18.NLP  Imdb reviews Dataset - part 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMDB reviews using the 8ksubwords pre tokenized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:TFDS datasets with text encoding are deprecated and will be removed in a future version. Instead, you should use the plain text version and tokenize the text using `tensorflow_text` (See: https://www.tensorflow.org/tutorials/tensorflow_text/intro#tfdata_example)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "\n",
    "imdb, info = tfds.load(\"imdb_reviews/subwords8k\",with_info = True,as_supervised=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = imdb['train'] , imdb['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = info.features['text'].encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<SubwordTextEncoder vocab_size=8185>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "938 -----> ab\n",
      "1282 -----> his\n",
      "227 -----> he\n",
      "231 -----> k \n",
      "760 -----> ma\n",
      "472 -----> ity \n",
      "9 -----> is \n",
      "3324 -----> nice\n",
      "7962 -----> !\n"
     ]
    }
   ],
   "source": [
    "ab = tokenizer.encode('abhishek maity is nice!')\n",
    "for i in ab:\n",
    "    print(i,\"----->\",tokenizer.decode([i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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'included_', 'hire', 'graph', 'gory_', 'favour', 'elde', 'dum', 'destroy_', 'destin', 'denti', 'consistent', 'cameo', 'betr', 'arrest', 'appea', 'animal', 'amen', 'accidentally', 'acce', 'Silv', 'Saturday_', 'ST_', 'Res', 'MGM', 'Korea', 'Fam', 'Asian_', 'Alle', 'zu', 'weeks', 'ticke', 'terrifi', 'table_', 'storytell', 'stopped_', 'steal', 'slash', 'shoe', 'select', 'rocke', 'roa', 'record_', 'previously', 'participa', 'okay_', 'ogr', 'official', 'nke', 'mistakes', 'misca', 'memorabl', 'logue', 'itat', 'ists_', 'intelligence_', 'ien', 'greate', 'ggy', 'gangster_', 'critical', 'closer', 'cartoons', 'boot', 'accepta', 'abu', 'TER', 'States', 'Roberts', 'LER', 'Jones_', 'Hat', 'Eri', 'Eliza', 'Coop', 'wes', 'uninteresting', 'tense', 'teet', 'suffers_', 'stranger', 'station_', 'scu', 'resid', 'rand', 'popula', 'ours', 'opene', 'occurr', 'non_', 'nominated_', 'mol', 'missi', 'memory_', 'memories_', 'maid', 'intri', 'inju', 'inevitabl', 'humans_', 'hanging_', 'gratuitous_', 'gas_', 'forme', 'direct', 'difficult', 'department', 'damag', 'creatures', 'cif', 'Warner', 'Titan', 'Matt_', 'Larr', 'KI', 'Hor', 'Holm', 'Fair', 'Drew', 'Andr', '1960', 'wri', 'vely', 'uls', 'travel_', 'trat', 'transf', 'timi', 'suspen', 'struggling', 'spoil_', 'slaps', 'sink', 'reti', 'reaction_', 'quest_', 'pilot_', 'narration', 'invite', 'hearing_', 'gm', 'gai', 'full', 'frankly', 'fairy', 'expe', 'dimension', 'dent_', 'deme', 'contest', 'conscious', 'cked', 'below_', 'ations', 'angel', 'alive', 'absurd_', 'Wer', 'Tha', 'Stewar', 'Play', 'Picture', 'Part', 'Martin', 'Franc', 'Fir', 'Fas', 'Ev', 'Cos', 'Carre', 'Bog', 'BU', 'Anne_', 'yan', 'writ', 'vit', 'vai', 'summ', 'ston', 'stin', 'stif', 'sensitive', 'rules', 'provided_', 'prostitut', 'pretentious_', 'poignan', 'pai', 'paced_', 'offi', 'nds_', 'mig', 'laughable_', 'instal', 'inati', 'forget', 'eit', 'defend', 'conse', 'beaut', 'Spr', 'Rol', 'Our_', 'NOT', 'Lugosi', 'Luci', 'Las', 'Imp', 'Ic', 'Earl', 'Davis_', 'Cod', '!)', 'twiste', 'sincer', 'sacrifice', 'references_', 'range_', 'purchase', 'orn', 'noise', 'neo', 'mecha', 'lun', 'insult_', 'fully', 'flicks_', 'fair', 'endless_', 'eeri', 'devot', 'curious_', 'comical', 'beth_', 'begin', 'aura', 'ase_', 'ach_', 'Sullivan', 'St', 'Sarah', 'London', 'Liv', 'Kee', 'Jackie_', 'Hong', 'Emil', 'Clair', 'China', 'California', 'Atlant', 'Alice', '\"?', '!!!!!!', 'xico', 'wick', 'visi', 'viewed_', 'uish', 'tribu', 'theatrical_', 'talks_', 'smile_', 'seven', 'reminisce', 'relie', 'rci', 'rah', 'pleasant_', 'plague', 'picio', 'ounce', 'murdered_', 'mul', 'mous_', 'mock', 'mira', 'mete', 'loss_', 'initia', 'iest_', 'health', 'harde', 'gran', 'goal', 'ghe', 'fy', 'fix', 'experienced', 'edy', 'deci', 'conflict_', 'compe', 'committed', 'cele', 'brick', 'bour', 'bers', 'berate', 'artist_', 'anth', 'Woody_', 'WWI', 'V_', 'TT', 'Sunday', 'Story_', 'Rob_', 'Rachel', 'Nin', 'Gree', 'Friday', 'Dev', 'Bros', 'Brana', ' : ', 'wha', 'vig', 'views', 'unconvincing', 'smi', 'sibl', 'quen', 'pointless', 'perp', 'particular', 'overwhelm', 'offered', 'nominat', 'naturally', 'locke', 'left', 'lady', 'ilt', 'iel', 'ication', 'historic', 'haunting', 'gem_', 'figures', 'figured_', 'evol', 'ery', 'eco', 'dynami', 'duct', 'doi', 'description', 'cultural', 'contrac', 'confide', 'combined', 'coin', 'cke', 'chosen_', 'amed', 'agon', 'Thomas_', 'THI', 'Nation', 'MOVIE', 'Lev', 'Jeff', 'Hoffman', 'Glen', 'Even', '1st_', ' ! ', 'yu', 'trappe', 'thir', 'tension', 'tail', 'table', 'split', 'sides', 'settle', 'schem', 'save', 'ruc', 'prime', 'posit', 'painte', 'ndi', 'marry_', 'kun', 'killing', 'isol', 'iot', 'intend', 'impres', 'horribly_', 'hing', 'heroi', 'gle_', 'fri', 'fitt', 'fighter', 'estin', 'ee_', 'drunk_', 'directly', 'dinos', 'chose_', 'changing', 'blonde_', 'benefi', 'award_', 'av', 'aki', 'ages', 'acter', 'VERY_', 'Ur', 'Tel', 'Superman_', 'Real', 'Phi', 'Palm', 'Nicol', 'Johnson', 'Jesus_', 'J_', 'Hes', 'Helen', 'Fun', 'Fle', 'Dir', 'Chap', 'vag', 'uncon', 'ues', 'types_', 'tical', 'sprin', 'sorts', 'securi', 'previ', 'porno', 'party', 'pare', 'method', 'medica', 'mber', 'landscape', 'jor', 'jail', 'imper', 'hunter', 'happening', 'gritty', 'gain_', 'flaws_', 'fak', 'extra', 'edited_', 'ecc', 'dragg', 'chie', 'cant_', 'breast', 'authorit', 'ated', 'ality', 'advise', 'advan', 'according_', 'Wors', 'Unlike', 'United_', 'Simon_', 'Riv', 'Pea', 'Michell', 'Exp', 'Child', 'Cham', 'Bourne', 'Basi', 'widow', 'walked_', 'upp', 'unforg', 'uld_', 'tting', 'till_', 'thy_', 'talents_', 'suspenseful', 'summer_', 'storm', 'screening', 'scare_', 'realizes_', 'rce', 'raw', 'qu', 'ngl', 'magic', 'lac', 'jobs', 'ister_', 'inti', 'inha', 'ill_', 'hands', 'grin', 'forward', 'examin', 'equent', 'emi', 'contact', 'concentrat', 'compu', 'competen', 'biograph', 'attach', 'amus', 'alik', 'activi', 'William', 'Myst', 'Luke_', 'Live', 'Life_', '15', 'zes', 'werewolf', 'warne', 'uring_', 'trilogy', 'swim', 'stumble', 'spite', 'spends_', 'sleep_', 'sist', 'sentence', 'rma', 'reward', 'reviewer_', 'pul', 'preten', 'performed', 'passing', 'par_', 'oph', 'livi', 'kinds_', 'journal', 'isticat', 'inva', 'idi', 'ham_', 'fte', 'few', 'featured', 'ern_', 'eag', 'dollars', 'disb', 'depth', 'cryin', 'cross_', 'content', 'contemporary_', 'colors', 'chee', 'because', 'asy', 'agent_', 'Willi', 'Warr', 'Ven', 'Vamp', 'Roch', 'ONE', 'Movie', 'Mau', 'Mass', 'MST', 'Hin', 'Hear', 'Gue', 'Gl', 'Freddy_', 'Definite', 'Captain_', 'BBC', '??? ', '80s_', '\"), ', 'wol', 'weekend', 'vampires', 'underst', 'tial_', 'terrorist', 'strength_', 'starre', 'soldier_', 'snow', 'sity', 'ruin_', 'retar', 'resu', 'required', 'recommended', 'ques', 'propo', 'presents_', 'perm', 'overt', 'olds', 'occas', 'nn_', 'nen', 'nei', 'mail', 'lost', 'lion', 'libr', 'inner_', 'headed', 'happy', 'guest', 'govern', 'friendly', 'explains', 'ens_', 'effectively', 'draw_', 'downright', 'dete', 'dde', 'dare', 'cring', 'courag', 'conspi', 'comedie', 'claims_', 'cide', 'chas', 'captivat', 'bite', 'bare', 'author_', 'addition', 'Vid', 'Rh', 'Oliv', 'Nata', 'Mexican', 'Keaton_', 'Iron', 'Barb', 'ALL_', '12', '!), ', 'worthwhile', 'weake', 'ung', 'understood_', 'unbelievable', 'superf', 'stolen', 'stereotypic', 'spoiler', 'sight', 'scares', 'rut', 'remove', 'remotely_', 'releva', 'prese', 'poke', 'ndou', 'mbla', 'lucky_', 'lling_', 'legendary', 'imagery', 'humou', 'hug', 'hired', 'heck', 'guilty', 'extras', 'expected', 'everywhere', 'dry_', 'drea', 'directed', 'dimensional_', 'ddi', 'dden', 'communica', 'cham', 'buddy', 'bank_', 'azi', 'algi', 'adventures', 'accurate_', 'accompan', 'Thom', 'Still_', 'Someone', 'Serious', 'SU', 'Phill', 'Perso', 'Patrick_', 'Lei', 'Jus', 'Gho', 'Get_', 'Freeman', 'Especially_', '?).', '...\"']\n"
     ]
    }
   ],
   "source": [
    "print(tokenizer.subwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8185\n"
     ]
    }
   ],
   "source": [
    "print(tokenizer.vocab_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Tokenized string is : [6307, 2327, 2934, 7961, 7979, 7975, 539, 48, 4249, 4429, 7, 2652, 8050, 7962]\n",
      "The Original String is :  Tensorflow 2.x from basics to mastery!\n"
     ]
    }
   ],
   "source": [
    "sample_string = \"Tensorflow 2.x from basics to mastery!\"\n",
    "\n",
    "tokenized_string = tokenizer.encode(sample_string)\n",
    "print('The Tokenized string is :',tokenized_string)\n",
    "\n",
    "original_string = tokenizer.decode(tokenized_string)\n",
    "print('The Original String is : ',original_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6307------>Ten\n",
      "2327------>sor\n",
      "2934------>flow\n",
      "7961------> \n",
      "7979------>2\n",
      "7975------>.\n",
      "539------>x \n",
      "48------>from \n",
      "4249------>basi\n",
      "4429------>cs \n",
      "7------>to \n",
      "2652------>master\n",
      "8050------>y\n",
      "7962------>!\n"
     ]
    }
   ],
   "source": [
    "for i in tokenized_string:\n",
    "    print('{}------>{}'.format(i,tokenizer.decode([i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "buffer_size = 10000\n",
    "batch_size = 64\n",
    "\n",
    "\n",
    "train_dataset = train_data.shuffle(buffer_size)\n",
    "train_dataset = train_dataset.padded_batch(batch_size,tf.compat.v1.data.get_output_shapes(train_dataset))\n",
    "\n",
    "test_dataset = test_data.padded_batch(batch_size,tf.compat.v1.data.get_output_shapes(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorShape([None, None]), TensorShape([None]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.compat.v1.data.get_output_shapes(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, None, 64)          523840    \n",
      "_________________________________________________________________\n",
      "global_average_pooling1d (Gl (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 6)                 390       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 7         \n",
      "=================================================================\n",
      "Total params: 524,237\n",
      "Trainable params: 524,237\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "embedding_dim = 64\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Embedding(tokenizer.vocab_size,embedding_dim),\n",
    "    tf.keras.layers.GlobalAveragePooling1D(),\n",
    "    tf.keras.layers.Dense(6,activation='relu'),\n",
    "    tf.keras.layers.Dense(1,activation='sigmoid')\n",
    "])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "391/391 [==============================] - 12s 30ms/step - loss: 0.6853 - accuracy: 0.5566 - val_loss: 0.6603 - val_accuracy: 0.7242\n",
      "Epoch 2/10\n",
      "391/391 [==============================] - 10s 26ms/step - loss: 0.5210 - accuracy: 0.8027 - val_loss: 0.4299 - val_accuracy: 0.8414\n",
      "Epoch 3/10\n",
      "391/391 [==============================] - 10s 26ms/step - loss: 0.3543 - accuracy: 0.8706 - val_loss: 0.3512 - val_accuracy: 0.8660\n",
      "Epoch 4/10\n",
      "391/391 [==============================] - 10s 26ms/step - loss: 0.2882 - accuracy: 0.8951 - val_loss: 0.3277 - val_accuracy: 0.8689\n",
      "Epoch 5/10\n",
      "391/391 [==============================] - 10s 27ms/step - loss: 0.2526 - accuracy: 0.9093 - val_loss: 0.3122 - val_accuracy: 0.8775\n",
      "Epoch 6/10\n",
      "391/391 [==============================] - 10s 26ms/step - loss: 0.2260 - accuracy: 0.9186 - val_loss: 0.3060 - val_accuracy: 0.8796\n",
      "Epoch 7/10\n",
      "391/391 [==============================] - 10s 26ms/step - loss: 0.2065 - accuracy: 0.9256 - val_loss: 0.3047 - val_accuracy: 0.8811\n",
      "Epoch 8/10\n",
      "391/391 [==============================] - 10s 25ms/step - loss: 0.1900 - accuracy: 0.9343 - val_loss: 0.3176 - val_accuracy: 0.8764\n",
      "Epoch 9/10\n",
      "391/391 [==============================] - 10s 25ms/step - loss: 0.1781 - accuracy: 0.9375 - val_loss: 0.3164 - val_accuracy: 0.8790\n",
      "Epoch 10/10\n",
      "391/391 [==============================] - 10s 25ms/step - loss: 0.1665 - accuracy: 0.9415 - val_loss: 0.3293 - val_accuracy: 0.8752\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 10\n",
    "model.compile(loss='binary_crossentropy',optimizer='Adam',metrics=['accuracy'])\n",
    "hist = model.fit(train_dataset,epochs=num_epochs,validation_data=test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20f2787ce48>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(hist.history['accuracy'])\n",
    "plt.plot(hist.history['val_accuracy'])\n",
    "plt.legend(['Train','Test'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20f6e7d5c08>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(hist.history['loss'])\n",
    "plt.plot(hist.history['val_loss'])\n",
    "plt.legend(['Train','Test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
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