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NLP_FINAL_Presentation.ipynb
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nlp_final_presentation.ipynb
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"cell_type": "markdown",
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"<a href=\"https://colab.research.google.com/gist/tensorvijay/7c2a05612c87a7b62ec01f72fcb32a2f/nlp_final_presentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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{
"cell_type": "code",
"metadata": {
"id": "HwF6X-aINf8H"
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from keras.models import Model\n",
"from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n",
"from keras.optimizers import RMSprop\n",
"from keras.preprocessing.text import Tokenizer\n",
"from keras.preprocessing import sequence\n",
"from keras.utils import to_categorical\n",
"from keras.callbacks import EarlyStopping\n",
"%matplotlib inline"
],
"execution_count": null,
"outputs": []
},
{
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"base_uri": "https://localhost:8080/",
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"outputId": "d6a454c2-27c8-47de-c2f6-9601cb8cfa5c"
},
"source": [
"df = pd.read_csv('/content/malayalam_news_train.csv',delimiter=',',encoding='utf_8_sig')\n",
"df.head()"
],
"execution_count": null,
"outputs": [
{
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"1 ഈ​​​സ്റ്റേ​​​ണ്‍ ക​​​റി പൗ​​​ഡ​​​റി​​​ന് പു​​​... business\n",
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"3 റയല്‍ മാഡ്രിഡിനെ ഞെട്ടിച്ച യുവതാരം ബ്രസീല്‍ ടീ... sports\n",
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"output_type": "execute_result",
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"3 റയല്‍ മാഡ്രിഡിനെ ഞെട്ടിച്ച യുവതാരം ബ്രസീല്‍ ടീ... sports\n",
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"source": [
"Y[0:10] #O business 1 entertainment 2 sports"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"array([[0],\n",
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"height": 349
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"id": "yCEIIIdBWOGf",
"outputId": "5a9e9d51-af76-4940-c580-971e727f996e"
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"source": [
"img=sns.countplot(df.label)\n",
"plt.xlabel('Label')\n",
"plt.title('Number of Classes')\n",
"plt.savefig('Malayalam_news_count')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
" FutureWarning\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAbsElEQVR4nO3de7gddX3v8fdHQLRyNynlHopYi1ZRIngvakWgVdCiQquAqEgrVU8PtnraR5CW1oqXU2qLxYpIVSiKCFVaRCqiCEKQFAJIDRcPCRFSUEFFKvA9f8xvyxD3zuyErL0S9vv1PPPsWb+5rO+atff67PnNrJlUFZIkrcyjxl2AJGntZ1hIkgYZFpKkQYaFJGmQYSFJGmRYSJIGGRaa9ZKckuQvx/TcSfLxJN9PctlqrqOSPGFN1yb1GRZa6yS5OcntSR7Xa3tjkgvHWNaoPA94CbBtVe0+2QxJtkrysSTLktyd5NtJ3tPfPtKoGRZaW60HvG3cRayqJOut4iI7ADdX1Y+nWN8WwCXAY4FnV9XGdOGyGbDTw6lVWhWGhdZWxwNHJdlsxQlJ5rWul/V7bRcmeWMbPzTJxUk+lOQHSW5M8pzWfkvbazlkhdXOSXJ++8/9q0l26K37SW3anUmuT/Lq3rRTkpyY5NwkPwZeOEm9Wyc5py2/OMmbWvsbgH8Cnp3kR0neM8l2+GPgbuC1VXUzQFXdUlVvq6qrJnmu305yZZK72ms9pjftMUk+meSOtl0uT7Jlb5vd2F7/TUl+v7fcYUmua11l501sm9aF9qG2Pe9KcnWSp0zyGvQIYFhobbUAuBA4ajWX3wO4Cng88GngdOCZwBOA1wIfTrJRb/7fB/4CmAMsBD4F0Lp6zm/r+GXgQOAfkuzSW/b3gOOAjYGvT1LL6cASYGvgAOCvkryoqj4GHAFcUlUbVdXRkyz7W8DnquqBab7uHwMH0+15/DbwB0n2b9MOATYFtmvb5QjgnvYaTwD2aXsuz2nbgCT7Af8HeCUwF/gacFpb317AC4AntvW+GrhjmnVqHWNYaG32buCPksxdjWVvqqqPV9X9wL/QfUAeW1X3VtWXgP+hC44JX6yqi6rqXuDP6P7b3w74Hbpuoo9X1X1VdSVwJvCq3rJnV9XFVfVAVf20X0Rbx3OBP62qn1bVQrq9iYOn+ToeDyyb7ouuqgur6upWy1V0H+y/2Sb/rK3vCVV1f1VdUVV3tWkPAE9J8tiqWlZV17T2I4C/rqrrquo+4K+AXdvexc/oAvJJQNo8065V6xbDQmutqloEfAF452osfltv/J62vhXb+nsWt/Se90fAnXR7AjsAe7Rumx8k+QHdXsivTLbsJLYG7qyqu3tt3wW2mebruAPYaprzkmSPJF9JsjzJD+k+7Oe0yf8MnAecnuTWJO9LskE7XvKaNu+yJF9M8qS2zA7A3/Ze+51AgG2q6j+ADwN/D9ye5KQkm0y3Vq1bDAut7Y4G3sRDP1wnDgb/Uq+t/+G9OrabGGndU1sAt9IFwVerarPesFFV/UFv2ZVduvlWYIskG/fatgeWTrOuLwOvSDLdv9VPA+cA21XVpsBH6D7cqaqfVdV7qmoXuq6m36Ht4VTVeVX1Erpg+jbw0ba+W4A3r/D6H1tV32jLnVBVuwG70HVHvWOadWodY1horVZVi+m6kd7aa1tO92H72iTrJTmMh39m0L5Jnpfk0XTHLi6tqlvo9myemOR1STZowzOT/Po0678F+Abw1+0A81OBNwCfnGZdHwQ2AT7RO7C8TZIPtnWtaGO6PZmfJtmd7ngKbbkXJvmNdsbWXXTdSA8k2TLJfu3Yxb3Aj+i6paALm3cleXJbx6ZJXtXGn9n2ZDagC/Cf9pbTI4xhoXXBscCK3yl4E91/sXcAT6b7QH44Pk23F3MnsBvdQXBa99FedAe2bwW+B/wNsOEqrPsgYF5b/izg6Kr68nQWrKo76fYCfgZ8M8ndwAXAD4HFkyzyh8Cxbb53A2f0pv0K8Fm6oLgO+Cpd19Sj6M66upXu9f8m8Aft+c+ie72nJ7kLWATs09a3Cd0eyPfputbuoDuLTY9A8eZHkqQh7llIkgYZFpKkQYaFJGmQYSFJGrT+8Czrpjlz5tS8efPGXYYkrTOuuOKK/66qSa+Y8IgNi3nz5rFgwYJxlyFJ64wk351qmt1QkqRBhoUkaZBhIUkaZFhIkgYZFpKkQYaFJGnQyMIiycnt3ryLem3/kmRhG25OMnHrxnlJ7ulN+0hvmd3avX0XJzkhSUZVsyRpcqP8nsUpdHfROnWioapeMzGe5AN0l1mecENV7TrJek6kuxz1N4Fzgb2BfxtBvZKkKYxsz6KqLqK7Nv4vaHsHr+bBG79PKslWwCZVdWl111I/Fdh/ZctIkta8cX2D+/nAbVX1nV7bjkmupLsxy59X1dfobqW5pDfPElZy7+IkhwOHA2y//fZrvGhJa9Zz/+654y7hEe/iP7p4jaxnXGFxEA/dq1gGbF9VdyTZDfj8xG0cV0VVnQScBDB//nzv6jQL/L9jf2PcJcwK27/76nGXoDGb8bBIsj7wSrpbVwJQVffS3fuXqroiyQ10N39fCmzbW3xbpn+je0nSGjKOU2d/C/h2Vf28eynJ3HYTeZL8KrAzcGNVLQPuSvKsdpzjYODsMdQsSbPaKE+dPQ24BPi1JEuSvKFNOpBfPLD9AuCqdirtZ4Ej2o3qobsB/T/R3Zz+BjwTSpJm3Mi6oarqoCnaD52k7UzgzCnmXwA8ZY0WJ0laJX6DW5I0yLCQJA0yLCRJgwwLSdIgw0KSNMiwkCQNMiwkSYMMC0nSIMNCkjTIsJAkDTIsJEmDDAtJ0iDDQpI0yLCQJA0yLCRJgwwLSdIgw0KSNMiwkCQNMiwkSYNGdg/udclu7zh13CU84l1x/MHjLkHSwzCyPYskJye5PcmiXtsxSZYmWdiGfXvT3pVkcZLrk7y01753a1uc5J2jqleSNLVRdkOdAuw9SfuHqmrXNpwLkGQX4EDgyW2Zf0iyXpL1gL8H9gF2AQ5q80qSZtDIuqGq6qIk86Y5+37A6VV1L3BTksXA7m3a4qq6ESDJ6W3ea9dwuZKklRjHAe4jk1zVuqk2b23bALf05lnS2qZqlyTNoJkOixOBnYBdgWXAB9bkypMcnmRBkgXLly9fk6uWpFltRsOiqm6rqvur6gHgozzY1bQU2K4367atbar2qdZ/UlXNr6r5c+fOXbPFS9IsNqNhkWSr3sNXABNnSp0DHJhkwyQ7AjsDlwGXAzsn2THJo+kOgp8zkzVLkkZ4gDvJacCewJwkS4CjgT2T7AoUcDPwZoCquibJGXQHru8D3lJV97f1HAmcB6wHnFxV14yqZknS5EZ5NtRBkzR/bCXzHwccN0n7ucC5a7A0SdIq8nIfkqRBhoUkaZBhIUkaZFhIkgYZFpKkQYaFJGmQYSFJGmRYSJIGGRaSpEGGhSRpkGEhSRpkWEiSBhkWkqRBhoUkaZBhIUkaZFhIkgYZFpKkQYaFJGmQYSFJGmRYSJIGGRaSpEEjC4skJye5PcmiXtvxSb6d5KokZyXZrLXPS3JPkoVt+Ehvmd2SXJ1kcZITkmRUNUuSJjfKPYtTgL1XaDsfeEpVPRX4L+BdvWk3VNWubTii134i8CZg5zasuE5J0oiNLCyq6iLgzhXavlRV97WHlwLbrmwdSbYCNqmqS6uqgFOB/UdRryRpauM8ZnEY8G+9xzsmuTLJV5M8v7VtAyzpzbOktU0qyeFJFiRZsHz58jVfsSTNUmMJiyR/BtwHfKo1LQO2r6qnA38MfDrJJqu63qo6qarmV9X8uXPnrrmCJWmWW3+mnzDJocDvAC9uXUtU1b3AvW38iiQ3AE8ElvLQrqptW5skaQbN6J5Fkr2BPwFeXlU/6bXPTbJeG/9VugPZN1bVMuCuJM9qZ0EdDJw9kzVLkka4Z5HkNGBPYE6SJcDRdGc/bQic386AvbSd+fQC4NgkPwMeAI6oqomD439Id2bVY+mOcfSPc0iSZsDIwqKqDpqk+WNTzHsmcOYU0xYAT1mDpUmSVpHf4JYkDTIsJEmDDAtJ0iDDQpI0yLCQJA0yLCRJgwwLSdIgw0KSNMiwkCQNMiwkSYMMC0nSIMNCkjTIsJAkDTIsJEmDDAtJ0iDDQpI0yLCQJA0yLCRJgwwLSdIgw0KSNMiwkCQNGmlYJDk5ye1JFvXatkhyfpLvtJ+bt/YkOSHJ4iRXJXlGb5lD2vzfSXLIKGuWJP2iaYVFkgum0zaJU4C9V2h7J3BBVe0MXNAeA+wD7NyGw4ET2/NsARwN7AHsDhw9ETCSpJmx0rBI8pj2YT0nyeZtr2CLJPOAbYZWXlUXAXeu0Lwf8Ik2/glg/177qdW5FNgsyVbAS4Hzq+rOqvo+cD6/GECSpBFaf2D6m4G3A1sDVwBp7XcBH17N59yyqpa18e8BW7bxbYBbevMtaW1Ttf+CJIfT7ZWw/fbbr2Z5kqQVrXTPoqr+tqp2BI6qql+tqh3b8LSqWt2w6K+/gHq46+mt76Sqml9V8+fOnbumVitJs97QngUAVfV3SZ4DzOsvU1WnrsZz3pZkq6pa1rqZbm/tS4HtevNt29qWAnuu0H7hajyvJGk1TfcA9z8D7weeBzyzDfNX8znPASbOaDoEOLvXfnA7K+pZwA9bd9V5wF7tmMnmwF6tTZI0Q6a1Z0EXDLu0bqNpS3Ia3V7BnCRL6M5qei9wRpI3AN8FXt1mPxfYF1gM/AR4PUBV3ZnkL4DL23zHVtWKB80lSSM03bBYBPwKsGxoxr6qOmiKSS+eZN4C3jLFek4GTl6V55YkrTnTDYs5wLVJLgPunWisqpePpCpJ0lplumFxzCiLkCSt3aZ7NtRXR12IJGntNa2wSHI3D34f4tHABsCPq2qTURUmSVp7THfPYuOJ8SShuzTHs0ZVlCRp7bLKV51t1276PN01myRJs8B0u6Fe2Xv4KLrvXfx0JBVJktY60z0b6mW98fuAm+m6oiRJs8B0j1m8ftSFSJLWXtO9NtS2Sc5qd727PcmZSbYddXGSpLXDdA9wf5zuQn9bt+FfW5skaRaYbljMraqPV9V9bTgF8IYRkjRLTDcs7kjy2iTrteG1wB2jLEyStPaYblgcRncp8e/RXXn2AODQEdUkSVrLTPfU2WOBQ6rq+wBJtqC7GdJhoypMkrT2mO6exVMnggK6GxIBTx9NSZKktc10w+JR7ZamwM/3LKa7VyJJWsdN9wP/A8AlST7THr8KOG40JUmS1jbT/Qb3qUkWAC9qTa+sqmtHV5YkaW0y7a6kFg4GhCTNQqt8ifKHK8mvJVnYG+5K8vYkxyRZ2mvft7fMu5IsTnJ9Ei+NLkkzbMYPUlfV9cCuAEnWA5YCZwGvBz5UVe/vz59kF+BA4Ml0lxr5cpInVtX9M1q4JM1iM75nsYIXAzdU1XdXMs9+wOlVdW9V3QQsBnafkeokScD4w+JA4LTe4yOTXJXk5N6putsAt/TmWdLafkGSw5MsSLJg+fLlo6lYkmahsYVFkkcDLwcmTsc9EdiJrotqGd3puqukqk6qqvlVNX/uXK9zKElryjj3LPYBvlVVtwFU1W1VdX9VPQB8lAe7mpYC2/WW27a1SZJmyDjD4iB6XVBJtupNewWwqI2fAxyYZMMkOwI7A5fNWJWSpPFcsiPJ44CXAG/uNb8vya5A0d3j+80AVXVNkjPovuNxH/AWz4SSpJk1lrCoqh8Dj1+h7XUrmf84vLyIJI3NuM+GkiStAwwLSdIgw0KSNMiwkCQNMiwkSYMMC0nSIMNCkjTIsJAkDTIsJEmDDAtJ0iDDQpI0yLCQJA0yLCRJgwwLSdIgw0KSNMiwkCQNMiwkSYMMC0nSIMNCkjTIsJAkDTIsJEmDxhYWSW5OcnWShUkWtLYtkpyf5Dvt5+atPUlOSLI4yVVJnjGuuiVpNhr3nsULq2rXqprfHr8TuKCqdgYuaI8B9gF2bsPhwIkzXqkkzWLjDosV7Qd8oo1/Ati/135qdS4FNkuy1TgKlKTZaJxhUcCXklyR5PDWtmVVLWvj3wO2bOPbALf0ll3S2h4iyeFJFiRZsHz58lHVLUmzzvpjfO7nVdXSJL8MnJ/k2/2JVVVJalVWWFUnAScBzJ8/f5WWlSRNbWx7FlW1tP28HTgL2B24baJ7qf28vc2+FNiut/i2rU2SNAPGEhZJHpdk44lxYC9gEXAOcEib7RDg7DZ+DnBwOyvqWcAPe91VkqQRG1c31JbAWUkmavh0Vf17ksuBM5K8Afgu8Oo2/7nAvsBi4CfA62e+ZEmavcYSFlV1I/C0SdrvAF48SXsBb5mB0iRJk1jbTp2VJK2FDAtJ0iDDQpI0yLCQJA0yLCRJgwwLSdIgw0KSNMiwkCQNMiwkSYMMC0nSIMNCkjTIsJAkDTIsJEmDDAtJ0iDDQpI0yLCQJA0yLCRJgwwLSdIgw0KSNMiwkCQNmvGwSLJdkq8kuTbJNUne1tqPSbI0ycI27Ntb5l1JFie5PslLZ7pmSZrt1h/Dc94H/O+q+laSjYErkpzfpn2oqt7fnznJLsCBwJOBrYEvJ3liVd0/o1VL0iw243sWVbWsqr7Vxu8GrgO2Wcki+wGnV9W9VXUTsBjYffSVSpImjPWYRZJ5wNOBb7amI5NcleTkJJu3tm2AW3qLLWGKcElyeJIFSRYsX758RFVL0uwztrBIshFwJvD2qroLOBHYCdgVWAZ8YFXXWVUnVdX8qpo/d+7cNVqvJM1mYwmLJBvQBcWnqupzAFV1W1XdX1UPAB/lwa6mpcB2vcW3bW2SpBkyjrOhAnwMuK6qPthr36o32yuARW38HODAJBsm2RHYGbhspuqVJI3nbKjnAq8Drk6ysLX9H+CgJLsCBdwMvBmgqq5JcgZwLd2ZVG/xTChJmlkzHhZV9XUgk0w6dyXLHAccN7KiJEkr5Te4JUmDDAtJ0iDDQpI0yLCQJA0yLCRJgwwLSdIgw0KSNMiwkCQNMiwkSYMMC0nSIMNCkjTIsJAkDTIsJEmDDAtJ0iDDQpI0yLCQJA0yLCRJgwwLSdIgw0KSNMiwkCQNMiwkSYPWmbBIsneS65MsTvLOcdcjSbPJOhEWSdYD/h7YB9gFOCjJLuOtSpJmj3UiLIDdgcVVdWNV/Q9wOrDfmGuSpFkjVTXuGgYlOQDYu6re2B6/Dtijqo5cYb7DgcPbw18Drp/RQmfOHOC/x12EVpvv37rtkfz+7VBVcyebsP5MVzJKVXUScNK46xi1JAuqav6469Dq8f1bt83W929d6YZaCmzXe7xta5MkzYB1JSwuB3ZOsmOSRwMHAueMuSZJmjXWiW6oqrovyZHAecB6wMlVdc2YyxqnR3xX2yOc79+6bVa+f+vEAW5J0nitK91QkqQxMiwkSYMMixFLMi/Jooe5jq2TfHZN1aS1U5I9kzxn3HWs65LsvzpXeJju9k/y8nFdcijJZkn+cBzPbVisA6rq1qo6YNx1aHSSrA/sCRgWD9/+dJcFmrZV2f5VdU5VvXf1SnvYNgPGEhZUlcMIB2Ae8G3gU8B1wGeBXwJuBua0eeYDF7bx3wQWtuFKYOO2jkVt+qHA54B/B74DvK/3XHsBlwDfAj4DbNTa3wtcC1wFvL+1vQpYBPwncNG4t9O6NgCPA77Ytt8i4DXtPX0fcDVwGfCE3u/Af7TtfwGwfWs/BfgI8M32nn6P7vtDC4Hn+x49ZHu/tm3ThcA/0p0V+SPguLZ9LgW2pPuwvxO4qc27Uxv+HbgC+BrwpGlu/5e1aVcCXwa2bMsdCny4t44TgG8ANwIHtPY9ga8CZ7f29wK/317D1cBObb65wJl0Xw+4HHhuaz8GOBm4sC3/1tZ+OnBPq/H4GX0Pxv1L8Egf2gdF9X4JTgaOYuqw+NfevBvRnd48j4eGxY3ApsBjgO/SfWFxDnAR8Lg2358C7wYeT3fZk4kz3zZrP68Gtum3OazS+/q7wEd7jzdt7+mftccHA1/ovaeHtPHDgM+38VOALwDrtcfHAEf11ul71L32X2/bcIP2+B/a9i3gZa3tfcCf97brAb3lLwB2buN7AP8xze2/ee/v5o3AB9r4oTw0LD5D10uzC9017KALix8AWwEb0oXQe9q0twH/t41/GnheG98euK5XyzfasnOAO4AN6H0WzPSwTnzP4hHglqq6uI1/EnjrSua9GPhgkk8Bn6uqJUlWnOeCqvohQJJrgR3odk93AS5u8z+abi/jh8BPgY8l+QLdH8fE85yS5Ay6/6q0aq4GPpDkb+hC4Wttu5/Wpp8GfKiNPxt4ZRv/Z7oPtgmfqar7p3gO36POi4HdgMvbNn4scDvwPzz4+3wF8JIVF0yyEd3exmd6f0cb9mZZ2fbfFviXJFvR/T3dNMV8n6+qB4Brk2zZa7+8qpa1Om4AvtTarwZe2MZ/C9ilV9smrWaAL1bVvcC9SW6n23MaG8NiZqz4ZZYC7uPBY0aP+fmEqvcm+SKwL90H/0vpPuz77u2N30/3PgY4v6oOWvHJk+xO9wd3AHAk8KKqOiLJHsBvA1ck2a2q7ljdFzjbVNV/JXkG3fv0l0kumJjUn20aq/rxSp7D96gT4BNV9a6HNCZHVfs3nAf/Dlb0KOAHVbXrFOuecvsDfwd8sKrOSbIn3X/7k+n/PWaK9gd6jx/o1foo4FlV9ZC/8RYek/2dj40HuGfG9kme3cZ/D/g6XZfFbq3tdydmTLJTVV1dVX9D14f5pGk+x6XAc5M8oa3ncUme2P5L2bSqzgX+F/C03vN8s6reDSznodfe0oAkWwM/qapPAscDz2iTXtP7eUkb/wbdJWqg67f+2hSrvZvuGNXEc/gedS4ADkjyywBJtkiyw0rm//l2rKq7gJuSvKotmyRPG1qu2ZQHr0F3yMOof2W+BPzRxIMkU4XahBVrnDGGxcy4HnhLkuvo+kFPBN4D/G2SBXT/NUx4e5JFSa4Cfgb823SeoKqW0/WlntaWvYQuaDYGvtDavg78cVvk+CRXt9N6v0F3kFDT9xvAZUkWAkcDf9naN2/b+m104Qzdh8HrW/vr2rTJ/CvwiiQLkzwf3yMAqupa4M+BL7VteD7dsYCpnA68I8mVSXaiC+g3JPlP4BqmvhfOitv/GLruqysY3SXJ3wrMT3JV61I+YmUztz3Li9tnxPEjqmlSXu5DWkOS3AzMr6pH6r0ONIu5ZyFJGuSehSRpkHsWkqRBhoUkaZBhIUkaZFhID1OSH63CvMckOWpU65dGxbCQJA0yLKQRSPKyJN9sXwz78grXDHpakkuSfCfJm3rLvCPJ5e0LWu8ZQ9nSlAwLaTS+TnfNn6fTfaP4T3rTngq8iO4Cg+9uN7faC9gZ2B3YFdgtyQtmuGZpSl5IUBqNlV2x9Oyquge4J8lX6ALieXT3I7myzbMRXXhcNHMlS1MzLKTRWNkVSye7CnGAv66qf5yZ8qRVYzeUNBoru2Lpfkkek+TxdDfJuRw4Dzhs4l4GSbaZuMqqtDZwz0J6+H4pyZLe4w/y4BVLv093S9Ude9OvAr5Cdwe0v6iqW4Fbk/w6cEm7l8GP6G4levvoy5eGeW0oSdIgu6EkSYMMC0nSIMNCkjTIsJAkDTIsJEmDDAtJ0iDDQpI06P8DuOqKXk3I+coAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "GflayapSehLv"
},
"source": [
"img.figure.savefig('count_plot.png')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "utkKBWgoenIh",
"outputId": "a1c16c8a-e779-43fb-9c80-728918eee24e"
},
"source": [
"X = df.headings\n",
"Y = df.label\n",
"le = LabelEncoder()\n",
"Y = le.fit_transform(Y)\n",
"Y = Y.reshape(-1,1)\n",
"Y"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0],\n",
" [0],\n",
" [2],\n",
" ...,\n",
" [2],\n",
" [1],\n",
" [1]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 156
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ME5lAtWPuEFx"
},
"source": [
"X_=[]\n",
"for i in X:\n",
" X_.append(i.replace('\\u200d',''))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "6at_-jqGuS1y",
"outputId": "11ce082c-1b26-4344-b688-c16fe9eebbb5"
},
"source": [
"X_[9]"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'\\nഒടുവില് ടി വി ചന്ദ്രന് അയ്യപ്പനോടുള്ള വാക്ക് പാലിച്ചു\\n \\n'"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
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{
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{
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" 'അടുത്ത': 141,\n",
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" 'എയര്': 143,\n",
" 'പരിക്ക്': 144,\n",
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" 'വിജയലക്ഷ്യം': 153,\n",
" 'ഗോവ': 154,\n",
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" 'തിരിച്ചെത്തി': 157,\n",
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" 'വമ്ബന്': 166,\n",
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" 'എങ്ങനെ': 211,\n",
" 'ആര്': 212,\n",
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" 'എത്തും': 215,\n",
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" 'മത്സരം': 217,\n",
" 'ലൂസിഫര്': 218,\n",
" 'താരങ്ങള്': 219,\n",
" 'നാളെ': 220,\n",
" 'ദേശീയ': 221,\n",
" 'ഫുട്ബോള്': 222,\n",
" 'സ്വന്തം': 223,\n",
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" 'ദിലീപ്': 225,\n",
" 'പിന്നില്': 226,\n",
" 'തന്റെ': 227,\n",
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" 'കിസാന്': 229,\n",
" 'മത്സരത്തില്': 230,\n",
" 'ബി': 231,\n",
" 'സത്യങ്ങള്': 232,\n",
" 'ഷെയ്ന്': 233,\n",
" 'ബാങ്കുകള്': 234,\n",
" 'പാക്': 235,\n",
" 'തിരിച്ചുവരവ്': 236,\n",
" 'രാഹുല്': 237,\n",
" 'വിലയില്': 238,\n",
" 'പ്രഖ്യാപിച്ചു': 239,\n",
" 'നിങ്ങളുടെ': 240,\n",
" 'ശ്രീശാന്തിന്റെ': 241,\n",
" 'സെമിയില്': 242,\n",
" 'മകന്': 243,\n",
" 'സുപ്രീംകോടതി': 244,\n",
" 'യുവന്റസ്': 245,\n",
" 'വ്യാപാര': 246,\n",
" 'ബിക്കിനി': 247,\n",
" 'ആര്യ': 248,\n",
" 'വെളിപ്പെടുത്തി': 249,\n",
" 'നേടി': 250,\n",
" 'സച്ചിന്': 251,\n",
" 'കീര്ത്തി': 252,\n",
" 'ട്രെയ്ലര്': 253,\n",
" '25': 254,\n",
" 'കാര്യങ്ങള്': 255,\n",
" '20': 256,\n",
" 'നടപടി': 257,\n",
" 'തുടങ്ങിയാല്': 258,\n",
" 'ചിലരൊക്കെ': 259,\n",
" 'ഉയര്ന്നു': 260,\n",
" 'ചിത്രീകരണം': 261,\n",
" 'വിധി': 262,\n",
" '12': 263,\n",
" '50': 264,\n",
" 'കടുത്ത': 265,\n",
" '3': 266,\n",
" 'ലോക': 267,\n",
" 'ടിക്കറ്റ്': 268,\n",
" 'നിന്നും': 269,\n",
" 'ചില': 270,\n",
" 'ചിത്രത്തില്': 271,\n",
" 'കൊണ്ട്': 272,\n",
" 'പ്രധാനമന്ത്രി': 273,\n",
" 'പാര്വതി': 274,\n",
" 'ലക്ഷ്യം': 275,\n",
" 'പണി': 276,\n",
" 'വിട്ടു': 277,\n",
" 'സാധ്യത': 278,\n",
" 'കരാര്': 279,\n",
" 'മാഞ്ചസ്റ്റര്': 280,\n",
" 'പലരും': 281,\n",
" 'ചെയ്ത്': 282,\n",
" 'കോണ്ഗ്രസ്': 283,\n",
" 'വിക്കറ്റ്': 284,\n",
" 'കേരളത്തില്': 285,\n",
" 'വീഴ്ത്തി': 286,\n",
" 'പറയാന്': 287,\n",
" 'കുടിക്കും': 288,\n",
" 'പ്രഖ്യാപിച്ച്': 289,\n",
" 'വോണ്': 290,\n",
" 'ബോളിവുഡ്': 291,\n",
" 'കളി': 292,\n",
" 'മറികടന്ന്': 293,\n",
" 'ഒന്നിക്കുന്നു': 294,\n",
" 'നീക്കി': 295,\n",
" 'ബിസിസിഐ': 296,\n",
" 'സെയ്ഫ്': 297,\n",
" 'ബംഗ്ലാദേശ്': 298,\n",
" '10': 299,\n",
" 'പറയുന്നു': 300,\n",
" 'പദ്ധതി': 301,\n",
" 'ആശ്വാസം': 302,\n",
" 'കേസ്': 303,\n",
" 'ഐലീഗ്': 304,\n",
" 'മാഡ്രിഡ്': 305,\n",
" 'ഗോകുല്': 306,\n",
" 'നായകന്': 307,\n",
" 'ഡി': 308,\n",
" 'ഫസ്റ്റ്': 309,\n",
" 'ദി': 310,\n",
" 'കാര്ഡ്': 311,\n",
" 'മാറ്റി': 312,\n",
" 'ഇന്ത്യയെ': 313,\n",
" 'നിങ്ങള്': 314,\n",
" 'രവി': 315,\n",
" 'ധോണിയെ': 316,\n",
" 'പന്ത്': 317,\n",
" 'പാക്കിസ്ഥാന്': 318,\n",
" 'ബിഎസ്എന്എല്': 319,\n",
" 'സുപ്രീം': 320,\n",
" '1000': 321,\n",
" 'വേണ്ട': 322,\n",
" 'നഷ്ടത്തില്': 323,\n",
" 'ഗോള്': 324,\n",
" 'പണം': 325,\n",
" 'ഓസീസിന്': 326,\n",
" 'കുതിപ്പ്': 327,\n",
" 'കുറഞ്ഞ': 328,\n",
" 'എഫ്സി': 329,\n",
" 'കോഹ്ലിയുടെ': 330,\n",
" 'എണ്ണം': 331,\n",
" 'നിര്ണായക': 332,\n",
" 'നിഫ്റ്റി': 333,\n",
" 'അലി': 334,\n",
" 'ചിത്രത്തിന്െറ': 335,\n",
" 'മുഖ്യമന്ത്രി': 336,\n",
" 'ലൂസിഫറിന്റെ': 337,\n",
" 'മെസി': 338,\n",
" 'നേട്ടത്തോടെ': 339,\n",
" 'ജീവിതം': 340,\n",
" 'ലോകത്തിലെ': 341,\n",
" 'വായ്പ': 342,\n",
" 'കേസില്': 343,\n",
" 'ഗംഭീര': 344,\n",
" 'പ്രധാന': 345,\n",
" 'ട്രെയിലര്': 346,\n",
" 'പ്രദര്ശനത്തിന്': 347,\n",
" 'നല്കിയ': 348,\n",
" '4': 349,\n",
" 'ആരാണ്': 350,\n",
" 'ദിവസം': 351,\n",
" 'സഹായം': 352,\n",
" '2019': 353,\n",
" 'കോഹ്': 354,\n",
" 'ടി20': 355,\n",
" 'സെഞ്ച്വറി': 356,\n",
" 'മാറ്റം': 357,\n",
" 'അന്താരാഷ്ട്ര': 358,\n",
" '000': 359,\n",
" 'ക്യാപ്റ്റന്': 360,\n",
" 'വിവാഹം': 361,\n",
" 'ബിജെപി': 362,\n",
" '273': 363,\n",
" 'കാര്': 364,\n",
" 'പൈസയും': 365,\n",
" 'നല്ല': 366,\n",
" 'കണ്ട്': 367,\n",
" 'കോലി': 368,\n",
" 'സമ്മാന്': 369,\n",
" 'വിജയം': 370,\n",
" 'പുരോഗമിക്കുന്നു': 371,\n",
" 'ചെയ്യാന്': 372,\n",
" 'ക്വാര്ട്ടര്': 373,\n",
" 'തടയാന്': 374,\n",
" 'ഒടുവില്': 375,\n",
" 'ഒപ്പം': 376,\n",
" 'ചിത്രവുമായി': 377,\n",
" 'പ്രിയാ': 378,\n",
" 'പട്ടികയില്': 379,\n",
" 'മാസ്': 380,\n",
" 'വൈറലാകുന്നു': 381,\n",
" 'കടന്നു': 382,\n",
" 'വഴി': 383,\n",
" 'താന്': 384,\n",
" 'ലൂസിഫറില്': 385,\n",
" 'ഞെട്ടിച്ച്': 386,\n",
" 'ബയേണ്': 387,\n",
" 'വരെ': 388,\n",
" '23': 389,\n",
" '6': 390,\n",
" 'തമിഴ്': 391,\n",
" 'രംഗത്ത്': 392,\n",
" 'ധരിച്ച്': 393,\n",
" 'പന്തില്': 394,\n",
" 'സിനിമയില്': 395,\n",
" 'ഇളവ്': 396,\n",
" 'മുന്പ്': 397,\n",
" 'ഓസ്ട്രേലിയന്': 398,\n",
" 'ജോസ്': 399,\n",
" '14': 400,\n",
" 'റെഡ്ഡി': 401,\n",
" 'തൊപ്പി': 402,\n",
" 'ഇടിഞ്ഞു': 403,\n",
" 'എല്ലാവരും': 404,\n",
" 'ഇന്ധന': 405,\n",
" 'യുടെ': 406,\n",
" 'ഉയര്ന്ന': 407,\n",
" 'പത്മഭൂഷണ്': 408,\n",
" 'മുമ്ബ്': 409,\n",
" 'ഒരുങ്ങി': 410,\n",
" 'വര്ധിച്ചു': 411,\n",
" 'റൊണാള്ഡോ': 412,\n",
" 'സയേഷ': 413,\n",
" 'ധോണിയുടെ': 414,\n",
" 'പോലും': 415,\n",
" 'വിന്ഡീസ്': 416,\n",
" 'ഓള്': 417,\n",
" 'ആസിഫ്': 418,\n",
" 'അറിയാം': 419,\n",
" 'മൂന്നു': 420,\n",
" 'ഓഹരികള്': 421,\n",
" 'നാം': 422,\n",
" 'നേരിയ': 423,\n",
" 'താരത്തെ': 424,\n",
" '500': 425,\n",
" '\\u200b': 426,\n",
" 'തേടി': 427,\n",
" 'ഡിലീറ്റഡ്': 428,\n",
" 'സീന്': 429,\n",
" 'അത്': 430,\n",
" 'വിജയലക്ഷ്മി': 431,\n",
" 'വളര്ച്ച': 432,\n",
" 'ലാല്': 433,\n",
" 'നിമിഷം': 434,\n",
" 'ശര്മ്മ': 435,\n",
" 'മോദിയുടെ': 436,\n",
" 'തുറന്ന്': 437,\n",
" 'വേണ്ടത്': 438,\n",
" 'ബെംഗളൂരു': 439,\n",
" 'അനില്': 440,\n",
" 'ഇന്ഷുറന്സ്': 441,\n",
" 'രണ്വീര്': 442,\n",
" 'വിദേശ': 443,\n",
" 'പ്രതികരണവുമായി': 444,\n",
" 'അഞ്ച്': 445,\n",
" 'കുമ്ബളങ്ങി': 446,\n",
" 'പ്ലസ്': 447,\n",
" 'ടോസ്': 448,\n",
" 'നിക്ഷേപം': 449,\n",
" 'ട്രെയിന്': 450,\n",
" 'കപൂര്': 451,\n",
" 'ജിയോ': 452,\n",
" 'യു': 453,\n",
" 'ടാറ്റ': 454,\n",
" 'പറ്റില്ലെങ്കില്': 455,\n",
" 'ചാംപ്യന്സ്': 456,\n",
" 'വേണം': 457,\n",
" 'രണ്ടു': 458,\n",
" 'അനു': 459,\n",
" 'ജെറ്റ്': 460,\n",
" 'എത്തുന്ന': 461,\n",
" 'ഇടിവ്': 462,\n",
" 'ശത്രുഘ്നന്': 463,\n",
" 'മുംബൈ': 464,\n",
" 'ഏറ്റുവാങ്ങി': 465,\n",
" 'ടെസ്റ്റ്': 466,\n",
" 'മുന്നറിയിപ്പുമായി': 467,\n",
" '100': 468,\n",
" 'പകരം': 469,\n",
" 'കീ': 470,\n",
" 'ടൊവിനോ': 471,\n",
" 'രാജാവും': 472,\n",
" 'ഇദയ': 473,\n",
" 'ചര്ച്ച': 474,\n",
" 'എന്നാല്': 475,\n",
" 'എന്നെ': 476,\n",
" 'ഒഴിവാക്കി': 477,\n",
" 'ഏറ്റെടുത്ത്': 478,\n",
" 'ഭാഗം': 479,\n",
" 'തിരികെ': 480,\n",
" 'തുറന്നു': 481,\n",
" 'രൂപയില്': 482,\n",
" 'ഇന്ധനവിലയില്': 483,\n",
" 'ഡല്ഹി': 484,\n",
" 'സൂപ്പര്താരം': 485,\n",
" 'റാഞ്ചി': 486,\n",
" 'വിമര്ശനവുമായി': 487,\n",
" '314': 488,\n",
" 'പുരസ്കാരം': 489,\n",
" 'ശ്രീനിവാസന്': 490,\n",
" 'ഓസ്ട്രേലിയക്ക്': 491,\n",
" 'ബാറ്റിങ്': 492,\n",
" 'ഗാംഗുലി': 493,\n",
" 'പദ്ധതിയുമായി': 494,\n",
" 'ധവാനും': 495,\n",
" 'സെമി': 496,\n",
" 'അച്ഛന്': 497,\n",
" 'ഗോപി': 498,\n",
" 'പൃഥ്വിരാജ്': 499,\n",
" 'എന്': 500,\n",
" 'റാഞ്ചിയില്': 501,\n",
" 'ടീമിന്': 502,\n",
" 'വരും': 503,\n",
" 'ജിഎസ്ടി': 504,\n",
" 'കാവ്യ': 505,\n",
" 'താരത്തിന്റെ': 506,\n",
" 'ഭാര്യ': 507,\n",
" 'സമീറ': 508,\n",
" 'തെന്നിന്ത്യന്': 509,\n",
" 'ആവശ്യപ്പെട്ട്': 510,\n",
" 'ബംഗളൂരു': 511,\n",
" 'രാജ്യത്ത്': 512,\n",
" 'നഷ്ടം': 513,\n",
" 'ലിസ്റ്റ്': 514,\n",
" 'ഇന്ദ്രജിത്ത്': 515,\n",
" 'റെയില്വേ': 516,\n",
" 'കോടികള്': 517,\n",
" 'ഇന്ത്യയിലെ': 518,\n",
" 'പേര്': 519,\n",
" 'ജയസൂര്യ': 520,\n",
" 'നിരക്കില്': 521,\n",
" 'എല്ലാ': 522,\n",
" 'സൈനിക': 523,\n",
" 'ധോണിയും': 524,\n",
" 'നായികയായി': 525,\n",
" 'സംപ്രേഷണാവകാശം': 526,\n",
" 'ഡീസലിന്': 527,\n",
" 'കുതിക്കുന്നു': 528,\n",
" 'സൂര്യയും': 529,\n",
" 'ഏഷ്യ': 530,\n",
" 'ലോകത്തെ': 531,\n",
" 'ഉയര്ന്ന്': 532,\n",
" 'സന്തോഷ്': 533,\n",
" 'പ്രത്യേക': 534,\n",
" 'ലുക്കില്': 535,\n",
" 'ക്ലബ്': 536,\n",
" 'ആദ്യം': 537,\n",
" 'സ്വര്ണം': 538,\n",
" 'മീഡിയയില്': 539,\n",
" 'വനിത': 540,\n",
" 'ഖവാജ': 541,\n",
" 'ഉയര്ത്തി': 542,\n",
" 'ഋഷഭ്': 543,\n",
" 'ബോളിവുഡിലേക്ക്': 544,\n",
" 'അരങ്ങേറ്റം': 545,\n",
" 'ധോണിക്ക്': 546,\n",
" 'വീട്ടില്': 547,\n",
" 'കെജിഎഫ്': 548,\n",
" 'ലൈവ്': 549,\n",
" 'ആക്ഷന്': 550,\n",
" 'അമ്മ': 551,\n",
" 'സിനിമയുടെ': 552,\n",
" 'തുടക്കമായി': 553,\n",
" 'പാന്': 554,\n",
" 'നോക്കി': 555,\n",
" 'മുന്നോട്ട്': 556,\n",
" 'സ്കോറിലേക്ക്': 557,\n",
" 'ചെയ്യുന്നത്': 558,\n",
" 'പ്രമുഖ': 559,\n",
" 'ലിവര്പൂള്': 560,\n",
" 'എന്തിന്': 561,\n",
" 'പരാതി': 562,\n",
" 'മറ്റൊരു': 563,\n",
" 'മഴ': 564,\n",
" 'ആരാധകരുടെ': 565,\n",
" 'ആഘോഷിച്ച്': 566,\n",
" 'പിന്വലിച്ചു': 567,\n",
" 'വിവാഹ': 568,\n",
" 'മെസ്സി': 569,\n",
" 'കണക്കുകള്': 570,\n",
" 'സൗദി': 571,\n",
" 'കാര്യം': 572,\n",
" 'ആരാധകര്ക്ക്': 573,\n",
" 'അഭിനയിക്കാന്': 574,\n",
" 'പറഞ്ഞ': 575,\n",
" 'ആര്മി': 576,\n",
" 'ഇതിഹാസം': 577,\n",
" 'യുഎസ്': 578,\n",
" 'മേഖല': 579,\n",
" 'പ്ലാന്': 580,\n",
" '30': 581,\n",
" 'റദ്ദാക്കി': 582,\n",
" 'കുറയും': 583,\n",
" 'പ്രീമിയം': 584,\n",
" 'ഓസ്ട്രേലിയയ്ക്ക്': 585,\n",
" 'ഇപ്പോള്': 586,\n",
" 'വെളിപ്പെടുത്തലുമായി': 587,\n",
" 'ഉദ്ഘാടനം': 588,\n",
" 'ഗ്രാമിന്': 589,\n",
" 'ഗോ': 590,\n",
" 'ഓഫര്': 591,\n",
" 'ആധാര്': 592,\n",
" 'പ്രകാശ്': 593,\n",
" 'പ്രതീക്ഷ': 594,\n",
" 'ഫിഫാ': 595,\n",
" 'ബാഴ്സലോണ': 596,\n",
" 'വിമാനങ്ങള്': 597,\n",
" 'ഗര്ഭകാലത്തെ': 598,\n",
" 'ബോഡി': 599,\n",
" 'മാസം': 600,\n",
" 'ലോകം': 601,\n",
" 'ശമ്ബളം': 602,\n",
" 'പുഷ്കരന്': 603,\n",
" 'മാക്സ്': 604,\n",
" 'ഇത്തവണ': 605,\n",
" 'തിളങ്ങി': 606,\n",
" 'മാറ്റങ്ങള്': 607,\n",
" 'വിനീത്': 608,\n",
" 'ആദ്യമായി': 609,\n",
" 'നിയന്ത്രണം': 610,\n",
" 'മലയാളി': 611,\n",
" 'മത്സരങ്ങള്': 612,\n",
" 'ചെയ്യാം': 613,\n",
" 'മണിയുടെ': 614,\n",
" 'നല്കിയത്': 615,\n",
" '32': 616,\n",
" 'ദിലീപിന്റെ': 617,\n",
" 'നിരാശ': 618,\n",
" 'ഓഫ്': 619,\n",
" 'തുടരുന്നു': 620,\n",
" 'പെട്രോളിന്': 621,\n",
" 'പ്രീമിയര്': 622,\n",
" 'നമ്ബി': 623,\n",
" 'രൂപയും': 624,\n",
" 'വണ്': 625,\n",
" 'നിധി': 626,\n",
" 'കോഴിക്കോട്': 627,\n",
" 'ഇടവേളയ്ക്ക്': 628,\n",
" 'അക്ബര്': 629,\n",
" 'ജയറാം': 630,\n",
" 'മേക്കിങ്': 631,\n",
" 'പൈസ': 632,\n",
" 'ക്രിക്കറ്റില്': 633,\n",
" 'നഷ്ടത്തോടെ': 634,\n",
" 'പന്തിന്': 635,\n",
" 'ജെ': 636,\n",
" 'അജയ്': 637,\n",
" 'ഡിസ്നിയുടെ': 638,\n",
" 'ന്റെ': 639,\n",
" 'സാനിയ': 640,\n",
" 'ജോലി': 641,\n",
" 'സിനിമയാകുന്നു': 642,\n",
" 'ഉസ്മാന്': 643,\n",
" 'ചരിത്ര': 644,\n",
" 'മൊഹാലിയില്': 645,\n",
" 'ബാഴ്സ': 646,\n",
" 'കുറിപ്പ്': 647,\n",
" 'റണ്': 648,\n",
" 'പക്ഷേ': 649,\n",
" 'വെടിക്കെട്ട്': 650,\n",
" 'കുറച്ചു': 651,\n",
" 'സ്വിസ്': 652,\n",
" 'യുണൈറ്റഡ്': 653,\n",
" 'റൊണാള്ഡോയുടെ': 654,\n",
" 'കൂട്ടുകെട്ട്': 655,\n",
" 'താഴ്ന്ന്': 656,\n",
" 'അച്ഛനെ': 657,\n",
" 'അറിയേണ്ടതെല്ലാം': 658,\n",
" 'ഇന്ത്യക്കു': 659,\n",
" 'കലങ്ക്': 660,\n",
" 'സ്പോര്ട്സ്': 661,\n",
" 'കൗണ്സില്': 662,\n",
" 'അവസരം': 663,\n",
" 'പാദത്തില്': 664,\n",
" 'ഇന്ത്യക്കെതിരെ': 665,\n",
" 'വര്ഷത്തെ': 666,\n",
" 'ദുല്ഖര്': 667,\n",
" 'മന്ത്രി': 668,\n",
" 'വി': 669,\n",
" 'ഡോളര്': 670,\n",
" 'അന്ന്': 671,\n",
" 'പേരില്': 672,\n",
" 'കേന്ദ്രം': 673,\n",
" 'അനുമതി': 674,\n",
" 'ഫോട്ടോ': 675,\n",
" 'കൂടുന്നു': 676,\n",
" 'രൂപയ്ക്ക്': 677,\n",
" 'ചെയ്യുന്ന': 678,\n",
" 'നാലു': 679,\n",
" 'ശ്രീശാന്ത്': 680,\n",
" 'ഹര്ജിയില്': 681,\n",
" 'സ്ഥാനം': 682,\n",
" 'ഇതാണ്': 683,\n",
" 'ഞങ്ങള്': 684,\n",
" 'കോ\\u200bടി': 685,\n",
" 'കനത്ത': 686,\n",
" 'താരങ്ങളുടെ': 687,\n",
" 'ബാറ്റ്': 688,\n",
" 'രാത്രി': 689,\n",
" 'സിന്ഹ': 690,\n",
" 'ബ്ലാസ്റ്റേഴ്സ്': 691,\n",
" 'നല്കും': 692,\n",
" 'പ്രൊമോ': 693,\n",
" 'പ്രവചനം': 694,\n",
" 'പോണ്': 695,\n",
" 'ശ്യാം': 696,\n",
" 'പുറത്താക്കി': 697,\n",
" 'ഫണ്ട്': 698,\n",
" 'കീഴടക്കി': 699,\n",
" 'നോട്ട്': 700,\n",
" 'കപ്പില്': 701,\n",
" 'ടീമിന്റെ': 702,\n",
" 'കാണാന്': 703,\n",
" '22': 704,\n",
" 'സണ്ണി': 705,\n",
" 'നല്കാന്': 706,\n",
" 'ആര്യയും': 707,\n",
" 'വരവ്': 708,\n",
" 'വെറും': 709,\n",
" 'ചെലവ്': 710,\n",
" 'ഏകദിനങ്ങളില്': 711,\n",
" 'സിംഗ്': 712,\n",
" 'സഞ്ജയ്': 713,\n",
" 'ബുക്ക്': 714,\n",
" 'വനിതകള്ക്ക്': 715,\n",
" 'ഒരുങ്ങുന്നു': 716,\n",
" 'അര്ജന്റീന': 717,\n",
" 'മൊബൈല്': 718,\n",
" '48': 719,\n",
" 'തനിക്ക്': 720,\n",
" 'വേഷത്തില്': 721,\n",
" '45': 722,\n",
" 'കൂട്ടി': 723,\n",
" '37': 724,\n",
" 'സ്വന്തമാക്കാന്': 725,\n",
" 'ഷാ': 726,\n",
" 'വില്ലനായി': 727,\n",
" 'തീയതി': 728,\n",
" 'ഓണ്ലൈന്': 729,\n",
" 'താരമായി': 730,\n",
" 'അംബാനിയുടെ': 731,\n",
" 'പരമ്ബരയില്': 732,\n",
" 'വര്ഷത്തിനുള്ളില്': 733,\n",
" 'ഇസ്പേഡ്': 734,\n",
" 'തുടര്ച്ചയായ': 735,\n",
" 'എക്സല്': 736,\n",
" 'സൈന': 737,\n",
" 'പിഴ': 738,\n",
" 'നായിക': 739,\n",
" 'റൗഡി': 740,\n",
" 'മോശം': 741,\n",
" 'പരമ്ബരയും': 742,\n",
" 'ചന്ദ': 743,\n",
" 'മേനോന്': 744,\n",
" 'ഡേ': 745,\n",
" 'കോട്ടയം': 746,\n",
" 'മഹീന്ദ്ര': 747,\n",
" 'രാജമൗലി': 748,\n",
" 'മാത്രമല്ല': 749,\n",
" 'മുകേഷ്': 750,\n",
" 'നേട്ടവുമായി': 751,\n",
" 'കാല്ലക്ഷം': 752,\n",
" 'നടത്തി': 753,\n",
" 'ഗംഭീര്': 754,\n",
" 'ടൂര്ണമെന്റ്': 755,\n",
" 'ആമസോണ്': 756,\n",
" 'തോല്പ്പിച്ച്': 757,\n",
" 'ഗാന്ധി': 758,\n",
" 'തട്ടിപ്പ്': 759,\n",
" '5000': 760,\n",
" 'ചെയ്യുന്നു': 761,\n",
" 'ഇനിയും': 762,\n",
" 'നേടിയ': 763,\n",
" 'പരാതിയുമായി': 764,\n",
" 'റോണോ': 765,\n",
" 'സ്വീകരിച്ച്': 766,\n",
" 'ചെല്സി': 767,\n",
" 'യൂറോപ്പ': 768,\n",
" 'പരാജയം': 769,\n",
" 'ഹിറ്റ്': 770,\n",
" 'വിരമിച്ചു': 771,\n",
" 'കഥ': 772,\n",
" 'വാര്ണര്': 773,\n",
" 'ആക്രമിച്ച': 774,\n",
" 'യോഗ്യത': 775,\n",
" '80': 776,\n",
" 'ഒത്തുകളി': 777,\n",
" 'കൊലപാതകത്തേക്കാള്': 778,\n",
" 'ആറു': 779,\n",
" 'ശ്രീശാന്തിന്': 780,\n",
" 'ഹെലികോപ്റ്റര്': 781,\n",
" 'ലൈഫ്': 782,\n",
" 'ജയിച്ചാല്': 783,\n",
" 'വിഡിയോ': 784,\n",
" 'മറികടന്നത്': 785,\n",
" 'മുക്കി': 786,\n",
" 'താരങ്ങള്ക്ക്': 787,\n",
" 'സര്പ്രൈസ്': 788,\n",
" 'അഫ്ഗാന്': 789,\n",
" 'ഒരുപാട്': 790,\n",
" 'മാച്ച്': 791,\n",
" 'ജവാന്മാരുടെ': 792,\n",
" 'ഒന്ന്': 793,\n",
" 'അക്കൗണ്ട്': 794,\n",
" 'പാട്ട്': 795,\n",
" 'ആഗോള': 796,\n",
" 'പ്രതിസന്ധി': 797,\n",
" 'മാസത്തെ': 798,\n",
" 'മാധവന്': 799,\n",
" 'സമ്മാനിച്ച്': 800,\n",
" 'വിജയ': 801,\n",
" 'കൈ': 802,\n",
" 'അടിച്ചു': 803,\n",
" 'ജൂണ്': 804,\n",
" 'വിമാന': 805,\n",
" 'പക്ഷെ': 806,\n",
" 'ഈസ്റ്റിന്': 807,\n",
" 'ലോട്ടറി': 808,\n",
" 'അടിച്ച്': 809,\n",
" 'നേടിയത്': 810,\n",
" 'നിരോധനം': 811,\n",
" 'പന്തിന്റെ': 812,\n",
" 'ഹര്ജി': 813,\n",
" 'കൊച്ചിയില്': 814,\n",
" 'നാട്ടില്': 815,\n",
" 'രണ്ടാമത്തെ': 816,\n",
" 'യൂബര്': 817,\n",
" 'വോട്ട്': 818,\n",
" 'മാറ്റമില്ല': 819,\n",
" 'അഞ്ചു': 820,\n",
" 'ഇലക്ട്രിക്': 821,\n",
" 'മലയാള': 822,\n",
" 'പട്ടാളത്തൊപ്പി': 823,\n",
" 'വാര്ത്ത': 824,\n",
" 'ശ്രീദേവി': 825,\n",
" 'ഹീറോ': 826,\n",
" 'സൈബര്': 827,\n",
" 'കടന്ന്': 828,\n",
" 'കര്ഷകര്ക്ക്': 829,\n",
" 'ബാറ്റിംഗ്': 830,\n",
" 'ചിത്ര': 831,\n",
" 'പരിശീലനം': 832,\n",
" 'പിന്മാറി': 833,\n",
" 'മേഖലയില്': 834,\n",
" 'ക്രിസ്റ്റ്യാനോ': 835,\n",
" 'ഇരട്ട': 836,\n",
" 'ആര്ബിഐ': 837,\n",
" 'പാളി': 838,\n",
" 'സംവിധാനം': 839,\n",
" 'മുതിര്ന്ന': 840,\n",
" 'ലോകകപ്പില്': 841,\n",
" 'പ്യാരേ': 842,\n",
" 'ഫെബ്രുവരിയില്': 843,\n",
" 'സൗഹൃദ': 844,\n",
" 'തോമസ്': 845,\n",
" 'ആറ്': 846,\n",
" 'കളിക്കില്ല': 847,\n",
" 'ആന്ഡ്': 848,\n",
" 'മതി': 849,\n",
" 'സ്പെഷ്യല്': 850,\n",
" 'തകര്ത്തടിച്ച്': 851,\n",
" 'കോഹ്ലിയും': 852,\n",
" '8': 853,\n",
" 'ഈസ്': 854,\n",
" 'യുവ': 855,\n",
" '40': 856,\n",
" 'പകുതി': 857,\n",
" 'മകള്': 858,\n",
" 'റാണിയിലെ': 859,\n",
" 'ഇഷ്ട': 860,\n",
" 'ഗ്രൂപ്പ്': 861,\n",
" 'സര്ഫ്': 862,\n",
" 'ഫേസ്ബുക്ക്': 863,\n",
" 'എത്തി': 864,\n",
" 'കാലം': 865,\n",
" 'ലോണ്': 866,\n",
" 'വാര്ഷിക': 867,\n",
" 'ബ്രഹ്മാണ്ഡ': 868,\n",
" 'ചാമ്ബ്യന്ഷിപ്പ്': 869,\n",
" 'ശ്രീകാന്തും': 870,\n",
" 'ഞാനായിരുന്നു': 871,\n",
" 'ലൂസിഫറിലെ': 872,\n",
" 'രണ്ടാമൂഴം': 873,\n",
" 'ആവശ്യം': 874,\n",
" 'ഓഫറുമായി': 875,\n",
" 'സാംസങ്': 876,\n",
" 'തെലുങ്ക്': 877,\n",
" 'പഞ്ചാബ്': 878,\n",
" '400': 879,\n",
" 'ദ': 880,\n",
" '200': 881,\n",
" 'പണപ്പെരുപ്പം': 882,\n",
" 'സിനദിന്': 883,\n",
" 'പങ്കുവെച്ച്': 884,\n",
" 'വര്ധനവ്': 885,\n",
" 'സഹകരണ': 886,\n",
" 'മാറുന്നു': 887,\n",
" 'തകര്ക്കാന്': 888,\n",
" 'പല': 889,\n",
" 'ആമിര്': 890,\n",
" 'ഖാന്': 891,\n",
" 'നയം': 892,\n",
" 'സൗത്ത്': 893,\n",
" '120': 894,\n",
" 'രാഷ്ട്രീയ': 895,\n",
" 'ട്വിസ്റ്റ്': 896,\n",
" 'ബാല': 897,\n",
" 'ാം': 898,\n",
" 'തോല്വിക്ക്': 899,\n",
" 'വെളിപ്പെടുത്തല്': 900,\n",
" 'എങ്കില്': 901,\n",
" 'ഇതാ': 902,\n",
" 'റെക്കോര്ഡുകള്': 903,\n",
" 'നിര്ഭയ': 904,\n",
" '16': 905,\n",
" 'ഇളയരാജയിലെ': 906,\n",
" 'അര്ദ്ധ': 907,\n",
" 'സിങ്': 908,\n",
" 'ഇംഗ്ലണ്ടിന്': 909,\n",
" 'അമര്': 910,\n",
" 'നടിയുടെ': 911,\n",
" 'ഓസ്കാര്': 912,\n",
" 'ഗോസ്': 913,\n",
" 'തുടരുമെന്ന്': 914,\n",
" 'കുടുംബങ്ങള്ക്ക്': 915,\n",
" 'ചികിത്സാ': 916,\n",
" '8000': 917,\n",
" 'പവര്': 918,\n",
" 'ടീമുകള്ക്ക്': 919,\n",
" 'ഗോളിന്': 920,\n",
" 'വനിതകള്': 921,\n",
" 'സംസ്ഥാനത്തെ': 922,\n",
" 'മണി': 923,\n",
" 'സേതുപതി': 924,\n",
" 'ദിനം': 925,\n",
" 'ഷോട്ട്': 926,\n",
" 'ആരംഭിച്ചത്': 927,\n",
" 'സ്മാര്ട്ട്': 928,\n",
" 'പാകിസ്ഥാന്': 929,\n",
" 'കുറിച്ചു': 930,\n",
" 'ക്ലോസ്': 931,\n",
" 'രക്ഷിക്കാന്': 932,\n",
" 'മുഹമ്മദ്': 933,\n",
" 'വിമര്ശനം': 934,\n",
" 'പെന്ഷന്': 935,\n",
" 'വരിച്ച': 936,\n",
" 'ബാങ്കിന്റെ': 937,\n",
" 'തോല്വിയില്': 938,\n",
" 'കൊമേഴ്സ്': 939,\n",
" 'വര്ധന': 940,\n",
" 'തെരഞ്ഞെടുപ്പിന്': 941,\n",
" 'നിരക്കുകള്': 942,\n",
" 'കാവ്യയോട്': 943,\n",
" 'ജയിക്കാന്': 944,\n",
" 'വിജയന്': 945,\n",
" 'മാറി': 946,\n",
" 'സൂചന': 947,\n",
" 'തുറന്നുപറഞ്ഞ്': 948,\n",
" 'ശിക്ഷ': 949,\n",
" 'രക്ഷകനായി': 950,\n",
" 'റ\\u200bബ\\u200bര്': 951,\n",
" 'ആദ്യപാദ': 952,\n",
" '3000': 953,\n",
" 'കേന്ദ്രസര്ക്കാര്': 954,\n",
" '800': 955,\n",
" 'യുവേഫ': 956,\n",
" 'വരുമാനത്തില്': 957,\n",
" 'കേരളം': 958,\n",
" 'ഗ്ലാമറസായി': 959,\n",
" 'നൈറ്റ്സിലെ': 960,\n",
" 'നൂറിന്': 961,\n",
" '17': 962,\n",
" 'സര്വീസ്': 963,\n",
" 'ഹോട്ടായി': 964,\n",
" 'സിനിമയിലെ': 965,\n",
" 'ല്': 966,\n",
" 'അവിശ്വസനീയ': 967,\n",
" 'ത്രില്ലര്': 968,\n",
" 'പാദ': 969,\n",
" 'സിറ്റിയും': 970,\n",
" 'സബ്സിഡി': 971,\n",
" 'ഐപിഎല്ലില്': 972,\n",
" 'ആശങ്ക': 973,\n",
" 'ബിസിനസ്': 974,\n",
" 'നായികമാര്': 975,\n",
" 'കൃഷ്ണന്': 976,\n",
" 'കന്നി': 977,\n",
" 'രാജ്യങ്ങള്': 978,\n",
" '70': 979,\n",
" 'സ്ഥാനത്ത്': 980,\n",
" 'അന്വേഷണം': 981,\n",
" 'പുറത്തായി': 982,\n",
" 'ഇന്ഷൂറന്സ്': 983,\n",
" 'ചെല്സിക്ക്': 984,\n",
" 'മൊഹാലി': 985,\n",
" 'ശ്രദ്ധ': 986,\n",
" 'കോണ്ഗ്രസിന്': 987,\n",
" 'ഷമ്മി': 988,\n",
" 'ഫെബ്രുവരി': 989,\n",
" 'മിന്നും': 990,\n",
" 'നിക്ഷേപ': 991,\n",
" 'വേള്ഡ്': 992,\n",
" 'സെന്റര്': 993,\n",
" 'ഇത്രയും': 994,\n",
" 'യഥാര്ത്ഥ': 995,\n",
" 'ധരിക്കുന്നത്': 996,\n",
" 'നീക്കം': 997,\n",
" 'ബൂം': 998,\n",
" 'മനസ്സ്': 999,\n",
" 'ലിയോണ്': 1000,\n",
" ...}"
]
},
"metadata": {
"tags": []
},
"execution_count": 161
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HP8dArWOf25Y",
"outputId": "3abb6239-04aa-4e2f-cd4e-7511f2f7a597"
},
"source": [
"sequences_matrix"
],
"execution_count": null,
"outputs": [
{
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"data": {
"text/plain": [
"array([[ 0, 0, 0, ..., 740, 7, 57],\n",
" [ 0, 0, 0, ..., 123, 119, 865],\n",
" [ 0, 0, 0, ..., 369, 626, 180],\n",
" ...,\n",
" [ 0, 0, 0, ..., 0, 0, 653],\n",
" [ 0, 0, 0, ..., 0, 0, 514],\n",
" [ 0, 0, 0, ..., 2, 68, 187]], dtype=int32)"
]
},
"metadata": {
"tags": []
},
"execution_count": 162
}
]
},
{
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"metadata": {
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"id": "OwnjXFXif-tM",
"outputId": "bbf1f04f-c845-473c-d05a-df49ae01d6b9"
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"source": [
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" [517],\n",
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" [124, 53, 720],\n",
" [118, 32, 373, 10],\n",
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" [1],\n",
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" ...]"
]
},
"metadata": {
"tags": []
},
"execution_count": 163
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kpfpq57oqIl8",
"outputId": "5875763b-0965-44cb-cbba-d95b660979a1"
},
"source": [
"sequences_matrix.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(4280, 150)"
]
},
"metadata": {
"tags": []
},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "o3SFDAA4zNHM",
"outputId": "f9898448-8a36-43e4-d505-fea137d263da"
},
"source": [
"Y_train.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(4280, 1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 31
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_G-P_Y49m1fj",
"outputId": "62a4fa8d-c061-4c88-e5b8-8427524304fe"
},
"source": [
"model = RNN()\n",
"model.summary()\n",
"model.compile(loss='categorical_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"functional_1\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"inputs (InputLayer) [(None, 150)] 0 \n",
"_________________________________________________________________\n",
"embedding (Embedding) (None, 150, 50) 50000 \n",
"_________________________________________________________________\n",
"lstm (LSTM) (None, 64) 29440 \n",
"_________________________________________________________________\n",
"FC1 (Dense) (None, 256) 16640 \n",
"_________________________________________________________________\n",
"activation (Activation) (None, 256) 0 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, 256) 0 \n",
"_________________________________________________________________\n",
"out_layer (Dense) (None, 3) 771 \n",
"_________________________________________________________________\n",
"activation_1 (Activation) (None, 3) 0 \n",
"=================================================================\n",
"Total params: 96,851\n",
"Trainable params: 96,851\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 865
},
"id": "6qITlIOKnGIT",
"outputId": "381d14b7-919d-4703-86ca-9dc4c9f44e9d"
},
"source": [
"from keras.utils import plot_model\n",
"plot_model(model,to_file='model.png',show_shapes=True)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Lkx1fWcOzUWW"
},
"source": [
"def RNN():\n",
" inputs = Input(name='inputs',shape=[max_len])\n",
" layer = Embedding(max_words,50,input_length=max_len)(inputs)\n",
" layer = LSTM(64)(layer)\n",
" layer = Dense(256,name='FC1')(layer)\n",
" layer = Activation('relu')(layer)\n",
" layer = Dropout(0.5)(layer)\n",
" layer = Dense(3,name='out_layer')(layer)\n",
" layer = Activation('sigmoid')(layer)\n",
" model = Model(inputs=inputs,outputs=layer)\n",
" return model"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GEpZjxD6zZpu",
"outputId": "420c36b5-e617-497e-b186-47333fb8c98d"
},
"source": [
"model = RNN()\n",
"model.summary()\n",
"model.compile(loss='categorical_crossentropy',optimizer=RMSprop(),metrics=['accuracy'])\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"functional_3\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"inputs (InputLayer) [(None, 150)] 0 \n",
"_________________________________________________________________\n",
"embedding_1 (Embedding) (None, 150, 50) 50000 \n",
"_________________________________________________________________\n",
"lstm_1 (LSTM) (None, 64) 29440 \n",
"_________________________________________________________________\n",
"FC1 (Dense) (None, 256) 16640 \n",
"_________________________________________________________________\n",
"activation_2 (Activation) (None, 256) 0 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 256) 0 \n",
"_________________________________________________________________\n",
"out_layer (Dense) (None, 3) 771 \n",
"_________________________________________________________________\n",
"activation_3 (Activation) (None, 3) 0 \n",
"=================================================================\n",
"Total params: 96,851\n",
"Trainable params: 96,851\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "0qAKYK5FsFjZ"
},
"source": [
"from keras.callbacks import ModelCheckpoint"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "sAHF2qXxsouD"
},
"source": [
"from keras.utils import to_categorical"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5lQBZGhgsscf"
},
"source": [
"Y_train_=to_categorical(Y_train)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "chhI9NyWrLyG"
},
"source": [
"filepath=\"/content/drive/MyDrive/Thirdsem/TimeSeries/NLP_malayalam.hdf5\"\n",
"checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
"callbacks_list = [checkpoint]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IrVbI3D7tTKe",
"outputId": "94085856-eac8-4915-cbd7-7f4fcfc14895"
},
"source": [
"Y_train_"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0., 0., 1.],\n",
" [0., 1., 0.],\n",
" [0., 1., 0.],\n",
" ...,\n",
" [0., 1., 0.],\n",
" [1., 0., 0.],\n",
" [1., 0., 0.]], dtype=float32)"
]
},
"metadata": {
"tags": []
},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ro6Aki3f0E3L",
"outputId": "cdcbf0de-7cdb-4ac5-f764-900d0212f2a4"
},
"source": [
"model.fit(sequences_matrix,Y_train_,batch_size=128,epochs=100,\n",
" validation_split=0.2,callbacks=callbacks_list)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"25/27 [==========================>...] - ETA: 0s - loss: 0.1382 - accuracy: 0.9378\n",
"Epoch 00001: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 17ms/step - loss: 0.1345 - accuracy: 0.9398 - val_loss: 0.4781 - val_accuracy: 0.8668\n",
"Epoch 2/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.1334 - accuracy: 0.9390\n",
"Epoch 00002: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1347 - accuracy: 0.9375 - val_loss: 0.4932 - val_accuracy: 0.8633\n",
"Epoch 3/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.1279 - accuracy: 0.9442\n",
"Epoch 00003: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1279 - accuracy: 0.9442 - val_loss: 0.5266 - val_accuracy: 0.8633\n",
"Epoch 4/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.1300 - accuracy: 0.9381\n",
"Epoch 00004: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1300 - accuracy: 0.9381 - val_loss: 0.5013 - val_accuracy: 0.8703\n",
"Epoch 5/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.1236 - accuracy: 0.9433\n",
"Epoch 00005: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1236 - accuracy: 0.9433 - val_loss: 0.5158 - val_accuracy: 0.8715\n",
"Epoch 6/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.1237 - accuracy: 0.9393\n",
"Epoch 00006: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.1225 - accuracy: 0.9401 - val_loss: 0.5905 - val_accuracy: 0.8657\n",
"Epoch 7/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.1233 - accuracy: 0.9460\n",
"Epoch 00007: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1233 - accuracy: 0.9460 - val_loss: 0.5670 - val_accuracy: 0.8633\n",
"Epoch 8/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.1239 - accuracy: 0.9448\n",
"Epoch 00008: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1239 - accuracy: 0.9448 - val_loss: 0.5843 - val_accuracy: 0.8703\n",
"Epoch 9/100\n",
"23/27 [========================>.....] - ETA: 0s - loss: 0.1200 - accuracy: 0.9416\n",
"Epoch 00009: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.1184 - accuracy: 0.9433 - val_loss: 0.6022 - val_accuracy: 0.8680\n",
"Epoch 10/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.1197 - accuracy: 0.9451\n",
"Epoch 00010: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.1197 - accuracy: 0.9451 - val_loss: 0.6314 - val_accuracy: 0.8680\n",
"Epoch 11/100\n",
"23/27 [========================>.....] - ETA: 0s - loss: 0.1191 - accuracy: 0.9446\n",
"Epoch 00011: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.1164 - accuracy: 0.9457 - val_loss: 0.6961 - val_accuracy: 0.8668\n",
"Epoch 12/100\n",
"24/27 [=========================>....] - ETA: 0s - loss: 0.1218 - accuracy: 0.9453\n",
"Epoch 00012: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.1205 - accuracy: 0.9451 - val_loss: 0.6681 - val_accuracy: 0.8692\n",
"Epoch 13/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.1137 - accuracy: 0.9483\n",
"Epoch 00013: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1153 - accuracy: 0.9474 - val_loss: 0.6778 - val_accuracy: 0.8692\n",
"Epoch 14/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.1139 - accuracy: 0.9460\n",
"Epoch 00014: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1139 - accuracy: 0.9460 - val_loss: 0.7301 - val_accuracy: 0.8598\n",
"Epoch 15/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.1125 - accuracy: 0.9480\n",
"Epoch 00015: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1128 - accuracy: 0.9474 - val_loss: 0.7073 - val_accuracy: 0.8657\n",
"Epoch 16/100\n",
"23/27 [========================>.....] - ETA: 0s - loss: 0.1084 - accuracy: 0.9474\n",
"Epoch 00016: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.1113 - accuracy: 0.9451 - val_loss: 0.7383 - val_accuracy: 0.8657\n",
"Epoch 17/100\n",
"22/27 [=======================>......] - ETA: 0s - loss: 0.1053 - accuracy: 0.9496\n",
"Epoch 00017: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.1090 - accuracy: 0.9477 - val_loss: 0.7407 - val_accuracy: 0.8657\n",
"Epoch 18/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.1087 - accuracy: 0.9448\n",
"Epoch 00018: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1087 - accuracy: 0.9448 - val_loss: 0.7974 - val_accuracy: 0.8645\n",
"Epoch 19/100\n",
"25/27 [==========================>...] - ETA: 0s - loss: 0.1104 - accuracy: 0.9466\n",
"Epoch 00019: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1095 - accuracy: 0.9471 - val_loss: 0.8063 - val_accuracy: 0.8598\n",
"Epoch 20/100\n",
"24/27 [=========================>....] - ETA: 0s - loss: 0.1068 - accuracy: 0.9489\n",
"Epoch 00020: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.1088 - accuracy: 0.9483 - val_loss: 0.7980 - val_accuracy: 0.8633\n",
"Epoch 21/100\n",
"24/27 [=========================>....] - ETA: 0s - loss: 0.1054 - accuracy: 0.9499\n",
"Epoch 00021: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.1110 - accuracy: 0.9480 - val_loss: 0.8156 - val_accuracy: 0.8586\n",
"Epoch 22/100\n",
"24/27 [=========================>....] - ETA: 0s - loss: 0.1033 - accuracy: 0.9505\n",
"Epoch 00022: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.1069 - accuracy: 0.9480 - val_loss: 0.8455 - val_accuracy: 0.8598\n",
"Epoch 23/100\n",
"22/27 [=======================>......] - ETA: 0s - loss: 0.1005 - accuracy: 0.9535\n",
"Epoch 00023: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1046 - accuracy: 0.9512 - val_loss: 0.8640 - val_accuracy: 0.8598\n",
"Epoch 24/100\n",
"23/27 [========================>.....] - ETA: 0s - loss: 0.1069 - accuracy: 0.9480\n",
"Epoch 00024: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1065 - accuracy: 0.9486 - val_loss: 0.9328 - val_accuracy: 0.8528\n",
"Epoch 25/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.1051 - accuracy: 0.9495\n",
"Epoch 00025: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1040 - accuracy: 0.9501 - val_loss: 0.9071 - val_accuracy: 0.8551\n",
"Epoch 26/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.1085 - accuracy: 0.9483\n",
"Epoch 00026: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1085 - accuracy: 0.9483 - val_loss: 0.9161 - val_accuracy: 0.8528\n",
"Epoch 27/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.1028 - accuracy: 0.9507\n",
"Epoch 00027: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1038 - accuracy: 0.9498 - val_loss: 0.9478 - val_accuracy: 0.8528\n",
"Epoch 28/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.1047 - accuracy: 0.9498\n",
"Epoch 00028: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1038 - accuracy: 0.9504 - val_loss: 0.9291 - val_accuracy: 0.8551\n",
"Epoch 29/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.1041 - accuracy: 0.9492\n",
"Epoch 00029: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1041 - accuracy: 0.9492 - val_loss: 0.9859 - val_accuracy: 0.8575\n",
"Epoch 30/100\n",
"25/27 [==========================>...] - ETA: 0s - loss: 0.1036 - accuracy: 0.9500\n",
"Epoch 00030: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.1042 - accuracy: 0.9495 - val_loss: 0.9958 - val_accuracy: 0.8575\n",
"Epoch 31/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.1036 - accuracy: 0.9477\n",
"Epoch 00031: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1034 - accuracy: 0.9477 - val_loss: 1.0271 - val_accuracy: 0.8528\n",
"Epoch 32/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.1029 - accuracy: 0.9498\n",
"Epoch 00032: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1029 - accuracy: 0.9498 - val_loss: 1.0209 - val_accuracy: 0.8528\n",
"Epoch 33/100\n",
"23/27 [========================>.....] - ETA: 0s - loss: 0.1011 - accuracy: 0.9487\n",
"Epoch 00033: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.1007 - accuracy: 0.9495 - val_loss: 1.0659 - val_accuracy: 0.8516\n",
"Epoch 34/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.1024 - accuracy: 0.9501\n",
"Epoch 00034: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1024 - accuracy: 0.9501 - val_loss: 1.0768 - val_accuracy: 0.8493\n",
"Epoch 35/100\n",
"23/27 [========================>.....] - ETA: 0s - loss: 0.1043 - accuracy: 0.9494\n",
"Epoch 00035: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1025 - accuracy: 0.9498 - val_loss: 1.1140 - val_accuracy: 0.8528\n",
"Epoch 36/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.1020 - accuracy: 0.9489\n",
"Epoch 00036: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.1013 - accuracy: 0.9492 - val_loss: 1.0928 - val_accuracy: 0.8528\n",
"Epoch 37/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.1003 - accuracy: 0.9495\n",
"Epoch 00037: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.1007 - accuracy: 0.9495 - val_loss: 1.1378 - val_accuracy: 0.8505\n",
"Epoch 38/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.1012 - accuracy: 0.9513\n",
"Epoch 00038: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.1001 - accuracy: 0.9515 - val_loss: 1.1400 - val_accuracy: 0.8505\n",
"Epoch 39/100\n",
"24/27 [=========================>....] - ETA: 0s - loss: 0.1035 - accuracy: 0.9479\n",
"Epoch 00039: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.1029 - accuracy: 0.9486 - val_loss: 1.1375 - val_accuracy: 0.8505\n",
"Epoch 40/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.1006 - accuracy: 0.9501\n",
"Epoch 00040: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1006 - accuracy: 0.9501 - val_loss: 1.1566 - val_accuracy: 0.8528\n",
"Epoch 41/100\n",
"24/27 [=========================>....] - ETA: 0s - loss: 0.1019 - accuracy: 0.9473\n",
"Epoch 00041: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.1016 - accuracy: 0.9474 - val_loss: 1.1586 - val_accuracy: 0.8493\n",
"Epoch 42/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.1000 - accuracy: 0.9489\n",
"Epoch 00042: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0995 - accuracy: 0.9492 - val_loss: 1.1871 - val_accuracy: 0.8516\n",
"Epoch 43/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0994 - accuracy: 0.9498\n",
"Epoch 00043: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0994 - accuracy: 0.9498 - val_loss: 1.1857 - val_accuracy: 0.8528\n",
"Epoch 44/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0987 - accuracy: 0.9507\n",
"Epoch 00044: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.0986 - accuracy: 0.9509 - val_loss: 1.2197 - val_accuracy: 0.8493\n",
"Epoch 45/100\n",
"25/27 [==========================>...] - ETA: 0s - loss: 0.1027 - accuracy: 0.9484\n",
"Epoch 00045: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1013 - accuracy: 0.9486 - val_loss: 1.2334 - val_accuracy: 0.8493\n",
"Epoch 46/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0998 - accuracy: 0.9504\n",
"Epoch 00046: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0999 - accuracy: 0.9501 - val_loss: 1.2155 - val_accuracy: 0.8540\n",
"Epoch 47/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.1038 - accuracy: 0.9492\n",
"Epoch 00047: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.1026 - accuracy: 0.9498 - val_loss: 1.2343 - val_accuracy: 0.8516\n",
"Epoch 48/100\n",
"23/27 [========================>.....] - ETA: 0s - loss: 0.1019 - accuracy: 0.9470\n",
"Epoch 00048: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.0988 - accuracy: 0.9501 - val_loss: 1.2501 - val_accuracy: 0.8505\n",
"Epoch 49/100\n",
"23/27 [========================>.....] - ETA: 0s - loss: 0.1001 - accuracy: 0.9497\n",
"Epoch 00049: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.0990 - accuracy: 0.9504 - val_loss: 1.2499 - val_accuracy: 0.8516\n",
"Epoch 50/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0972 - accuracy: 0.9516\n",
"Epoch 00050: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.0986 - accuracy: 0.9509 - val_loss: 1.2780 - val_accuracy: 0.8505\n",
"Epoch 51/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0983 - accuracy: 0.9498\n",
"Epoch 00051: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0985 - accuracy: 0.9498 - val_loss: 1.2644 - val_accuracy: 0.8516\n",
"Epoch 52/100\n",
"22/27 [=======================>......] - ETA: 0s - loss: 0.0954 - accuracy: 0.9499\n",
"Epoch 00052: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0975 - accuracy: 0.9492 - val_loss: 1.2760 - val_accuracy: 0.8528\n",
"Epoch 53/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0987 - accuracy: 0.9495\n",
"Epoch 00053: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0985 - accuracy: 0.9492 - val_loss: 1.2874 - val_accuracy: 0.8516\n",
"Epoch 54/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0987 - accuracy: 0.9498\n",
"Epoch 00054: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0987 - accuracy: 0.9498 - val_loss: 1.2812 - val_accuracy: 0.8470\n",
"Epoch 55/100\n",
"23/27 [========================>.....] - ETA: 0s - loss: 0.1024 - accuracy: 0.9480\n",
"Epoch 00055: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.0987 - accuracy: 0.9512 - val_loss: 1.3147 - val_accuracy: 0.8516\n",
"Epoch 56/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0959 - accuracy: 0.9510\n",
"Epoch 00056: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0971 - accuracy: 0.9498 - val_loss: 1.2956 - val_accuracy: 0.8493\n",
"Epoch 57/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0975 - accuracy: 0.9495\n",
"Epoch 00057: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0975 - accuracy: 0.9495 - val_loss: 1.3062 - val_accuracy: 0.8505\n",
"Epoch 58/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0978 - accuracy: 0.9489\n",
"Epoch 00058: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.0978 - accuracy: 0.9489 - val_loss: 1.2946 - val_accuracy: 0.8516\n",
"Epoch 59/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0976 - accuracy: 0.9486\n",
"Epoch 00059: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0969 - accuracy: 0.9495 - val_loss: 1.2975 - val_accuracy: 0.8505\n",
"Epoch 60/100\n",
"23/27 [========================>.....] - ETA: 0s - loss: 0.0964 - accuracy: 0.9514\n",
"Epoch 00060: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0969 - accuracy: 0.9501 - val_loss: 1.3299 - val_accuracy: 0.8505\n",
"Epoch 61/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0979 - accuracy: 0.9492\n",
"Epoch 00061: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0965 - accuracy: 0.9501 - val_loss: 1.3054 - val_accuracy: 0.8435\n",
"Epoch 62/100\n",
"23/27 [========================>.....] - ETA: 0s - loss: 0.0989 - accuracy: 0.9501\n",
"Epoch 00062: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0966 - accuracy: 0.9509 - val_loss: 1.3035 - val_accuracy: 0.8481\n",
"Epoch 63/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0963 - accuracy: 0.9498\n",
"Epoch 00063: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0963 - accuracy: 0.9498 - val_loss: 1.3560 - val_accuracy: 0.8458\n",
"Epoch 64/100\n",
"25/27 [==========================>...] - ETA: 0s - loss: 0.0984 - accuracy: 0.9484\n",
"Epoch 00064: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0958 - accuracy: 0.9498 - val_loss: 1.3375 - val_accuracy: 0.8481\n",
"Epoch 65/100\n",
"23/27 [========================>.....] - ETA: 0s - loss: 0.0947 - accuracy: 0.9497\n",
"Epoch 00065: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.0955 - accuracy: 0.9504 - val_loss: 1.3753 - val_accuracy: 0.8493\n",
"Epoch 66/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0968 - accuracy: 0.9519\n",
"Epoch 00066: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0963 - accuracy: 0.9521 - val_loss: 1.3081 - val_accuracy: 0.8458\n",
"Epoch 67/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0961 - accuracy: 0.9501\n",
"Epoch 00067: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0950 - accuracy: 0.9509 - val_loss: 1.3750 - val_accuracy: 0.8435\n",
"Epoch 68/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0961 - accuracy: 0.9504\n",
"Epoch 00068: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0961 - accuracy: 0.9504 - val_loss: 1.3975 - val_accuracy: 0.8446\n",
"Epoch 69/100\n",
"25/27 [==========================>...] - ETA: 0s - loss: 0.0956 - accuracy: 0.9509\n",
"Epoch 00069: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0970 - accuracy: 0.9506 - val_loss: 1.3552 - val_accuracy: 0.8481\n",
"Epoch 70/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0944 - accuracy: 0.9513\n",
"Epoch 00070: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0946 - accuracy: 0.9512 - val_loss: 1.3561 - val_accuracy: 0.8458\n",
"Epoch 71/100\n",
"24/27 [=========================>....] - ETA: 0s - loss: 0.1010 - accuracy: 0.9482\n",
"Epoch 00071: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.0991 - accuracy: 0.9498 - val_loss: 1.3675 - val_accuracy: 0.8446\n",
"Epoch 72/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0932 - accuracy: 0.9510\n",
"Epoch 00072: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0942 - accuracy: 0.9504 - val_loss: 1.4022 - val_accuracy: 0.8423\n",
"Epoch 73/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0958 - accuracy: 0.9509\n",
"Epoch 00073: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0958 - accuracy: 0.9509 - val_loss: 1.4034 - val_accuracy: 0.8423\n",
"Epoch 74/100\n",
"25/27 [==========================>...] - ETA: 0s - loss: 0.0951 - accuracy: 0.9503\n",
"Epoch 00074: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 11ms/step - loss: 0.0958 - accuracy: 0.9495 - val_loss: 1.4234 - val_accuracy: 0.8411\n",
"Epoch 75/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0955 - accuracy: 0.9509\n",
"Epoch 00075: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0955 - accuracy: 0.9509 - val_loss: 1.3805 - val_accuracy: 0.8411\n",
"Epoch 76/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0936 - accuracy: 0.9506\n",
"Epoch 00076: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0936 - accuracy: 0.9506 - val_loss: 1.3605 - val_accuracy: 0.8446\n",
"Epoch 77/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0968 - accuracy: 0.9501\n",
"Epoch 00077: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0965 - accuracy: 0.9501 - val_loss: 1.4142 - val_accuracy: 0.8458\n",
"Epoch 78/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0951 - accuracy: 0.9509\n",
"Epoch 00078: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0951 - accuracy: 0.9509 - val_loss: 1.3971 - val_accuracy: 0.8435\n",
"Epoch 79/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0944 - accuracy: 0.9519\n",
"Epoch 00079: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0945 - accuracy: 0.9521 - val_loss: 1.4374 - val_accuracy: 0.8435\n",
"Epoch 80/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0951 - accuracy: 0.9525\n",
"Epoch 00080: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0938 - accuracy: 0.9530 - val_loss: 1.4235 - val_accuracy: 0.8400\n",
"Epoch 81/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0939 - accuracy: 0.9516\n",
"Epoch 00081: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.0943 - accuracy: 0.9509 - val_loss: 1.4518 - val_accuracy: 0.8423\n",
"Epoch 82/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0955 - accuracy: 0.9501\n",
"Epoch 00082: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.0952 - accuracy: 0.9501 - val_loss: 1.4203 - val_accuracy: 0.8435\n",
"Epoch 83/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0956 - accuracy: 0.9501\n",
"Epoch 00083: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.0947 - accuracy: 0.9509 - val_loss: 1.4482 - val_accuracy: 0.8411\n",
"Epoch 84/100\n",
"25/27 [==========================>...] - ETA: 0s - loss: 0.0937 - accuracy: 0.9516\n",
"Epoch 00084: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0939 - accuracy: 0.9515 - val_loss: 1.4858 - val_accuracy: 0.8411\n",
"Epoch 85/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0939 - accuracy: 0.9516\n",
"Epoch 00085: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0947 - accuracy: 0.9512 - val_loss: 1.4758 - val_accuracy: 0.8388\n",
"Epoch 86/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0957 - accuracy: 0.9509\n",
"Epoch 00086: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0957 - accuracy: 0.9509 - val_loss: 1.4355 - val_accuracy: 0.8411\n",
"Epoch 87/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0946 - accuracy: 0.9501\n",
"Epoch 00087: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0946 - accuracy: 0.9501 - val_loss: 1.4159 - val_accuracy: 0.8400\n",
"Epoch 88/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0954 - accuracy: 0.9519\n",
"Epoch 00088: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0953 - accuracy: 0.9512 - val_loss: 1.3881 - val_accuracy: 0.8388\n",
"Epoch 89/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0956 - accuracy: 0.9510\n",
"Epoch 00089: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0937 - accuracy: 0.9518 - val_loss: 1.4357 - val_accuracy: 0.8411\n",
"Epoch 90/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0965 - accuracy: 0.9506\n",
"Epoch 00090: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0965 - accuracy: 0.9506 - val_loss: 1.4409 - val_accuracy: 0.8400\n",
"Epoch 91/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0946 - accuracy: 0.9501\n",
"Epoch 00091: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0940 - accuracy: 0.9506 - val_loss: 1.4469 - val_accuracy: 0.8400\n",
"Epoch 92/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0938 - accuracy: 0.9509\n",
"Epoch 00092: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0938 - accuracy: 0.9509 - val_loss: 1.4692 - val_accuracy: 0.8411\n",
"Epoch 93/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0945 - accuracy: 0.9510\n",
"Epoch 00093: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.0943 - accuracy: 0.9512 - val_loss: 1.4402 - val_accuracy: 0.8435\n",
"Epoch 94/100\n",
"27/27 [==============================] - ETA: 0s - loss: 0.0941 - accuracy: 0.9518\n",
"Epoch 00094: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0941 - accuracy: 0.9518 - val_loss: 1.4783 - val_accuracy: 0.8364\n",
"Epoch 95/100\n",
"23/27 [========================>.....] - ETA: 0s - loss: 0.0914 - accuracy: 0.9497\n",
"Epoch 00095: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0935 - accuracy: 0.9509 - val_loss: 1.4417 - val_accuracy: 0.8400\n",
"Epoch 96/100\n",
"25/27 [==========================>...] - ETA: 0s - loss: 0.0948 - accuracy: 0.9513\n",
"Epoch 00096: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.0933 - accuracy: 0.9524 - val_loss: 1.4876 - val_accuracy: 0.8423\n",
"Epoch 97/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0949 - accuracy: 0.9513\n",
"Epoch 00097: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.0935 - accuracy: 0.9521 - val_loss: 1.4260 - val_accuracy: 0.8388\n",
"Epoch 98/100\n",
"26/27 [===========================>..] - ETA: 0s - loss: 0.0929 - accuracy: 0.9510\n",
"Epoch 00098: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 12ms/step - loss: 0.0938 - accuracy: 0.9498 - val_loss: 1.4577 - val_accuracy: 0.8388\n",
"Epoch 99/100\n",
"24/27 [=========================>....] - ETA: 0s - loss: 0.0909 - accuracy: 0.9528\n",
"Epoch 00099: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 13ms/step - loss: 0.0929 - accuracy: 0.9524 - val_loss: 1.4315 - val_accuracy: 0.8388\n",
"Epoch 100/100\n",
"24/27 [=========================>....] - ETA: 0s - loss: 0.0923 - accuracy: 0.9525\n",
"Epoch 00100: val_loss did not improve from 0.35729\n",
"27/27 [==============================] - 0s 14ms/step - loss: 0.0935 - accuracy: 0.9509 - val_loss: 1.4691 - val_accuracy: 0.8411\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7fa4f21e57f0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 36
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VVmwmT-CvmVc"
},
"source": [
"import keras"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "j0Rc9l0FurQq"
},
"source": [
"mm=keras.models.load_model('/content/drive/MyDrive/Thirdsem/TimeSeries/NLP_malayalam.hdf5')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "HKbk7VdOwAxL"
},
"source": [
"test_sequences = tok.texts_to_sequences(X_val)\n",
"test_sequences_matrix = sequence.pad_sequences(test_sequences,maxlen=max_len)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "AhliZhnowFuL"
},
"source": [
"y_pred=np.argmax(mm.predict(test_sequences_matrix),1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ZJE9uxXQ0mzd"
},
"source": [
"y_pred_rn=np.argmax(model.predict(test_sequences_matrix),1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "fuBkJQkY6y5h"
},
"source": [
"from sklearn.metrics import confusion_matrix"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "BxkTwCWw_HEJ"
},
"source": [
"CF=confusion_matrix(y_pred_rn,Y_val)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KZGRmqCk_Rkx",
"outputId": "628d124b-1d24-41de-adbb-c67f28322c46"
},
"source": [
"CF #saved model"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[171, 10, 5],\n",
" [ 13, 215, 5],\n",
" [ 51, 30, 256]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 76
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xZ_cZjJKwx5S",
"outputId": "9a4fba1c-044a-48d8-ae5a-0a5334a19e53"
},
"source": [
"CF"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[203, 25, 29],\n",
" [ 13, 213, 12],\n",
" [ 19, 17, 225]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 84
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ans1pR7CA_qB"
},
"source": [
"def metrics(CF):\n",
" TN=[]\n",
" D=np.diag(CF)\n",
" DS=np.sum(D)\n",
" D_=np.diagflat(D)\n",
" DF=CF-D_\n",
" FP=np.sum(DF,1)\n",
" FN=np.sum(DF,0)\n",
" TP=D\n",
" for i in D:\n",
" TN.append(DS-i)\n",
" A=np.divide(TP+TN,TP+TN+FP+FN)\n",
" P=np.divide(TP,TP+FP)\n",
" R=np.divide(TP,TP+FN)\n",
" F1=np.divide(2*(precision * recall),(precision + recall))\n",
" return A,P,R,F1"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0fe22B2wCBhI"
},
"source": [
"import seaborn as sb"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 280
},
"id": "nVriuPU4y7Bj",
"outputId": "809310ca-e8e3-45cc-861e-39e03bf4920a"
},
"source": [
"accuracy,precision,recall,f1score=metrics(CF) #for running model\n",
"CR={'accuracy':accuracy,'precision':precision,'recall':recall,'f1_score':f1score}\n",
"data=pd.DataFrame(CR)\n",
"#0 Business, 2 sports, 1 Entertainment\n",
"newdata=data.rename(index={0:'Business',1:'sports',2:'Entertainment'})\n",
"img=sb.heatmap(newdata,annot=True,fmt='f',robust=True,linewidths=2, cmap=\"YlGnBu\")\n",
"img.figure.savefig('CB_25.png')"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PzYC4r90zeY8",
"outputId": "e1ae7b88-3c2d-4cd2-9f68-ccf894203026"
},
"source": [
"np.mean(f1score) # for the running model"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.8477301860632295"
]
},
"metadata": {
"tags": []
},
"execution_count": 68
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 280
},
"id": "aiqdRtIdzktx",
"outputId": "c015dabb-e79d-4673-da7b-4f05e9314f0e"
},
"source": [
"accuracy,precision,recall,f1score=metrics(CF) #for running model\n",
"CR={'accuracy':accuracy,'precision':precision,'recall':recall,'f1_score':f1score}\n",
"data=pd.DataFrame(CR)\n",
"#0 Business, 2 sports, 1 Entertainment\n",
"newdata=data.rename(index={0:'Business',1:'sports',2:'Entertainment'})\n",
"img=sb.heatmap(newdata,annot=True,fmt='f',robust=True,linewidths=2, cmap=\"YlGnBu\")\n",
"img.figure.savefig('NLP_saved_model.png')"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kTx5AIl30XYL",
"outputId": "13bffa76-5cac-411f-8173-74a8fc91b3c8"
},
"source": [
"np.mean(f1score) # for the saved model"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.8477301860632295"
]
},
"metadata": {
"tags": []
},
"execution_count": 78
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 280
},
"id": "XCUpvHKQBGXN",
"outputId": "02a1b05a-86da-475e-b7df-14f423d34581"
},
"source": [
"accuracy,precision,recall=metrics(CF) #for saved model\n",
"CR={'accuracy':accuracy,'precision':precision,'recall':recall}\n",
"data=pd.DataFrame(CR)\n",
"#0 Business, 2 sports, 1 Entertainment\n",
"newdata=data.rename(index={0:'Business',1:'sports',2:'Entertainment'})\n",
"img=sb.heatmap(newdata,annot=True,fmt='f',robust=True,linewidths=2, cmap=\"YlGnBu\")\n",
"img.figure.savefig('CB_25.png')"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 280
},
"id": "QqJTJwhs7Q-m",
"outputId": "366f8392-258d-4afe-a43a-3437a9f54995"
},
"source": [
"accuracy,precision,recall,f1score=metrics(CF) #for running model\n",
"CR={'accuracy':accuracy,'precision':precision,'recall':recall,'f1score':f1score}\n",
"data=pd.DataFrame(CR)\n",
"#0 Business, 1 sports, 2 Entertainment\n",
"newdata=data.rename(index={0:'Business',1:'sports',2:'Entertainment'})\n",
"img=sb.heatmap(newdata,annot=True,fmt='f',robust=True,linewidths=2, cmap=\"YlGnBu\")\n",
"img.figure.savefig('Saved_model.png')"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iBn7xjGt1QD5"
},
"source": [
"#saved model is better"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gUWqy_IP1SZS",
"outputId": "3306acfc-05cc-4e95-8fdb-2393c1695e96"
},
"source": [
"mm.summary()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"functional_3\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"inputs (InputLayer) [(None, 150)] 0 \n",
"_________________________________________________________________\n",
"embedding_1 (Embedding) (None, 150, 50) 50000 \n",
"_________________________________________________________________\n",
"lstm_1 (LSTM) (None, 64) 29440 \n",
"_________________________________________________________________\n",
"FC1 (Dense) (None, 256) 16640 \n",
"_________________________________________________________________\n",
"activation_2 (Activation) (None, 256) 0 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 256) 0 \n",
"_________________________________________________________________\n",
"out_layer (Dense) (None, 3) 771 \n",
"_________________________________________________________________\n",
"activation_3 (Activation) (None, 3) 0 \n",
"=================================================================\n",
"Total params: 96,851\n",
"Trainable params: 96,851\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "3QJHBVIn2boQ",
"outputId": "e0f14ad5-e821-4469-88e0-7a115fc77af9"
},
"source": [
"df = pd.read_csv('../content/malayalam_news_test.csv',delimiter=',',encoding='utf_8_sig')\n",
"df.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>headings</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>MAL_0001</td>\n",
" <td>അന്താരാഷ്ട്ര ഇന്റര്‍നെറ്റ് സേവനം താല്‍ക്കാലികമ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>MAL_0002</td>\n",
" <td>മഞ്ഞ് ചതിച്ചാശാനേ!!!!, ഹാന്നോവറിന്റെ പ്രതീക്ഷക...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>MAL_0003</td>\n",
" <td>12 സീസണ്‍ എട്ടു ചാമ്ബ്യന്മാര്‍, പ്രവചിക്കാന്‍ ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MAL_0004</td>\n",
" <td>\\nമുംബൈ സിറ്റിയോടു തോറ്റിട്ടും യോഗ്യത നേടി ഗോവ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MAL_0005</td>\n",
" <td>മദ്യപിച്ചെത്തിയ നടന്മാര്‍ തമ്മില്‍ കയ്യാങ്കളി;...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID headings\n",
"0 MAL_0001 അന്താരാഷ്ട്ര ഇന്റര്‍നെറ്റ് സേവനം താല്‍ക്കാലികമ...\n",
"1 MAL_0002 മഞ്ഞ് ചതിച്ചാശാനേ!!!!, ഹാന്നോവറിന്റെ പ്രതീക്ഷക...\n",
"2 MAL_0003 12 സീസണ്‍ എട്ടു ചാമ്ബ്യന്മാര്‍, പ്രവചിക്കാന്‍ ...\n",
"3 MAL_0004 \\nമുംബൈ സിറ്റിയോടു തോറ്റിട്ടും യോഗ്യത നേടി ഗോവ...\n",
"4 MAL_0005 മദ്യപിച്ചെത്തിയ നടന്മാര്‍ തമ്മില്‍ കയ്യാങ്കളി;..."
]
},
"metadata": {
"tags": []
},
"execution_count": 99
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CrHrckDR2v7H",
"outputId": "40c6fa81-7103-4f2d-ae6b-3f09dad3d187"
},
"source": [
"df.count"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<bound method DataFrame.count of ID headings\n",
"0 MAL_0001 അന്താരാഷ്ട്ര ഇന്റര്‍നെറ്റ് സേവനം താല്‍ക്കാലികമ...\n",
"1 MAL_0002 മഞ്ഞ് ചതിച്ചാശാനേ!!!!, ഹാന്നോവറിന്റെ പ്രതീക്ഷക...\n",
"2 MAL_0003 12 സീസണ്‍ എട്ടു ചാമ്ബ്യന്മാര്‍, പ്രവചിക്കാന്‍ ...\n",
"3 MAL_0004 \\nമുംബൈ സിറ്റിയോടു തോറ്റിട്ടും യോഗ്യത നേടി ഗോവ...\n",
"4 MAL_0005 മദ്യപിച്ചെത്തിയ നടന്മാര്‍ തമ്മില്‍ കയ്യാങ്കളി;...\n",
"... ... ...\n",
"1255 MAL_1256 സാക്ഷാല്‍ വിരാട് കോഹ്ലിയെ പോലും ഞെട്ടിച്ച്‌ ജസ...\n",
"1256 MAL_1257 ചാമ്ബ്യന്‍സ് ലീഗ്; ബയേണിനെ തകര്‍ത്ത് ലിവര്‍പൂള...\n",
"1257 MAL_1258 20 രൂപയുടെ നാണയമിറക്കാന്‍ കേന്ദ്ര ധനകാര്യ മന്ത...\n",
"1258 MAL_1259 ഇന്ത്യന്‍ സമ്ബദ് വ്യവസ്ഥ 7.3 ശതമാനം വളര്‍ച്ച ന...\n",
"1259 MAL_1260 വൈ ദിസ് കൊലച്ചതി? ഒറ്റ രാത്രികൊണ്ട് ലക്ഷക്കണക്...\n",
"\n",
"[1260 rows x 2 columns]>"
]
},
"metadata": {
"tags": []
},
"execution_count": 100
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "a8lueO9-3CrH"
},
"source": [
"X_test=df.headings"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PZ6HM7C83MaD"
},
"source": [
"T_ind=df.ID"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "63xK0kwg3S12",
"outputId": "338f24c7-88e6-4492-b953-5d1fd3467f1b"
},
"source": [
"T_ind"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 MAL_0001\n",
"1 MAL_0002\n",
"2 MAL_0003\n",
"3 MAL_0004\n",
"4 MAL_0005\n",
" ... \n",
"1255 MAL_1256\n",
"1256 MAL_1257\n",
"1257 MAL_1258\n",
"1258 MAL_1259\n",
"1259 MAL_1260\n",
"Name: ID, Length: 1260, dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 116
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7BO2gFl93iZk"
},
"source": [
"eval_sequences = tok.texts_to_sequences(X_val)\n",
"eval_sequences_matrix = sequence.pad_sequences(test_sequences,maxlen=max_len)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "FGj8IJxS8pZO"
},
"source": [
"from keras.utils import to_categorical"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "HZOJmwlt88cV"
},
"source": [
"Y_train_=to_categorical(Y_train)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JwMb4DS97ck1",
"outputId": "0100d191-cd0d-49d4-84cf-e6f5b8d4c21c"
},
"source": [
"Y_train_.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(4280, 3)"
]
},
"metadata": {
"tags": []
},
"execution_count": 190
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rZvcnbQh7Z-f"
},
"source": [
"X_=[]\n",
"for i in X_test:\n",
" X_.append(i.replace('\\u200d',''))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "M7e21iGj39zA"
},
"source": [
"X__=[]\n",
"for i in X_:\n",
" X__.append(i.replace('\\u200c',''))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "n2yDvuC131fM"
},
"source": [
"X_test=np.asarray(X__)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "I0gNlzgA50jn",
"outputId": "ee0deafd-1b6b-4a42-d155-c35267da4f3b"
},
"source": [
"X_test.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1260,)"
]
},
"metadata": {
"tags": []
},
"execution_count": 119
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "fcnhi3n-4ZV5"
},
"source": [
"eval_sequences = tok.texts_to_sequences(X_test)\n",
"eval_sequences_matrix = sequence.pad_sequences(eval_sequences,maxlen=max_len)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "dnIEVLaN7JlX"
},
"source": [
"S_label=np.argmax(mm.predict(eval_sequences_matrix),1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6N-ajn9E0vb6",
"outputId": "7b1e5693-bb52-4ec6-b2c1-cab486f92564"
},
"source": [
"S_label.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1260,)"
]
},
"metadata": {
"tags": []
},
"execution_count": 123
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7XdgLLx37WpZ"
},
"source": [
"S_L=np.zeros_like(S_label)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3-HwPEF-5CkF"
},
"source": [
"CR={'ID':T_ind,'Predicted_Labels':S_label}\n",
"data=pd.DataFrame(CR)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "_OC062BX6HLC",
"outputId": "2f536904-5663-42a9-fad8-1f8cc7150add"
},
"source": [
"data.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>Predicted_Labels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>MAL_0001</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>MAL_0002</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>MAL_0003</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MAL_0004</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MAL_0005</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID Predicted_Labels\n",
"0 MAL_0001 0\n",
"1 MAL_0002 0\n",
"2 MAL_0003 2\n",
"3 MAL_0004 2\n",
"4 MAL_0005 1"
]
},
"metadata": {
"tags": []
},
"execution_count": 140
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "l8xf10gp6Hp1"
},
"source": [
"newdata=data.rename(data.Predicted_Labels=={0:'Business',1:'Entertainment',2:'Sports'})"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hXJjdw7O7Fwn"
},
"source": [
"newdata=data.replace({'Predicted_Labels': {0:'Business',1:'Entertainment',2:'Sports'}})"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "68WQC_H27HD-",
"outputId": "52f1f7f5-18f9-4095-f70d-b7a07d540d53"
},
"source": [
"data.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>Predicted_Labels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>MAL_0001</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>MAL_0002</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>MAL_0003</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MAL_0004</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MAL_0005</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID Predicted_Labels\n",
"0 MAL_0001 0\n",
"1 MAL_0002 0\n",
"2 MAL_0003 2\n",
"3 MAL_0004 2\n",
"4 MAL_0005 1"
]
},
"metadata": {
"tags": []
},
"execution_count": 150
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"id": "tiWjmCo-8edF",
"outputId": "9e1d6fe8-00eb-40b4-b4fb-87f1a8d10972"
},
"source": [
"newdata.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>Predicted_Labels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>MAL_0001</td>\n",
" <td>Business</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>MAL_0002</td>\n",
" <td>Business</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>MAL_0003</td>\n",
" <td>Sports</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MAL_0004</td>\n",
" <td>Sports</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>MAL_0005</td>\n",
" <td>Entertainment</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID Predicted_Labels\n",
"0 MAL_0001 Business\n",
"1 MAL_0002 Business\n",
"2 MAL_0003 Sports\n",
"3 MAL_0004 Sports\n",
"4 MAL_0005 Entertainment"
]
},
"metadata": {
"tags": []
},
"execution_count": 152
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "k4oc1vjY-zjt"
},
"source": [
"newdata.to_csv('19015_Task_1_submission-1.csv')"
],
"execution_count": null,
"outputs": []
}
]
}
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