deeplearning-assignment2.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import pandas\n",
    "\n",
    "dftrain = pandas.read_csv('/Users/ceric/Downloads/deeplearning/project2/data/train.txt', ' ', header=None)\n",
    "dfval = pandas.read_csv('/Users/ceric/Downloads/deeplearning/project2/data/val.txt', ' ', header=None)\n",
    "dftest = pandas.read_csv('/Users/ceric/Downloads/deeplearning/project2/data/test.txt', ' ', header=None)\n"
   ]
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   "execution_count": 24,
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      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./flower_photos/test/410421672_563550467c.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./flower_photos/test/17990320484_93bba345d2_m.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./flower_photos/test/5212885371_fe27c406a2_n.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./flower_photos/test/13997641965_80d5dab542_n.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./flower_photos/test/8739657154_6db14796c9.jpg</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                                                   0\n",
       "0      ./flower_photos/test/410421672_563550467c.jpg\n",
       "1  ./flower_photos/test/17990320484_93bba345d2_m.jpg\n",
       "2   ./flower_photos/test/5212885371_fe27c406a2_n.jpg\n",
       "3  ./flower_photos/test/13997641965_80d5dab542_n.jpg\n",
       "4     ./flower_photos/test/8739657154_6db14796c9.jpg"
      ]
     },
     "execution_count": 24,
     "metadata": {},
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   ],
   "source": [
    "dftest.head()"
   ]
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    {
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       "(2569, 2)"
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    "\n"
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      "('/Users/ceric/Downloads/deeplearning/project2/data/./flower_photos/test/5863698305_04a4277401_n.jpg', '/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/test/daisy/5863698305_04a4277401_n.jpg')\n",
      "('/Users/ceric/Downloads/deeplearning/project2/data/./flower_photos/test/7164500544_332b75aa3b.jpg', '/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/test/daisy/7164500544_332b75aa3b.jpg')\n",
      "('/Users/ceric/Downloads/deeplearning/project2/data/./flower_photos/test/3115889021_053f3b8e5a.jpg', '/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/test/daisy/3115889021_053f3b8e5a.jpg')\n",
      "('/Users/ceric/Downloads/deeplearning/project2/data/./flower_photos/test/22244161124_53e457bb66_n.jpg', '/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/test/daisy/22244161124_53e457bb66_n.jpg')\n",
      "('/Users/ceric/Downloads/deeplearning/project2/data/./flower_photos/test/3731075939_6c92d7fe68_m.jpg', '/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/test/daisy/3731075939_6c92d7fe68_m.jpg')\n",
      "('/Users/ceric/Downloads/deeplearning/project2/data/./flower_photos/test/6208851904_9d916ebb32_n.jpg', '/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/test/daisy/6208851904_9d916ebb32_n.jpg')\n",
      "('/Users/ceric/Downloads/deeplearning/project2/data/./flower_photos/test/799964360_7e07a227ea_n.jpg', '/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/test/daisy/799964360_7e07a227ea_n.jpg')\n",
      "('/Users/ceric/Downloads/deeplearning/project2/data/./flower_photos/test/14674743211_f68b13f6d9.jpg', '/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/test/daisy/14674743211_f68b13f6d9.jpg')\n",
      "('/Users/ceric/Downloads/deeplearning/project2/data/./flower_photos/test/13560152823_9da5e48c87_m.jpg', '/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/test/daisy/13560152823_9da5e48c87_m.jpg')\n",
      "('/Users/ceric/Downloads/deeplearning/project2/data/./flower_photos/test/3530500952_9f94fb8b9c_m.jpg', '/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/test/daisy/3530500952_9f94fb8b9c_m.jpg')\n",
      "('/Users/ceric/Downloads/deeplearning/project2/data/./flower_photos/test/16510864164_3afa8ac37f.jpg', '/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/test/daisy/16510864164_3afa8ac37f.jpg')\n",
      "('/Users/ceric/Downloads/deeplearning/project2/data/./flower_photos/test/14003401241_543535b385.jpg', '/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/test/daisy/14003401241_543535b385.jpg')\n"
     ]
    }
   ],
   "source": [
    "from shutil import copyfile\n",
    "\n",
    "basepath = '/Users/ceric/Downloads/deeplearning/project2/data/'\n",
    "\n",
    "pre_basepath = '/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/'\n",
    "pre_train = pre_basepath + 'train'\n",
    "pre_val = pre_basepath + 'val'\n",
    "pre_test = pre_basepath + 'test'\n",
    "\n",
    "def copyToFolder(df, pre_path, fortest):\n",
    "    for index, row in df.iterrows():\n",
    "        srcpath = basepath + row[0]\n",
    "        #label = row[1]\n",
    "        tok = row[0].split('/')\n",
    "        catname = tok[2]\n",
    "        filename = tok[3]\n",
    "\n",
    "        topath = pre_path + '/' + str(catname) + '/' + filename\n",
    "        \n",
    "        if(fortest == True):\n",
    "            topath = pre_path + '/' + 'daisy' + '/' + filename\n",
    "            \n",
    "        print (srcpath, topath)\n",
    "        copyfile(srcpath, topath)\n",
    "        \n",
    "        \n",
    "#copyToFolder(dftrain, pre_train)\n",
    "#copyToFolder(dfval, pre_val)\n",
    "copyToFolder(dftest, pre_test, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#integration the predictions and the ordering of generating the TFRecords\n",
    "\n",
    "predictions = pandas.read_csv('/Users/ceric/Downloads/predictions.txt', ' ', header=None)\n",
    "myfilenames = pandas.read_csv('/Users/ceric/Downloads/deeplearning/project2/preprocessed-data/filenames2.txt', ' ', header=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(551, 1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(551, 1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myfilenames.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new = [myfilenames, predictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new = pandas.concat(new, axis=1)\n",
    "new.columns = ['file', 'label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>551 rows × 2 columns</p>\n",
       "</div>"
      ],
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       "526   8956863946_f96be02aae_n.jpg      1\n",
       "527     8980145452_efbd6e3b04.jpg      1\n",
       "528   8989067485_aab399460b_n.jpg      1\n",
       "529   9159362388_c6f4cf3812_n.jpg      2\n",
       "530     9160289562_ab2718d19b.jpg      2\n",
       "531   9161647994_e39b65cb9c_n.jpg      0\n",
       "532     9216286876_289a4779f7.jpg      3\n",
       "533   9300754115_dd79670066_n.jpg      2\n",
       "534     9302733302_2cb92cf275.jpg      3\n",
       "535   9309388105_12c0b8dd54_m.jpg      4\n",
       "536     9310226774_d1b8f5d9c9.jpg      0\n",
       "537     9339697826_88c9c4dc50.jpg      3\n",
       "538     9404876600_04f6d37685.jpg      4\n",
       "539   9432335346_e298e47713_n.jpg      3\n",
       "540   9448615838_04078d09bf_n.jpg      3\n",
       "541   9497774935_a7daec5433_n.jpg      3\n",
       "542   9558630626_52a1b7d702_m.jpg      3\n",
       "543   9593034725_0062f0d24e_n.jpg      0\n",
       "544     9599534035_ae4df582b6.jpg      3\n",
       "545     9610374042_bb16cded3d.jpg      3\n",
       "546     9613826015_f345354874.jpg      1\n",
       "547     9617087594_ec2a9b16f6.jpg      1\n",
       "548   9726260379_4e8ee66875_m.jpg      1\n",
       "549        9818247_e2eac18894.jpg      1\n",
       "550   9870557734_88eb3b9e3b_n.jpg      3\n",
       "\n",
       "[551 rows x 2 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this is the TFRecord ordering with predictions\n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "onlyfilename = dftest[0].map(lambda x: x.split('/')[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dftest_new = pandas.concat([dftest, onlyfilename], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dftest_new.columns = ['full_path', 'file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dftest_final = pandas.merge(dftest_new, new, on='file')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>full_path</th>\n",
       "      <th>file</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./flower_photos/test/410421672_563550467c.jpg</td>\n",
       "      <td>410421672_563550467c.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./flower_photos/test/17990320484_93bba345d2_m.jpg</td>\n",
       "      <td>17990320484_93bba345d2_m.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./flower_photos/test/5212885371_fe27c406a2_n.jpg</td>\n",
       "      <td>5212885371_fe27c406a2_n.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./flower_photos/test/13997641965_80d5dab542_n.jpg</td>\n",
       "      <td>13997641965_80d5dab542_n.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./flower_photos/test/8739657154_6db14796c9.jpg</td>\n",
       "      <td>8739657154_6db14796c9.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>./flower_photos/test/13235124703_a7e1266e44.jpg</td>\n",
       "      <td>13235124703_a7e1266e44.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>./flower_photos/test/3584414925_1e6c4b61db_n.jpg</td>\n",
       "      <td>3584414925_1e6c4b61db_n.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>./flower_photos/test/3459346147_faffff51c7_n.jpg</td>\n",
       "      <td>3459346147_faffff51c7_n.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>./flower_photos/test/9432335346_e298e47713_n.jpg</td>\n",
       "      <td>9432335346_e298e47713_n.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>./flower_photos/test/19443726008_8c9c68efa7_m.jpg</td>\n",
       "      <td>19443726008_8c9c68efa7_m.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>./flower_photos/test/8686332852_c6dcb2e86b.jpg</td>\n",
       "      <td>8686332852_c6dcb2e86b.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>./flower_photos/test/4267024012_295e7141a3_n.jpg</td>\n",
       "      <td>4267024012_295e7141a3_n.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>./flower_photos/test/13946048982_4e6ec56987.jpg</td>\n",
       "      <td>13946048982_4e6ec56987.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>./flower_photos/test/10555749515_13a12a026e.jpg</td>\n",
       "      <td>10555749515_13a12a026e.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>./flower_photos/test/2093263381_afd51358a3.jpg</td>\n",
       "      <td>2093263381_afd51358a3.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>./flower_photos/test/4279989256_9a48c0d194_n.jpg</td>\n",
       "      <td>4279989256_9a48c0d194_n.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>./flower_photos/test/15026703621_e15e9d55f0_n.jpg</td>\n",
       "      <td>15026703621_e15e9d55f0_n.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>./flower_photos/test/4522764992_e9d70b82c1_m.jpg</td>\n",
       "      <td>4522764992_e9d70b82c1_m.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>./flower_photos/test/3372748508_e5a4eacfcb_n.jpg</td>\n",
       "      <td>3372748508_e5a4eacfcb_n.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>./flower_photos/test/9617087594_ec2a9b16f6.jpg</td>\n",
       "      <td>9617087594_ec2a9b16f6.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>./flower_photos/test/10791227_7168491604.jpg</td>\n",
       "      <td>10791227_7168491604.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>./flower_photos/test/18111636378_856027a7b8_n.jpg</td>\n",
       "      <td>18111636378_856027a7b8_n.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>./flower_photos/test/6108118824_5b0231a56d.jpg</td>\n",
       "      <td>6108118824_5b0231a56d.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>./flower_photos/test/10712722853_5632165b04.jpg</td>\n",
       "      <td>10712722853_5632165b04.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>./flower_photos/test/135994133_4f306fe4bf_n.jpg</td>\n",
       "      <td>135994133_4f306fe4bf_n.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>./flower_photos/test/4610018126_21f438d2dc_m.jpg</td>\n",
       "      <td>4610018126_21f438d2dc_m.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>./flower_photos/test/5795159787_ebb51a5e75.jpg</td>\n",
       "      <td>5795159787_ebb51a5e75.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>./flower_photos/test/2498632196_e47a472d5a.jpg</td>\n",
       "      <td>2498632196_e47a472d5a.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>./flower_photos/test/122450705_9885fff3c4_n.jpg</td>\n",
       "      <td>122450705_9885fff3c4_n.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>./flower_photos/test/20481273479_d459834a3e_n.jpg</td>\n",
       "      <td>20481273479_d459834a3e_n.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>./flower_photos/test/8908062479_449200a1b4.jpg</td>\n",
       "      <td>8908062479_449200a1b4.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>./flower_photos/test/3614805920_7a6610aa4b_n.jpg</td>\n",
       "      <td>3614805920_7a6610aa4b_n.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>./flower_photos/test/8524505682_bda885af3a_n.jpg</td>\n",
       "      <td>8524505682_bda885af3a_n.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>./flower_photos/test/15760153042_a2a90e9da5_m.jpg</td>\n",
       "      <td>15760153042_a2a90e9da5_m.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>./flower_photos/test/11465213433_847c4fa261.jpg</td>\n",
       "      <td>11465213433_847c4fa261.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>./flower_photos/test/18587334446_ef1021909b_n.jpg</td>\n",
       "      <td>18587334446_ef1021909b_n.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>./flower_photos/test/19599413676_fc9ee2640e.jpg</td>\n",
       "      <td>19599413676_fc9ee2640e.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>./flower_photos/test/16018886851_c32746cb72.jpg</td>\n",
       "      <td>16018886851_c32746cb72.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>./flower_photos/test/14087792403_f34f37ba3b_m.jpg</td>\n",
       "      <td>14087792403_f34f37ba3b_m.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>./flower_photos/test/738207467_fc59cfcd9b_z.jpg</td>\n",
       "      <td>738207467_fc59cfcd9b_z.jpg</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>./flower_photos/test/20596941736_f2c5f496cf.jpg</td>\n",
       "      <td>20596941736_f2c5f496cf.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>./flower_photos/test/5419629292_2f06e4b295.jpg</td>\n",
       "      <td>5419629292_2f06e4b295.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>./flower_photos/test/130684941_d1abfa3be6_m.jpg</td>\n",
       "      <td>130684941_d1abfa3be6_m.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>./flower_photos/test/2462476884_58c617b26a.jpg</td>\n",
       "      <td>2462476884_58c617b26a.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>./flower_photos/test/15030133005_9728102622_z.jpg</td>\n",
       "      <td>15030133005_9728102622_z.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>./flower_photos/test/3045046293_57f6d52065_m.jpg</td>\n",
       "      <td>3045046293_57f6d52065_m.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>./flower_photos/test/20619292635_9857a12d54.jpg</td>\n",
       "      <td>20619292635_9857a12d54.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>./flower_photos/test/14058811536_f29cd7bd58_n.jpg</td>\n",
       "      <td>14058811536_f29cd7bd58_n.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>./flower_photos/test/5863698305_04a4277401_n.jpg</td>\n",
       "      <td>5863698305_04a4277401_n.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>./flower_photos/test/7164500544_332b75aa3b.jpg</td>\n",
       "      <td>7164500544_332b75aa3b.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>./flower_photos/test/3115889021_053f3b8e5a.jpg</td>\n",
       "      <td>3115889021_053f3b8e5a.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>./flower_photos/test/22244161124_53e457bb66_n.jpg</td>\n",
       "      <td>22244161124_53e457bb66_n.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>./flower_photos/test/3731075939_6c92d7fe68_m.jpg</td>\n",
       "      <td>3731075939_6c92d7fe68_m.jpg</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>./flower_photos/test/6208851904_9d916ebb32_n.jpg</td>\n",
       "      <td>6208851904_9d916ebb32_n.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>./flower_photos/test/799964360_7e07a227ea_n.jpg</td>\n",
       "      <td>799964360_7e07a227ea_n.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>./flower_photos/test/14674743211_f68b13f6d9.jpg</td>\n",
       "      <td>14674743211_f68b13f6d9.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>./flower_photos/test/13560152823_9da5e48c87_m.jpg</td>\n",
       "      <td>13560152823_9da5e48c87_m.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>./flower_photos/test/3530500952_9f94fb8b9c_m.jpg</td>\n",
       "      <td>3530500952_9f94fb8b9c_m.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>./flower_photos/test/16510864164_3afa8ac37f.jpg</td>\n",
       "      <td>16510864164_3afa8ac37f.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>./flower_photos/test/14003401241_543535b385.jpg</td>\n",
       "      <td>14003401241_543535b385.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>551 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             full_path  \\\n",
       "0        ./flower_photos/test/410421672_563550467c.jpg   \n",
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       "..                                                 ...   \n",
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       "\n",
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       "\n",
       "[551 rows x 3 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftest_final.to_csv('/Users/ceric/Downloads/deeplearning/project2/full_path_predictions.csv')\n",
    "dftest_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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