xiangze/waifu2x_params.ipynb

## waifu2x_params.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:c509441dffe3e000d27b234f563456ac8003004efbe374d7b67ec999c9ef5bd9"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json, sys, numpy as np\n",
      "%pylab inline\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#https://marcan.st/transf/scale2.0x_model.json\n",
      "model = json.load(open(\"scale2.0x_model.json\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "model\u306e\u8981\u7d20"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for k,v in model[0].items():\n",
      "    print k"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "weight\n",
        "kH\n",
        "nOutputPlane\n",
        "bias\n",
        "kW\n",
        "nInputPlane\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "\u4fc2\u6570\u306e\u6b21\u5143"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for l in model:\n",
      "    print np.array(l['weight']).shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(32, 1, 3, 3)\n",
        "(32, 32, 3, 3)\n",
        "(64, 32, 3, 3)\n",
        "(64, 64, 3, 3)\n",
        "(128, 64, 3, 3)\n",
        "(128, 128, 3, 3)\n",
        "(1, 128, 3, 3)\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for l in model:\n",
      "    ws=np.array(l['weight']).flatten()\n",
      "    num=len(ws)\n",
      "    nonzero=sum([1 if(abs(i)<0.001) else 0 for i in ws ])\n",
      "    print num,nonzero,float(nonzero)/num"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "288 2 0.00694444444444\n",
        "9216 68 0.00737847222222\n",
        "18432 176 0.00954861111111\n",
        "36864 353 0.00957573784722\n",
        "73728"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 775 0.0105116102431\n",
        "147456"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1711 0.0116034613715\n",
        "1152 2 0.00173611111111\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for l in model:\n",
      "    ws=np.array(l['weight']).flatten()\n",
      "    pylab.hist(ws)\n",
      "    pylab.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x8ce5dd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x8ce4710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x8ddf410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x69ff1d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x69ff8d0>"
       ]
      },
      {
       "metadata": {},
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       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x698e810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x3893d90>"
       ]
      }
     ],
     "prompt_number": 19
    }
   ],
   "metadata": {}
  }
 ]
}