Sample code to train a embedding layer using tensorflow

Word embedding sample.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Raw corpus definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "corpus_raw = 'Fred is a software engineer . Xander is also a software engineer . Fred can write software programe .  Xander knows a lot of machine learning algorithms '\n",
    "# convert to lower case\n",
    "corpus_raw = corpus_raw.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### encode the words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'is': 0,\n",
       " 'knows': 1,\n",
       " 'lot': 2,\n",
       " 'algorithms': 3,\n",
       " 'also': 4,\n",
       " 'a': 5,\n",
       " 'fred': 6,\n",
       " 'of': 7,\n",
       " 'software': 8,\n",
       " 'can': 9,\n",
       " 'learning': 10,\n",
       " 'xander': 11,\n",
       " 'engineer': 12,\n",
       " 'write': 13,\n",
       " 'machine': 14,\n",
       " 'programe': 15}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = []\n",
    "for word in corpus_raw.split():\n",
    "    if word != '.': # because we don't want to treat . as a word\n",
    "        words.append(word)\n",
    "words = set(words) # so that all duplicate words are removed\n",
    "word2int = {}\n",
    "int2word = {}\n",
    "vocab_size = len(words) # gives the total number of unique words\n",
    "for i,word in enumerate(words):\n",
    "    word2int[word] = i\n",
    "    int2word[i] = word\n",
    "\n",
    "word2int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['fred', 'is', 'a', 'software', 'engineer'],\n",
       " ['xander', 'is', 'also', 'a', 'software', 'engineer'],\n",
       " ['fred', 'can', 'write', 'software', 'programe'],\n",
       " ['xander', 'knows', 'a', 'lot', 'of', 'machine', 'learning', 'algorithms']]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# raw sentences is a list of sentences.\n",
    "raw_sentences = corpus_raw.split('.')\n",
    "sentences = []\n",
    "for sentence in raw_sentences:\n",
    "    sentences.append(sentence.split())\n",
    "\n",
    "sentences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Trainning Data for embedding, skip ngram, using window size 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['fred', 'is'],\n",
       " ['fred', 'a'],\n",
       " ['is', 'fred'],\n",
       " ['is', 'a'],\n",
       " ['is', 'software'],\n",
       " ['a', 'fred'],\n",
       " ['a', 'is'],\n",
       " ['a', 'software'],\n",
       " ['a', 'engineer'],\n",
       " ['software', 'is'],\n",
       " ['software', 'a'],\n",
       " ['software', 'engineer'],\n",
       " ['engineer', 'a'],\n",
       " ['engineer', 'software'],\n",
       " ['xander', 'is'],\n",
       " ['xander', 'also'],\n",
       " ['is', 'xander'],\n",
       " ['is', 'also'],\n",
       " ['is', 'a'],\n",
       " ['also', 'xander'],\n",
       " ['also', 'is'],\n",
       " ['also', 'a'],\n",
       " ['also', 'software'],\n",
       " ['a', 'is'],\n",
       " ['a', 'also'],\n",
       " ['a', 'software'],\n",
       " ['a', 'engineer'],\n",
       " ['software', 'also'],\n",
       " ['software', 'a'],\n",
       " ['software', 'engineer'],\n",
       " ['engineer', 'a'],\n",
       " ['engineer', 'software'],\n",
       " ['fred', 'can'],\n",
       " ['fred', 'write'],\n",
       " ['can', 'fred'],\n",
       " ['can', 'write'],\n",
       " ['can', 'software'],\n",
       " ['write', 'fred'],\n",
       " ['write', 'can'],\n",
       " ['write', 'software'],\n",
       " ['write', 'programe'],\n",
       " ['software', 'can'],\n",
       " ['software', 'write'],\n",
       " ['software', 'programe'],\n",
       " ['programe', 'write'],\n",
       " ['programe', 'software'],\n",
       " ['xander', 'knows'],\n",
       " ['xander', 'a'],\n",
       " ['knows', 'xander'],\n",
       " ['knows', 'a'],\n",
       " ['knows', 'lot'],\n",
       " ['a', 'xander'],\n",
       " ['a', 'knows'],\n",
       " ['a', 'lot'],\n",
       " ['a', 'of'],\n",
       " ['lot', 'knows'],\n",
       " ['lot', 'a'],\n",
       " ['lot', 'of'],\n",
       " ['lot', 'machine'],\n",
       " ['of', 'a'],\n",
       " ['of', 'lot'],\n",
       " ['of', 'machine'],\n",
       " ['of', 'learning'],\n",
       " ['machine', 'lot'],\n",
       " ['machine', 'of'],\n",
       " ['machine', 'learning'],\n",
       " ['machine', 'algorithms'],\n",
       " ['learning', 'of'],\n",
       " ['learning', 'machine'],\n",
       " ['learning', 'algorithms'],\n",
       " ['algorithms', 'machine'],\n",
       " ['algorithms', 'learning']]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "WINDOW_SIZE = 2\n",
    "for sentence in sentences:\n",
    "    for word_index, word in enumerate(sentence):\n",
    "        for nb_word in sentence[max(word_index - WINDOW_SIZE, 0) : min(word_index + WINDOW_SIZE, len(sentence)) + 1] : \n",
    "            if nb_word != word:\n",
    "                data.append([word, nb_word])\n",
    "data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "encoding with on hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [1. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n",
      "[[1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# function to convert numbers to one hot vectors\n",
    "def to_one_hot(data_point_index, vocab_size):\n",
    "    temp = np.zeros(vocab_size)\n",
    "    temp[data_point_index] = 1\n",
    "    return temp\n",
    "x_train = [] # input word\n",
    "y_train = [] # output word\n",
    "for data_word in data:\n",
    "    x_train.append(to_one_hot(word2int[ data_word[0] ], vocab_size))\n",
    "    y_train.append(to_one_hot(word2int[ data_word[1] ], vocab_size))\n",
    "# convert them to numpy arrays\n",
    "x_train = np.asarray(x_train)\n",
    "y_train = np.asarray(y_train)\n",
    "print(x_train)\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the shape of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(72, 16) (72, 16)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape, y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train the embedding layer with TF 1.0 lazy evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0311 17:57:10.152336 139999039043328 deprecation.py:323] From /opt/conda/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:63: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "loss is :  1.4826959\n",
      "loss is :  1.4826949\n",
      "loss is :  1.4826939\n",
      "loss is :  1.4826928\n",
      "loss is :  1.4826919\n",
      "loss is :  1.482691\n",
      "loss is :  1.48269\n",
      "loss is :  1.4826889\n",
      "loss is :  1.4826881\n",
      "loss is :  1.482687\n",
      "loss is :  1.4826859\n",
      "loss is :  1.4826851\n",
      "loss is :  1.482684\n",
      "loss is :  1.482683\n",
      "loss is :  1.482682\n",
      "loss is :  1.482681\n",
      "loss is :  1.4826801\n",
      "loss is :  1.482679\n",
      "loss is :  1.482678\n",
      "loss is :  1.482677\n",
      "loss is :  1.4826761\n",
      "loss is :  1.4826751\n",
      "loss is :  1.4826742\n",
      "loss is :  1.4826733\n",
      "loss is :  1.4826722\n",
      "loss is :  1.4826713\n",
      "loss is :  1.4826703\n",
      "loss is :  1.4826694\n",
      "loss is :  1.4826684\n",
      "loss is :  1.4826673\n",
      "loss is :  1.4826664\n",
      "loss is :  1.4826654\n",
      "loss is :  1.4826643\n",
      "loss is :  1.4826635\n",
      "loss is :  1.4826626\n",
      "loss is :  1.4826615\n",
      "loss is :  1.4826605\n",
      "loss is :  1.4826595\n",
      "loss is :  1.4826585\n",
      "loss is :  1.4826576\n",
      "loss is :  1.4826566\n",
      "loss is :  1.4826556\n",
      "loss is :  1.4826548\n"
     ]
    }
   ],
   "source": [
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n",
    "\n",
    "# making placeholders for x_train and y_train\n",
    "x = tf.placeholder(tf.float32, shape=(None, vocab_size))\n",
    "y_label = tf.placeholder(tf.float32, shape=(None, vocab_size))\n",
    "\n",
    "EMBEDDING_DIM = 5 # you can choose your own number\n",
    "W1 = tf.Variable(tf.random_normal([vocab_size, EMBEDDING_DIM]))\n",
    "b1 = tf.Variable(tf.random_normal([EMBEDDING_DIM])) #bias\n",
    "hidden_representation = tf.add(tf.matmul(x,W1), b1)\n",
    "\n",
    "W2 = tf.Variable(tf.random_normal([EMBEDDING_DIM, vocab_size]))\n",
    "b2 = tf.Variable(tf.random_normal([vocab_size]))\n",
    "prediction = tf.nn.softmax(tf.add( tf.matmul(hidden_representation, W2), b2))\n",
    "\n",
    "sess = tf.Session()\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init) #make sure you do this!\n",
    "# define the loss function:\n",
    "cross_entropy_loss = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(prediction), reduction_indices=[1]))\n",
    "# define the training step:\n",
    "train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy_loss)\n",
    "n_iters = 10000\n",
    "# train for n_iter iterations\n",
    "for _ in range(n_iters):\n",
    "    sess.run(train_step, feed_dict={x: x_train, y_label: y_train})\n",
    "    print('loss is : ', sess.run(cross_entropy_loss, feed_dict={x: x_train, y_label: y_train}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.23982283  0.77702165  1.8975285   0.5108667  -0.43468612]\n",
      " [-1.0705326   2.852689    0.18506023  0.8469611  -1.8208697 ]\n",
      " [-1.385144   -3.365414    0.9495529  -0.05046546 -0.14475018]\n",
      " [ 0.55278856 -2.013035   -1.7999253   2.8386216   0.47975594]\n",
      " [-3.058676    1.8981671   1.5947797   0.5567552   2.7379334 ]\n",
      " [ 0.40750992  1.1368086   2.0928793  -2.8163285   1.0754099 ]\n",
      " [-3.0863605   1.0802759  -3.0569534   0.18604219  1.8195069 ]\n",
      " [-0.05245042  0.09491912 -1.4299512   1.3472636  -2.0740066 ]\n",
      " [-0.9871204   0.09714027 -0.80345935 -0.23076862  1.222522  ]\n",
      " [ 1.7065194   1.7000102  -0.05846833  2.2065213   0.94157976]\n",
      " [ 1.2891977  -2.4178238   1.0065038  -0.01903009 -1.6489551 ]\n",
      " [-2.5148993  -0.36801454  2.0228522  -1.5881424   0.28695273]\n",
      " [-1.0856471  -0.14916275  2.4379945   2.9938548   0.82928115]\n",
      " [ 2.631486   -0.6465525  -0.17753276 -0.14255512  2.7014139 ]\n",
      " [ 1.3588654  -1.6569685  -1.822983   -1.5710166  -1.1546688 ]\n",
      " [ 0.48103023  1.4211856  -0.14430964  3.766582    2.0554762 ]]\n"
     ]
    }
   ],
   "source": [
    "vectors_tf1 = sess.run(W1 + b1)\n",
    "\n",
    "# if you work it out, you will see that it has the same effect as running the node hidden representation\n",
    "print(vectors_tf1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "software\n",
      "is\n",
      "learning\n"
     ]
    }
   ],
   "source": [
    "def euclidean_dist(vec1, vec2):\n",
    "    return np.sqrt(np.sum((vec1-vec2)**2))\n",
    "\n",
    "def find_closest(word_index, vectors):\n",
    "    min_dist = 10000 # to act like positive infinity\n",
    "    min_index = -1\n",
    "    query_vector = vectors[word_index]\n",
    "    for index, vector in enumerate(vectors):\n",
    "        if euclidean_dist(vector, query_vector) < min_dist and not np.array_equal(vector, query_vector):\n",
    "            min_dist = euclidean_dist(vector, query_vector)\n",
    "            min_index = index\n",
    "    return min_index\n",
    "\n",
    "print(int2word[find_closest(word2int['fred'], vectors_tf1)])\n",
    "print(int2word[find_closest(word2int['xander'], vectors_tf1)])\n",
    "print(int2word[find_closest(word2int['machine'], vectors_tf1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.0 Keras API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "embedding_dim=32\n",
    "\n",
    "# The Embedding layer takes at least two arguments:\n",
    "# the number of possible words in the vocabulary, here 1000 (1 + maximum word index),\n",
    "# and the dimensionality of the embeddings, here 32.\n",
    "embedding_layer = layers.Embedding(vocab_size, embedding_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Dense)            (None, 32)                544       \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 16)                528       \n",
      "=================================================================\n",
      "Total params: 1,072\n",
      "Trainable params: 1,072\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "  layers.Dense(embedding_dim, input_shape=(vocab_size,), name='embedding'),\n",
    "  layers.Dense(vocab_size, activation='softmax'),\n",
    "])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "72/72 [==============================] - 0s 2ms/sample - loss: 0.2367 - acc: 0.9375\n",
      "Epoch 2/1000\n",
      "72/72 [==============================] - 0s 110us/sample - loss: 0.2359 - acc: 0.9375\n",
      "Epoch 3/1000\n",
      "72/72 [==============================] - 0s 123us/sample - loss: 0.2352 - acc: 0.9375\n",
      "Epoch 4/1000\n",
      "72/72 [==============================] - 0s 183us/sample - loss: 0.2346 - acc: 0.9375\n",
      "Epoch 5/1000\n",
      "72/72 [==============================] - 0s 152us/sample - loss: 0.2341 - acc: 0.9375\n",
      "Epoch 6/1000\n",
      "72/72 [==============================] - 0s 151us/sample - loss: 0.2335 - acc: 0.9375\n",
      "Epoch 7/1000\n",
      "72/72 [==============================] - 0s 160us/sample - loss: 0.2329 - acc: 0.9375\n",
      "Epoch 8/1000\n",
      "72/72 [==============================] - 0s 167us/sample - loss: 0.2323 - acc: 0.9375\n",
      "Epoch 9/1000\n",
      "72/72 [==============================] - 0s 195us/sample - loss: 0.2318 - acc: 0.9375\n",
      "Epoch 10/1000\n",
      "72/72 [==============================] - 0s 163us/sample - loss: 0.2313 - acc: 0.9375\n",
      "Epoch 11/1000\n",
      "72/72 [==============================] - 0s 142us/sample - loss: 0.2307 - acc: 0.9375\n",
      "Epoch 12/1000\n",
      "72/72 [==============================] - 0s 145us/sample - loss: 0.2302 - acc: 0.9375\n",
      "Epoch 13/1000\n",
      "72/72 [==============================] - 0s 164us/sample - loss: 0.2297 - acc: 0.9375\n",
      "Epoch 14/1000\n",
      "72/72 [==============================] - 0s 140us/sample - loss: 0.2292 - acc: 0.9375\n",
      "Epoch 15/1000\n",
      "72/72 [==============================] - 0s 187us/sample - loss: 0.2287 - acc: 0.9375\n",
      "Epoch 16/1000\n",
      "72/72 [==============================] - 0s 141us/sample - loss: 0.2282 - acc: 0.9375\n",
      "Epoch 17/1000\n",
      "72/72 [==============================] - 0s 167us/sample - loss: 0.2277 - acc: 0.9375\n",
      "Epoch 18/1000\n",
      "72/72 [==============================] - 0s 165us/sample - loss: 0.2272 - acc: 0.9375\n",
      "Epoch 19/1000\n",
      "72/72 [==============================] - 0s 136us/sample - loss: 0.2267 - acc: 0.9375\n",
      "Epoch 20/1000\n",
      "72/72 [==============================] - 0s 150us/sample - loss: 0.2262 - acc: 0.9375\n",
      "Epoch 21/1000\n",
      "72/72 [==============================] - 0s 171us/sample - loss: 0.2257 - acc: 0.9375\n",
      "Epoch 22/1000\n",
      "72/72 [==============================] - 0s 143us/sample - loss: 0.2252 - acc: 0.9375\n",
      "Epoch 23/1000\n",
      "72/72 [==============================] - 0s 159us/sample - loss: 0.2247 - acc: 0.9375\n",
      "Epoch 24/1000\n",
      "72/72 [==============================] - 0s 144us/sample - loss: 0.2242 - acc: 0.9375\n",
      "Epoch 25/1000\n",
      "72/72 [==============================] - 0s 170us/sample - loss: 0.2237 - acc: 0.9375\n",
      "Epoch 26/1000\n",
      "72/72 [==============================] - 0s 147us/sample - loss: 0.2232 - acc: 0.9375\n",
      "Epoch 27/1000\n",
      "72/72 [==============================] - 0s 189us/sample - loss: 0.2227 - acc: 0.9375\n",
      "Epoch 28/1000\n",
      "72/72 [==============================] - 0s 164us/sample - loss: 0.2222 - acc: 0.9375\n",
      "Epoch 29/1000\n",
      "72/72 [==============================] - 0s 184us/sample - loss: 0.2217 - acc: 0.9375\n",
      "Epoch 30/1000\n",
      "72/72 [==============================] - 0s 204us/sample - loss: 0.2212 - acc: 0.9375\n",
      "Epoch 31/1000\n",
      "72/72 [==============================] - 0s 312us/sample - loss: 0.2207 - acc: 0.9375\n",
      "Epoch 32/1000\n",
      "72/72 [==============================] - 0s 144us/sample - loss: 0.2202 - acc: 0.9375\n",
      "Epoch 33/1000\n",
      "72/72 [==============================] - 0s 143us/sample - loss: 0.2197 - acc: 0.9375\n",
      "Epoch 34/1000\n",
      "72/72 [==============================] - 0s 150us/sample - loss: 0.2192 - acc: 0.9375\n",
      "Epoch 35/1000\n",
      "72/72 [==============================] - 0s 141us/sample - loss: 0.2187 - acc: 0.9375\n",
      "Epoch 36/1000\n",
      "72/72 [==============================] - 0s 165us/sample - loss: 0.2182 - acc: 0.9375\n",
      "Epoch 37/1000\n",
      "72/72 [==============================] - 0s 175us/sample - loss: 0.2177 - acc: 0.9375\n",
      "Epoch 38/1000\n",
      "72/72 [==============================] - 0s 141us/sample - loss: 0.2173 - acc: 0.9375\n",
      "Epoch 39/1000\n",
      "72/72 [==============================] - 0s 120us/sample - loss: 0.2168 - acc: 0.9375\n",
      "Epoch 40/1000\n",
      "72/72 [==============================] - 0s 131us/sample - loss: 0.2163 - acc: 0.9375\n",
      "Epoch 41/1000\n",
      "72/72 [==============================] - 0s 115us/sample - loss: 0.2158 - acc: 0.9375\n",
      "Epoch 42/1000\n",
      "72/72 [==============================] - 0s 146us/sample - loss: 0.2153 - acc: 0.9375\n",
      "Epoch 43/1000\n",
      "72/72 [==============================] - 0s 107us/sample - loss: 0.2148 - acc: 0.9375\n",
      "Epoch 44/1000\n",
      "72/72 [==============================] - 0s 123us/sample - loss: 0.2144 - acc: 0.9375\n",
      "Epoch 45/1000\n",
      "72/72 [==============================] - 0s 118us/sample - loss: 0.2139 - acc: 0.9375\n",
      "Epoch 46/1000\n",
      "72/72 [==============================] - 0s 136us/sample - loss: 0.2134 - acc: 0.9375\n",
      "Epoch 47/1000\n",
      "72/72 [==============================] - 0s 143us/sample - loss: 0.2129 - acc: 0.9375\n",
      "Epoch 48/1000\n",
      "72/72 [==============================] - 0s 134us/sample - loss: 0.2124 - acc: 0.9375\n",
      "Epoch 49/1000\n",
      "72/72 [==============================] - 0s 107us/sample - loss: 0.2119 - acc: 0.9375\n",
      "Epoch 50/1000\n",
      "72/72 [==============================] - 0s 137us/sample - loss: 0.2115 - acc: 0.9375\n",
      "Epoch 51/1000\n",
      "72/72 [==============================] - 0s 126us/sample - loss: 0.2110 - acc: 0.9375\n",
      "Epoch 52/1000\n",
      "72/72 [==============================] - 0s 156us/sample - loss: 0.2105 - acc: 0.9375\n",
      "Epoch 53/1000\n",
      "72/72 [==============================] - 0s 140us/sample - loss: 0.2100 - acc: 0.9375\n",
      "Epoch 54/1000\n",
      "72/72 [==============================] - 0s 124us/sample - loss: 0.2095 - acc: 0.9375\n",
      "Epoch 55/1000\n",
      "72/72 [==============================] - 0s 133us/sample - loss: 0.2090 - acc: 0.9375\n",
      "Epoch 56/1000\n",
      "72/72 [==============================] - 0s 159us/sample - loss: 0.2085 - acc: 0.9375\n",
      "Epoch 57/1000\n",
      "72/72 [==============================] - 0s 119us/sample - loss: 0.2081 - acc: 0.9375\n",
      "Epoch 58/1000\n",
      "72/72 [==============================] - 0s 147us/sample - loss: 0.2076 - acc: 0.9375\n",
      "Epoch 59/1000\n",
      "72/72 [==============================] - 0s 145us/sample - loss: 0.2071 - acc: 0.9375\n",
      "Epoch 60/1000\n",
      "72/72 [==============================] - 0s 160us/sample - loss: 0.2066 - acc: 0.9375\n",
      "Epoch 61/1000\n",
      "72/72 [==============================] - 0s 141us/sample - loss: 0.2061 - acc: 0.9375\n",
      "Epoch 62/1000\n",
      "72/72 [==============================] - 0s 138us/sample - loss: 0.2057 - acc: 0.9375\n",
      "Epoch 63/1000\n",
      "72/72 [==============================] - 0s 145us/sample - loss: 0.2051 - acc: 0.9375\n",
      "Epoch 64/1000\n",
      "72/72 [==============================] - 0s 130us/sample - loss: 0.2047 - acc: 0.9375\n",
      "Epoch 65/1000\n",
      "72/72 [==============================] - 0s 144us/sample - loss: 0.2042 - acc: 0.9375\n",
      "Epoch 66/1000\n",
      "72/72 [==============================] - 0s 150us/sample - loss: 0.2037 - acc: 0.9375\n",
      "Epoch 67/1000\n",
      "72/72 [==============================] - 0s 128us/sample - loss: 0.2032 - acc: 0.9375\n",
      "Epoch 68/1000\n",
      "72/72 [==============================] - 0s 138us/sample - loss: 0.2028 - acc: 0.9375\n",
      "Epoch 69/1000\n",
      "72/72 [==============================] - 0s 136us/sample - loss: 0.2023 - acc: 0.9375\n",
      "Epoch 70/1000\n",
      "72/72 [==============================] - 0s 127us/sample - loss: 0.2019 - acc: 0.9375\n",
      "Epoch 71/1000\n",
      "72/72 [==============================] - 0s 101us/sample - loss: 0.2014 - acc: 0.9375\n",
      "Epoch 72/1000\n",
      "72/72 [==============================] - 0s 125us/sample - loss: 0.2010 - acc: 0.9375\n",
      "Epoch 73/1000\n",
      "72/72 [==============================] - 0s 139us/sample - loss: 0.2005 - acc: 0.9375\n",
      "Epoch 74/1000\n",
      "72/72 [==============================] - 0s 115us/sample - loss: 0.2001 - acc: 0.9375\n",
      "Epoch 75/1000\n",
      "72/72 [==============================] - 0s 141us/sample - loss: 0.1996 - acc: 0.9375\n",
      "Epoch 76/1000\n",
      "72/72 [==============================] - 0s 122us/sample - loss: 0.1992 - acc: 0.9375\n",
      "Epoch 77/1000\n",
      "72/72 [==============================] - 0s 120us/sample - loss: 0.1988 - acc: 0.9375\n",
      "Epoch 78/1000\n",
      "72/72 [==============================] - 0s 124us/sample - loss: 0.1983 - acc: 0.9375\n",
      "Epoch 79/1000\n",
      "72/72 [==============================] - 0s 96us/sample - loss: 0.1979 - acc: 0.9375\n",
      "Epoch 80/1000\n",
      "72/72 [==============================] - 0s 126us/sample - loss: 0.1975 - acc: 0.9375\n",
      "Epoch 81/1000\n",
      "72/72 [==============================] - 0s 135us/sample - loss: 0.1971 - acc: 0.9375\n",
      "Epoch 82/1000\n",
      "72/72 [==============================] - 0s 125us/sample - loss: 0.1966 - acc: 0.9375\n",
      "Epoch 83/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72/72 [==============================] - 0s 129us/sample - loss: 0.1962 - acc: 0.9375\n",
      "Epoch 84/1000\n",
      "72/72 [==============================] - 0s 161us/sample - loss: 0.1958 - acc: 0.9375\n",
      "Epoch 85/1000\n",
      "72/72 [==============================] - 0s 144us/sample - loss: 0.1954 - acc: 0.9375\n",
      "Epoch 86/1000\n",
      "72/72 [==============================] - 0s 117us/sample - loss: 0.1950 - acc: 0.9375\n",
      "Epoch 87/1000\n",
      "72/72 [==============================] - 0s 142us/sample - loss: 0.1946 - acc: 0.9375\n",
      "Epoch 88/1000\n",
      "72/72 [==============================] - 0s 128us/sample - loss: 0.1942 - acc: 0.9375\n",
      "Epoch 89/1000\n",
      "72/72 [==============================] - 0s 128us/sample - loss: 0.1938 - acc: 0.9375\n",
      "Epoch 90/1000\n",
      "72/72 [==============================] - 0s 100us/sample - loss: 0.1934 - acc: 0.9375\n",
      "Epoch 91/1000\n",
      "72/72 [==============================] - 0s 106us/sample - loss: 0.1930 - acc: 0.9375\n",
      "Epoch 92/1000\n",
      "72/72 [==============================] - 0s 149us/sample - loss: 0.1926 - acc: 0.9375\n",
      "Epoch 93/1000\n",
      "72/72 [==============================] - 0s 118us/sample - loss: 0.1922 - acc: 0.9375\n",
      "Epoch 94/1000\n",
      "72/72 [==============================] - 0s 119us/sample - loss: 0.1919 - acc: 0.9375\n",
      "Epoch 95/1000\n",
      "72/72 [==============================] - 0s 119us/sample - loss: 0.1915 - acc: 0.9375\n",
      "Epoch 96/1000\n",
      "72/72 [==============================] - 0s 146us/sample - loss: 0.1911 - acc: 0.9375\n",
      "Epoch 97/1000\n",
      "72/72 [==============================] - 0s 129us/sample - loss: 0.1907 - acc: 0.9375\n",
      "Epoch 98/1000\n",
      "72/72 [==============================] - 0s 121us/sample - loss: 0.1904 - acc: 0.9375\n",
      "Epoch 99/1000\n",
      "72/72 [==============================] - 0s 117us/sample - loss: 0.1900 - acc: 0.9375\n",
      "Epoch 100/1000\n",
      "72/72 [==============================] - 0s 157us/sample - loss: 0.1896 - acc: 0.9375\n",
      "Epoch 101/1000\n",
      "72/72 [==============================] - 0s 120us/sample - loss: 0.1892 - acc: 0.9375\n",
      "Epoch 102/1000\n",
      "72/72 [==============================] - 0s 132us/sample - loss: 0.1888 - acc: 0.9375\n",
      "Epoch 103/1000\n",
      "72/72 [==============================] - 0s 143us/sample - loss: 0.1885 - acc: 0.9375\n",
      "Epoch 104/1000\n",
      "72/72 [==============================] - 0s 154us/sample - loss: 0.1881 - acc: 0.9375\n",
      "Epoch 105/1000\n",
      "72/72 [==============================] - 0s 128us/sample - loss: 0.1877 - acc: 0.9375\n",
      "Epoch 106/1000\n",
      "72/72 [==============================] - 0s 121us/sample - loss: 0.1873 - acc: 0.9375\n",
      "Epoch 107/1000\n",
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     ]
    },
    {
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     ]
    },
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     ]
    },
    {
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     "text": [
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     ]
    },
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     ]
    },
    {
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     "text": [
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     ]
    },
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     ]
    },
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     ]
    },
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     ]
    },
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     ]
    },
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     ]
    },
    {
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      "Epoch 972/1000\n",
      "72/72 [==============================] - 0s 91us/sample - loss: 0.1475 - acc: 0.9375\n",
      "Epoch 973/1000\n",
      "72/72 [==============================] - 0s 105us/sample - loss: 0.1475 - acc: 0.9375\n",
      "Epoch 974/1000\n",
      "72/72 [==============================] - 0s 126us/sample - loss: 0.1475 - acc: 0.9375\n",
      "Epoch 975/1000\n",
      "72/72 [==============================] - 0s 119us/sample - loss: 0.1475 - acc: 0.9375\n",
      "Epoch 976/1000\n",
      "72/72 [==============================] - 0s 99us/sample - loss: 0.1475 - acc: 0.9375\n",
      "Epoch 977/1000\n",
      "72/72 [==============================] - 0s 119us/sample - loss: 0.1475 - acc: 0.9375\n",
      "Epoch 978/1000\n",
      "72/72 [==============================] - 0s 115us/sample - loss: 0.1475 - acc: 0.9375\n",
      "Epoch 979/1000\n",
      "72/72 [==============================] - 0s 108us/sample - loss: 0.1474 - acc: 0.9375\n",
      "Epoch 980/1000\n",
      "72/72 [==============================] - 0s 116us/sample - loss: 0.1474 - acc: 0.9375\n",
      "Epoch 981/1000\n",
      "72/72 [==============================] - 0s 82us/sample - loss: 0.1474 - acc: 0.9375\n",
      "Epoch 982/1000\n",
      "72/72 [==============================] - 0s 145us/sample - loss: 0.1473 - acc: 0.9375\n",
      "Epoch 983/1000\n",
      "72/72 [==============================] - 0s 115us/sample - loss: 0.1473 - acc: 0.9349\n",
      "Epoch 984/1000\n",
      "72/72 [==============================] - 0s 113us/sample - loss: 0.1473 - acc: 0.9375\n",
      "Epoch 985/1000\n",
      "72/72 [==============================] - 0s 95us/sample - loss: 0.1473 - acc: 0.9375\n",
      "Epoch 986/1000\n",
      "72/72 [==============================] - 0s 108us/sample - loss: 0.1473 - acc: 0.9358\n",
      "Epoch 987/1000\n",
      "72/72 [==============================] - 0s 125us/sample - loss: 0.1473 - acc: 0.9375\n",
      "Epoch 988/1000\n",
      "72/72 [==============================] - 0s 120us/sample - loss: 0.1473 - acc: 0.9375\n",
      "Epoch 989/1000\n",
      "72/72 [==============================] - 0s 110us/sample - loss: 0.1473 - acc: 0.9375\n",
      "Epoch 990/1000\n",
      "72/72 [==============================] - 0s 120us/sample - loss: 0.1473 - acc: 0.9375\n",
      "Epoch 991/1000\n",
      "72/72 [==============================] - 0s 112us/sample - loss: 0.1474 - acc: 0.9375\n",
      "Epoch 992/1000\n",
      "72/72 [==============================] - 0s 113us/sample - loss: 0.1473 - acc: 0.9375\n",
      "Epoch 993/1000\n",
      "72/72 [==============================] - 0s 111us/sample - loss: 0.1473 - acc: 0.9375\n",
      "Epoch 994/1000\n",
      "72/72 [==============================] - 0s 117us/sample - loss: 0.1474 - acc: 0.9375\n",
      "Epoch 995/1000\n",
      "72/72 [==============================] - 0s 103us/sample - loss: 0.1474 - acc: 0.9375\n",
      "Epoch 996/1000\n",
      "72/72 [==============================] - 0s 123us/sample - loss: 0.1474 - acc: 0.9375\n",
      "Epoch 997/1000\n",
      "72/72 [==============================] - 0s 102us/sample - loss: 0.1474 - acc: 0.9375\n",
      "Epoch 998/1000\n",
      "72/72 [==============================] - 0s 102us/sample - loss: 0.1473 - acc: 0.9375\n",
      "Epoch 999/1000\n",
      "72/72 [==============================] - 0s 110us/sample - loss: 0.1474 - acc: 0.9375\n",
      "Epoch 1000/1000\n",
      "72/72 [==============================] - 0s 106us/sample - loss: 0.1474 - acc: 0.9375\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(\n",
    "    x_train,\n",
    "    y_train,\n",
    "    epochs=1000,\n",
    "    batch_size=32,\n",
    "    validation_split=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.21893698, -1.2934344 , -0.4463426 ,  0.83723754, -0.24482442,\n",
       "         0.07841764, -0.83055675,  0.07825156,  0.1209636 , -0.2181137 ,\n",
       "         0.26916376, -0.5992932 , -0.5164871 , -1.0136564 , -0.04871304,\n",
       "         0.2896396 , -0.6351596 , -0.3111065 , -0.0669952 , -0.14435269,\n",
       "        -0.8458535 ,  0.13493033,  0.18488666,  0.5270504 ,  0.33647123,\n",
       "         0.17199217,  0.22049606, -0.01914184,  0.39120317, -0.10352875,\n",
       "        -0.8246435 , -0.41335976]], dtype=float32)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the vector of the first word and predict it\n",
    "v = np.array([to_one_hot(0,vocab_size)])\n",
    "# get the embedding layer mode \n",
    "embedding_layer = tf.keras.Model(inputs=model.input,\n",
    "                                 outputs=model.get_layer(name='embedding').output)\n",
    "embedding_layer.predict(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.21893698, -1.2934344 , -0.4463426 ,  0.83723754, -0.24482442,\n",
       "         0.07841764, -0.83055675,  0.07825156,  0.1209636 , -0.2181137 ,\n",
       "         0.26916376, -0.5992932 , -0.5164871 , -1.0136564 , -0.04871304,\n",
       "         0.2896396 , -0.6351596 , -0.3111065 , -0.0669952 , -0.14435269,\n",
       "        -0.8458535 ,  0.13493033,  0.18488666,  0.5270504 ,  0.33647123,\n",
       "         0.17199217,  0.22049606, -0.01914184,  0.39120317, -0.10352875,\n",
       "        -0.8246435 , -0.41335976],\n",
       "       [ 0.1885516 , -1.1718698 ,  0.33295792,  0.5056582 , -0.25515926,\n",
       "         0.6727002 ,  0.12430872,  0.90458274,  0.28651145, -0.3949632 ,\n",
       "         0.48001453,  0.02680906,  0.05885297, -0.24642178, -0.94839925,\n",
       "        -0.06086197, -0.60224205, -0.19180785, -0.12334496,  0.4183089 ,\n",
       "         0.17984357, -0.9045588 ,  0.9754231 ,  0.87124383,  0.547851  ,\n",
       "         0.27847183,  0.02304809, -0.12650774, -0.06359787,  0.41584826,\n",
       "        -1.109606  , -0.08813327],\n",
       "       [-0.04553003,  0.47522658,  0.57269317, -0.02492407, -0.69584274,\n",
       "        -0.96281487,  0.23452829,  0.39008582,  0.4659747 , -0.5043241 ,\n",
       "        -0.65596354, -0.40427226,  0.05281794, -1.0615344 , -0.16924615,\n",
       "        -0.41836655,  0.11454618,  0.10337722, -0.20654073,  0.6149285 ,\n",
       "        -0.01949787,  0.14291534, -0.5618439 ,  0.91535985,  0.5161731 ,\n",
       "         0.19091165, -0.25092083, -0.43377632,  1.134396  ,  0.00127929,\n",
       "         0.4951113 ,  0.03672975],\n",
       "       [ 0.15612864,  0.27414367,  0.76527816, -0.8327178 ,  0.01122323,\n",
       "        -0.6093072 ,  0.541864  ,  0.04557411,  0.2720576 ,  0.33265275,\n",
       "        -0.08485171, -0.16802216, -0.04436316,  0.12900999, -0.30765343,\n",
       "         0.3656359 , -0.36249653, -0.46926704, -0.47748417,  0.76826054,\n",
       "         0.5160184 , -0.03095649,  0.49595547,  0.35120404,  0.49718955,\n",
       "        -0.7233785 ,  1.3116633 , -0.45781147,  0.9889394 ,  0.7166417 ,\n",
       "         0.88427556, -0.09726012],\n",
       "       [ 0.17479022, -0.28269953,  0.5449167 ,  0.9891989 , -1.2329127 ,\n",
       "         0.64056766, -0.6567069 ,  0.11747167,  0.8026403 ,  0.51198596,\n",
       "         0.10361061,  0.73427594, -0.01541121, -0.4035501 , -0.30172285,\n",
       "         0.1086521 ,  0.22866231, -0.60236937, -0.21520595,  0.26645064,\n",
       "        -0.24574238,  0.31592444,  0.31603885,  0.32098848, -0.3904145 ,\n",
       "         1.2523776 ,  0.07707014, -0.19062021, -0.06130613,  0.05600531,\n",
       "        -1.0036404 , -0.72715414],\n",
       "       [ 0.06142621, -0.01602063, -0.53600013,  1.2234012 , -0.39029518,\n",
       "         0.08679277, -0.29600868,  0.7921473 , -0.32207587, -0.45200247,\n",
       "        -0.15589939,  0.10373681,  0.46044785, -0.1410485 ,  0.17299809,\n",
       "         0.9229716 ,  0.353022  , -0.15229401, -0.10831208, -0.0923834 ,\n",
       "         0.09416185, -0.330385  , -0.47357956, -0.0708054 , -1.0005044 ,\n",
       "         0.48248184,  0.04383809,  0.53776777,  0.23681581,  0.04046299,\n",
       "        -0.00125185, -0.25200605],\n",
       "       [-0.08004175,  0.3920791 ,  0.48737758, -0.08531712, -0.19709195,\n",
       "         0.36441943,  0.2371748 , -0.1502755 ,  1.0108126 , -0.4545221 ,\n",
       "        -0.6195923 ,  0.26835874, -1.0725632 ,  0.06940606, -0.0723866 ,\n",
       "         0.05227641, -0.14783172, -0.7103933 ,  0.7923666 ,  0.16245043,\n",
       "         0.44325995,  0.5458426 , -0.51220953, -0.3436653 ,  0.0835903 ,\n",
       "         0.16329473, -0.24749523, -0.84743387,  0.22527349, -0.592352  ,\n",
       "        -0.9287725 , -0.92847055],\n",
       "       [ 0.39840704, -0.361648  ,  0.8616877 ,  0.09213915, -0.09824692,\n",
       "        -0.47577497,  0.7506807 ,  0.2956781 , -0.23390916, -0.53279305,\n",
       "         0.34058195, -0.10593308,  0.06011377, -0.1086987 , -0.7807761 ,\n",
       "         0.2854612 , -0.2783858 , -1.0871813 ,  0.07964367,  1.0597554 ,\n",
       "         0.515299  ,  0.05230841,  0.58580375,  0.4869448 ,  0.5540305 ,\n",
       "        -0.2708305 ,  0.70388794, -0.93591756,  0.5809147 ,  0.51810205,\n",
       "        -0.02593321, -0.13712421],\n",
       "       [ 0.22349744,  0.19505116, -0.26670337, -0.38231397, -0.3630561 ,\n",
       "         0.51211095,  0.05102493, -0.21613649,  0.65460336, -0.5719154 ,\n",
       "         0.43377236, -0.46376908, -0.7025174 ,  0.630774  ,  0.36292744,\n",
       "        -0.05469324, -0.48480245, -0.31724182,  1.065361  ,  0.00141209,\n",
       "        -0.5395677 , -0.0543528 , -0.2882796 , -0.21058704, -0.30969286,\n",
       "         0.41859138, -0.9352021 , -0.4190526 ,  0.08664404,  0.05590807,\n",
       "        -0.51357305, -0.8414515 ],\n",
       "       [ 0.04886441, -0.19785176, -0.00559095,  0.510252  ,  0.00756273,\n",
       "         0.45433182, -0.7839428 , -0.28771985, -0.30854112, -0.77679634,\n",
       "        -0.3904449 ,  0.36273438, -0.5745548 , -0.5100043 ,  0.79803646,\n",
       "         0.54808617, -0.7969347 , -0.0661571 , -0.10232905, -0.60598475,\n",
       "        -0.26093203,  0.51871246, -0.03555146, -0.279886  ,  0.315085  ,\n",
       "        -0.21651636,  0.76861995, -0.3181329 , -0.7340261 , -0.8260999 ,\n",
       "         0.22799923, -0.7208367 ],\n",
       "       [-0.5374069 ,  0.33167908,  0.99819666, -0.26682538, -0.10293901,\n",
       "        -0.14646067,  0.01563773, -0.2306942 ,  0.05519595, -0.29118568,\n",
       "        -0.57614636, -0.46637523,  0.64640266,  0.110843  , -0.5639424 ,\n",
       "        -0.41853705,  0.65505207, -0.33368084, -0.75432146, -0.08513066,\n",
       "         0.9020224 ,  0.46797946,  0.1319004 ,  0.4341759 ,  0.598019  ,\n",
       "        -0.1479104 ,  0.02174886,  0.6034775 ,  0.5862478 , -0.5470749 ,\n",
       "         0.42985046,  0.5108988 ],\n",
       "       [ 0.74269074,  0.09282599, -0.5176006 ,  0.34035388, -0.5385621 ,\n",
       "        -0.81594235, -0.7548084 ,  0.33703083,  0.47710857,  0.29553446,\n",
       "         0.00469988,  0.08536939, -0.0637202 , -0.42500633, -0.9399647 ,\n",
       "         0.38526207,  0.07159257, -0.20358038,  0.32036823,  0.8847766 ,\n",
       "         0.207034  ,  0.31908363, -0.8558333 ,  0.30166113, -0.0808869 ,\n",
       "         0.43138993, -0.7998552 , -0.6924016 ,  0.7452146 ,  0.12991482,\n",
       "        -0.12180841, -0.7732408 ],\n",
       "       [ 0.5001608 , -0.8223302 ,  0.798917  ,  0.55441284, -0.9811436 ,\n",
       "        -0.3092105 , -0.7324277 , -0.32616177,  0.16543674, -0.9791828 ,\n",
       "         0.58559096,  0.18315288, -0.5587305 , -0.48344976,  0.16107067,\n",
       "        -0.50393033, -0.6190009 , -1.0812049 , -0.4852864 ,  0.03127082,\n",
       "        -0.06758878, -0.12312064,  0.62206924,  0.21393651,  0.15693237,\n",
       "         1.1520922 ,  0.4556535 , -0.38028282,  0.5663264 , -0.86096627,\n",
       "        -0.54521424, -0.77824295],\n",
       "       [ 0.36744094, -0.9327359 ,  0.3541948 ,  0.00426194,  0.36432597,\n",
       "         0.53290236, -0.25535253, -0.548267  ,  0.29314128, -0.40824872,\n",
       "        -0.34143275,  0.2641328 , -0.6594604 , -0.68374074,  0.77668124,\n",
       "        -0.45811835, -0.20412506,  0.1694414 , -0.02096919,  0.01827241,\n",
       "        -0.26675874,  0.55053604,  0.40815574, -0.6126729 , -0.91108584,\n",
       "         0.40716428,  0.04525526,  0.49495363, -0.44476697, -1.2288107 ,\n",
       "        -0.3418437 ,  0.78013057],\n",
       "       [ 0.6419539 ,  0.39047137,  0.76531154,  0.17178738, -0.1839744 ,\n",
       "        -0.42946076,  0.5659198 ,  0.6042179 , -0.5334292 , -0.43334338,\n",
       "        -0.17404857,  0.3535536 ,  0.0617433 ,  0.59134567, -0.09836366,\n",
       "        -0.18837713,  0.2250188 , -0.5821938 , -1.0633137 ,  0.3287856 ,\n",
       "         0.78365874, -0.5918353 ,  0.4281573 , -0.45297402,  0.49795213,\n",
       "        -0.70865166, -0.44333422,  0.6712176 ,  0.34538382,  0.01692916,\n",
       "         0.03808287,  0.49698168],\n",
       "       [-0.10098727, -0.09477388,  0.33302212,  0.14720342, -0.32677883,\n",
       "         0.06979229, -0.63225836, -0.52978396, -0.15189841, -0.68582135,\n",
       "         0.06043746,  0.56723267,  0.21149763, -0.23642766,  0.70426553,\n",
       "         0.38782963, -0.44382197, -0.49771792,  0.05566399, -0.72491753,\n",
       "         0.28054485,  0.382624  ,  0.24434558, -0.5751589 ,  0.44114575,\n",
       "         0.67321604,  0.8998965 , -0.9133763 , -0.47504738, -0.9288186 ,\n",
       "         0.11276649, -0.64059883]], dtype=float32)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_words = []\n",
    "for word in word2int:\n",
    "    all_words.append(to_one_hot(word2int[word],vocab_size))\n",
    "\n",
    "vector = np.array(all_words)\n",
    "vectors_tf2 = embedding_layer.predict(vector)\n",
    "vectors_tf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "software\n",
      "lot\n",
      "learning\n"
     ]
    }
   ],
   "source": [
    "print(int2word[find_closest(word2int['fred'], vectors_tf2)])\n",
    "print(int2word[find_closest(word2int['xander'], vectors_tf2)])\n",
    "print(int2word[find_closest(word2int['machine'], vectors_tf2)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualize embedding using TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from sklearn import preprocessing\n",
    "\n",
    "def dimension_reduction(input_vector):\n",
    "    model = TSNE(n_components=2, random_state=0)\n",
    "    np.set_printoptions(suppress=True)\n",
    "    vectors = model.fit_transform(input_vector)\n",
    "\n",
    "    normalizer = preprocessing.Normalizer()\n",
    "    vectors =  normalizer.fit_transform(vectors, 'l2')\n",
    "    return vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "sns.set()\n",
    "vectors = dimension_reduction(vectors_tf1)\n",
    "dataset = pd.DataFrame(data=vectors, columns=['x','y'])\n",
    "ax = sns.scatterplot(x=\"x\", y=\"y\", data=dataset)\n",
    "for word in words:\n",
    "    ax.annotate(word, (vectors[word2int[word]][0],vectors[word2int[word]][1] ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vectors = dimension_reduction(vectors_tf2)\n",
    "dataset = pd.DataFrame(data=vectors, columns=['x','y'])\n",
    "ax = sns.scatterplot(x=\"x\", y=\"y\", data=dataset)\n",
    "for word in words:\n",
    "    ax.annotate(word, (vectors[word2int[word]][0],vectors[word2int[word]][1] ))"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}