Keras_sentiment_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "## The IMDB Reviews Dataset\n",
    "____\n",
    "\n",
    "____"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.python.keras.datasets import imdb\n",
    "(x_train,y_train),(x_test,y_test)= imdb.load_data(num_words=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(x_train[0])\n",
    "print(y_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes_name=['Negative','Positive']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_index = imdb.get_word_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4822"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_index['hello']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decoding the Reviews\n",
    "___\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rev_word_index = dict((value,key) for key,value in word_index.items())\n",
    "def decode(review):\n",
    "    text = '';\n",
    "    for i in review:\n",
    "        text+= rev_word_index[i]\n",
    "        text+=' '\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"the as you with out themselves powerful lets loves their becomes reaching had journalist of lot from anyone to have after out atmosphere never more room and it so heart shows to years of every never going and help moments or of every chest visual movie except her was several of enough more with is now current film as you of mine potentially unfortunately of you than him that with out themselves her get for was camp of you movie sometimes movie that with scary but and to story wonderful that in seeing in character to of 70s musicians with heart had shadows they of here that with her serious to have does when from why what have critics they is you that isn't one will very to as itself with other and in of seen over landed for anyone of and br show's to whether from than out themselves history he name half some br of and odd was two most of mean for 1 any an boat she he should is thought frog but of script you not while history he heart to real at barrel but when from one bit then have two of script their with her nobody most that with wasn't to with armed acting watch an for with heartfelt film want an \""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decode(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len of 1st train 218\n",
      "len of 2st train 189\n",
      "len of 1st test 68\n",
      "len of 2st test 260\n"
     ]
    }
   ],
   "source": [
    "def show_len():\n",
    "    print('len of 1st train', len(x_train[0]))\n",
    "    print('len of 2st train', len(x_train[1]))\n",
    "    print('len of 1st test', len(x_test[0]))\n",
    "    print('len of 2st test', len(x_test[1]))\n",
    "show_len()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "##  Padding the Examples\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_index['the']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.python.keras.preprocessing.sequence import pad_sequences\n",
    "x_train = pad_sequences(x_train,value=word_index['the'],padding='post',maxlen=256)\n",
    "x_test = pad_sequences(x_test,value=1,padding='post',maxlen=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len of 1st train 256\n",
      "len of 2st train 256\n",
      "len of 1st test 256\n",
      "len of 2st test 256\n"
     ]
    }
   ],
   "source": [
    "show_len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"the as you with out themselves powerful lets loves their becomes reaching had journalist of lot from anyone to have after out atmosphere never more room and it so heart shows to years of every never going and help moments or of every chest visual movie except her was several of enough more with is now current film as you of mine potentially unfortunately of you than him that with out themselves her get for was camp of you movie sometimes movie that with scary but and to story wonderful that in seeing in character to of 70s musicians with heart had shadows they of here that with her serious to have does when from why what have critics they is you that isn't one will very to as itself with other and in of seen over landed for anyone of and br show's to whether from than out themselves history he name half some br of and odd was two most of mean for 1 any an boat she he should is thought frog but of script you not while history he heart to real at barrel but when from one bit then have two of script their with her nobody most that with wasn't to with armed acting watch an for with heartfelt film want an the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the \""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decode(x_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Word Embeddings\n",
    "\n",
    "\n",
    "## Creating and Training the Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, None, 16)          160000    \n",
      "_________________________________________________________________\n",
      "global_average_pooling1d (Gl (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 160,289\n",
      "Trainable params: 160,289\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.keras import Sequential\n",
    "from tensorflow.python.keras.layers  import Dense,Embedding, GlobalAveragePooling1D\n",
    "\n",
    "model = Sequential(\n",
    "    [\n",
    "        Embedding(10000,16),\n",
    "        GlobalAveragePooling1D(),\n",
    "        Dense(16,activation='relu'),\n",
    "        Dense(1,activation='sigmoid')\n",
    "    ]\n",
    ")\n",
    "model.compile(\n",
    "    loss ='binary_crossentropy',\n",
    "    optimizer = 'adam',\n",
    "    metrics = ['accuracy']\n",
    ")\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19."
     ]
    }
   ],
   "source": [
    "from tensorflow.python.keras.callbacks import LambdaCallback\n",
    "simple_log = LambdaCallback(on_epoch_end= lambda e, l: print(e, end='.'))\n",
    "E =20\n",
    "h = model.fit(\n",
    "x_train,y_train,\n",
    "    validation_split=0.3,\n",
    "    epochs=E,callbacks=[simple_log],\n",
    "    verbose=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Predictions and Evaluation\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.plot(range(E),h.history['acc'],label='Training')\n",
    "plt.plot(range(E),h.history['val_acc'],label='Test')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000/25000 [==============================] - 1s 46us/step\n",
      "Test acc 82.708\n"
     ]
    }
   ],
   "source": [
    "loss,acc = model.evaluate(x_test,y_test)\n",
    "print(\"Test acc {}\".format(acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "pred = model.predict(np.expand_dims(x_test[0],axis=0))\n",
    "print(classes_name[np.argmax(pred[0])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"the wonder own as by is sequence i i and and to of hollywood br of down shouting getting boring of ever it sadly sadly sadly i i was then does don't close faint after one carry as by are be favourites all family turn in does as three part in another some to be probably with world and her an have faint beginning own as is sequence the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the the \""
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decode(x_test[0])"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}