\n",
    "                f.close()\n",
    "                labels.append(label_id)\n",
    "\n",
    "print('Found %s texts.' % len(texts))\n",
    "\n",
    "# finally, vectorize the text samples into a 2D integer tensor\n",
    "tokenizer = Tokenizer(num_words=MAX_NB_WORDS)\n",
    "tokenizer.fit_on_texts(texts)\n",
    "sequences = tokenizer.texts_to_sequences(texts)\n",
    "\n",
    "word_index = tokenizer.word_index\n",
    "print('Found %s unique tokens.' % len(word_index))\n",
    "\n",
    "data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "\n",
    "labels = to_categorical(np.asarray(labels))\n",
    "print('Shape of data tensor:', data.shape)\n",
    "print('Shape of label tensor:', labels.shape)\n",
    "\n",
    "# split the data into a training set and a validation set\n",
    "indices = np.arange(data.shape[0])\n",
    "np.random.shuffle(indices)\n",
    "data = data[indices]\n",
    "labels = labels[indices]\n",
    "num_validation_samples = int(VALIDATION_SPLIT * data.shape[0])\n",
    "\n",
    "x_train = data[:-num_validation_samples]\n",
    "y_train = labels[:-num_validation_samples]\n",
    "x_val = data[-num_validation_samples:]\n",
    "y_val = labels[-num_validation_samples:]\n",
    "\n",
    "print('Preparing embedding matrix.')\n",
    "\n",
    "# prepare embedding matrix\n",
    "num_words = min(MAX_NB_WORDS, len(word_index))\n",
    "embedding_matrix = np.zeros((num_words, EMBEDDING_DIM))\n",
    "for word, i in word_index.items():\n",
    "    if i >= MAX_NB_WORDS:\n",
    "        continue\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        # words not found in embedding index will be all-zeros.\n",
    "        embedding_matrix[i] = embedding_vector\n",
    "\n",
    "# load pre-trained word embeddings into an Embedding layer\n",
    "# note that we set trainable = False so as to keep the embeddings fixed\n",
    "embedding_layer = Embedding(num_words,\n",
    "                            EMBEDDING_DIM,\n",
    "                            weights=[embedding_matrix],\n",
    "                            input_length=MAX_SEQUENCE_LENGTH,\n",
    "                            trainable=False)\n",
    "\n",
    "print('Training model.')\n",
    "\n",
    "# train a 1D convnet with global maxpooling\n",
    "sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
    "embedded_sequences = embedding_layer(sequence_input)\n",
    "x = Conv1D(128, 5, activation='relu')(embedded_sequences)\n",
    "x = MaxPooling1D(5)(x)\n",
    "x = Conv1D(128, 5, activation='relu')(x)\n",
    "x = MaxPooling1D(5)(x)\n",
    "x = Conv1D(128, 5, activation='relu')(x)\n",
    "x = MaxPooling1D(35)(x)\n",
    "x = Flatten()(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "preds = Dense(len(labels_index), activation='softmax')(x)\n",
    "\n",
    "model = Model(sequence_input, preds)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['acc'])\n",
    "\n",
    "model.fit(x_train, y_train,\n",
    "          batch_size=128,\n",
    "          epochs=10,\n",
    "          validation_data=(x_val, y_val))"
   ]
  }
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