Untitled2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word Embedding Techniques using Embedding Layer in Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Libraries USed Tensorflow> 2.0  and keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "##tensorflow >2.0\n",
    "from tensorflow.keras.preprocessing.text import one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### sentences\n",
    "sent=[  'the glass of milk',\n",
    "     'the glass of juice',\n",
    "     'the cup of tea',\n",
    "    'I am a good boy',\n",
    "     'I am a good developer',\n",
    "     'understand the meaning of words',\n",
    "     'your videos are good',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['the glass of milk',\n",
       " 'the glass of juice',\n",
       " 'the cup of tea',\n",
       " 'I am a good boy',\n",
       " 'I am a good developer',\n",
       " 'understand the meaning of words',\n",
       " 'your videos are good']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Vocabulary size\n",
    "voc_size=10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One Hot Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6654, 998, 8966, 1609], [6654, 998, 8966, 1428], [6654, 4519, 8966, 4736], [8807, 9358, 6927, 7257, 6349], [8807, 9358, 6927, 7257, 801], [5774, 6654, 257, 8966, 4241], [4044, 1506, 8798, 7257]]\n"
     ]
    }
   ],
   "source": [
    "onehot_repr=[one_hot(words,voc_size)for words in sent] \n",
    "print(onehot_repr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word Embedding Represntation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Embedding\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    0    0    0 6654  998 8966 1609]\n",
      " [   0    0    0    0 6654  998 8966 1428]\n",
      " [   0    0    0    0 6654 4519 8966 4736]\n",
      " [   0    0    0 8807 9358 6927 7257 6349]\n",
      " [   0    0    0 8807 9358 6927 7257  801]\n",
      " [   0    0    0 5774 6654  257 8966 4241]\n",
      " [   0    0    0    0 4044 1506 8798 7257]]\n"
     ]
    }
   ],
   "source": [
    "sent_length=8\n",
    "embedded_docs=pad_sequences(onehot_repr,padding='pre',maxlen=sent_length)\n",
    "print(embedded_docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim=10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Embedding(voc_size,10,input_length=sent_length))\n",
    "model.compile('adam','mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_10\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_9 (Embedding)      (None, 8, 10)             100000    \n",
      "=================================================================\n",
      "Total params: 100,000\n",
      "Trainable params: 100,000\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-3.78632322e-02 -2.62806062e-02  2.97411121e-02 -3.30717117e-02\n",
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      "    5.14941290e-03  1.52081959e-02]\n",
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      "   -3.30929384e-02 -4.77949157e-02]]\n",
      "\n",
      " [[-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
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      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
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      "    2.71405019e-02  9.45134088e-03  2.37812735e-02  4.17690389e-02\n",
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      "   -1.21230260e-02  3.76243480e-02 -1.16624944e-02 -2.14108825e-02\n",
      "    4.65453304e-02  1.53732188e-02]\n",
      "  [ 3.27601545e-02 -6.37691095e-03  3.90734412e-02 -1.91246755e-02\n",
      "    2.17718594e-02 -4.63032499e-02  8.00942257e-03 -3.11566722e-02\n",
      "   -4.86455113e-03 -4.84352373e-02]\n",
      "  [ 2.47278921e-02 -1.74773335e-02  2.73607112e-02  3.32159512e-02\n",
      "    3.40201892e-02  3.07315923e-02  3.27118374e-02  2.95767300e-02\n",
      "    3.36540863e-03 -1.39380470e-02]]\n",
      "\n",
      " [[-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-3.78632322e-02 -2.62806062e-02  2.97411121e-02 -3.30717117e-02\n",
      "    2.71405019e-02  9.45134088e-03  2.37812735e-02  4.17690389e-02\n",
      "    5.14941290e-03  1.52081959e-02]\n",
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      "   -1.26881972e-02  1.82384253e-03]\n",
      "  [ 3.27601545e-02 -6.37691095e-03  3.90734412e-02 -1.91246755e-02\n",
      "    2.17718594e-02 -4.63032499e-02  8.00942257e-03 -3.11566722e-02\n",
      "   -4.86455113e-03 -4.84352373e-02]\n",
      "  [ 2.06373222e-02 -4.02337313e-02 -7.88780302e-03  4.99007814e-02\n",
      "    6.30392879e-03 -4.51510027e-03 -4.75921407e-02  4.11981009e-02\n",
      "    2.95249373e-03 -2.82982830e-02]]\n",
      "\n",
      " [[-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-1.45982131e-02  3.89718898e-02  8.12438875e-03  2.98856981e-02\n",
      "   -4.28516045e-02  3.53246070e-02  4.52651493e-02  4.48638909e-02\n",
      "    8.75295326e-03  7.60734081e-04]\n",
      "  [ 4.34498824e-02 -1.39420740e-02 -3.63176242e-02  1.51205100e-02\n",
      "   -5.29961661e-03  3.06736305e-03  2.74117328e-02 -5.53436205e-03\n",
      "    1.96599998e-02  2.70034112e-02]\n",
      "  [ 3.74776237e-02  1.71419419e-02  8.23931769e-03  1.07321031e-02\n",
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      "\n",
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      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
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      "   -3.65974903e-02 -2.37902757e-02]\n",
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      "    1.96599998e-02  2.70034112e-02]\n",
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      "  [ 3.44808139e-02 -3.24114189e-02 -4.27851342e-02 -2.97762640e-02\n",
      "    2.60863453e-03  1.32247098e-02 -1.52253993e-02 -1.80003159e-02\n",
      "    2.13337280e-02  1.47879384e-02]]\n",
      "\n",
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      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
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      "   -3.65974903e-02 -2.37902757e-02]\n",
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      "   -3.88347134e-02 -3.49127762e-02]]\n",
      "\n",
      " [[-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.25554439e-03 -1.59294531e-03 -4.71415296e-02  4.42524664e-02\n",
      "   -9.73954052e-03 -4.32581306e-02  4.00710814e-02 -1.43285990e-02\n",
      "   -3.65974903e-02 -2.37902757e-02]\n",
      "  [-4.73472364e-02 -3.81088741e-02 -2.09466461e-02 -2.04950571e-02\n",
      "    1.39485039e-02  4.69478406e-02  1.68476589e-02 -3.53824347e-04\n",
      "   -4.28333394e-02 -3.28581110e-02]\n",
      "  [-1.94661859e-02 -2.65732538e-02  1.35865100e-02  3.72302271e-02\n",
      "   -6.92135096e-03  3.96609344e-02 -2.66522169e-02  4.32638861e-02\n",
      "    4.62255217e-02  3.84382121e-02]\n",
      "  [ 2.43262388e-02 -3.90543789e-03 -3.17700654e-02  2.07458772e-02\n",
      "    1.68472528e-03  3.81677859e-02 -2.90758908e-05  2.38945819e-02\n",
      "   -4.55065966e-02 -4.36516404e-02]\n",
      "  [ 4.71589826e-02 -1.86485425e-02  4.58842032e-02  2.64571793e-02\n",
      "   -3.14603336e-02  1.91541426e-02 -3.84818092e-02 -3.24337035e-02\n",
      "   -2.62893792e-02 -3.42335850e-02]]]\n"
     ]
    }
   ],
   "source": [
    "print(model.predict(embedded_docs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    0,    0,    0, 6654,  998, 8966, 1609])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedded_docs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.00425554 -0.00159295 -0.04714153  0.04425247 -0.00973954 -0.04325813\n",
      "   0.04007108 -0.0143286  -0.03659749 -0.02379028]\n",
      " [-0.00425554 -0.00159295 -0.04714153  0.04425247 -0.00973954 -0.04325813\n",
      "   0.04007108 -0.0143286  -0.03659749 -0.02379028]\n",
      " [-0.00425554 -0.00159295 -0.04714153  0.04425247 -0.00973954 -0.04325813\n",
      "   0.04007108 -0.0143286  -0.03659749 -0.02379028]\n",
      " [-0.00425554 -0.00159295 -0.04714153  0.04425247 -0.00973954 -0.04325813\n",
      "   0.04007108 -0.0143286  -0.03659749 -0.02379028]\n",
      " [-0.03786323 -0.02628061  0.02974111 -0.03307171  0.0271405   0.00945134\n",
      "   0.02378127  0.04176904  0.00514941  0.0152082 ]\n",
      " [ 0.04834186  0.04388311 -0.02802253 -0.01475487 -0.01212303  0.03762435\n",
      "  -0.01166249 -0.02141088  0.04654533  0.01537322]\n",
      " [ 0.03276015 -0.00637691  0.03907344 -0.01912468  0.02177186 -0.04630325\n",
      "   0.00800942 -0.03115667 -0.00486455 -0.04843524]\n",
      " [-0.04173617  0.03438064  0.02880521 -0.01896455  0.0323303  -0.00109453\n",
      "  -0.01675171 -0.00941917 -0.03309294 -0.04779492]]\n"
     ]
    }
   ],
   "source": [
    "print(model.predict(embedded_docs)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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