2_model_resnet34.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle Competition:  Iceberg or Ship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook 2:\n",
    "## run resnet34 architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 476\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu 120926 Jan  8 22:22 1_getdata.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu 223609 Jan  8 23:56 2_model_resnet34.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu 134887 Jan  8 21:26 baseline.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    185 Jan  8 22:18 README.md\r\n"
     ]
    }
   ],
   "source": [
    "!ls -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put these at the top of every notebook, to get automatic reloading and inline plotting\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.imports import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This file contains all the main external libs we'll use\n",
    "from fastai.imports import *\n",
    "from fastai.transforms import *\n",
    "from fastai.conv_learner import *\n",
    "from fastai.model import *\n",
    "from fastai.dataset import *\n",
    "from fastai.sgdr import *\n",
    "from fastai.plots import *\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import shutil\n",
    "from IPython.display import FileLink, FileLinks\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 1973260\r\n",
      "drwxrwxr-x  2 ubuntu ubuntu       4096 Jan  8 22:29 models\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu     117951 Oct 23 17:27 sample_submission.csv\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu      38566 Jan  7 20:36 sample_submission.csv.7z\r\n",
      "drwxrwxr-x  2 ubuntu ubuntu     274432 Jan  8 21:46 test\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu 1521771850 Oct 23 17:27 test.json\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu  257127394 Jan  7 20:36 test.json.7z\r\n",
      "drwxrwxr-x 14 ubuntu ubuntu       4096 Jan  8 23:54 tmp\r\n",
      "drwxrwxr-x  4 ubuntu ubuntu       4096 Jan  8 21:45 train\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu  196313674 Oct 23 17:23 train.json\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu   44932785 Jan  7 20:36 train.json.7z\r\n",
      "drwxrwxr-x  4 ubuntu ubuntu       4096 Jan  8 21:45 valid\r\n"
     ]
    }
   ],
   "source": [
    "#!ls /home/nidhin/data/iceberg\n",
    "!ls -l /home/ubuntu/data/iceberg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PATH = \"/home/nidhin/data/iceberg/\"\n",
    "PATH = \"/home/ubuntu/data/iceberg/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 1973260\r\n",
      "drwxrwxr-x  2 ubuntu ubuntu       4096 Jan  8 22:29 models\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu     117951 Oct 23 17:27 sample_submission.csv\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu      38566 Jan  7 20:36 sample_submission.csv.7z\r\n",
      "drwxrwxr-x  2 ubuntu ubuntu     274432 Jan  8 21:46 test\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu 1521771850 Oct 23 17:27 test.json\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu  257127394 Jan  7 20:36 test.json.7z\r\n",
      "drwxrwxr-x 14 ubuntu ubuntu       4096 Jan  8 23:54 tmp\r\n",
      "drwxrwxr-x  4 ubuntu ubuntu       4096 Jan  8 21:45 train\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu  196313674 Oct 23 17:23 train.json\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu   44932785 Jan  7 20:36 train.json.7z\r\n",
      "drwxrwxr-x  4 ubuntu ubuntu       4096 Jan  8 21:45 valid\r\n"
     ]
    }
   ],
   "source": [
    "!ls -l {PATH}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) resnet34, `sz=75`\n",
    "### run default model with original image size\n",
    "- resnet34 is the one to start with, and probably end with. it is fast and accurate. If you can't get a good result with resnet34, something else is probably wrong  \n",
    "\n",
    "----> It's true accuracy of ~ 0.75 is pretty good for a first time model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Number of Epochs** - how many times do you want your algorithm to go through your images and read them? Start with 1. If that doesn't work, then run more.\n",
    "- **Learning Rate** - this is the only one that is complex to pick. With gradient descent, can pick out which direction to move downhill and take a small step in that direction. Mathematically, we take the derivative of the function. Learning rate is what do you multiply the derivative by."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose a learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "arch=resnet34\n",
    "sz=75\n",
    "data  = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz))\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c40ea2b70fbf4b029b48746ce1f29a9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   0.         1.94689  160.14763    0.53646]             \n",
      "\n"
     ]
    }
   ],
   "source": [
    "lrf = learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TZ1BXxoOlDTZs4GtYZBGpVss37GTXvv6Kas6B4i7aiohUlfaOLhrqari4zAdLG0yBLyJSID9Y2oUnTqOlsajLnGVDgS8iUuDZrgzP7dhX9mPfD0WBLyJSoL1jKwCXV8hwCoUU+CIiBdo7ulg8dzIzJ5b/YGmDKfBFREJdPb08sWk3V1Zgcw4o8EVEDmrPj32vwBcRqWztHV3MnzaORTPGJ11KJBT4IiJApm+A33du54pTZ1bMYGmDKfBFRIAHn+nmQLayBksbTIEvIkLQHXPKuHpeNr9yBksbTIEvIlVv34GBYLC0U2ZW1GBpg1XuVyYiUqSv/CZFT+8Abzl3btKlREqBLyJVbd22vXx92TquPWs2L5s/NelyIqXAF5Gq5e585p6naayr4cZXn5J0OZFT4ItI1bp/VZoHnunmhitOYsaEyhtKYTAFvohUpd7+LDfd+zQnzRzP28+fn3Q5saiswZ5FRIr0tQc72bhjP3e++zzqK7hnTqHq+CpFRAps3LGPrz7QyWsXH8/5J05LupzYKPBFpOrcdG8HtTXGP1xV+RdqCynwRaSq/OaZNO0dXXzwskXMmtScdDmxUuCLSNXoG8hy0z0dLJzewrtefkLS5cROF21FpGrc8tA61m3by+3XnUNDXfWd70b2FZvZN8wsbWZPRbUPEZFibd61n3/7dYolpx/HxSe1Jl1OIqL8E/ctYEmE7y8iUrT/+9NV5Nz5x6tPTbqUxEQW+O6+DNgR1fuLiBTr4dQ2fvqnLbz/0hcxZ8q4pMtJTPU1YolIVTkwkONTdz/NvKnjuP7ihUmXk6jEA9/Mrjez5Wa2vLu7O+lyRKTC3Pa79aTSGT712tNoqq9NupxEJR747r7U3dvcva21tTovpIhINNI9vXzx/me57JQZvPLUyp26sFiJB76ISFT+6Wer6M86n3rtaUmXUhKi7JZ5J/B74GQz22Rm74pqXyIigz2ydjs/XrmZ97xiIfOntSRdTkmI7INX7v7mqN5bRGQkA9ngQu3syc2875IXJV1OyVCTjohUnO/8YQOrt+7hf119Ks0N1X2htpACX0QqSveePr7Q/iwXLZrOq04/LulySooCX0Qqyud+sZre/iyfvuZ0zCzpckqKAl9EKsZjz+3kBys2cd3LT+DE1vFJl1NyFPgiUhGyOeeTP3mKmRMb+eBli5IupyQp8EWk7PX2Z/ngnY/x1PM9fOI1pzG+USO/D0VHRUTK2vZMH+++fTmPb9zFJ646lde+ZFbSJZUsBb6IlK112/byzm/+kS27e/n3t5zFq1+ssB+JAl9EytLy9Tt49+3LMTO+++7zeNn8KUmXVPIU+CJSdn765BY+8v2VzJ7czDf/+mwWTNfQCcVQ4ItI2XB3li5byz//fDVt86ew9O1tTG1pSLqssqHAF5GykB8f545HnuM1L57FF/5ycdWPbz9WCnwRKXl7+wb44J2P8+vVad5z8UI+vuQUamr0KdqxUuCLSElL9/Ry3W2P0rG5h//9Z2fwV+fNT7qksqXAF5GS9WzXHt75zUfZue8At77jbC49ZUbSJZU1Bb6IlKSHU9t477dX0NRQy/ffcz5nzJ6UdEllT4EvIiXnhys2ceNdT7KwtYVvvvMcZk9uTrqkiqDAF5GS4e586Vdr+OL9a7jgxGl89W0vY1JzfdJlVQwFvogkbiCb45dPd7H0obU8sXEXbzhrDv987YtpqNP4jseSAl9EErO3b4DvL9/Irb9dx6ad+5k/bRz/9PoX8+Zz5mrykggo8EUkdlt39/Kt363nu49soKd3gLb5U/jH15zGFafNpFb96yOjwBeR2Kza0sPXH1rLPU9sJptzlpxxHH9z0ULOmqeBz+KgwBeRSLk7y9Zs45aH1vLQmm2Ma6jlrefO57oLT2DetHFJl1dVFPgiEom+gSx3r9zMrb9dx+qte5gxoZGPLTmZt54zn0nj1PMmCQp8ETmm0j29/GDFJm773XrSe/o45bgJ3PwXi7lm8fHqdZMwBb6IHDF3p7M7w/L1O3l0/U6Wb9jBhu37ALho0XRu/ovFXLRounrclAgFvogUrW8gy1PP7z4Y8Cs27GDnvn4AprU00LZgCm87dz6vOLmVk2ZOSLhaGUyBLyLD2r2vnxXP7QjCff1OVm7axYGBHAALp7dw+akzOXvBVNoWTOGE6S06ky9xCnyRKndgIMfW3b1s2rWP53fu5/ld+9m0cz9/2rSbZ7r2AFBXY5wxexJvP28+bWHATx/fmHDlMlaRBr6ZLQG+BNQCt7j7Z6Pcn4i80N6+AZ7ftZ/nd+5nU3gfPN/H87v2k97Th/vh28yY0MjJx03g6pfMom3BVM6cO5nmBs0uVe4iC3wzqwW+AlwBbAIeNbO73b0jqn2KVCp3J9M3QE/vAD37+4Nb/nFvPz37B8L7Q8937e9ny+797Arb2PPqa41Zk5qZPbmZixa1MntyM7OnNDMnvD9uUhONdQr3ShTlGf45QMrd1wKY2X8CrwMU+JIodyfnkHMPbrmCxw653KHH7k7WnWwuuA3knIGsM5DLkc05/dn88uB58JqTzeUOrtufzdE7kKOvP0vfQC64HXycpbc/uO/rz9Eb3udf29+fpWf/AHt6+8n5yF9XS0MtE5vrmdhUz8TmOo6f1MRZ8yYze0oQ7nOmNDN78jhaJzRq+IIqFWXgzwY2FjzfBJwbxY6u/vJD9PbnonjrSPng/6PHsu1R7fjo3ruwbj9seeF7+NDLD3vsh72H+6Ht8uv5Ydt4uM6h0HYPtwiX59wPvk9+XcJwdyA7WmrGpLGuhqb6Whrramisr6GprpbG+hoa62ppqq9hYnP9wXUmNtUdFuTB/eHPJzTVUVerPu4ysigDf6hTiBf8tpnZ9cD1APPmzTuiHb2odTz92dL4RR6zozjROppztNF6U4z23oWb22HLbcjlHLa+HXyP/OL8ZoYdemwFWxx8PVheYxY+PvReh5YFyy3cIL+8xoya8LXamkOPa8yorQnXC9eprTn0uCZcv64mf19DXe3wz+traw5bv7625rAwb6itUW8WSUSUgb8JmFvwfA6wefBK7r4UWArQ1tZ2RKn9xTe99Eg2ExGpKlH+D/gosMjMTjCzBuBNwN0R7k9EREYQ2Rm+uw+Y2QeAXxJ0y/yGuz8d1f5ERGRkkfbDd/efAT+Lch8iIlIcXdYXEakSCnwRkSqhwBcRqRIKfBGRKqHAFxGpEnY0H+8/1sxsD/BM0nUMYTqwLekihqC6xkZ1jY3qGpuk6prv7q3FrFhq4+E/4+5tSRcxmJktV13FU11jo7rGRnUdOTXpiIhUCQW+iEiVKLXAX5p0AcNQXWOjusZGdY2N6jpCJXXRVkREolNqZ/giIhKR2APfzJaY2TNmljKzG4d4vdHMvhe+/oiZLYihprlm9hszW2VmT5vZh4dY5xIz221mK8PbJ6Ouq2Df683sT+F+lw/xupnZ/w+P2ZNmdlYMNZ1ccCxWmlmPmd0waJ1YjpmZfcPM0mb2VMGyqWbWbmZrwvspw2z7jnCdNWb2jhjq+ryZrQ6/Tz8ys8nDbDvi9zyCuj5tZs8XfK+uGmbbEX9/I6jrewU1rTezlcNsG+XxGjIfSuFnbMzcPbYbwTDJncBCoAF4Ajht0DrvA74WPn4T8L0Y6poFnBU+ngA8O0RdlwD3xnm8Cva9Hpg+wutXAT8nmBDqPOCRBL6vWwn6A8d+zICLgbOApwqWfQ64MXx8I/AvQ2w3FVgb3k8JH0+JuK4rgbrw8b8MVVcx3/MI6vo08NEivs8j/v4e67oGvf4F4JMJHK8h86EUfsbGeov7DP/gxObufgDIT2xe6HXAbeHjHwKvtIjng3P3Le7+WPh4D7CKYE7ecvE64HYP/AGYbGazYtz/K4FOd98Q4z4PcvdlwI5Biwt/jm4D/myITV8FtLv7DnffCbQDS6Ksy93vc/eB8OkfCGaCi9Uwx6sYxfz+RlJXmAF/Cdx5rPZXrBHyIfGfsbGKO/CHmth8cLAeXCf8xdgNTIulOiBsQnop8MgQL59vZk+Y2c/N7PS4aiKYC/g+M1thwRzAgxVzXKP0Job/RUzqmM109y0Q/MICM4ZYJ+njdh3Bf2ZDGe17HoUPhE1N3ximeSLJ43UR0OXua4Z5PZbjNSgfyuFn7DBxB34xE5sXNfl5FMxsPHAXcIO79wx6+TGCJovFwJeBH8dRU+hCdz8LeDXwfjO7eNDrSR6zBuAa4AdDvJzkMStGksftE8AAcMcwq4z2PT/WvgqcCJwJbCFoPhksseMFvJmRz+4jP16j5MOwmw2xLLGukXEHfjETmx9cx8zqgEkc2b+fY2Jm9QTfzDvc/b8Gv+7uPe6eCR//DKg3s+lR1xXub3N4nwZ+RPCvdaGiJoyPyKuBx9y9a/ALSR4zoCvfrBXep4dYJ5HjFl64uxp4q4cNvYMV8T0/pty9y92z7p4Dvj7M/pI6XnXAtcD3hlsn6uM1TD6U7M/YcOIO/GImNr8byF/J/nPg18P9UhwrYfvgrcAqd//XYdY5Ln8twczOITh226OsK9xXi5lNyD8muOj31KDV7gbeboHzgN35fzVjMOyZV1LHLFT4c/QO4CdDrPNL4EozmxI2YVwZLouMmS0BPg5c4+77hlmnmO/5sa6r8JrP64fZXzG/v1G4HFjt7puGejHq4zVCPpTkz9iI4r5KTNCj5FmCq/2fCJfdRPALANBE0DyQAv4ILIyhppcT/Jv1JLAyvF0FvBd4b7jOB4CnCXom/AG4IKbjtTDc5xPh/vPHrLA2A74SHtM/AW0x1TaOIMAnFSyL/ZgR/MHZAvQTnFG9i+C6z6+ANeH91HDdNuCWgm2vC3/WUsA7Y6grRdCmm/85y/dIOx742Ujf84jr+nb4s/MkQZDNGlxX+PwFv79R1hUu/1b+Z6pg3TiP13D5kPjP2Fhv+qStiEiV0CdtRUSqhAJfRKRKKPBFRKqEAl9EpEoo8EVEqoQCXyqGmf0uvF9gZm85xu/9D0PtS6ScqFumVBwzu4Rg5Merx7BNrbtnR3g94+7jj0V9IknRGb5UDDPLhA8/C1wUjo3+ETOrtWAc+kfDwcHeE65/STjO+XcJPnSEmf04HIDr6fwgXGb2WaA5fL87CvcVfrr582b2VDge+xsL3vsBM/uhBePf31HwqePPmllHWMvNcR4jqW51SRcgEoEbKTjDD4N7t7ufbWaNwMNmdl+47jnAGe6+Lnx+nbvvMLNm4FEzu8vdbzSzD7j7mUPs61qCAccWA9PDbZaFr70UOJ1g7JSHgQvNrINg6IJT3N1tmAlQRKKgM3ypBlcSjDW0kmBY22nAovC1PxaEPcCHzCw/FMTcgvWG83LgTg8GHusCHgTOLnjvTR4MSLYSWAD0AL3ALWZ2LTDkeDoiUVDgSzUw4IPufmZ4O8Hd82f4ew+uFLT9Xw6c78GQzo8TjO002nsPp6/gcZZgpqsBgv8q7iKYMOMXY/pKRI6CAl8q0R6Cqejyfgn8j3CIW8zspHBUxcEmATvdfZ+ZnUIwXWRef377QZYBbwyvE7QSTNP3x+EKC8dUn+TBcNE3EDQHicRCbfhSiZ4EBsKmmW8BXyJoTnksvHDazdDT0f0CeK+ZPQk8Q9Csk7cUeNLMHnP3txYs/xFwPsFIjQ58zN23hn8whjIB+ImZNRH8d/CRI/sSRcZO3TJFRKqEmnRERKqEAl9EpEoo8EVEqoQCX0SkSijwRUSqhAJfRKRKKPBFRKqEAl9EpEr8N4Lh9h2d9YTvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1aab10940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aRM0fSpmjEXdfGbwXAK8QaUL7AXcf5e7Z7p6dmZlZhd2QmnDjgK60P6gBd7wzh93FpWGXIyL7IZZBMhXoamadzSyFSCiMLrPMcmAAgJl1JxIk+cF4AnARkb4VgmlJZtYiGE4GzgXmILVeg5RE7h3ck0XrtvH0xNywyxGR/RCzIHH3YuDXwFhgPpGzs+aa2T1mdn6w2EhguJnNJHLkcaXvfbh3XyDP3aN/q6QCY81sFjADWAk8Hat9kJp16mEtObtnax757DuWbdgedjkiUkm29/d23ZWdne05OTlhlyGVsGZLIQMe+pzsTs144apj0bkUIuExs2nunl3RcrqyXeJK64w0Rp7ZjfHf5vPB7NVhlyMilaAgkbhzxUmd6NmuCb9/bx5bC4vCLkdEKqAgkbiTmGDcP6QX67ft4qGxC8MuR0QqoCCRuNS7fVN+dsLBvPjVMmau2Bx2OSLyIxQkErdGntWNzMap/Pbt2RSX6NoSkXilIJG41SQtmd+ddzhzV23lxcnLwi5HRPZBQSJxbVCv1vQ7NJOHPl7I6i07wy5HRMqhIJG4ZmbcO7gnxaXOPe/NC7scESmHgkTiXsfmDblhQFc+nLOGfy9YG3Y5IlKGgkRqheF9sujasjF3vTOXnbtLwi5HRKJUKkjM7EYza2IRz5rZN2Z2ZqyLE9kjJSmBP1zQk5Wbd/LwZ9+FXY6IRKnsEcnP3X0rcCaQCVwF/DFmVYmU4/is5lx0THuemZjLwjUFYZcjIoHKBsmeO+cNAp5395mU/+AqkZi6fVB30tOSuOPt2ZSW1v0bjorUBpUNkmlm9jGRIBlrZumArhCTGtesUQq3D+pOzrJNvJGzouIVRCTmKhskVwO3Ace6+w4gmUjzlkiNu+iY9hzXuRkPfLiADdt2hV2OSL1X2SA5EVjo7pvN7DLgTmBL7MoS2Tcz4/4hPdmxu5j7xswPuxyReq+yQfIEsMPMjgBuBZYBL8asKpEKHNIynRF9s/jXNyv5cvH6sMsRqdcqGyTFwSNwBwMPu/vDQHrsyhKp2PWndaVjs4bc+c4cdhXr2hKRsFQ2SArM7HbgcuADM0sk0k8iEpq05ETuGXw4ufnbeWp8btjliNRblQ2Si4FdRK4nWQO0Ax6MWVUildS/W0vO6d2Gv41bxNL128MuR6ReqlSQBOHxMpBhZucChe6uPhKJC/9zbg9SExO46905RFpgRaQmVfYWKT8FvgYuAn4KTDGzC2NZmEhltWqSxi1ndWPid+sZPXNV2OWI1DuVbdq6g8g1JFe4+8+A44C7KlrJzAaa2UIzW2Rmt5Uzv6OZjTOz6WY2y8wGBdMvNbMZUa9SMzsymHeMmc0OtvmImekKe+GyEw6md/sM7n1/Plt2FoVdjki9UtkgSXD3dVHjGypaN+iQfww4G+gBDDOzHmUWuxN4w92PAoYCjwO4+8vufqS7H0mkg3+pu88I1nkCGAF0DV4DK7kPUoclJhj3D+nFxu27eHDsgrDLEalXKhskH5nZWDO70syuBD4AxlSwznHAInfPdffdwGtETh+O5kCTYDgDKK9dYhjwKoCZtQGauPvk4HTkF4ELKrkPUsf1bJfBFSd14uUpy5m+fFPY5YjUG5XtbP9/wCigN3AEMMrdf1PBau2A6Jsh5QXTot0NXGZmeUSC6fpytnMxQZAE6+dVsE2px0ae2Y1W6Wnc8fYcikt0OziRmlDpB1u5+1vufrO73+Tub1dilfL6LsqeUjMMeMHd2xO5IeRLZvafmszseGCHu8/Zj23uWXeEmeWYWU5+fn4lypW6oHFqEr87rwfzVm/lhS+Xhl2OSL1QUT9HgZltLedVYGZbK9h2HtAharw9P2y6uhp4A8DdJwNpQIuo+UPZezSyZ5vtK9gmwfZGuXu2u2dnZmZWUKrUJQN7tua0w1ryf598y6rNO8MuR6TO+9Egcfd0d29Szivd3Zv82LrAVKCrmXU2sxQioTC6zDLLgQEAZtadSJDkB+MJRE43fi2qntVErrI/IThb62fAu5XeW6kXzIzfn384pe7cPXpu2OWI1Hkxe2a7uxcDvwbGAvOJnJ0118zuMbPzg8VGAsPNbCaRI48rfe8VZX2BPHcve++L64BngEXAYuDDWO2D1F4dmjXkxgGH8vG8tXwyb23Y5YjUaVYfrgTOzs72nJycsMuQGlZUUso5j0xk+64SPrm5Lw1TksIuSaRWMbNp7p5d0XIxOyIRCVtyYgL3D+nFys07+eun34VdjkidpSCROi27UzOGHtuBZyctYf7qis4PEZEDoSCROu83Aw8jo0Eyv317NqWldb8pV6SmKUikzjuoUQp3DOrO9OWbeW3qiopXEJH9oiCReuEnR7fjhKxm/PHD+eQX7Aq7HJE6RUEi9YKZ8YcLerGzqIT7PpgXdjkidYqCROqNQ1o25rp+XXhnxiq+WLQ+7HJE6gwFidQrvzz1EA5u3pA735lDYVFJ2OWI1AkKEqlX0pIT+cMFPVmyfjtPjl8cdjkidYKCROqdPl0zOf+Itjw+bjG5+dvCLkek1lOQSL1057ndSU1O4M535lAfbhMkEksKEqmXWqancevAw/hy8QbenVHukwhEpJIUJFJvXXJcR47o0JQ/fDCPLxevZ/mGHewqVge8yP7S7VCl3kpMMO4f0pMhj3/JJU9P+c/0Fo1TaJPRgDYZaZFX0z3DkffWGWkkJ+pvMJE9FCRSrx3eNoOJt57Kt2sLWL25kNVbClm9ZSerthSydMN2Ji/eQMGu4u+tYwYtGqfSNgiX1hlptG26N2jaNG1Aq/RUkhQ2Uk8oSKTea9UkjVZN0vY5v6CwiDVbClm1pZDVm3f+J2xWbylkUf42Jn6Xz/bd328SS7BIP8wPQiajAW2aRo50WqankZhgsd49kZhTkIhUID0tmfS0ZLq2Si93vruztbA4CJudrN5cyJrgqGb1lp0sWFPAuAX57CxzAWRigtEqPZXWwVFM24w0WmdE3vc0p7VonKqwkbinIBGpIjMjo0EyGQ2S6dZ632GzZWfR3qazzYXfC555q7by6by17Cou/d56SQlGqyZ7+2raBn00bTIa0LZpZLhFo1QSFDYSIgWJSA0wM5o2TKFpwxS6t2lS7jLuzqYdRZFms817m89Wbylk1eadzMrbzNi5hewuEzYpiQm0ykilTZM9zWZ7TxRo2zTSh9O8UQpmChuJDQWJSJwwM5o1SqFZoxQOb5tR7jLuzobtuyNHM5v3Bs2e0Plm+SbWbFlNUcn3L7JMSUqInHHWJBIuHQ5qwOCj2tEls3FN7JrUcVYfrurNzs72nJycsMsQqRGlpZGw2duEFgmZVVuCvpvNhazZWkipO2f2aMU1/bpwdMeDwi5b4pCZTXP37IqW0xGJSB2TkGBkpqeSmZ5K7/blL7N+2y5e/HIpf5+8jLFz13Jcp2Zc0y+LU7u1VH+L7LeYnuhuZgPNbKGZLTKz28qZ39HMxpnZdDObZWaDoub1NrPJZjbXzGabWVow/fNgmzOCV8tY7oNIXdSicSo3n9mNL287jf85twcrN+/k6r/nMPDhCbw5Le8H/TAiPyZmTVtmlgh8C5wB5AFTgWHuPi9qmVHAdHd/wsx6AGPcvZOZJQHfAJe7+0wzaw5sdvcSM/scuMXdK91WpaYtkR9XVFLKB7NW8+T4xSxYU0CbjDSuPqUzQ4/rSONUNVzUV5Vt2orlEclxwCJ3z3X33cBrwOAyyziw5xSWDGDP3fPOBGa5+0wAd9/g7roJkkiMJCcmcMFR7fjwxj68cNWxdGreiD98MJ8TH/iMP320gHUFhWGXKHEsln9qtANWRI3nAceXWeZu4GMzux5oBJweTD8UcDMbC2QCr7n7n6LWe97MSoC3gD94fThjQKQGmBn9u7Wkf7eWzFixmVETFvPE+MU8M2kJ/3V0e0b0zaJzi0ZhlylxJpZHJOX12JX9hT8MeMHd2wODgJfMLIFIwJ0CXBq8DzGzAcE6l7p7L6BP8Lq83A83G2FmOWaWk5+fX/W9EalnjuzQlMcvPYZ/j+zPhce0561v8jjtoc+57h/TmLFic9jlSRyJZZDkAR2ixtuzt+lqj6uBNwDcfTKQBrQI1h3v7uvdfQcwBjg6WG5l8F4AvEKkCe0H3H2Uu2e7e3ZmZma17ZRIfdO5RSPuH9KLL35zGr/qfwhfLFrPBY99wdBRkxm3cJ0eDCYxDZKpQFcz62xmKcBQYHSZZZYDAwDMrDuRIMkHxgK9zaxh0PHeD5hnZklm1iJYPhk4F5gTw30QkUBmeiq3nNWNL28fwJ3ndGfZhh1c9fxUzn54Iv/6Jo+iEp3pVV/F9ILE4HTevwKJwHPufp+Z3QPkuPvo4Eytp4HGRJq9bnX3j4N1LwNuD6aPcfdbzawRMAFIDrb5KXBzRR3xOmtLpPoVlZTy3sxVPDU+l4VrC2ibkcbVfbIYemwHGulMrzqhsmdt6cp2EakSd+fzhfk8OX4xU5ZsJKNBMj878WCuOKkTLRqnhl2eVIGCJIqCRKRmTF++iafG5zJ23hpSEhO48Jj2DO+TRSed6VUrKUiiKEhEalZu/jaenpjLW9NWUlxaytk923BNvyx6t28admmyHxQkURQkIuFYV1DIC18s5aWvllFQWMyJWc25tn8X+nZtodva1wIKkigKEpFwFRQW8drXK3h20hLWbC3ksNbpXNuvC+f0bkOynm0ftxQkURQkIvFhd3Ep785YyagJuXy3bhvtmjbgF306c/GxHWiYojO94o2CJIqCRCS+lJY64xau48nxi5m6dBNNGybzsxM7ccWJB9NcZ3rFDQVJFAWJSPyatmwjT43P5eN5a0lNSuCn2R0Y3ieLjs0bhl1avacgiaIgEYl/i9Zt4+kJubw9PXKm16Bebbi2Xxd6tiv/scMSewqSKAoSkdpj7dZCnv9iKS9/tYyCXcWcckgLrumXxSmH6EyvmqYgiaIgEal9thYW8eqU5Tw7aQnrCnZxeNsmXNOvC4N6tiZJZ3rVCAVJFAWJSO21q7iEd6ev4qkJi1mcv532BzVgeJ8sfprdgQYpiWGXV6cpSKIoSERqv9JS57MFkTO9pi3bxEENk7nipE787MRONGuUEnZ5dZKCJIqCRKRuyVm6kSfH5/Lp/LWkJSdwcXYHftEniw7NdKZXdVKQRFGQiNRN360tYNSEXN6ZsZJSh3N6tWFE3yyd6VVNFCRRFCQidduaLYU898USXpmynG27iunTtQXX9uvCSV2a60yvKlCQRFGQiNQPW3YW8cqU5Tz3xRLyC3bRs10TrunbhbN1ptcBUZBEUZCI1C+FRSW8Mz1yT6/c9dvp2Kwhw/tGnt6om0RWnoIkioJEpH4qLXU+nreWJ8cvZsaKzRzaqjH3DO7JCVnNwy6tVqhskCiaRaTOSkgwBvZszdu/PImnf5bNjt0lDB31Ff/92nTWFRSGXV6doSARkTrPzDijRys+uakfN5x2CGNmr2HAn8fz3KQlFJeUhl1eracgEZF6o0FKIjef2Y2xN/XlqIMP4p7353Huo5PIWbox7NJqNQWJiNQ7nVs04u9XHcuTlx3N1p1FXPjkZG7550zWb9sVdmm1koJEROolM2NgzzZ8OrIf1/XvwrszVnLanz/npclLKSmt+ychVaeYBomZDTSzhWa2yMxuK2d+RzMbZ2bTzWyWmQ2KmtfbzCab2Vwzm21macH0Y4LxRWb2iOlqIxGpgoYpSfxm4GF8eGNferbL4K535zL4sUlMX74p7NJqjZgFiZklAo8BZwM9gGFm1qPMYncCb7j7UcBQ4PFg3STgH8C17n440B8oCtZ5AhgBdA1eA2O1DyJSfxzSsjEv/+J4Hh12FPkFuxjy+Jfc9tYsNm7fHXZpcS+WRyTHAYvcPdfddwOvAYPLLONAk2A4A1gVDJ8JzHL3mQDuvsHdS8ysDdDE3Sd75AKYF4ELYrgPIlKPmBnnHdGWz0b2Z3ifzvxzWh6nPfQ5r0xZTqmau/YplkHSDlgRNZ4XTIt2N3CZmeUBY4Drg+mHAm5mY83sGzO7NWqbeRVsU0SkShqnJnHHOT0Yc0MfDm2Vzm/fns2QJ75kdt6WsEuLS7EMkvL6LspG+jDgBXdvDwwCXjKzBCAJOAW4NHgfYmYDKrnNyIc5RUMLAAANeElEQVSbjTCzHDPLyc/PP9B9EJF6rFvrdF4fcQJ/vfhIVm7ayfmPTeLOd2azZUdRxSvXI7EMkjygQ9R4e/Y2Xe1xNfAGgLtPBtKAFsG64919vbvvIHK0cnQwvX0F2yTY3ih3z3b37MzMzGrYHRGpj8yMC45qx79v6ceVJ3XilSnLOfWhz3kjZ4WauwKxDJKpQFcz62xmKUQ600eXWWY5MADAzLoTCZJ8YCzQ28waBh3v/YB57r4aKDCzE4KztX4GvBvDfRARAaBJWjK/O+9w3r++D51bNOLWN2dx0VOTmbtKzV0xCxJ3LwZ+TSQU5hM5O2uumd1jZucHi40EhpvZTOBV4EqP2AT8H5EwmgF84+4fBOtcBzwDLAIWAx/Gah9ERMrq0bYJ/7zmRB68sDdL12/nvEcncffouWwtrL/NXbr7r4jIAdqyo4g/f7yQf0xZRvNGqdxxzmFccGS7OvMwLd39V0QkxjIaJnPvBT0Z/atTaHdQA256fSYXj/qKhWsKwi6tRilIRESqqFf7DN6+7iQe+Ekvvl1bwKBHJnLfB/PYtqs47NJqhIJERKQaJCQYw47ryLiR/flpdnuembSEAQ99znszV1HXuxAUJCIi1eigRik88JPe/Ou6k8hMT+X6V6dz6TNTWLSu7jZ3KUhERGLgqI4H8e6vTuHeC3oyZ+UWzn54In/8cAE7dte95i4FiYhIjCQmGJefcDD/vqU/FxzZjifHL+b0h8bz4ezVdaq5S0EiIhJjLRqn8uBFR/DmtSeS0TCF617+hiuen8qS9dvDLq1aKEhERGpIdqdmvPfrk/ndeT2YvmwTZ/1lAg99vJCdu0vCLq1KFCQiIjUoKTGBq07uzGe39OOc3m149N+LOOMv4/lk3tqwSztgChIRkRC0TE/jLxcfyesjTqBhSiLDX8zh6hemsnzDjrBL228KEhGREB2f1ZwPbujDHYO681XuBs74y3ge/vQ7CotqT3OXgkREJGTJiQkM75vFZyP7c0aPVvzl0285668TGLdwXdilVYqCREQkTrTOSONvlxzNy784nqQE46rnpzLixRzyNsV3c5eCREQkzpx8SAs+vLEvvxl4GBO/W8/p/zeex8YtYldxfDZ3KUhEROJQSlIC1/Xvwqcj+3Fqt5Y8OHYhZ/91IhO/i79HhytIRETiWLumDXjismN44apjKXXn8me/5lcvf8PqLTvDLu0/FCQiIrVA/24tGXtTX0aecSifzl/LgIfG89T4xewuLg27NAWJiEhtkZqUyPUDuvLpzf04qUsLHvhwAYMemciXi9eHWpeCRESklunQrCHPXJHNs1dks6u4hEuensINr05n3dbCUOpRkIiI1FIDurfik5v6ceOArnw0dw2nPTSeZyctobikZpu7FCQiIrVYWnIiN51xKB//d1+yOx3Eve/P49xHJzF16cYaq0FBIiJSB3Rq0YjnrzyWpy4/hoLCYi56cjI3vzGDzTt2x/yzYxokZjbQzBaa2SIzu62c+R3NbJyZTTezWWY2KJjeycx2mtmM4PVk1DqfB9vcM69lLPdBRKS2MDPOOrw1n9zcl1+d2oVvlm0iJSn2xwtJsdqwmSUCjwFnAHnAVDMb7e7zoha7E3jD3Z8wsx7AGKBTMG+xux+5j81f6u45MSpdRKRWa5iSxP876zBuGNCV1KTEmH9eLKPqOGCRu+e6+27gNWBwmWUcaBIMZwCrYliPiEi9UhMhArENknbAiqjxvGBatLuBy8wsj8jRyPVR8zoHTV7jzaxPmfWeD5q17jIzq+7CRUSk8mIZJOX9gi/7tPthwAvu3h4YBLxkZgnAaqCjux8F3Ay8YmZ7jlwudfdeQJ/gdXm5H242wsxyzCwnPz/+7k0jIlJXxDJI8oAOUePt+WHT1dXAGwDuPhlIA1q4+y533xBMnwYsBg4NxlcG7wXAK0Sa0H7A3Ue5e7a7Z2dmZlbbTomIyPfFMkimAl3NrLOZpQBDgdFlllkODAAws+5EgiTfzDKDznrMLAvoCuSaWZKZtQimJwPnAnNiuA8iIlKBmJ215e7FZvZrYCyQCDzn7nPN7B4gx91HAyOBp83sJiLNXle6u5tZX+AeMysGSoBr3X2jmTUCxgYhkgh8Cjwdq30QEZGKmXvZbou6Jzs723NydLawiMj+MLNp7p5d0XK6sl1ERKqkXhyRmFk+sGw/VskAtuznx+zPOpVZtgUQ7r2hw3Ug/wY1oSbrqu7Pqo7tHeg29J2KD/v773Cwu1d8tpK761XmBYyK5TqVWZZIP1LoP4va9G9Q1+qq7s+qju0d6Db0nYqPV6z+/6ppq3zvxXidA9l+fROvP6OarKu6P6s6tneg29B3Kj7E5OdUL5q2aiMzy/FKdHKJSOXoOxU7OiKJX6PCLkCkjtF3KkZ0RCIiIlWiIxIREakSBYmIiFSJgkRERKpEQVILmdkFZva0mb1rZmeGXY9IbWdmWWb2rJm9GXYttZGCpIaZ2XNmts7M5pSZ/qPPt4/m7u+4+3DgSuDiGJYrEveq6TuV6+5Xx7bSuktnbdWw4M7G24AX3b1nMC0R+Jao59sTeehXIvBAmU383N3XBes9BLzs7t/UUPkicaeav1NvuvuFNVV7XRGz28hL+dx9gpl1KjP5P8+3BzCz14DB7v4AkWeufE/weOE/Ah8qRKS+q47vlFSNmrbiQ2Webx/teuB04EIzuzaWhYnUUvv1nTKz5mb2JHCUmd0e6+LqGh2RxIfKPN9+7wz3R4BHYleOSK23v9+pDYD+KDtAOiKJD5V5vr2IVJ6+UzVIQRIfKvN8exGpPH2napCCpIaZ2avAZKCbmeWZ2dXuXgzseb79fOANd58bZp0itYW+U+HT6b8iIlIlOiIREZEqUZCIiEiVKEhERKRKFCQiIlIlChIREakSBYmIiFSJgkTijpltq4HPOL+iW4vH4DP7m9lJB7DeUWb2TDB8pZn9rfqr239m1qnsrdvLWSbTzD6qqZokHAoSqbOCW4mXy91Hu/sfY/CZP3b/uv7AfgcJ8Fvg0QMqKGTung+sNrOTw65FYkdBInHNzP6fmU01s1lm9vuo6e+Y2TQzm2tmI6KmbzOze8xsCnCimS01s9+b2TdmNtvMDguW+89f9mb2gpk9YmZfmlmumV0YTE8ws8eDz3jfzMbsmVemxs/N7H4zGw/caGbnmdkUM5tuZp+aWavgNufXAjeZ2Qwz6xP8tf5WsH9Ty/tla2bpQG93n1nOvIPN7LPgZ/OZmXUMpncxs6+Cbd5T3hGemTUysw/MbKaZzTGzi4PpxwY/h5lm9rWZpQdHHhODn+E35R1VmVmimT0Y9W91TdTsd4BLy/0HlrrB3fXSK65ewLbg/UxgFJE7uSYA7wN9g3nNgvcGwBygeTDuwE+jtrUUuD4Y/iXwTDB8JfC3YPgF4J/BZ/Qg8hwLgAuBMcH01sAm4MJy6v0ceDxq/CD23jXiF8BDwfDdwC1Ry70CnBIMdwTml7PtU4G3osaj634PuCIY/jnwTjD8PjAsGL52z8+zzHb/C3g6ajwDSAFygWODaU2I3CG8IZAWTOsK5ATDnYA5wfAI4M5gOBXIAToH4+2A2WH/v9Irdi/dRl7i2ZnBa3ow3pjIL7IJwA1mNiSY3iGYvgEoAd4qs51/Be/TgJ/s47PecfdSYJ6ZtQqmnQL8M5i+xszG/Uitr0cNtwdeN7M2RH45L9nHOqcDPSLPKQOgiZmlu3tB1DJtgPx9rH9i1P68BPwpavoFwfArwJ/LWXc28Gcz+1/gfXefaGa9gNXuPhXA3bdC5OgF+JuZHUnk53toOds7E+gddcSWQeTfZAmwDmi7j32QOkBBIvHMgAfc/anvTTTrT+SX8InuvsPMPgfSgtmF7l5SZju7gvcS9v1/flfUsJV5r4ztUcOPAv/n7qODWu/exzoJRPZh549sdyd7960ilb5xnrt/a2bHAIOAB8zsYyJNUOVt4yZgLXBEUHNhOcsYkSO/seXMSyOyH1JHqY9E4tlY4Odm1hjAzNqZWUsif+1uCkLkMOCEGH3+JOC/gr6SVkQ6yysjA1gZDF8RNb0ASI8a/5jIHWoBCP7iL2s+cMg+PudLIrdHh0gfxKRg+CsiTVdEzf8eM2sL7HD3fxA5YjkaWAC0NbNjg2XSg5MHMogcqZQClxN57nlZY4HrzCw5WPfQ4EgGIkcwP3p2l9RuChKJW+7+MZGmmclmNht4k8gv4o+AJDObBdxL5BdnLLxF5AFJc4CngCnAlkqsdzfwTzObCKyPmv4eMGRPZztwA5AddE7Po5wn9Ln7AiAj6HQv6wbgquDncDlwYzD9v4GbzexrIk1j5dXcC/jazGYAdwB/cPfdwMXAo2Y2E/iEyNHE48AVZvYVkVDYXs72ngHmAd8EpwQ/xd6jv1OBD8pZR+oI3UZe5EeYWWN332ZmzYGvgZPdfU0N13ATUODuz1Ry+YbATnd3MxtKpON9cEyL/PF6JgCD3X1TWDVIbKmPROTHvW9mTYl0mt9b0yESeAK4aD+WP4ZI57gBm4mc0RUKM8sk0l+kEKnDdEQiIiJVoj4SERGpEgWJiIhUiYJERESqREEiIiJVoiAREZEqUZCIiEiV/H9KN7Y8InxwdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1ef4746d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### precompute=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "292e3cb487b7441da7fcbabfcbb52e88",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=6), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.75508  0.46643  0.77604]                      \n",
      "[ 1.       0.65084  0.40912  0.81771]                      \n",
      "[ 2.       0.6196   0.41578  0.79427]                      \n",
      "[ 3.       0.59421  0.40262  0.81771]                      \n",
      "[ 4.       0.58294  0.42992  0.82552]                      \n",
      "[ 5.       0.56092  0.39568  0.83333]                      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# create a learner\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=False)\n",
    "learn.fit(0.01, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### precompute=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2e850025c5648a0a69f5a973f934378",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.8353   0.49865  0.76302]                     \n",
      "[ 1.       0.75103  0.50165  0.76562]                     \n",
      "[ 2.       0.66908  0.44567  0.79167]                      \n",
      "[ 3.       0.62526  0.43623  0.77865]                      \n",
      "[ 4.       0.58316  0.40806  0.80729]                     \n",
      "\n"
     ]
    }
   ],
   "source": [
    "data  = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz), test_name='test')\n",
    "\n",
    "# create a learner\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(0.01, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lrf=learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learn.sched.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving model - data augmentation\n",
    "If you try training for more epochs, you'll notice that we start to *overfit*, which means that our model is learning to recognize the specific images in the training set, rather than generalizaing such that we also get good results on the validation set. One way to fix this is to effectively create more data, through *data augmentation*. This refers to randomly changing the images in ways that shouldn't impact their interpretation, such as horizontal flipping, zooming, and rotating.\n",
    "\n",
    "We can do this by passing `aug_tfms` (*augmentation transforms*) to `tfms_from_model`, with a list of functions to apply that randomly change the image however we wish. For photos that are largely taken from the side (e.g. most photos of dogs and cats, as opposed to photos taken from the top down, such as satellite imagery) we can use the pre-defined list of functions `transforms_side_on`. We can also specify random zooming of images up to specified scale by adding the `max_zoom` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_augs():\n",
    "    data = ImageClassifierData.from_paths(PATH, bs=2, tfms=tfms, num_workers=1)\n",
    "    x,_ = next(iter(data.aug_dl))\n",
    "    return data.trn_ds.denorm(x)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ims = np.stack([get_augs() for i in range(6)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1af3e6be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(ims, rows=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new data object that includes augmentation in the transforms\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "324b7e669fb24d8984cae67d0137d91f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.74313  0.55602  0.78906]                     \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(1e-2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.precompute=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f5ad8a221694f6f821fd09bdd4f3087",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=8), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.6369   0.4642   0.78385]                      \n",
      "[ 1.       0.61301  0.46768  0.78125]                      \n",
      "[ 2.       0.60963  0.4995   0.74219]                      \n",
      "[ 3.       0.62941  0.46663  0.78125]                      \n",
      "[ 4.       0.64633  0.61842  0.71615]                      \n",
      "[ 5.       0.60944  0.56978  0.73177]                      \n",
      "[ 6.       0.60198  0.43637  0.79948]                      \n",
      "[ 7.       0.58591  0.44646  0.78646]                      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(1e-2, 4, cycle_len=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1a90c0278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 21/21 [00:04<00:00,  4.85it/s]\n",
      "100%|██████████| 6/6 [00:01<00:00,  5.52it/s]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "24fc38169c8947d880c2cb690b61b69e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.82022  0.52906  0.75521]                     \n",
      "[ 1.       0.71406  0.45046  0.79948]                     \n",
      "[ 2.       0.65477  0.44598  0.78906]                      \n",
      "[ 3.       0.61044  0.4699   0.78646]                      \n",
      "[ 4.       0.57422  0.45854  0.80469]                     \n",
      "\n"
     ]
    }
   ],
   "source": [
    "sz=150\n",
    "tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.1)\n",
    "arch=resnet34\n",
    "\n",
    "\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(0.01, 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a63e129e750d48ca9ef46e1d9d851227",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.74851  0.52915  0.77865]                     \n",
      "[ 1.       0.62714  0.46133  0.79167]                      \n",
      "[ 2.       0.61331  0.48226  0.82292]                     \n",
      "[ 3.       0.58417  0.46668  0.79427]                      \n",
      "[ 4.       0.56412  0.47685  0.79688]                      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "sz=150\n",
    "tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.3)\n",
    "arch=resnet34\n",
    "\n",
    "\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(0.01, 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b78076eb75c947e2a1a12b14079e6632",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.79448  0.4753   0.7474 ]                     \n",
      "[ 1.       0.6773   0.43598  0.75781]                     \n",
      "[ 2.       0.61891  0.43777  0.7526 ]                      \n",
      "[ 3.       0.56005  0.43328  0.76042]                     \n",
      "[ 4.       0.53163  0.39025  0.78906]                     \n",
      "\n"
     ]
    }
   ],
   "source": [
    "sz=200\n",
    "tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.3)\n",
    "arch=resnet34\n",
    "\n",
    "\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(0.01, 5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25b5cd51839a4378ac485bfda87bd7fa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=15), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.71768  0.45812  0.77604]                     \n",
      "[ 1.       0.59081  0.43442  0.78385]                      \n",
      "[ 2.       0.56973  0.40699  0.79427]                      \n",
      "[ 3.       0.57567  0.45593  0.79167]                     \n",
      "[ 4.       0.54321  0.37333  0.82292]                     \n",
      "[ 5.       0.52835  0.3895   0.82552]                      \n",
      "[ 6.       0.52982  0.44553  0.79948]                     \n",
      "[ 7.       0.49836  0.41541  0.80469]                     \n",
      "[ 8.       0.48344  0.38498  0.80208]                     \n",
      "[ 9.       0.45579  0.35471  0.82812]                     \n",
      "[ 10.        0.45414   0.38399   0.81771]                 \n",
      "[ 11.        0.42462   0.40983   0.82552]                 \n",
      "[ 12.        0.44617   0.36813   0.82552]                 \n",
      "[ 13.        0.45096   0.40077   0.8099 ]                 \n",
      "[ 14.        0.42649   0.376     0.82031]                 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "sz=250\n",
    "tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.15)\n",
    "arch=resnet34\n",
    "\n",
    "\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(0.01, 15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a61c9a43a76b485e9d083a060693777f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.74844  0.55948  0.58073]                     \n",
      "[ 1.       0.6959   0.49039  0.80729]                     \n",
      "[ 2.       0.644    0.48945  0.79688]                      \n",
      "[ 3.       0.58865  0.41913  0.82292]                     \n",
      "[ 4.       0.53976  0.36452  0.84375]                     \n",
      "\n"
     ]
    }
   ],
   "source": [
    "sz=300\n",
    "tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.25)\n",
    "arch=resnet34\n",
    "\n",
    "\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(0.01, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 21/21 [00:16<00:00,  1.28it/s]\n",
      "100%|██████████| 6/6 [00:04<00:00,  1.39it/s]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61e6f05d8809469fbb1d1a5933abaa6b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=20), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.61897  0.48585  0.79427]                     \n",
      "[ 1.       0.5604   0.37532  0.8099 ]                      \n",
      "[ 2.       0.55367  0.39825  0.82812]                     \n",
      "[ 3.       0.56613  0.40228  0.83333]                     \n",
      "[ 4.       0.5307   0.35779  0.82552]                     \n",
      "[ 5.       0.5295   0.3536   0.82552]                      \n",
      "[ 6.       0.50396  0.3472   0.83073]                      \n",
      "[ 7.       0.48926  0.36424  0.83073]                     \n",
      "[ 8.       0.47307  0.35414  0.84115]                     \n",
      "[ 9.       0.46583  0.32324  0.84115]                     \n",
      "[ 10.        0.45637   0.38688   0.85417]                 \n",
      "[ 11.        0.46575   0.33224   0.85156]                 \n",
      "[ 12.        0.44698   0.32958   0.84635]                 \n",
      "[ 13.        0.42827   0.39059   0.8125 ]                 \n",
      "[ 14.        0.41616   0.4295    0.81771]                  \n",
      "[ 15.        0.41004   0.34692   0.82031]                 \n",
      "[ 16.        0.40226   0.30368   0.83854]                 \n",
      "[ 17.        0.38395   0.35782   0.83594]                 \n",
      "[ 18.        0.374     0.40275   0.83333]                  \n",
      "[ 19.        0.37378   0.31332   0.84896]                 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# transforms = top down\n",
    "sz=350\n",
    "tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_top_down, max_zoom=1.1)\n",
    "arch=resnet34\n",
    "\n",
    "\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(0.01, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb6505a926274bf1be08542c41192e69",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=20), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.89811  0.66582  0.66146]                     \n",
      "[ 1.       0.79593  0.6148   0.5651 ]                     \n",
      "[ 2.       0.73209  0.58069  0.60156]                     \n",
      "[ 3.       0.66504  0.55096  0.59375]                     \n",
      "[ 4.       0.62734  0.54098  0.59375]                     \n",
      "[ 5.       0.59382  0.512    0.76562]                     \n",
      "[ 6.       0.56218  0.50094  0.77344]                     \n",
      "[ 7.       0.54107  0.50097  0.78906]                      \n",
      "[ 8.       0.54222  0.47884  0.79427]                     \n",
      "[ 9.       0.52484  0.48452  0.79688]                     \n",
      "[ 10.        0.51995   0.50241   0.63281]                 \n",
      "[ 11.        0.48889   0.47393   0.79427]                  \n",
      "[ 12.        0.49995   0.47213   0.79688]                 \n",
      "[ 13.        0.49352   0.49357   0.64844]                 \n",
      "[ 14.        0.48413   0.47261   0.80208]                 \n",
      "[ 15.        0.47066   0.48002   0.8099 ]                 \n",
      "[ 16.        0.46425   0.46485   0.80208]                 \n",
      "[ 17.        0.47806   0.4742    0.8099 ]                  \n",
      "[ 18.        0.47067   0.44509   0.82292]                  \n",
      "[ 19.        0.46273   0.45987   0.80729]                 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "sz=300\n",
    "tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.1)\n",
    "arch=resnet34\n",
    "\n",
    "\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(0.001, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da06e9e42afa4de78a168f4bfa787cc7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=20), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.73921  0.68444  0.60156]                     \n",
      "[ 1.       0.68056  0.45354  0.82552]                      \n",
      "[ 2.       0.68815  0.42104  0.83073]                     \n",
      "[ 3.       0.61453  0.46441  0.80729]                     \n",
      "[ 4.       0.58373  0.39951  0.83594]                     \n",
      "[ 5.       0.5685   0.38131  0.85938]                     \n",
      "[ 6.       0.56345  0.47061  0.79948]                      \n",
      "[ 7.       0.54819  0.39482  0.83333]                     \n",
      "[ 8.       0.5042   0.45958  0.65885]                     \n",
      "[ 9.       0.4857   0.4605   0.69531]                     \n",
      "[ 10.        0.46008   0.39426   0.86458]                 \n",
      "[ 11.        0.4413    0.52706   0.64583]                 \n",
      "[ 12.        0.43078   0.47595   0.65625]                  \n",
      "[ 13.        0.42612   0.40406   0.83854]                 \n",
      "[ 14.        0.42306   0.38567   0.84896]                  \n",
      "[ 15.        0.42459   0.34915   0.85938]                  \n",
      "[ 16.        0.40864   0.39301   0.86458]                  \n",
      "[ 17.        0.41731   0.48463   0.6849 ]                  \n",
      "[ 18.        0.41647   0.36071   0.84896]                 \n",
      "[ 19.        0.42396   0.3531    0.85938]                 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "sz=300\n",
    "tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.1)\n",
    "arch=resnet34\n",
    "\n",
    "\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(0.01, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "188b88dbdaa748409969cc67cc323848",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=20), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.65531  0.48181  0.79948]                      \n",
      "[ 1.       0.56399  0.4624   0.80729]                      \n",
      "[ 2.       0.51748  0.37813  0.80208]                     \n",
      "[ 3.       0.5281   0.32703  0.83073]                     \n",
      "[ 4.       0.50944  0.37132  0.82031]                     \n",
      "[ 5.       0.50438  0.34015  0.81771]                     \n",
      "[ 6.       0.49455  0.39384  0.8125 ]                     \n",
      "[ 7.       0.46982  0.35196  0.84635]                     \n",
      "[ 8.       0.48585  0.3789   0.81771]                     \n",
      "[ 9.       0.45298  0.36026  0.83333]                     \n",
      "[ 10.        0.42237   0.34081   0.82812]                  \n",
      "[ 11.        0.40618   0.33461   0.84635]                 \n",
      "[ 12.        0.40139   0.39234   0.83333]                 \n",
      "[ 13.        0.40033   0.38171   0.84115]                 \n",
      "[ 14.        0.43229   0.35294   0.83333]                 \n",
      "[ 15.        0.42195   0.42581   0.84375]                 \n",
      "[ 16.        0.4023    0.34461   0.83854]                 \n",
      "[ 17.        0.37381   0.32944   0.84635]                 \n",
      "[ 18.        0.35069   0.33057   0.83594]                 \n",
      "[ 19.        0.36263   0.35024   0.84375]                 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# transforms = top down\n",
    "sz=350\n",
    "tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_top_down, max_zoom=1.2)\n",
    "arch=resnet34\n",
    "\n",
    "\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(0.01, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8bea550319484f129ba467c43f865e84",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=25), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.89946  0.60649  0.70573]                      \n",
      "[ 1.       0.81841  0.48429  0.82031]                     \n",
      "[ 2.       0.70871  0.43515  0.83333]                     \n",
      "[ 3.       0.64972  0.42043  0.84115]                     \n",
      "[ 4.       0.62848  0.39616  0.84896]                     \n",
      "[ 5.       0.58858  0.38537  0.83854]                     \n",
      "[ 6.       0.58344  0.3716   0.83594]                     \n",
      "[ 7.       0.56136  0.3676   0.83594]                     \n",
      "[ 8.       0.54183  0.36144  0.84115]                      \n",
      "[ 9.       0.52628  0.35863  0.84115]                     \n",
      "[ 10.        0.5021    0.37068   0.83854]                 \n",
      "[ 11.        0.48463   0.35917   0.83854]                  \n",
      "[ 12.        0.47375   0.35408   0.84115]                  \n",
      "[ 13.        0.45457   0.35579   0.83333]                 \n",
      "[ 14.        0.44998   0.33694   0.84896]                  \n",
      "[ 15.        0.44012   0.35764   0.84115]                 \n",
      "[ 16.        0.45868   0.3449    0.83594]                 \n",
      "[ 17.        0.45524   0.33255   0.84635]                 \n",
      "[ 18.        0.43815   0.33408   0.85156]                  \n",
      "[ 19.        0.43994   0.33387   0.83854]                 \n",
      "[ 20.        0.43328   0.33328   0.83854]                 \n",
      "[ 21.        0.42178   0.34481   0.83333]                 \n",
      "[ 22.        0.42677   0.32975   0.85417]                 \n",
      "[ 23.        0.41775   0.34918   0.84896]                 \n",
      "[ 24.        0.41083   0.33724   0.83333]                 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# transforms = top down\n",
    "sz=320\n",
    "tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.2)\n",
    "arch=resnet34\n",
    "\n",
    "\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(0.001, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81f42fa356b34fefad40806889bcde07",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=25), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.86722  0.66464  0.51302]                     \n",
      "[ 1.       0.76956  0.5865   0.57552]                     \n",
      "[ 2.       0.6893   0.53885  0.76823]                      \n",
      "[ 3.       0.64056  0.52174  0.75781]                     \n",
      "[ 4.       0.6126   0.51165  0.77604]                     \n",
      "[ 5.       0.58547  0.49639  0.78906]                      \n",
      "[ 6.       0.58114  0.47564  0.79427]                     \n",
      "[ 7.       0.57392  0.45657  0.79167]                     \n",
      "[ 8.       0.55553  0.47193  0.79427]                     \n",
      "[ 9.       0.53558  0.43976  0.82031]                      \n",
      "[ 10.        0.53412   0.4428    0.82031]                 \n",
      "[ 11.        0.52585   0.45195   0.80729]                 \n",
      "[ 12.        0.50604   0.45552   0.8125 ]                  \n",
      "[ 13.        0.50109   0.4505    0.8151 ]                 \n",
      "[ 14.        0.48187   0.46074   0.82552]                  \n",
      "[ 15.        0.47918   0.43574   0.82552]                  \n",
      "[ 16.        0.47532   0.45311   0.82031]                 \n",
      "[ 17.        0.4702    0.43589   0.8151 ]                 \n",
      "[ 18.        0.46397   0.41861   0.83073]                 \n",
      "[ 19.        0.45352   0.42988   0.83854]                  \n",
      "[ 20.        0.46342   0.42058   0.83594]                  \n",
      "[ 21.        0.48024   0.39997   0.83333]                 \n",
      "[ 22.        0.4822    0.41375   0.83854]                 \n",
      "[ 23.        0.49624   0.42459   0.83854]                  \n",
      "[ 24.        0.49922   0.41931   0.84375]                 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# transforms = top down\n",
    "sz=300\n",
    "tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.3)\n",
    "arch=resnet34\n",
    "\n",
    "\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(0.001, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "258e532ffd30442291e244230ed0ced5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=25), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.84901  0.69598  0.63021]                     \n",
      "[ 1.       0.76471  0.59476  0.71875]                      \n",
      "[ 2.       0.71238  0.54819  0.74479]                     \n",
      "[ 3.       0.65642  0.49274  0.78125]                      \n",
      "[ 4.       0.60729  0.47282  0.77604]                      \n",
      "[ 5.       0.58316  0.45909  0.78906]                      \n",
      "[ 6.       0.55684  0.4659   0.79948]                     \n",
      "[ 7.       0.5417   0.4385   0.80729]                     \n",
      "[ 8.       0.54251  0.43341  0.79948]                     \n",
      "[ 9.       0.53137  0.42596  0.79688]                     \n",
      "[ 10.        0.53712   0.41881   0.8151 ]                  \n",
      "[ 11.        0.51457   0.42911   0.81771]                  \n",
      "[ 12.        0.50938   0.42097   0.82292]                 \n",
      "[ 13.        0.50296   0.40719   0.82292]                  \n",
      "[ 14.        0.51268   0.41509   0.8099 ]                 \n",
      "[ 15.        0.51389   0.40509   0.83333]                  \n",
      "[ 16.        0.51969   0.42082   0.82812]                 \n",
      "[ 17.        0.50182   0.40355   0.81771]                 \n",
      "[ 18.        0.48145   0.40825   0.82812]                 \n",
      "[ 19.        0.4702    0.42373   0.81771]                 \n",
      "[ 20.        0.4578    0.41945   0.8125 ]                 \n",
      "[ 21.        0.45531   0.41084   0.81771]                  \n",
      "[ 22.        0.4488    0.3979    0.82552]                  \n",
      "[ 23.        0.44505   0.40774   0.82812]                  \n",
      "[ 24.        0.43673   0.39708   0.83854]                 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# transforms = top down\n",
    "sz=300\n",
    "tfms = tfms_from_model(resnet34, sz, aug_tfms=transforms_side_on, max_zoom=1.4)\n",
    "arch=resnet34\n",
    "\n",
    "\n",
    "data = ImageClassifierData.from_paths(PATH, tfms=tfms)\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True)\n",
    "learn.fit(0.001, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log loss, accuracy                           \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.47877670776928016, 0.7819314641744548)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_preds,y = learn.TTA()\n",
    "preds = np.mean(np.exp(log_preds),axis=0)\n",
    "print(\"log loss, accuracy\")\n",
    "(metrics.log_loss(y,preds), accuracy(preds,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[117  47]\n",
      " [ 54 103]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f71e3f03390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_predicted= np.argmax(preds,axis=1)\n",
    "cm = confusion_matrix(y, y_predicted)\n",
    "\n",
    "\n",
    "plot_confusion_matrix(cm, data.classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plots(ims, figsize=(12,6), rows=1, titles=None):\n",
    "    f = plt.figure(figsize=figsize)\n",
    "    for i in range(len(ims)):\n",
    "        sp = f.add_subplot(rows, len(ims)//rows, i+1)\n",
    "        sp.axis('Off')\n",
    "        if titles is not None: sp.set_title(titles[i], fontsize=16)\n",
    "        plt.imshow(ims[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Band 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01b706e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Band 1\")\n",
    "plots(X_all[:,:,:,0],rows=2, titles=y_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Band 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7eff4f3d2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Band 2\")\n",
    "plots(X_all[:,:,:,1],rows=2, titles=y_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Band 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f019b6bd860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Band 3\")\n",
    "plots(X_all[:,:,:,2],rows=2, titles=y_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Bands\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01b76c3d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"All Bands\")\n",
    "plots(X_all[:,:,:],rows=2, titles=y_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    851\n",
       "1    753\n",
       "Name: is_iceberg, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see breakdown of classes in training dataset\n",
    "train_df['is_iceberg'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.530549\n",
       "1    0.469451\n",
       "Name: is_iceberg, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get percents\n",
    "train_df['is_iceberg'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# test set prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    }
   ],
   "source": [
    "log_preds,y = learn.TTA(is_test=True)\n",
    "probs = np.exp(log_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 8424, 2)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_test_file(probs,test_df):\n",
    "    probs = np.mean(probs,axis=0)\n",
    "    df = pd.DataFrame(probs)\n",
    "    df.columns = data.classes\n",
    "    df['id'] = test_df['id']\n",
    "    df=df.drop('ship',axis=1)\n",
    "    df=df.rename(columns={'ice':'is_iceberg'})\n",
    "    SUBM = f'{PATH}sub/'\n",
    "    os.makedirs(SUBM, exist_ok=True)\n",
    "    df.to_csv(f'{SUBM}subm.gz', compression='gzip', index=False)\n",
    "    FileLink(f'{SUBM}subm.gz')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "make_test_file(probs,test_df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
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