Gist to itorch notebook to help visualize weights for CNN.

Visualize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "require 'torch'\n",
    "require 'nn'\n",
    "require 'nngraph'\n",
    "require 'itorch'\n",
    "-- exotics\n",
    "require 'loadcaffe'\n",
    "-- local imports\n",
    "local utils = require 'misc.utils'\n",
    "require 'misc.DataLoader'\n",
    "require 'misc.DataLoaderRaw'\n",
    "require 'misc.LanguageModel'\n",
    "local net_utils = require 'misc.net_utils'\n",
    "require 'cutorch'\n",
    "require 'cunn'\n",
    "require 'cudnn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nil\t\n"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkpoint = torch.load('model_id.t7')\n",
    "cnn = checkpoint.protos.cnn\n",
    "-- print(cnn.modules[1].weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 64\n",
       "  3\n",
       "  3\n",
       "  3\n",
       "[torch.LongStorage of size 4]\n",
       "\n"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight = cnn.modules[1].weight\n",
    "print(weight:size())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
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     "metadata": {
      "image/png": {
       "height": 33,
       "width": 3
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = image.toDisplayTensor{input=weight}\n",
    "\n",
    "im = image.scale(im, im:size(1), im:size(2),'bilinear')\n",
    "itorch.image(im)\n",
    "image.save(\"filters.png\",im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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