03_New_Time_Series_Data_Augmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "created by Ignacio Oguiza - email: oguiza@gmail.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:01:03.209484Z",
     "start_time": "2019-10-04T13:01:03.203526Z"
    },
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "utils.load_extension('collapsible_headings/main')\n",
       "utils.load_extension('hide_input/main')\n",
       "utils.load_extension('autosavetime/main')\n",
       "utils.load_extension('execute_time/ExecuteTime')\n",
       "utils.load_extension('code_prettify/code_prettify')\n",
       "utils.load_extension('scroll_down/main')\n",
       "utils.load_extension('jupyter-js-widgets/extension')\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "utils.load_extension('collapsible_headings/main')\n",
    "utils.load_extension('hide_input/main')\n",
    "utils.load_extension('autosavetime/main')\n",
    "utils.load_extension('execute_time/ExecuteTime')\n",
    "utils.load_extension('code_prettify/code_prettify')\n",
    "utils.load_extension('scroll_down/main')\n",
    "utils.load_extension('jupyter-js-widgets/extension')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New data augmentation techniques: cutout, mixup & cutmix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Purpose 😇"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As you may know, Jeremy Howard claims in his excellent fastai course that data augmentation is perhaps the most important regularization technique when training a model for Computer Vision, second only to getting more data samples (which is often costly or just impossible).\n",
    "\n",
    "During the last 2 years a number of data augmentation techniques have been developed with excellent results in vision datasets.\n",
    "\n",
    "In this notebook we'll see how you can easily apply some of this new data augmentation techniques to time series using fastai, fastai_timeseries and torchtimeseries.models library available at [timeseriesAI](https://github.com/timeseriesAI/timeseriesAI)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Import libraries 📚"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:01:03.547660Z",
     "start_time": "2019-10-04T13:01:03.215967Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:01:05.580832Z",
     "start_time": "2019-10-04T13:01:03.551178Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fastai_timeseries import *\n",
    "from torchtimeseries.models import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Prepare data 🔢"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "First we'll create a databunch for the 'OliveOil' UCR dataset. You can select any other dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:01:05.926099Z",
     "start_time": "2019-10-04T13:01:05.584351Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TSDataBunch;\n",
       "\n",
       "Train: LabelList (30 items)\n",
       "x: TimeSeriesList\n",
       "TimeSeries(ch=1, seq_len=470),TimeSeries(ch=1, seq_len=470),TimeSeries(ch=1, seq_len=470),TimeSeries(ch=1, seq_len=470),TimeSeries(ch=1, seq_len=470)\n",
       "y: CategoryList\n",
       "1,1,1,1,1\n",
       "Path: .;\n",
       "\n",
       "Valid: LabelList (30 items)\n",
       "x: TimeSeriesList\n",
       "TimeSeries(ch=1, seq_len=470),TimeSeries(ch=1, seq_len=470),TimeSeries(ch=1, seq_len=470),TimeSeries(ch=1, seq_len=470),TimeSeries(ch=1, seq_len=470)\n",
       "y: CategoryList\n",
       "1,1,1,1,1\n",
       "Path: .;\n",
       "\n",
       "Test: None"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsid = 'Beef'\n",
    "db = create_UCR_databunch(dsid)\n",
    "db"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Once the databunch's been built, we can easy visualize time series and their classes using the show_batch method.\n",
    "\n",
    "☣️ Remember that these charts represent different time series with their respective labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:01:09.114502Z",
     "start_time": "2019-10-04T13:01:05.929458Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "db.show_batch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Build learner 🏗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now I'll create a learner object. As a model I'll use the new Inceptiontime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:17:20.087652Z",
     "start_time": "2019-10-04T13:17:20.039006Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "learn = Learner(db, InceptionTime(db.features, db.c), metrics=accuracy, \n",
    "                loss_func=LabelSmoothingCrossEntropy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Train 🚵🏼‍♀️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T15:19:51.381705Z",
     "start_time": "2019-10-04T15:11:40.527184Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.697133</td>\n",
       "      <td>1.636551</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.658765</td>\n",
       "      <td>1.634878</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.628749</td>\n",
       "      <td>1.633871</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.603675</td>\n",
       "      <td>1.633338</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.581709</td>\n",
       "      <td>1.633209</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.562016</td>\n",
       "      <td>1.633017</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.543772</td>\n",
       "      <td>1.632773</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.526412</td>\n",
       "      <td>1.632591</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.509713</td>\n",
       "      <td>1.632315</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.493474</td>\n",
       "      <td>1.631614</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.477614</td>\n",
       "      <td>1.630401</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.462065</td>\n",
       "      <td>1.628628</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.446795</td>\n",
       "      <td>1.626409</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.431797</td>\n",
       "      <td>1.623053</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.417064</td>\n",
       "      <td>1.619603</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.402596</td>\n",
       "      <td>1.613142</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.388477</td>\n",
       "      <td>1.609142</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.374838</td>\n",
       "      <td>1.595912</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.361802</td>\n",
       "      <td>1.584519</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.348744</td>\n",
       "      <td>1.574727</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.335601</td>\n",
       "      <td>1.558694</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.322362</td>\n",
       "      <td>1.539030</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.309687</td>\n",
       "      <td>1.525543</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.297099</td>\n",
       "      <td>1.525444</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.287485</td>\n",
       "      <td>1.487910</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.278417</td>\n",
       "      <td>1.451224</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.267326</td>\n",
       "      <td>1.419333</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.255071</td>\n",
       "      <td>1.531612</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.245143</td>\n",
       "      <td>1.428932</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.233745</td>\n",
       "      <td>1.673071</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.222732</td>\n",
       "      <td>1.317958</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.210769</td>\n",
       "      <td>1.317483</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.197816</td>\n",
       "      <td>1.636617</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.183761</td>\n",
       "      <td>1.968882</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.169847</td>\n",
       "      <td>1.360843</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.156803</td>\n",
       "      <td>1.217057</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.142337</td>\n",
       "      <td>2.626117</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.130414</td>\n",
       "      <td>1.677359</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.122333</td>\n",
       "      <td>1.932176</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.116616</td>\n",
       "      <td>1.541641</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.105934</td>\n",
       "      <td>2.903922</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.094385</td>\n",
       "      <td>5.271531</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.082686</td>\n",
       "      <td>5.667654</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.070797</td>\n",
       "      <td>4.902615</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.058120</td>\n",
       "      <td>7.664695</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.047126</td>\n",
       "      <td>4.719625</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.040817</td>\n",
       "      <td>3.125497</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.033824</td>\n",
       "      <td>5.493695</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.025297</td>\n",
       "      <td>6.714429</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.016156</td>\n",
       "      <td>7.241136</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.004743</td>\n",
       "      <td>6.179959</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>0.993645</td>\n",
       "      <td>8.953449</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>0.982641</td>\n",
       "      <td>11.618028</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>0.972119</td>\n",
       "      <td>8.661292</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>0.961185</td>\n",
       "      <td>7.684217</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0.949132</td>\n",
       "      <td>9.823573</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>0.937367</td>\n",
       "      <td>10.694403</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>0.925187</td>\n",
       "      <td>12.210872</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>0.913146</td>\n",
       "      <td>14.403736</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>0.901192</td>\n",
       "      <td>13.310433</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>0.889542</td>\n",
       "      <td>12.824086</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>0.879126</td>\n",
       "      <td>14.573278</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>0.870193</td>\n",
       "      <td>11.141336</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>0.861951</td>\n",
       "      <td>4.968851</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>0.854350</td>\n",
       "      <td>9.333371</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0.847897</td>\n",
       "      <td>2.665716</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>0.842686</td>\n",
       "      <td>2.313952</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0.834853</td>\n",
       "      <td>3.875152</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>0.827271</td>\n",
       "      <td>2.235546</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>0.819326</td>\n",
       "      <td>2.601296</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>0.811829</td>\n",
       "      <td>3.508690</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>0.804304</td>\n",
       "      <td>4.183553</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>0.797064</td>\n",
       "      <td>3.399729</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>0.789246</td>\n",
       "      <td>2.493759</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>0.781297</td>\n",
       "      <td>1.851689</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>0.773465</td>\n",
       "      <td>1.665282</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.765998</td>\n",
       "      <td>5.534258</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.758704</td>\n",
       "      <td>1.918662</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.750972</td>\n",
       "      <td>2.003186</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.743452</td>\n",
       "      <td>1.611230</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.736011</td>\n",
       "      <td>1.628435</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.728675</td>\n",
       "      <td>1.465575</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.721576</td>\n",
       "      <td>0.969776</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.714612</td>\n",
       "      <td>0.898719</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.707672</td>\n",
       "      <td>1.032310</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.700603</td>\n",
       "      <td>1.012637</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.693883</td>\n",
       "      <td>1.637516</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.687245</td>\n",
       "      <td>1.379606</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.680597</td>\n",
       "      <td>0.982019</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.674161</td>\n",
       "      <td>1.008814</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.667775</td>\n",
       "      <td>1.266140</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.661540</td>\n",
       "      <td>1.079461</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.655445</td>\n",
       "      <td>0.770133</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.649447</td>\n",
       "      <td>0.776157</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.643603</td>\n",
       "      <td>0.780240</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.637886</td>\n",
       "      <td>0.794417</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.632302</td>\n",
       "      <td>0.794495</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.626835</td>\n",
       "      <td>0.836716</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.621506</td>\n",
       "      <td>0.952855</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.616283</td>\n",
       "      <td>1.031987</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.611199</td>\n",
       "      <td>0.992564</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>0.606229</td>\n",
       "      <td>0.990812</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>0.601378</td>\n",
       "      <td>1.038975</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>0.596639</td>\n",
       "      <td>0.925851</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>0.592018</td>\n",
       "      <td>0.839252</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>0.587503</td>\n",
       "      <td>0.846729</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>0.583092</td>\n",
       "      <td>0.876285</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>0.578791</td>\n",
       "      <td>0.867612</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>0.574593</td>\n",
       "      <td>0.865421</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>0.570497</td>\n",
       "      <td>0.861011</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>0.566498</td>\n",
       "      <td>0.860369</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>0.562593</td>\n",
       "      <td>0.856723</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>0.558781</td>\n",
       "      <td>0.857777</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>0.555063</td>\n",
       "      <td>0.861587</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>0.551431</td>\n",
       "      <td>0.864382</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>0.547886</td>\n",
       "      <td>0.866298</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>0.544424</td>\n",
       "      <td>0.866298</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>0.541043</td>\n",
       "      <td>0.876166</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>0.537743</td>\n",
       "      <td>0.877081</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>0.534521</td>\n",
       "      <td>0.870586</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>0.531374</td>\n",
       "      <td>0.871706</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>0.528300</td>\n",
       "      <td>0.868688</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>0.525299</td>\n",
       "      <td>0.864780</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>0.522367</td>\n",
       "      <td>0.863685</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>0.519504</td>\n",
       "      <td>0.860523</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>0.516706</td>\n",
       "      <td>0.858484</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>0.513973</td>\n",
       "      <td>0.856415</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>0.511304</td>\n",
       "      <td>0.855038</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>0.508696</td>\n",
       "      <td>0.854263</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>0.506147</td>\n",
       "      <td>0.853070</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>0.503657</td>\n",
       "      <td>0.851796</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>0.501223</td>\n",
       "      <td>0.852567</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>0.498845</td>\n",
       "      <td>0.849896</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>0.496521</td>\n",
       "      <td>0.847939</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>0.494250</td>\n",
       "      <td>0.847190</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>0.492030</td>\n",
       "      <td>0.846976</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>0.489860</td>\n",
       "      <td>0.848106</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>0.487739</td>\n",
       "      <td>0.849875</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>0.485665</td>\n",
       "      <td>0.849752</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>0.483638</td>\n",
       "      <td>0.850015</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>0.481656</td>\n",
       "      <td>0.851885</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>0.479719</td>\n",
       "      <td>0.855177</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>0.477824</td>\n",
       "      <td>0.856939</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>0.475971</td>\n",
       "      <td>0.856017</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>0.474160</td>\n",
       "      <td>0.854898</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>0.472389</td>\n",
       "      <td>0.854715</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>0.470656</td>\n",
       "      <td>0.854882</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>0.468962</td>\n",
       "      <td>0.854378</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>0.467305</td>\n",
       "      <td>0.853540</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0.465685</td>\n",
       "      <td>0.853437</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>0.464100</td>\n",
       "      <td>0.854188</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>0.462550</td>\n",
       "      <td>0.855247</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>0.461033</td>\n",
       "      <td>0.855636</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>0.459550</td>\n",
       "      <td>0.855190</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>0.458098</td>\n",
       "      <td>0.854677</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>0.456679</td>\n",
       "      <td>0.854630</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>0.455290</td>\n",
       "      <td>0.855154</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>0.453931</td>\n",
       "      <td>0.855961</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>0.452602</td>\n",
       "      <td>0.856542</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>0.451301</td>\n",
       "      <td>0.856716</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>0.450029</td>\n",
       "      <td>0.856649</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>0.448783</td>\n",
       "      <td>0.856666</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>0.447565</td>\n",
       "      <td>0.856919</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>0.446372</td>\n",
       "      <td>0.857375</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>0.445206</td>\n",
       "      <td>0.857843</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>0.444064</td>\n",
       "      <td>0.858171</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>0.442946</td>\n",
       "      <td>0.858309</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>0.441853</td>\n",
       "      <td>0.858343</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>0.440783</td>\n",
       "      <td>0.858372</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>0.439735</td>\n",
       "      <td>0.858487</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>0.438710</td>\n",
       "      <td>0.858700</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>0.437707</td>\n",
       "      <td>0.858980</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>0.436725</td>\n",
       "      <td>0.859262</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>0.435763</td>\n",
       "      <td>0.859469</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>0.434822</td>\n",
       "      <td>0.859581</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>0.433901</td>\n",
       "      <td>0.859597</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>0.433000</td>\n",
       "      <td>0.859544</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>0.432117</td>\n",
       "      <td>0.859459</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>0.431253</td>\n",
       "      <td>0.859385</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>0.430408</td>\n",
       "      <td>0.859341</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>0.429580</td>\n",
       "      <td>0.859343</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>0.428769</td>\n",
       "      <td>0.859393</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182</td>\n",
       "      <td>0.427976</td>\n",
       "      <td>0.859490</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183</td>\n",
       "      <td>0.427199</td>\n",
       "      <td>0.859620</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>184</td>\n",
       "      <td>0.426438</td>\n",
       "      <td>0.859767</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>0.425694</td>\n",
       "      <td>0.859917</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>0.424965</td>\n",
       "      <td>0.860061</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>0.424251</td>\n",
       "      <td>0.860193</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>0.423552</td>\n",
       "      <td>0.860311</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>0.422868</td>\n",
       "      <td>0.860409</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>0.422198</td>\n",
       "      <td>0.860496</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>0.421542</td>\n",
       "      <td>0.860570</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>0.420899</td>\n",
       "      <td>0.860630</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>0.420270</td>\n",
       "      <td>0.860679</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>0.419654</td>\n",
       "      <td>0.860717</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>195</td>\n",
       "      <td>0.419051</td>\n",
       "      <td>0.860749</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>196</td>\n",
       "      <td>0.418461</td>\n",
       "      <td>0.860777</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>197</td>\n",
       "      <td>0.417882</td>\n",
       "      <td>0.860800</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>0.417316</td>\n",
       "      <td>0.860822</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>0.416761</td>\n",
       "      <td>0.860841</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "83.3% is a pretty good result with the Beef dataset. But let's see if we can improve it even further by using data augmentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Applying data augmentation techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "In some cases, data augmentation is applied to a single time series. Changes are applied to that individual time series. One of these techniques is Cutout.\n",
    "\n",
    "More recently, new data augmentations have appeared that combine a time series with another randomly selected time series, blending both in some way. 2 important techniques applicable to time series are Mixup and CutMix.\n",
    "\n",
    "All these techniques work really well in images, but are not still often used with time series. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Data augmentation: Single Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "You'll see that applying these techniques is super easy. You only need to add the required callback."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Cutout (DeVries, 2017)\n",
    "https://arxiv.org/abs/1708.04552"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This is a single item transformation, where **a random section of a time series is is replaced by zero**. \n",
    "\n",
    "You can apply all thes techniques in 2 ways (the result is exactly the same):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:05:04.492141Z",
     "start_time": "2019-10-04T13:05:04.441980Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "learn = Learner(db, InceptionTime(db.features, db.c))\n",
    "learn.cutout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:05:04.540294Z",
     "start_time": "2019-10-04T13:05:04.497539Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "learn = Learner(db, InceptionTime(db.features, db.c)).cutout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Since you cannot see the impact of the technique, I've built a function (show_tfms) to be able to easily visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:05:05.756951Z",
     "start_time": "2019-10-04T13:05:04.542813Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = Learner(db, InceptionTime(db.features, db.c)).cutout().show_tfms();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "☣️ Remember that all these are examples of the **same time series, once cutout has been applied**. All techniques in this notebook are applied randomly on the fly, thus generating an endless amount of variations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "These techniques have a parameter that define the amount of change from the original time series. It's called **alpha**. \n",
    "\n",
    "For cutout, the default alpha is set to 1, but you can modify it up or down, depending on how much regularization you want to apply."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:05:07.177022Z",
     "start_time": "2019-10-04T13:05:05.759900Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = Learner(db, InceptionTime(db.features, db.c)).cutout(alpha=1).show_tfms();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:05:08.347012Z",
     "start_time": "2019-10-04T13:05:07.179379Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = Learner(db, InceptionTime(db.features, db.c)).cutout(alpha=.2).show_tfms();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:05:09.715400Z",
     "start_time": "2019-10-04T13:05:08.349582Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = Learner(db, InceptionTime(db.features, db.c)).cutout(alpha=2.).show_tfms();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The default value is reasonable, but feel free to modify it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Data augmentation: Multi Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "There are at least a couple of things multiTS data transforms have in common: \n",
    "\n",
    "- they combine 2 or more TS to create a new synthetic TS\n",
    "- unlike previous techniques like cutout, the entire TS provides informative datapoints. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Mixup (Zhang, 2018)\n",
    "https://arxiv.org/abs/1710.09412"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Mixup blends two time series randomly drawn from our training data. A weight λ (between .5-1) is assigned to the first sample, and 1-λ to the second one. \n",
    "Despite its simplicity, mixup allows a new state-of-the-art performance in the CIFAR-10, CIFAR- 100, and ImageNet-2012 image classification datasets, and can also improve performance in time series problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:05:10.941102Z",
     "start_time": "2019-10-04T13:05:09.718202Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = Learner(db, InceptionTime(db.features, db.c)).mixup(alpha=.4).show_tfms();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Mixup creates time series that look very 'real', based on a weighted average of 2 time series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "The parameter for mixup is called alpha, and it's default value set to .4. Usual values range between .2-.4, although you can use any number greater than 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T10:02:36.324133Z",
     "start_time": "2019-07-08T10:02:36.283230Z"
    },
    "hidden": true
   },
   "source": [
    "### Cutmix (Yun, 2019)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "https://arxiv.org/abs/1905.04899\n",
    "\n",
    "Cutmix is similar to Cutout, as a single patch is cut and pasted into a different training Time Series.\n",
    "\n",
    "CutMix consistently outperforms the state-of-the-art augmentation strategies on CIFAR and ImageNet classification tasks, as well as on the ImageNet weakly- supervised localization task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:05:12.225095Z",
     "start_time": "2019-10-04T13:05:10.943348Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = Learner(db, InceptionTime(db.features, db.c)).cutmix(alpha=1.).show_tfms();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "For cutmix the default value of alpha is also 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## How to train using data augmentation?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "It's super easy! The only thing you need to do is: \n",
    "\n",
    "1. First you will create your ImageDataBunch as you would normally do.\n",
    "2. The you will create the learner as usual, but you will add to it the selected augmentation you have selected (cutmix, mixup or cutmix). You can only select one of these new data augmentations at a time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Mixup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:25:40.779991Z",
     "start_time": "2019-10-04T13:25:40.702759Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dsis = 'Beef'\n",
    "db = create_UCR_databunch(dsid)\n",
    "learn = Learner(db, InceptionTime(db.features, db.c), metrics=accuracy, \n",
    "                loss_func=LabelSmoothingCrossEntropy()).mixup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "3. If you want to visualize the effect data augmentation before training (to adjust alpha for example), just add show_tfms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T13:05:13.492676Z",
     "start_time": "2019-10-04T13:05:12.299052Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.show_tfms();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-08T18:38:24.617897Z",
     "start_time": "2019-07-08T18:38:24.597298Z"
    },
    "hidden": true
   },
   "source": [
    "That's it!!. \n",
    "You are now ready to train with data augmentation!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T15:09:25.004119Z",
     "start_time": "2019-10-04T15:01:10.561677Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.689828</td>\n",
       "      <td>1.619471</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.673922</td>\n",
       "      <td>1.618781</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.647962</td>\n",
       "      <td>1.618125</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.631207</td>\n",
       "      <td>1.617363</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.611909</td>\n",
       "      <td>1.616646</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.595307</td>\n",
       "      <td>1.615953</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.578687</td>\n",
       "      <td>1.615269</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.563238</td>\n",
       "      <td>1.614500</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.548130</td>\n",
       "      <td>1.613729</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.534638</td>\n",
       "      <td>1.612713</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.522140</td>\n",
       "      <td>1.611550</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.512481</td>\n",
       "      <td>1.610328</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.499381</td>\n",
       "      <td>1.608953</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.486657</td>\n",
       "      <td>1.607466</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.474970</td>\n",
       "      <td>1.605166</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.464807</td>\n",
       "      <td>1.602398</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.450602</td>\n",
       "      <td>1.599006</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.441010</td>\n",
       "      <td>1.592957</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.431140</td>\n",
       "      <td>1.585209</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.420655</td>\n",
       "      <td>1.575528</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.411091</td>\n",
       "      <td>1.568540</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.401187</td>\n",
       "      <td>1.557126</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.393381</td>\n",
       "      <td>1.547969</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.386891</td>\n",
       "      <td>1.509804</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.380682</td>\n",
       "      <td>1.517822</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.372284</td>\n",
       "      <td>1.507112</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.363400</td>\n",
       "      <td>1.529526</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.356471</td>\n",
       "      <td>1.546933</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.345878</td>\n",
       "      <td>1.476422</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.338359</td>\n",
       "      <td>1.489565</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.328831</td>\n",
       "      <td>1.370115</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.319710</td>\n",
       "      <td>1.492780</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.310245</td>\n",
       "      <td>1.909312</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.304384</td>\n",
       "      <td>1.942166</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.298426</td>\n",
       "      <td>1.449292</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.296161</td>\n",
       "      <td>1.392294</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.287528</td>\n",
       "      <td>1.707830</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.279770</td>\n",
       "      <td>1.918631</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.270381</td>\n",
       "      <td>1.791980</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.259860</td>\n",
       "      <td>3.575068</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.254035</td>\n",
       "      <td>2.539945</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.245580</td>\n",
       "      <td>1.968613</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.237224</td>\n",
       "      <td>2.108842</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.228153</td>\n",
       "      <td>2.946331</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.220387</td>\n",
       "      <td>3.205979</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.212358</td>\n",
       "      <td>3.939463</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.208177</td>\n",
       "      <td>5.770495</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.201450</td>\n",
       "      <td>3.143615</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.195719</td>\n",
       "      <td>2.494654</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.189962</td>\n",
       "      <td>4.610262</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.184943</td>\n",
       "      <td>3.094051</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.178857</td>\n",
       "      <td>4.785711</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.173128</td>\n",
       "      <td>5.147786</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.166027</td>\n",
       "      <td>4.195990</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.158504</td>\n",
       "      <td>4.618266</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.154156</td>\n",
       "      <td>5.224677</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1.146860</td>\n",
       "      <td>5.487449</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>1.143896</td>\n",
       "      <td>3.752536</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1.139026</td>\n",
       "      <td>3.471848</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>1.132638</td>\n",
       "      <td>3.616133</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.127449</td>\n",
       "      <td>3.148611</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>1.119508</td>\n",
       "      <td>2.473933</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>1.111254</td>\n",
       "      <td>2.150029</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>1.103141</td>\n",
       "      <td>1.433178</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>1.095411</td>\n",
       "      <td>1.055387</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>1.088881</td>\n",
       "      <td>1.327046</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>1.081982</td>\n",
       "      <td>1.605395</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>1.079995</td>\n",
       "      <td>2.084538</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>1.077057</td>\n",
       "      <td>2.170066</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>1.073893</td>\n",
       "      <td>2.078325</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>1.070387</td>\n",
       "      <td>5.268016</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>1.065864</td>\n",
       "      <td>10.335102</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>1.064703</td>\n",
       "      <td>4.640256</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>1.061717</td>\n",
       "      <td>1.847973</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>1.060507</td>\n",
       "      <td>1.547381</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>1.057674</td>\n",
       "      <td>1.439578</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>1.055321</td>\n",
       "      <td>1.494718</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>1.054715</td>\n",
       "      <td>2.734326</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>1.049378</td>\n",
       "      <td>1.831880</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>1.048055</td>\n",
       "      <td>1.342813</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>1.045389</td>\n",
       "      <td>1.058503</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>1.038875</td>\n",
       "      <td>2.418780</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>1.036652</td>\n",
       "      <td>3.206536</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>1.034173</td>\n",
       "      <td>2.932166</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>1.030047</td>\n",
       "      <td>2.636901</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>1.027156</td>\n",
       "      <td>2.956616</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>1.020752</td>\n",
       "      <td>3.017718</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>1.016966</td>\n",
       "      <td>2.859704</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>1.011848</td>\n",
       "      <td>2.513342</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>1.007957</td>\n",
       "      <td>2.365700</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>1.003033</td>\n",
       "      <td>2.211102</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>1.000160</td>\n",
       "      <td>2.693544</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.995621</td>\n",
       "      <td>2.895486</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.995153</td>\n",
       "      <td>2.610945</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.991598</td>\n",
       "      <td>2.286873</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.988696</td>\n",
       "      <td>2.728762</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.987154</td>\n",
       "      <td>2.344796</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.983275</td>\n",
       "      <td>3.232719</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.980970</td>\n",
       "      <td>1.335799</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.977147</td>\n",
       "      <td>1.167713</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.972813</td>\n",
       "      <td>1.170871</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>0.971278</td>\n",
       "      <td>1.362768</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>0.969613</td>\n",
       "      <td>1.461215</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>0.965762</td>\n",
       "      <td>1.476665</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>0.960326</td>\n",
       "      <td>2.355848</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>0.955352</td>\n",
       "      <td>2.981581</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>0.949955</td>\n",
       "      <td>3.107812</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>0.946355</td>\n",
       "      <td>2.925362</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>0.940663</td>\n",
       "      <td>2.415063</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>0.938540</td>\n",
       "      <td>1.688057</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>0.937894</td>\n",
       "      <td>1.362045</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>0.933151</td>\n",
       "      <td>1.243008</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>0.932395</td>\n",
       "      <td>1.176888</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>0.928695</td>\n",
       "      <td>1.349458</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>0.926381</td>\n",
       "      <td>1.627350</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>0.921778</td>\n",
       "      <td>1.600521</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>0.917558</td>\n",
       "      <td>1.366820</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>0.912869</td>\n",
       "      <td>1.430818</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>0.909506</td>\n",
       "      <td>1.488323</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>0.908174</td>\n",
       "      <td>1.238361</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>0.906753</td>\n",
       "      <td>0.972730</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>0.902651</td>\n",
       "      <td>0.901888</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>0.899289</td>\n",
       "      <td>0.816972</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>0.894561</td>\n",
       "      <td>0.944673</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>0.892816</td>\n",
       "      <td>0.783612</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>0.887467</td>\n",
       "      <td>0.708474</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>0.885093</td>\n",
       "      <td>0.726909</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>0.881646</td>\n",
       "      <td>0.995866</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>0.875917</td>\n",
       "      <td>1.191868</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>0.871996</td>\n",
       "      <td>1.267549</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>0.869246</td>\n",
       "      <td>1.369829</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>0.867821</td>\n",
       "      <td>1.436767</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>0.864062</td>\n",
       "      <td>1.160231</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>0.859628</td>\n",
       "      <td>1.105737</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>0.859078</td>\n",
       "      <td>0.851334</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>0.858720</td>\n",
       "      <td>0.778529</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>0.856945</td>\n",
       "      <td>1.147635</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>0.854129</td>\n",
       "      <td>1.129824</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>0.852220</td>\n",
       "      <td>0.979698</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>0.847723</td>\n",
       "      <td>0.809028</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>0.843133</td>\n",
       "      <td>0.673137</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>0.839500</td>\n",
       "      <td>0.736410</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>0.835289</td>\n",
       "      <td>0.999134</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>0.833026</td>\n",
       "      <td>1.256068</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>0.833735</td>\n",
       "      <td>1.237204</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>0.834965</td>\n",
       "      <td>1.282824</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>0.833917</td>\n",
       "      <td>1.396549</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>0.828235</td>\n",
       "      <td>1.317177</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>0.827091</td>\n",
       "      <td>1.111997</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0.824293</td>\n",
       "      <td>0.851436</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>0.823835</td>\n",
       "      <td>0.715306</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>0.822530</td>\n",
       "      <td>0.689193</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>0.817367</td>\n",
       "      <td>0.732036</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>0.817202</td>\n",
       "      <td>0.856252</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>0.816193</td>\n",
       "      <td>0.948537</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>0.813613</td>\n",
       "      <td>1.066483</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>0.811144</td>\n",
       "      <td>1.068854</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>0.810831</td>\n",
       "      <td>1.071055</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>0.808077</td>\n",
       "      <td>0.977296</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>0.804630</td>\n",
       "      <td>0.838821</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>0.802261</td>\n",
       "      <td>0.772696</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>0.802727</td>\n",
       "      <td>0.791702</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>0.798813</td>\n",
       "      <td>0.835375</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>0.797573</td>\n",
       "      <td>0.865562</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>0.798201</td>\n",
       "      <td>0.891660</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>0.795692</td>\n",
       "      <td>0.892899</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>0.793362</td>\n",
       "      <td>0.867864</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>0.792883</td>\n",
       "      <td>0.810626</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>0.791444</td>\n",
       "      <td>0.769112</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>0.788628</td>\n",
       "      <td>0.742704</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>0.787844</td>\n",
       "      <td>0.720856</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>0.784512</td>\n",
       "      <td>0.715271</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>0.782812</td>\n",
       "      <td>0.724146</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>0.780747</td>\n",
       "      <td>0.742088</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>0.779653</td>\n",
       "      <td>0.765256</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>0.777027</td>\n",
       "      <td>0.789565</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>0.777006</td>\n",
       "      <td>0.810619</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>0.777498</td>\n",
       "      <td>0.812250</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>0.774280</td>\n",
       "      <td>0.812091</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>0.774260</td>\n",
       "      <td>0.810058</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>0.773745</td>\n",
       "      <td>0.804928</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>0.771602</td>\n",
       "      <td>0.797569</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182</td>\n",
       "      <td>0.770738</td>\n",
       "      <td>0.778828</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183</td>\n",
       "      <td>0.769796</td>\n",
       "      <td>0.761000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>184</td>\n",
       "      <td>0.769606</td>\n",
       "      <td>0.746166</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>0.769999</td>\n",
       "      <td>0.735135</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>0.769042</td>\n",
       "      <td>0.724371</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>0.765944</td>\n",
       "      <td>0.714545</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>0.766425</td>\n",
       "      <td>0.709452</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>0.765466</td>\n",
       "      <td>0.703008</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>0.761368</td>\n",
       "      <td>0.699016</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>0.759790</td>\n",
       "      <td>0.696969</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>0.758059</td>\n",
       "      <td>0.694304</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>0.756838</td>\n",
       "      <td>0.691365</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>0.756011</td>\n",
       "      <td>0.691939</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>195</td>\n",
       "      <td>0.755389</td>\n",
       "      <td>0.692854</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>196</td>\n",
       "      <td>0.754549</td>\n",
       "      <td>0.691379</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>197</td>\n",
       "      <td>0.754667</td>\n",
       "      <td>0.690486</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>0.754686</td>\n",
       "      <td>0.692739</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>0.753276</td>\n",
       "      <td>0.694158</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "☣️ I've built a chart to compare performance with and without mixup. \n",
    "As you can see, and this has occurred in many experiments I've done, it takes longer to get a high level of performance with mixup, but then it tends to keep growing more than without mixup. \n",
    "This is something to take into account when designing your experiments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T15:49:50.275328Z",
     "start_time": "2019-10-04T15:49:50.220838Z"
    },
    "hidden": true
   },
   "source": [
    "<img src=\"./images/beef_mixup.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Cutmix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T17:06:15.191324Z",
     "start_time": "2019-10-04T16:58:05.162269Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.706453</td>\n",
       "      <td>1.624805</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.685710</td>\n",
       "      <td>1.624133</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.661669</td>\n",
       "      <td>1.623917</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.638445</td>\n",
       "      <td>1.623724</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.624182</td>\n",
       "      <td>1.623581</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.614200</td>\n",
       "      <td>1.623360</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.604189</td>\n",
       "      <td>1.623062</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.591671</td>\n",
       "      <td>1.622744</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.579674</td>\n",
       "      <td>1.622297</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.569131</td>\n",
       "      <td>1.621474</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.561477</td>\n",
       "      <td>1.620105</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.550535</td>\n",
       "      <td>1.618287</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.537226</td>\n",
       "      <td>1.616055</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.533442</td>\n",
       "      <td>1.613569</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.529642</td>\n",
       "      <td>1.610154</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.526031</td>\n",
       "      <td>1.606566</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.516714</td>\n",
       "      <td>1.601131</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.509571</td>\n",
       "      <td>1.593389</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.506086</td>\n",
       "      <td>1.585316</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.502734</td>\n",
       "      <td>1.573562</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.499156</td>\n",
       "      <td>1.554533</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.488737</td>\n",
       "      <td>1.531936</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.484465</td>\n",
       "      <td>1.516183</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.481679</td>\n",
       "      <td>1.487968</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.480139</td>\n",
       "      <td>1.481755</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.474577</td>\n",
       "      <td>1.457397</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.471540</td>\n",
       "      <td>1.453811</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.470596</td>\n",
       "      <td>1.458764</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.469030</td>\n",
       "      <td>1.463857</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.466600</td>\n",
       "      <td>1.523658</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.464954</td>\n",
       "      <td>1.616127</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.463124</td>\n",
       "      <td>1.639686</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.458457</td>\n",
       "      <td>1.663750</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.452542</td>\n",
       "      <td>1.680489</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.448779</td>\n",
       "      <td>1.987832</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.443480</td>\n",
       "      <td>1.724338</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.439493</td>\n",
       "      <td>2.210143</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.436260</td>\n",
       "      <td>2.561839</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.435892</td>\n",
       "      <td>1.457669</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.433157</td>\n",
       "      <td>1.703312</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.431497</td>\n",
       "      <td>2.224642</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.430971</td>\n",
       "      <td>2.875991</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.428263</td>\n",
       "      <td>2.325544</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.427699</td>\n",
       "      <td>1.876932</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.422279</td>\n",
       "      <td>1.727904</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.418207</td>\n",
       "      <td>1.762134</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.416281</td>\n",
       "      <td>1.538334</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.415849</td>\n",
       "      <td>1.947046</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.412131</td>\n",
       "      <td>1.897876</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.409524</td>\n",
       "      <td>2.145555</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.404963</td>\n",
       "      <td>6.031844</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.396030</td>\n",
       "      <td>6.310513</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.396604</td>\n",
       "      <td>5.209888</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.394261</td>\n",
       "      <td>4.222475</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.391686</td>\n",
       "      <td>2.320999</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.388773</td>\n",
       "      <td>1.465874</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1.386416</td>\n",
       "      <td>2.058067</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>1.386596</td>\n",
       "      <td>3.500876</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1.384054</td>\n",
       "      <td>2.610673</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>1.384070</td>\n",
       "      <td>2.270039</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.383814</td>\n",
       "      <td>1.708934</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>1.380550</td>\n",
       "      <td>1.421436</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>1.382743</td>\n",
       "      <td>1.412282</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>1.384651</td>\n",
       "      <td>2.413202</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>1.380379</td>\n",
       "      <td>2.628878</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>1.380742</td>\n",
       "      <td>2.428576</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>1.378306</td>\n",
       "      <td>1.427382</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>1.377688</td>\n",
       "      <td>1.407880</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>1.377784</td>\n",
       "      <td>1.648727</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>1.374915</td>\n",
       "      <td>1.851315</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>1.372057</td>\n",
       "      <td>1.959223</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>1.367003</td>\n",
       "      <td>1.710080</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>1.366259</td>\n",
       "      <td>1.444495</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>1.366753</td>\n",
       "      <td>1.294768</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>1.362236</td>\n",
       "      <td>1.499587</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>1.359724</td>\n",
       "      <td>2.356512</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>1.359129</td>\n",
       "      <td>2.694739</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>1.358984</td>\n",
       "      <td>2.540057</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>1.357662</td>\n",
       "      <td>2.659967</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>1.360360</td>\n",
       "      <td>2.046504</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>1.356875</td>\n",
       "      <td>1.603783</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>1.356482</td>\n",
       "      <td>1.273809</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>1.354857</td>\n",
       "      <td>1.436357</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>1.352965</td>\n",
       "      <td>1.385521</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>1.348206</td>\n",
       "      <td>1.277132</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>1.347547</td>\n",
       "      <td>1.401034</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>1.346372</td>\n",
       "      <td>1.559262</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>1.344510</td>\n",
       "      <td>1.635159</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>1.344424</td>\n",
       "      <td>1.650026</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>1.342335</td>\n",
       "      <td>1.612467</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>1.341733</td>\n",
       "      <td>1.306708</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>1.338287</td>\n",
       "      <td>1.169277</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>1.337811</td>\n",
       "      <td>1.212823</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>1.337693</td>\n",
       "      <td>1.435008</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>1.331457</td>\n",
       "      <td>1.337413</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>1.331445</td>\n",
       "      <td>1.105399</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>1.331121</td>\n",
       "      <td>1.171597</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>1.324570</td>\n",
       "      <td>1.264037</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>1.322947</td>\n",
       "      <td>1.286684</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>1.323301</td>\n",
       "      <td>1.347077</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>1.321393</td>\n",
       "      <td>1.297375</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>1.319361</td>\n",
       "      <td>1.191885</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>1.315395</td>\n",
       "      <td>1.189938</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>1.315025</td>\n",
       "      <td>1.259681</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>1.308935</td>\n",
       "      <td>1.349674</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>1.305654</td>\n",
       "      <td>1.378004</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>1.305785</td>\n",
       "      <td>1.346191</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>1.305715</td>\n",
       "      <td>1.356849</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>1.304183</td>\n",
       "      <td>1.541104</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>1.302752</td>\n",
       "      <td>1.282268</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>1.303141</td>\n",
       "      <td>1.279333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>1.300831</td>\n",
       "      <td>1.186200</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>1.300369</td>\n",
       "      <td>1.150524</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>1.304650</td>\n",
       "      <td>1.354581</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>1.303918</td>\n",
       "      <td>1.443647</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>1.302446</td>\n",
       "      <td>1.253269</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>1.299625</td>\n",
       "      <td>1.254723</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>1.297959</td>\n",
       "      <td>1.355177</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>1.295360</td>\n",
       "      <td>1.413822</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>1.294235</td>\n",
       "      <td>1.272771</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>1.294245</td>\n",
       "      <td>1.135344</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>1.290968</td>\n",
       "      <td>1.179739</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>1.291180</td>\n",
       "      <td>1.224161</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>1.288947</td>\n",
       "      <td>1.178296</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>1.286780</td>\n",
       "      <td>1.094188</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>1.278990</td>\n",
       "      <td>1.079829</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>1.280690</td>\n",
       "      <td>1.146522</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>1.277659</td>\n",
       "      <td>1.112404</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>1.275256</td>\n",
       "      <td>1.077389</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>1.274448</td>\n",
       "      <td>1.371475</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>1.273802</td>\n",
       "      <td>1.661546</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>1.275651</td>\n",
       "      <td>1.745858</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>1.273929</td>\n",
       "      <td>1.663937</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>1.270650</td>\n",
       "      <td>1.477462</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>1.269193</td>\n",
       "      <td>1.260925</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>1.267992</td>\n",
       "      <td>1.182368</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>1.265650</td>\n",
       "      <td>1.268212</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>1.263543</td>\n",
       "      <td>1.349007</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>1.262578</td>\n",
       "      <td>1.366768</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>1.260029</td>\n",
       "      <td>1.291980</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>1.258410</td>\n",
       "      <td>1.213087</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>1.260078</td>\n",
       "      <td>1.172351</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>1.258358</td>\n",
       "      <td>1.161846</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>1.252187</td>\n",
       "      <td>1.153422</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>1.251021</td>\n",
       "      <td>1.143518</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>1.251198</td>\n",
       "      <td>1.133091</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>1.249392</td>\n",
       "      <td>1.119584</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>1.248203</td>\n",
       "      <td>1.107645</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>1.247276</td>\n",
       "      <td>1.119978</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>1.243595</td>\n",
       "      <td>1.146225</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>1.241543</td>\n",
       "      <td>1.176757</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>1.238316</td>\n",
       "      <td>1.200777</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>1.237871</td>\n",
       "      <td>1.213209</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>1.235683</td>\n",
       "      <td>1.203346</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>1.233224</td>\n",
       "      <td>1.185343</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>1.232618</td>\n",
       "      <td>1.150263</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>1.233111</td>\n",
       "      <td>1.124269</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>1.232996</td>\n",
       "      <td>1.082658</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>1.231997</td>\n",
       "      <td>1.048401</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>1.232146</td>\n",
       "      <td>1.027407</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>1.232651</td>\n",
       "      <td>1.024449</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>1.228856</td>\n",
       "      <td>1.025052</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>1.230592</td>\n",
       "      <td>1.037613</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>1.230132</td>\n",
       "      <td>1.037835</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>1.226062</td>\n",
       "      <td>1.028810</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>1.220277</td>\n",
       "      <td>1.017511</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>1.219127</td>\n",
       "      <td>1.005891</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>1.217834</td>\n",
       "      <td>0.992963</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>1.215868</td>\n",
       "      <td>0.988810</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>1.210998</td>\n",
       "      <td>0.987222</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>1.205445</td>\n",
       "      <td>0.989531</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>1.202443</td>\n",
       "      <td>0.994882</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>1.200605</td>\n",
       "      <td>0.996973</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>1.199470</td>\n",
       "      <td>0.996149</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>1.196583</td>\n",
       "      <td>0.994541</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>1.195204</td>\n",
       "      <td>0.992158</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>1.194456</td>\n",
       "      <td>0.990747</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>1.192639</td>\n",
       "      <td>0.986806</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>1.189554</td>\n",
       "      <td>0.983644</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>1.188267</td>\n",
       "      <td>0.980869</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>1.185815</td>\n",
       "      <td>0.978657</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>1.185371</td>\n",
       "      <td>0.974919</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182</td>\n",
       "      <td>1.183739</td>\n",
       "      <td>0.973566</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183</td>\n",
       "      <td>1.179749</td>\n",
       "      <td>0.973964</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>184</td>\n",
       "      <td>1.177038</td>\n",
       "      <td>0.974452</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>1.167234</td>\n",
       "      <td>0.976603</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>1.166255</td>\n",
       "      <td>0.980606</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>1.164219</td>\n",
       "      <td>0.982645</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>1.161877</td>\n",
       "      <td>0.983812</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>1.160285</td>\n",
       "      <td>0.987663</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>1.158639</td>\n",
       "      <td>0.989382</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>1.157610</td>\n",
       "      <td>0.992286</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>1.156571</td>\n",
       "      <td>0.991146</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>1.157177</td>\n",
       "      <td>0.990459</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>1.159675</td>\n",
       "      <td>0.990053</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>195</td>\n",
       "      <td>1.161296</td>\n",
       "      <td>0.990250</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>196</td>\n",
       "      <td>1.160810</td>\n",
       "      <td>0.989652</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>197</td>\n",
       "      <td>1.161151</td>\n",
       "      <td>0.988033</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>1.160224</td>\n",
       "      <td>0.986137</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>1.159139</td>\n",
       "      <td>0.985020</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = Learner(db, InceptionTime(db.features, db.c), metrics=accuracy, \n",
    "                loss_func=LabelSmoothingCrossEntropy()).cutmix()\n",
    "learn.fit_one_cycle(200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Cutout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T17:14:58.872555Z",
     "start_time": "2019-10-04T17:06:54.019636Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.660396</td>\n",
       "      <td>1.620012</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.637469</td>\n",
       "      <td>1.619628</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.631980</td>\n",
       "      <td>1.619524</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.618567</td>\n",
       "      <td>1.619128</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.604060</td>\n",
       "      <td>1.618873</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.591112</td>\n",
       "      <td>1.618706</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.577234</td>\n",
       "      <td>1.618439</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.566691</td>\n",
       "      <td>1.618009</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.574861</td>\n",
       "      <td>1.617688</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.565786</td>\n",
       "      <td>1.617103</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.553699</td>\n",
       "      <td>1.616126</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.540023</td>\n",
       "      <td>1.614528</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.527577</td>\n",
       "      <td>1.612250</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.518896</td>\n",
       "      <td>1.608595</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.507092</td>\n",
       "      <td>1.604372</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.501131</td>\n",
       "      <td>1.597838</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.504705</td>\n",
       "      <td>1.588038</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.493358</td>\n",
       "      <td>1.578237</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.483621</td>\n",
       "      <td>1.567515</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.474008</td>\n",
       "      <td>1.555968</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.465212</td>\n",
       "      <td>1.544741</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.454736</td>\n",
       "      <td>1.529409</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.443652</td>\n",
       "      <td>1.509047</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.434206</td>\n",
       "      <td>1.488219</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.424903</td>\n",
       "      <td>1.487186</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.414110</td>\n",
       "      <td>1.454912</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.403112</td>\n",
       "      <td>1.411377</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.400361</td>\n",
       "      <td>1.454942</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.394396</td>\n",
       "      <td>2.853700</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.386278</td>\n",
       "      <td>3.738343</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.380608</td>\n",
       "      <td>3.026152</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.375208</td>\n",
       "      <td>2.086259</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.372596</td>\n",
       "      <td>2.029034</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.368522</td>\n",
       "      <td>1.776222</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.364299</td>\n",
       "      <td>1.564647</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.356729</td>\n",
       "      <td>1.687982</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.349027</td>\n",
       "      <td>1.828559</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.341715</td>\n",
       "      <td>1.832533</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.331327</td>\n",
       "      <td>1.929781</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.324214</td>\n",
       "      <td>1.609313</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.316651</td>\n",
       "      <td>3.501971</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.309119</td>\n",
       "      <td>4.311689</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.313026</td>\n",
       "      <td>13.606978</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.311430</td>\n",
       "      <td>9.567814</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.308879</td>\n",
       "      <td>7.534220</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.306159</td>\n",
       "      <td>3.707384</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.301580</td>\n",
       "      <td>2.191738</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.295914</td>\n",
       "      <td>2.324064</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.307469</td>\n",
       "      <td>9.194708</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.302346</td>\n",
       "      <td>4.097297</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.296714</td>\n",
       "      <td>3.400907</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.291286</td>\n",
       "      <td>4.134081</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.292245</td>\n",
       "      <td>9.854806</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.291874</td>\n",
       "      <td>32.586937</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.288160</td>\n",
       "      <td>29.200527</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.284828</td>\n",
       "      <td>16.745173</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1.283797</td>\n",
       "      <td>11.435283</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>1.277688</td>\n",
       "      <td>9.402293</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1.272753</td>\n",
       "      <td>7.304148</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>1.266980</td>\n",
       "      <td>5.996444</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.264276</td>\n",
       "      <td>6.278485</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>1.258912</td>\n",
       "      <td>11.868817</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>1.253075</td>\n",
       "      <td>9.906275</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>1.252298</td>\n",
       "      <td>8.497372</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>1.248089</td>\n",
       "      <td>8.165295</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>1.244134</td>\n",
       "      <td>5.703846</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>1.238663</td>\n",
       "      <td>3.617159</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>1.232986</td>\n",
       "      <td>2.547888</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>1.228218</td>\n",
       "      <td>2.249779</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>1.234005</td>\n",
       "      <td>1.480027</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>1.244705</td>\n",
       "      <td>5.166591</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>1.243025</td>\n",
       "      <td>9.313371</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>1.242977</td>\n",
       "      <td>9.378866</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>1.242423</td>\n",
       "      <td>7.334600</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>1.243052</td>\n",
       "      <td>6.318696</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>1.247443</td>\n",
       "      <td>5.345894</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>1.245416</td>\n",
       "      <td>4.845418</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>1.244565</td>\n",
       "      <td>5.320508</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>1.241501</td>\n",
       "      <td>3.297099</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>1.236092</td>\n",
       "      <td>3.492207</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>1.233410</td>\n",
       "      <td>3.885850</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>1.227951</td>\n",
       "      <td>3.243068</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>1.225131</td>\n",
       "      <td>1.910741</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>1.223312</td>\n",
       "      <td>1.487790</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>1.216726</td>\n",
       "      <td>1.447462</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>1.220145</td>\n",
       "      <td>1.574987</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>1.218359</td>\n",
       "      <td>2.194073</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>1.215006</td>\n",
       "      <td>2.299147</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>1.210736</td>\n",
       "      <td>2.274392</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>1.207281</td>\n",
       "      <td>2.524296</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>1.205115</td>\n",
       "      <td>2.238126</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>1.204487</td>\n",
       "      <td>2.013067</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>1.199850</td>\n",
       "      <td>2.660136</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>1.196869</td>\n",
       "      <td>3.244203</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>1.194110</td>\n",
       "      <td>3.232043</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>1.192502</td>\n",
       "      <td>2.691142</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>1.195390</td>\n",
       "      <td>2.010396</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>1.190254</td>\n",
       "      <td>1.854620</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>1.184024</td>\n",
       "      <td>1.996308</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>1.177545</td>\n",
       "      <td>1.911293</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>1.174493</td>\n",
       "      <td>1.540360</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>1.167482</td>\n",
       "      <td>1.370175</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>1.159704</td>\n",
       "      <td>1.308586</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>1.153548</td>\n",
       "      <td>1.548130</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>1.146871</td>\n",
       "      <td>1.348858</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>1.146403</td>\n",
       "      <td>1.077422</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>1.141683</td>\n",
       "      <td>1.196881</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>1.135765</td>\n",
       "      <td>1.291074</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>1.129827</td>\n",
       "      <td>1.262042</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>1.123418</td>\n",
       "      <td>1.167186</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>1.116877</td>\n",
       "      <td>1.232698</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>1.113724</td>\n",
       "      <td>1.866791</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>1.111017</td>\n",
       "      <td>2.025032</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>1.109310</td>\n",
       "      <td>1.887846</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>1.102472</td>\n",
       "      <td>1.619525</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>1.095449</td>\n",
       "      <td>1.380898</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>1.090489</td>\n",
       "      <td>1.227257</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>1.085784</td>\n",
       "      <td>1.403344</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>1.078969</td>\n",
       "      <td>1.733581</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>1.074235</td>\n",
       "      <td>1.987759</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>1.083529</td>\n",
       "      <td>1.523327</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>1.083109</td>\n",
       "      <td>1.330128</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>1.078421</td>\n",
       "      <td>1.807096</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>1.077411</td>\n",
       "      <td>2.611065</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>1.076933</td>\n",
       "      <td>3.256675</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>1.078660</td>\n",
       "      <td>3.346977</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>1.073926</td>\n",
       "      <td>2.891910</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>1.072688</td>\n",
       "      <td>2.565595</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>1.067860</td>\n",
       "      <td>2.095694</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>1.068164</td>\n",
       "      <td>1.778149</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>1.066739</td>\n",
       "      <td>1.461159</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>1.063635</td>\n",
       "      <td>1.250543</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>1.061969</td>\n",
       "      <td>1.226142</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>1.057962</td>\n",
       "      <td>1.190449</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>1.051249</td>\n",
       "      <td>1.223222</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>1.046239</td>\n",
       "      <td>1.219140</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>1.043587</td>\n",
       "      <td>1.158116</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>1.037572</td>\n",
       "      <td>1.107074</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>1.034447</td>\n",
       "      <td>1.136540</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>1.027728</td>\n",
       "      <td>1.185949</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>1.027412</td>\n",
       "      <td>1.308161</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>1.021058</td>\n",
       "      <td>1.282450</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>1.017411</td>\n",
       "      <td>1.306550</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>1.016686</td>\n",
       "      <td>1.263149</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>1.010793</td>\n",
       "      <td>1.198263</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>1.008476</td>\n",
       "      <td>1.317031</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>1.005065</td>\n",
       "      <td>1.481030</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>0.999182</td>\n",
       "      <td>1.471093</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>0.997035</td>\n",
       "      <td>1.308852</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0.994885</td>\n",
       "      <td>1.158710</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>0.988591</td>\n",
       "      <td>1.089001</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>0.983139</td>\n",
       "      <td>1.057717</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>0.980064</td>\n",
       "      <td>1.021482</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>0.976560</td>\n",
       "      <td>1.005424</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>0.974256</td>\n",
       "      <td>1.056330</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>0.970273</td>\n",
       "      <td>1.198120</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>0.965092</td>\n",
       "      <td>1.395134</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>0.962309</td>\n",
       "      <td>1.325926</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>0.958749</td>\n",
       "      <td>1.232951</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>0.953150</td>\n",
       "      <td>1.236645</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>0.948714</td>\n",
       "      <td>1.141429</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>0.943281</td>\n",
       "      <td>1.050981</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>0.942111</td>\n",
       "      <td>1.027893</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>0.935896</td>\n",
       "      <td>1.021700</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>0.934182</td>\n",
       "      <td>1.058105</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>0.931000</td>\n",
       "      <td>1.137445</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>0.934147</td>\n",
       "      <td>1.215132</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>0.930111</td>\n",
       "      <td>1.241952</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>0.926065</td>\n",
       "      <td>1.256834</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>0.920515</td>\n",
       "      <td>1.266970</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>0.916091</td>\n",
       "      <td>1.295894</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>0.916563</td>\n",
       "      <td>1.379379</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>0.913004</td>\n",
       "      <td>1.357460</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>0.909355</td>\n",
       "      <td>1.322893</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>0.907756</td>\n",
       "      <td>1.289818</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>0.905712</td>\n",
       "      <td>1.310621</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>0.906727</td>\n",
       "      <td>1.289430</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>0.906684</td>\n",
       "      <td>1.339022</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>0.906677</td>\n",
       "      <td>1.321404</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>0.903140</td>\n",
       "      <td>1.323079</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>0.899265</td>\n",
       "      <td>1.351478</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>0.895234</td>\n",
       "      <td>1.308878</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182</td>\n",
       "      <td>0.894889</td>\n",
       "      <td>1.251764</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183</td>\n",
       "      <td>0.893129</td>\n",
       "      <td>1.269285</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>184</td>\n",
       "      <td>0.887717</td>\n",
       "      <td>1.244808</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>0.884424</td>\n",
       "      <td>1.245035</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>0.888531</td>\n",
       "      <td>1.224573</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>0.885721</td>\n",
       "      <td>1.242810</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>0.883915</td>\n",
       "      <td>1.238724</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>0.880226</td>\n",
       "      <td>1.209234</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>0.877577</td>\n",
       "      <td>1.236441</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>0.874377</td>\n",
       "      <td>1.232569</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>0.871007</td>\n",
       "      <td>1.249061</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>0.873851</td>\n",
       "      <td>1.186095</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>0.869425</td>\n",
       "      <td>1.165819</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>195</td>\n",
       "      <td>0.865002</td>\n",
       "      <td>1.161543</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>196</td>\n",
       "      <td>0.863245</td>\n",
       "      <td>1.160168</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>197</td>\n",
       "      <td>0.861162</td>\n",
       "      <td>1.164579</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>0.859056</td>\n",
       "      <td>1.186687</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>0.862104</td>\n",
       "      <td>1.236917</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = Learner(db, InceptionTime(db.features, db.c), metrics=accuracy, \n",
    "                loss_func=LabelSmoothingCrossEntropy()).cutout()\n",
    "learn.fit_one_cycle(200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Scheduled data transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "As a bonus, for those of you who enjoy more complex approaches, I've created a function that will allow you to automatically adjust the value of the alpha parameter during training. Let's see how it works.\n",
    "\n",
    "☣️ Please, bear in mind that the minimum value of alpha for mixup is > 0 (this is a current fastai constraint). So if you want to start from 0, you can use something like .001 instead. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T14:01:31.542666Z",
     "start_time": "2019-10-04T14:01:29.353675Z"
    },
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " alpha between 0.001 and 1.0 in iters 0.00 to 0.70\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='0' class='' max='200', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      0.00% [0/200 00:00<00:00]\n",
       "    </div>\n",
       "    \n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='0' class='progress-bar-interrupted' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      Interrupted\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = Learner(db, InceptionTime(db.features, db.c), metrics=accuracy,\n",
    "               loss_func=LabelSmoothingCrossEntropy())\n",
    "\n",
    "tfm_fn = mixup\n",
    "sch_param='alpha'\n",
    "sch_val = (.001, 1.)              # values of parameter alpha (initial, final)\n",
    "sch_iter = (0., .7)               # percent of training epochs (start, end)\n",
    "sch_func = partial(annealing_cos) # annealing_cos, None = annealing_linear, cosine_annealing\n",
    "plot = True\n",
    "test = True                       # set to True for adjusting the values. When ready to train set to False\n",
    "sch_tfm_cb = partial(TfmScheduler, tfm_fn=tfm_fn, sch_param=sch_param, sch_val=sch_val, \n",
    "                      sch_iter=sch_iter, sch_func=sch_func, plot=plot, test=test)\n",
    "learn.callback_fns.append(sch_tfm_cb)\n",
    "\n",
    "learn.fit_one_cycle(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-04T15:35:35.032122Z",
     "start_time": "2019-10-04T15:27:19.765842Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " alpha between 0.001 and 1.0 in iters 0.00 to 0.70\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.626387</td>\n",
       "      <td>1.627592</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.601632</td>\n",
       "      <td>1.626852</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.580567</td>\n",
       "      <td>1.626486</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.561646</td>\n",
       "      <td>1.626253</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.543889</td>\n",
       "      <td>1.625805</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.526808</td>\n",
       "      <td>1.625060</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.510128</td>\n",
       "      <td>1.624043</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.493622</td>\n",
       "      <td>1.622928</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.477247</td>\n",
       "      <td>1.621706</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.461012</td>\n",
       "      <td>1.620356</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.444964</td>\n",
       "      <td>1.618782</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.429263</td>\n",
       "      <td>1.616995</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.415805</td>\n",
       "      <td>1.614870</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.401853</td>\n",
       "      <td>1.613464</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.388338</td>\n",
       "      <td>1.609434</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.374339</td>\n",
       "      <td>1.610430</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.362183</td>\n",
       "      <td>1.602663</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.348864</td>\n",
       "      <td>1.601656</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.335258</td>\n",
       "      <td>1.606995</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.321574</td>\n",
       "      <td>1.596013</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.306098</td>\n",
       "      <td>1.592437</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1.293900</td>\n",
       "      <td>1.581123</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1.279845</td>\n",
       "      <td>1.564399</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1.271103</td>\n",
       "      <td>1.500022</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1.261507</td>\n",
       "      <td>1.535057</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.248613</td>\n",
       "      <td>1.472087</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>1.237349</td>\n",
       "      <td>1.417059</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1.225417</td>\n",
       "      <td>1.435310</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1.214563</td>\n",
       "      <td>1.365397</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1.203867</td>\n",
       "      <td>1.452246</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.194183</td>\n",
       "      <td>1.263637</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>1.185022</td>\n",
       "      <td>1.283553</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>1.171834</td>\n",
       "      <td>1.517067</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>1.163586</td>\n",
       "      <td>1.597277</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>1.149949</td>\n",
       "      <td>1.694383</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1.138015</td>\n",
       "      <td>1.358102</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>1.124267</td>\n",
       "      <td>2.482912</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1.120735</td>\n",
       "      <td>1.297349</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1.109981</td>\n",
       "      <td>2.298659</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1.103910</td>\n",
       "      <td>4.613716</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.101009</td>\n",
       "      <td>9.654345</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1.094292</td>\n",
       "      <td>5.961040</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1.091424</td>\n",
       "      <td>4.990852</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>1.085680</td>\n",
       "      <td>6.741884</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>1.078918</td>\n",
       "      <td>1.311232</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.077116</td>\n",
       "      <td>3.062059</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>1.070217</td>\n",
       "      <td>1.283510</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>1.064868</td>\n",
       "      <td>3.715569</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>1.059177</td>\n",
       "      <td>1.724050</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1.054424</td>\n",
       "      <td>4.899724</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>1.054437</td>\n",
       "      <td>2.283245</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>1.053111</td>\n",
       "      <td>2.297719</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>1.050523</td>\n",
       "      <td>2.093637</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>1.047141</td>\n",
       "      <td>1.727470</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>1.046703</td>\n",
       "      <td>4.376657</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>1.043961</td>\n",
       "      <td>6.196053</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>1.039703</td>\n",
       "      <td>6.152739</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>1.035884</td>\n",
       "      <td>8.199553</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>1.033973</td>\n",
       "      <td>9.609694</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>1.030891</td>\n",
       "      <td>6.227965</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.028900</td>\n",
       "      <td>5.746769</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>1.027948</td>\n",
       "      <td>5.763941</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>62</td>\n",
       "      <td>1.028211</td>\n",
       "      <td>2.939988</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>1.028534</td>\n",
       "      <td>1.482004</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64</td>\n",
       "      <td>1.029271</td>\n",
       "      <td>4.230787</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>1.029912</td>\n",
       "      <td>6.466330</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>1.027463</td>\n",
       "      <td>3.925814</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>1.025617</td>\n",
       "      <td>3.920921</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>1.022813</td>\n",
       "      <td>3.472798</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>1.018501</td>\n",
       "      <td>3.049520</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>70</td>\n",
       "      <td>1.018678</td>\n",
       "      <td>2.491311</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>1.017646</td>\n",
       "      <td>1.477216</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>1.015955</td>\n",
       "      <td>2.422115</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>73</td>\n",
       "      <td>1.011675</td>\n",
       "      <td>1.438160</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>1.006047</td>\n",
       "      <td>1.375284</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>1.002805</td>\n",
       "      <td>1.802636</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>0.998354</td>\n",
       "      <td>3.754055</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>0.994339</td>\n",
       "      <td>2.482006</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>0.994594</td>\n",
       "      <td>1.123771</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>0.992726</td>\n",
       "      <td>1.100370</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.993869</td>\n",
       "      <td>1.098626</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>0.989084</td>\n",
       "      <td>0.969644</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>0.986687</td>\n",
       "      <td>1.031124</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>0.985856</td>\n",
       "      <td>1.389311</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>0.982548</td>\n",
       "      <td>1.677026</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>0.980049</td>\n",
       "      <td>1.984690</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>0.977262</td>\n",
       "      <td>2.663417</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>0.974976</td>\n",
       "      <td>2.417084</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>88</td>\n",
       "      <td>0.975138</td>\n",
       "      <td>1.385765</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>0.972034</td>\n",
       "      <td>1.563319</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.969463</td>\n",
       "      <td>1.633355</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0.968935</td>\n",
       "      <td>2.039129</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0.967603</td>\n",
       "      <td>1.541844</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0.968810</td>\n",
       "      <td>1.315756</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0.970300</td>\n",
       "      <td>3.284852</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0.971463</td>\n",
       "      <td>3.589286</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0.971568</td>\n",
       "      <td>2.353341</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0.970776</td>\n",
       "      <td>2.591434</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>0.970075</td>\n",
       "      <td>2.927469</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0.971289</td>\n",
       "      <td>2.609849</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.974466</td>\n",
       "      <td>2.325409</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>0.974490</td>\n",
       "      <td>2.081188</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>0.973824</td>\n",
       "      <td>1.359118</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>0.972219</td>\n",
       "      <td>1.090843</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>104</td>\n",
       "      <td>0.971711</td>\n",
       "      <td>1.190391</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>0.972649</td>\n",
       "      <td>1.588080</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>0.974650</td>\n",
       "      <td>1.835901</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>0.973514</td>\n",
       "      <td>1.870666</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>0.972411</td>\n",
       "      <td>1.652206</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>0.974086</td>\n",
       "      <td>1.278639</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>0.974933</td>\n",
       "      <td>1.040625</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>0.972952</td>\n",
       "      <td>1.021651</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>0.971806</td>\n",
       "      <td>1.343572</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>113</td>\n",
       "      <td>0.969928</td>\n",
       "      <td>1.387259</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>0.967772</td>\n",
       "      <td>1.570803</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>0.965366</td>\n",
       "      <td>1.621146</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>0.962725</td>\n",
       "      <td>1.714004</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>0.960849</td>\n",
       "      <td>1.907067</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>0.957004</td>\n",
       "      <td>1.591835</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>0.958164</td>\n",
       "      <td>1.680010</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>0.958625</td>\n",
       "      <td>1.225325</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>0.956625</td>\n",
       "      <td>1.107982</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>0.957369</td>\n",
       "      <td>1.121521</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>0.955815</td>\n",
       "      <td>1.202939</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>0.954944</td>\n",
       "      <td>1.234500</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>0.954373</td>\n",
       "      <td>1.474302</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126</td>\n",
       "      <td>0.951846</td>\n",
       "      <td>1.589271</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>0.951891</td>\n",
       "      <td>1.673985</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>0.953131</td>\n",
       "      <td>1.567316</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>0.953709</td>\n",
       "      <td>1.088511</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>0.953542</td>\n",
       "      <td>1.128953</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>0.952610</td>\n",
       "      <td>1.575795</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>0.953830</td>\n",
       "      <td>1.743010</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>0.951794</td>\n",
       "      <td>1.571326</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>0.949696</td>\n",
       "      <td>1.577728</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>0.952297</td>\n",
       "      <td>1.815940</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>0.952972</td>\n",
       "      <td>2.064868</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>0.955628</td>\n",
       "      <td>2.435957</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>0.954675</td>\n",
       "      <td>2.641927</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>139</td>\n",
       "      <td>0.954418</td>\n",
       "      <td>2.548305</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>0.955395</td>\n",
       "      <td>1.842673</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>0.955617</td>\n",
       "      <td>1.097640</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>0.954719</td>\n",
       "      <td>0.925584</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>0.956634</td>\n",
       "      <td>0.877834</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>0.954160</td>\n",
       "      <td>0.841679</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>0.954152</td>\n",
       "      <td>0.808143</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>0.954123</td>\n",
       "      <td>0.787732</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>0.951483</td>\n",
       "      <td>0.767450</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>0.949662</td>\n",
       "      <td>0.753072</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0.947428</td>\n",
       "      <td>0.766489</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>0.948786</td>\n",
       "      <td>0.804269</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>0.946424</td>\n",
       "      <td>0.854882</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>152</td>\n",
       "      <td>0.947040</td>\n",
       "      <td>0.908327</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>0.945913</td>\n",
       "      <td>0.951015</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>0.945124</td>\n",
       "      <td>1.022153</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>155</td>\n",
       "      <td>0.943171</td>\n",
       "      <td>1.066825</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>0.942373</td>\n",
       "      <td>1.054384</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>0.940294</td>\n",
       "      <td>0.997566</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>0.940491</td>\n",
       "      <td>0.971229</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>0.939682</td>\n",
       "      <td>0.846206</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>0.939346</td>\n",
       "      <td>0.764268</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>0.937229</td>\n",
       "      <td>0.754534</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>0.933547</td>\n",
       "      <td>0.787042</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>0.933203</td>\n",
       "      <td>0.857130</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>164</td>\n",
       "      <td>0.933637</td>\n",
       "      <td>0.915972</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>0.931348</td>\n",
       "      <td>0.919065</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>166</td>\n",
       "      <td>0.931428</td>\n",
       "      <td>0.859337</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>0.929779</td>\n",
       "      <td>0.789867</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>0.928110</td>\n",
       "      <td>0.753117</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>0.926101</td>\n",
       "      <td>0.738458</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>0.925353</td>\n",
       "      <td>0.755080</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>0.926029</td>\n",
       "      <td>0.771436</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>0.925250</td>\n",
       "      <td>0.785918</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>0.924132</td>\n",
       "      <td>0.790192</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>0.924556</td>\n",
       "      <td>0.791276</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>0.923391</td>\n",
       "      <td>0.790005</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>0.921544</td>\n",
       "      <td>0.790712</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>0.919461</td>\n",
       "      <td>0.794095</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>0.917881</td>\n",
       "      <td>0.796023</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>0.917527</td>\n",
       "      <td>0.795494</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>0.915659</td>\n",
       "      <td>0.784916</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>0.915729</td>\n",
       "      <td>0.777742</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182</td>\n",
       "      <td>0.914645</td>\n",
       "      <td>0.770701</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183</td>\n",
       "      <td>0.913537</td>\n",
       "      <td>0.767288</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>184</td>\n",
       "      <td>0.912305</td>\n",
       "      <td>0.764935</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>0.910081</td>\n",
       "      <td>0.762620</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>0.908561</td>\n",
       "      <td>0.763817</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>0.909015</td>\n",
       "      <td>0.764751</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>0.908268</td>\n",
       "      <td>0.764663</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>0.907292</td>\n",
       "      <td>0.765498</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>0.905085</td>\n",
       "      <td>0.766115</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>0.904604</td>\n",
       "      <td>0.768526</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>192</td>\n",
       "      <td>0.904123</td>\n",
       "      <td>0.769853</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>193</td>\n",
       "      <td>0.903383</td>\n",
       "      <td>0.774133</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>0.901851</td>\n",
       "      <td>0.778949</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>195</td>\n",
       "      <td>0.901167</td>\n",
       "      <td>0.782591</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>196</td>\n",
       "      <td>0.900324</td>\n",
       "      <td>0.784429</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>197</td>\n",
       "      <td>0.898247</td>\n",
       "      <td>0.786330</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>198</td>\n",
       "      <td>0.897429</td>\n",
       "      <td>0.791879</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>199</td>\n",
       "      <td>0.897475</td>\n",
       "      <td>0.795262</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>00:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "learn = Learner(db, InceptionTime(db.features, db.c), metrics=accuracy,\n",
    "               loss_func=LabelSmoothingCrossEntropy())\n",
    "\n",
    "tfm_fn = mixup\n",
    "sch_param='alpha'\n",
    "sch_val = (.001, 1.)              # values of parameter alpha (initial, final)\n",
    "sch_iter = (0., .7)               # percent of training epochs (start, end)\n",
    "sch_func = partial(annealing_cos) # annealing_cos, None = annealing_linear, cosine_annealing\n",
    "plot = True\n",
    "test = False                      # set to True for adjusting the values. When ready to train set to False\n",
    "sch_tfm_cb = partial(TfmScheduler, tfm_fn=tfm_fn, sch_param=sch_param, sch_val=sch_val, \n",
    "                      sch_iter=sch_iter, sch_func=sch_func, plot=plot, test=test)\n",
    "learn.callback_fns.append(sch_tfm_cb)\n",
    "\n",
    "learn.fit_one_cycle(200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "In this case, the scheduled approach didn't improve performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "This is all for now! I hope you find this as useful as I have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
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