hunan-rostomyan/config_perplexity.ipynb

## config_perplexity.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import set_matplotlib_formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "set_matplotlib_formats('retina')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expected performance on PTB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Based on Zaremba 2015\n",
    "config_performance = np.array([\n",
    "        [13, 37.99, 121.39, 115.91],\n",
    "        [39, 48.45, 86.16, 82.07],\n",
    "        [55, 37.87, 82.62, 78.29]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a52f2b0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 458,
       "width": 1004
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = config_performance[:, 0]\n",
    "train_error, valid_error, test_error = config_performance[:, 1:].T\n",
    "\n",
    "sns.set_style('white')\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "labels = ['Small', 'Medium', 'Large']\n",
    "for label in labels:\n",
    "    label_i = labels.index(label)\n",
    "    plt.axvline(epochs[label_i], color='#eeeeee', zorder=0)\n",
    "\n",
    "plt.scatter(epochs, train_error, color=sns.xkcd_rgb['medium green'], label='Train', s=40)\n",
    "plt.scatter(epochs, valid_error, color=sns.xkcd_rgb['denim blue'], label='Validation', s=40)\n",
    "plt.scatter(epochs, test_error, color=sns.xkcd_rgb['pale red'], label='Test', s=40)\n",
    "\n",
    "plt.xticks(epochs, labels, rotation='horizontal')\n",
    "plt.ylabel('Expected perplexity')\n",
    "plt.xlabel('Configuration size')\n",
    "plt.legend(loc='upper right', frameon=True)\n",
    "plt.savefig('config_perplexity.png', format='png', dpi=800)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}