bug_tf.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Bug when convolving (conv2d) images of certain sizes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Windows 7 Professional, Service Pack 1, GPU: Nvidia Titan X (Pascal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a checkerboard image for demonstration\n",
    "\n",
    "import numpy as np\n",
    "w = 3676\n",
    "h = 1832\n",
    "delta=100\n",
    "data = np.zeros((h,w)).astype(\"float32\")\n",
    "\n",
    "for i in range(w):\n",
    "    for j in range(h):\n",
    "        if (((i//delta)%2==0 and(j//delta)%2==0) or ((i//delta)%2==1 and(j//delta)%2==1)):\n",
    "            data[j,i]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ff47908>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TkjQoaUjS1N8cZ2ZmLWn1W0TX8Kt7AQeBhRHxmqR+4NuSLphp0YjYAGyAxk4Mm5lZc5pu\nApJOAP4Y6B8bi4gjwJG0vF3SPuBcYBSYX3r6/DRmZmY91MrhoD8EXoiIdw/zSOqTNCstnw0sBl6K\niIPAm5KWpfMI1wAPtvC7zcysDRq5RHQT8H/ABySNSLouPbSa408IXwLsTJeMfhO4ISLGTip/Gvh3\nYBjYh68MMjPruew/LDYwMBCeWayedXLMVMU6OWbKrU6OmVqdWWxoaKg9HxYzM7NfX24CZmY1lv3h\nIElvAXt6nWOGzgBe7XWIJlQxdxUzQzVzVzEzVDN3q5l/JyL6Glmx1c8JdMOeiBjodYiZkDRUtcxQ\nzdxVzAzVzF3FzFDN3N3M7MNBZmY15iZgZlZjVWgCG3odoAlVzAzVzF3FzFDN3FXMDNXM3bXM2Z8Y\nNjOzzqnCnoCZmXVItk1A0gpJe9JMZOt6nWe8NKPas2kGtaE0drqkrZL2pp9z0rjSbGrDknZKurBL\nGSeaFW7GGSWtTevvlbS2R7lvkTRamrVuZemx9Sn3HknLS+Ndew9JWiDpUUnPSdot6TNpPNvtPUXm\n3Lf1eyQ9IemZlPvv0/giSY+nDPdKOimNz073h9PjZ033erqc+y5JL5e295I03p33SERkdwNmUXy/\n0NnAScAzwPm9zjUu437gjHFjXwTWpeV1wD+n5ZUU35UkYBnweJcyXgJcCOxqNiNwOvBS+jknLc/p\nQe5bgL+aYN3z0/tjNrAovW9mdfs9BJwJXJiWTwVeTNmy3d5TZM59Wws4JS2fSDFb4TLgPmB1Gr8D\n+Iu0/GngjrS8Grh3qtfTg9x3AVdNsH5X3iO57gksBYYj4qWI+AWwGVjV40yNWAXcnZbv5tjsaauA\ne6KwDThNxWxrHRUTzwo304zLga0R8XpE/BjYSjE1aLdzT2YVsDkijkTEyxRfULiULr+HIuJgRDyV\nlt8CnqeYOCnb7T1F5snksq0jIn6a7p6YbgH8AcUXV8Lx23rsz+CbwMckaYrX0+3ck+nKeyTXJjAP\nOFC6P+VMZD0SwHclbZc0mMbmRvG12QA/BOam5Zxez0wz5pT9prRbvHHssAoZ5k6HGz5M8T+9Smzv\ncZkh820taZaKbys+TPGP4D7gJxFxdIIM7+ZLj78B/FYOuSNibHv/Q9reX5E0e3zucfnamjvXJlAF\nF0XEhcDlwI2SLik/GMV+W9aXXlUhY8ntwDnAEooZ7G7tbZyJSToFuB/4bES8WX4s1+09Qebst3VE\nvB0RSygmqFoKnNfjSA0Zn1vSB4H1FPl/l+IQz990M1OuTWAUWFC6n91MZBExmn4eBh6geCMeGjvM\nk34eTqvn9HpmmjGL7BFxKP0Fege4k2O77dnklnQixT+mX4+Ib6XhrLf3RJmrsK3HRMRPgEeBj1Ic\nLhn7Kpxyhnfzpcd/E3iNPHKvSIflIoqZGf+DLm/vXJvAk8DidLb/JIqTOVt6nOldkk6WdOrYMnAZ\nsIsi49iZ+rUcmz1tC3BNOtu/DHijdIig22aa8WHgMklz0mGBy9JYV407h3IlxfaGIvfqdAXIIorZ\n7J6gy++hdIz5a8DzEfHl0kPZbu/JMldgW/dJOi0tvxf4OMX5jEeBq9Jq47f12J/BVcD3017ZZK+n\nm7lfKP0nQRTnMcrbu/PvkWbPKHf6RnFm/EWKY32f63WecdnOpriq4Blg91g+iuOMjwB7ge8Bp8ex\nqwK+ml7Ls8BAl3Juotid/yXFccPrmskIXEtx0mwY+GSPcv9nyrUz/eU4s7T+51LuPcDlvXgPARdR\nHOrZCexIt5U5b+8pMue+rT8EPJ3y7QL+Lo2fTfGP+DDwDWB2Gn9Puj+cHj97utfT5dzfT9t7F/Bf\nHLuCqCvvEX9i2MysxnI9HGRmZl3gJmBmVmNuAmZmNeYmYGZWY24CZmY15iZgZlZjbgJmZjXmJmBm\nVmP/Dw5WbhTmvhWhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29e57eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing the image\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "plt.imshow(data, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# preparing for tf convolution\n",
    "\n",
    "import tensorflow as tf\n",
    "data_tens = data[np.newaxis,...,np.newaxis]\n",
    "\n",
    "weights = tf.Variable(tf.truncated_normal([1,1,1,1], stddev=0.1))\n",
    "y = tf.nn.conv2d(data_tens, weights, [1,1,1,1], padding='SAME')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evalutating the output\n",
    "\n",
    "data_convolved = 0\n",
    "with tf.Session():\n",
    "    tf.global_variables_initializer().run()\n",
    "    data_convolved = y.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x30118940>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ozry7MWfozrzblnPlPydgZmaTpxuOBMzMbJJUtghIWippb+pEtqrT+QyXOqrtTB3U+lJs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\nzEXAzCxjLgJmZhlzETAzy5iLgJlZxv4f6wOlolEayXIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x300d94e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the convolved image \n",
    "\n",
    "plt.imshow(data_convolved[0,:,:,0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}