Pandas Graphing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import IPython.display\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "text = '/home/cc/Voice-Identification/speaker10_training.log'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.rename(columns={'acc':'Training Acc', 'loss': 'Training Loss', 'val_acc':'Validation Acc', 'val_loss':'Validation Loss'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>Training Acc</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Acc</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.337719</td>\n",
       "      <td>2.016422</td>\n",
       "      <td>0.779070</td>\n",
       "      <td>0.776449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.788012</td>\n",
       "      <td>0.647710</td>\n",
       "      <td>0.895349</td>\n",
       "      <td>0.327201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.884503</td>\n",
       "      <td>0.330732</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>0.219564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.935673</td>\n",
       "      <td>0.198895</td>\n",
       "      <td>0.918605</td>\n",
       "      <td>0.212255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.954678</td>\n",
       "      <td>0.142989</td>\n",
       "      <td>0.941860</td>\n",
       "      <td>0.190121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.970760</td>\n",
       "      <td>0.097007</td>\n",
       "      <td>0.930233</td>\n",
       "      <td>0.172630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.973684</td>\n",
       "      <td>0.093890</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>0.166869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.983918</td>\n",
       "      <td>0.070073</td>\n",
       "      <td>0.941860</td>\n",
       "      <td>0.160245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.988304</td>\n",
       "      <td>0.047548</td>\n",
       "      <td>0.965116</td>\n",
       "      <td>0.138859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.988304</td>\n",
       "      <td>0.042477</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>0.130341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.995614</td>\n",
       "      <td>0.031284</td>\n",
       "      <td>0.965116</td>\n",
       "      <td>0.126845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.989766</td>\n",
       "      <td>0.032881</td>\n",
       "      <td>0.965116</td>\n",
       "      <td>0.118630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.992690</td>\n",
       "      <td>0.024677</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>0.089497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.997076</td>\n",
       "      <td>0.030279</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>0.095983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.994152</td>\n",
       "      <td>0.021925</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>0.116793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    epoch  Training Acc  Training Loss  Validation Acc  Validation Loss\n",
       "0       0      0.337719       2.016422        0.779070         0.776449\n",
       "1       1      0.788012       0.647710        0.895349         0.327201\n",
       "2       2      0.884503       0.330732        0.953488         0.219564\n",
       "3       3      0.935673       0.198895        0.918605         0.212255\n",
       "4       4      0.954678       0.142989        0.941860         0.190121\n",
       "5       5      0.970760       0.097007        0.930233         0.172630\n",
       "6       6      0.973684       0.093890        0.953488         0.166869\n",
       "7       7      0.983918       0.070073        0.941860         0.160245\n",
       "8       8      0.988304       0.047548        0.965116         0.138859\n",
       "9       9      0.988304       0.042477        0.953488         0.130341\n",
       "10     10      0.995614       0.031284        0.965116         0.126845\n",
       "11     11      0.989766       0.032881        0.965116         0.118630\n",
       "12     12      0.992690       0.024677        0.976744         0.089497\n",
       "13     13      0.997076       0.030279        0.953488         0.095983\n",
       "14     14      0.994152       0.021925        0.953488         0.116793"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XRwivsNsVKqsslJsslFdaKDdVU2GyUlFlcfR6bI6epSMAO55brHYsta/Z6gdo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f97008990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = df.plot(x='epoch', ylim=(0, 1.5))\n",
    "p.set_axis_bgcolor('w')\n",
    "p.grid(color='k', linestyle='--', linewidth=0.25)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy and Loss Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}