7_5_resnext50_more_epoch_update_sz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle Competition:  Iceberg or Ship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# resnext50 architecture \n",
    "\n",
    "## start with image size of 75, increase epochs, update size recurrently\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 4400\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu 1405706 Jan 15 01:35 1b_getdata-csv.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  625023 Jan 14 18:47 1_getdata.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  245619 Jan 14 18:36 2_model_resnet34.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu   13742 Jan 15 01:35 3_model_resnext50.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu   14220 Jan 13 21:14 4_model_nasnet.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu   64965 Jan 14 19:40 4_resnext_resize.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  147078 Jan 14 19:57 5_resnet18_resize.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  138629 Jan 14 19:52 6_resnext50_resize.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  205624 Jan 15 02:37 7_1_resnext50_more_epoch_submitted.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  210001 Jan 15 18:51 7_2_resnext50_more_epoch.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  108386 Jan 20 18:46 7_5_resnext50_more_epoch_update_sz.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  179449 Jan 14 21:28 7_resnext50_sz_100.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  201400 Jan 15 02:17 8_resnext50_more_epoch_sz300.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  137540 Jan 14 18:36 baseline.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    3299 Jan 15 01:35 helper.py\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  767392 Jan 14 18:36 other_groverpr.ipynb\r\n",
      "drwxrwxr-x 2 ubuntu ubuntu    4096 Jan 15 01:42 __pycache__\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    1492 Jan 15 14:56 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",
    "import helper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PATH = \"/home/nidhin/data/iceberg/\"\n",
    "PATH = os.path.expanduser(\"~/data/iceberg/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 1973264\r\n",
      "drwxrwxr-x  2 ubuntu ubuntu       4096 Jan  9 00:38 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       4096 Jan 15 14:20 sub\r\n",
      "drwxrwxr-x  2 ubuntu ubuntu     274432 Jan 14 18:44 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 13 ubuntu ubuntu       4096 Jan 15 16:52 tmp\r\n",
      "drwxrwxr-x  4 ubuntu ubuntu       4096 Jan 14 18:43 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 14 18:43 valid\r\n"
     ]
    }
   ],
   "source": [
    "!ls -l {PATH}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "arch=resnext50\n",
    "sz=75\n",
    "bs=32\n",
    "lr = 0.005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e17318a7feb0425a9491983a287a5be9",
       "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, max=6), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def get_data(sz, bs):\n",
    "    tfms = tfms_from_model(arch, sz, aug_tfms=transforms_top_down, max_zoom=1.10)\n",
    "    data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=bs,test_name='test')\n",
    "    return data\n",
    "\n",
    "data = get_data(sz, bs)\n",
    "data = data.resize(int(sz*1.1), 'tmp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tfms = tfms_from_model(arch, sz, aug_tfms=transforms_side_on, max_zoom=1.1)\n",
    "#data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=bs,test_name='test',)\n",
    "\n",
    "learn = ConvLearner.pretrained(arch, data, precompute=True, ps=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning Rate Finder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "43ab6121d2224ea5b8371a4de678e1c6",
       "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": [
      " 61%|██████    | 25/41 [00:00<00:00, 26.91it/s, loss=3.68] \n",
      "                                                          \r"
     ]
    }
   ],
   "source": [
    "lrf = learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73fe8dada0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2q+oOVa0H5gMzW6yjQPPkTglAcWs7FJE0IF5VP1FVBZ4Dzu/csE1nGJGewLPXTOTVH05mSJ+e3P3GJs74/VLmryygobHJ7fCMCWoF5TX0io4gKTbS7VAA/yaSdKDQ63ORU+btbuByESkCFgE3ey0b5Nzyel9ETvXaZ1Eb+zQBZNzAZOZdN4kXfnAKfeJjuOO19Uz/w/v8M283TU3qdnjGBKX8smoGJMcSKHf2/ZlIfJ1hy2+O2cBcVc0AzgWeF5EwoAQY4Nzyuh14UUTi27lPz8FF5ohIrojklpaWHvdJmI4TEaYOSeH1G6fw9JXjiYkM59b5eVz7t1WWTIw5Dp7p4wOjfQT8m0iKgEyvzxl8/dbV94EFAKr6CRADpKhqnaqWOeWrgc+Boc4+M9rYJ852T6rqeFUdn5oaGH2tuzsRYXpOXxbdcio/P3cYS7eU8sSyHW6HZUxQaWpSCvcfCpiGdvBvIlkFZIvIIBGJwtOYvrDFOgXAWQAiMgxPIikVkVSnsR4ROQFPo/oOVS0BqkRkktNb60rgn348B+MHYWHCD04dxLkj+/Hg21tYW1jhdkjGBI09lbXUNzQFTNdf8GMiUdUG4CZgMbAZT++sjSJyj4ic56z2Y+A6EVkLzAOudhrRpwHrnPJXgBtUtdzZ5ofA08B2PDWVN/11DsZ/RIT7LhhF3/gYbpm/hoN11kXYmPY40mMrOTB6bIGfp0hR1UV4GtG9y37h9X4TMNXHdq8Crx5ln7nAiM6N1LghITaSP146hllPfsIv/7mRBy8Z7XZIxgS8ggAbQwI2st24bOKgZG46M5tXPy3in3m73Q7HmICXX15NRJjQPzFw5rWzRGJcd8uZQxg3MIm7Xt9gMwkb04aC8kOkJ/UgIjxwvr4DJxLTbUWEh/GnS8eAwK3z19iARWNaUeCMIQkklkhMQMhMjuW3F4zk04IKHnp3m9vhGBOw8ssD5zkkzSyRmIDx3dH9uXhcBn9esp3lO8rcDseYgHPg0GEqag4H1GBEsERiAszd5w0nq3dPbnspj4oae66JMd6OTB9vNRJjjq5ndAQPzxrLvoN13PHqejzDiowx4D19fOCMIQFLJCYAjcxI4Cdnn8hbG/cwf1Vh2xsY000UBNgDrZpZIjEB6bpTT+DU7BR+9cZGtu896HY4xgSEgvJqUnpF0Svar2PJj5klEhOQwsKEBy8eTWxUBLfMW2MPxTIGz62tzABrHwFLJCaA9YmP4fcXjWJTSSX3v7nF7XCMcV1BeQ0DLZEYc2zOGtaXq6dk8dePdrJky163wzHGNfUNTRRXHGJAgDxe15slEhPw7jjnJE7qF8dPX15LaVWd2+EY44rdFYdo0sDr+guWSEwQiIkM55HZY6mqbeAnL6+1pyqabunI9PEB1mMLLJGYIJHdN47/950c3t9ayl8/2ul2OMZ0uYKyagBrIzGmIy47ZQBn5/Tl/rc+Y9nWUrfDMaZL5ZfVEBMZRmpctNuhfI0lEhM0RIT7LxzF4NReXDt3Ff9Y0zXPL1lfdICbXvyUh/6zjbWFFXZrzbiiebJGz1PGA0u7RrWIyK3As0AVnsfcjgXuUNW3/RibMV+T1DOKBTdMZs5zufzopTz2HazjB6ee4LfjLVxbzE9fXktkeBj/Xl/CH/+zld49o5g2NJXThqYybWgqyT2j/HZ8Y5oVltcE3NQozdo7PPJaVX1IRL4FpALX4EkslkhMl4uPiWTuNRO5fUEev/n3Zr6orOXOc4YRFtZ5f6k1NSkPvrOFR5d8zsSsZB67/GQE+GDbPpZu2cv7W0t5fc1uRGBURiKnD03l9BNTGZWRSHgnxmEMgKpSUF7DlMEpbofiU3sTSfP/jHOBZ1V1rQRi/cp0G56eXCeT2msjT32wk9KqOh64aDRRER2/W3uwroEfzc/jP5u/YNaETO6ZOeLIfs8fm875Y9NpbFLW7z7A+1tKWbp1Lw+/t42H3t1GUmwkp2Z7ksq0oamk9Aq8+9km+JQerKOmvjEge2xB+xPJahF5GxgE3CkicUCbj7ETkRnAQ0A48LSq/q7F8gHA34BEZ507VHWRiHwT+B0QBdQDP1XV95xtlgJpwCFnN2erqo1U64bCw4S7zxvuGQG/eAtl1fU8fvm4Ds1DVFBWww+eW8XnpdXc/d0crpqS5fOedHiYMCYzkTGZidw6PZv91fUs21bK+1tKeX9rKQvXFiMCI9MTGJuZSFpiD9ISYujv/OwbH0NkAD0q1QS2wgCdrLFZe//HfR8YA+xQ1RoRScZze+uoRCQceBT4JlAErBKRhaq6yWu1u4AFqvq4iOQAi4AsYB/wXVUtFpERwGIg3Wu7y1Q1t52xmxAmIvz3GUNIjYvmztfWM/vJ5Tx7zYTjqgl8/Pk+bnzhU1Thb9dM5BvZ7b+NkNQziplj0pk5Jp2mJmVjcSVLt+xl6dZSXvt0N1V1DV9ZP0wgNS6atIQe9E+MIS3hq4mmf2IPUntFd+rtOhO8vpw+PrgTyWQgT1WrReRy4GQ8NY3WTAS2q+oOABGZD8wEvBOJAvHO+wSgGEBV13itsxGIEZFoVbVhzcanS8ZnktIrihtf+JQLH/+Y566dyMBjmEri+eX5/GrhRrJSevL0lePJSjn+Rs2wMGFkRgIjMxK4+axsAKpqD1NyoJbiikOUHKilpOIQxQdqKTlwiM9Kqnjvs73UHv5qJT8mMoxvj+zPZZMGMDYzMSB765iukV9WgwhkJPVwOxSf2ptIHgdGi8ho4H+AZ4DngNNa2SYd8H6YRBFwSot17gbeFpGbgZ7AdB/7uRBY0yKJPCsijcCrwG/Unn5kgDNP6suL103i2rmruPDxj5l7zURGpCe0us3hxibuXriRF1YUcOZJffjTrDHEx0R2emxxMZHExUQytG+cz+WqSkXNYYoPHKKkwpNgNhZX8sbaYl79tIhhafFcdsoAzh+bHnBTiBv/KyyvoX9CD6Ijwt0Oxaf23qRtcL6sZwIPqepDgO//EV/y9edTyy/82cBcVc3A05D/vIgciUlEhgP3A9d7bXOZqo4ETnVeV/g8uMgcEckVkdzSUhu81l2cPCCJV26YQnREOJc+8Qkfbtt31HXLq+u5/OkVvLCigOtPO4GnrhzvlyTSHiJCUs8ohvdPYHpOX66YnMXvLhzFip9P594LRgBw1z82cMq9/+F/X1/PxuIDrsRp3JFfXkNmcmDWRqD9iaRKRO7E86X9b6f9o63/cUVAptfnDJxbV16+DywAUNVPgBggBUBEMoDXgStV9fPmDVR1t/OzCngRzy20r1HVJ1V1vKqOT01NbddJmtAwpE8vXrtxCpnJsVwzdyX/zPv6wMXNJZWc9+cPWVNYwR8vHc2d5wwLyG67vaIjuOyUgSy65Ru8fuMUzhmZxquri/j2wx9y/qMf8XJuIYfq7VktoS6/rIaBATqGBNqfSC4F6vCMJ9mD57bV79vYZhWQLSKDRCQKmAUsbLFOAXAWgIgMw5NISkUkEfg3cKeqftS8sohEiEhzookEvgNsaOc5mG6kb3wML10/mZMHJHHr/Dye/mDHkWWLN+7hwsc/pr6hiQXXT+aCsRkuRto+IsLYAUn838WjWfm/0/nFd3Koqj3MT19Zxym//Y/zJMkqt8M0flBT38C+g3UB22MLQNrbvCAifYEJzseV7elyKyLnAn/C07X3r6p6r4jcA+Sq6kKnp9ZTQC88t73+R1XfFpG7gDuBbV67OxuoBpbhqQ2FA/8BblfVVv8kGz9+vObmWiev7qj2cCO3vZTHmxv2cP20E+gVHcGD72xldEYCT1wxnn4JMW6HeNxUlZU7y/n7igLe2lDC4UbllEHJXDZpIDOG9+uUMTXGfZ/tqWTGnz7gkdlj+e7o/l16bBFZrarj21yvPYlERC7BUwNZiqft41Q8Yzte6WCcXcISSffW2KTcvXAjzy/PB2DmmP7cf+EoYiIDs+HyeOw7WMcrq4t4cUUBBeU1zBjej79cMc7tsEwnWLxxD9c/v5qFN01lVEZilx67vYmkvd0/fg5MaK6FiEgqntpAUCQS072Fhwn3zBzO0L69ALh80sCQ60qb0iuaG04bzJxTT+D+tz7jiWU7+Lz0IINTe7kdmumg5sGIodBGEtbiVlbZMWxrjOtEhCsmZ3HFZN8j1UNFWJjwg1NPICo8jL99vMvtcEwnyC+rIT4mgoRYd3oUtkd7k8FbIrJYRK4WkavxNIQv8l9YxpjjlRoXzXdH9+eV1UVU1h52OxzTQfnlNcc0uNYN7UokqvpT4ElgFDAaeFJVf+bPwIwxx++aqVnU1DeyYFVh2yubgLaj9GDATtbYrN23p1T1VVW9XVVvU9XX/RmUMaZjRqQnMCErib99sotGexBX0Co7WEfR/kOMymh9hga3tZpIRKRKRCp9vKpEpLKrgjTGHLurpwyisPwQ731mk2MHq7zCCgDGZCa5HEnrWk0kqhqnqvE+XnGqGt/atsYYd31reF/6J8Tw7Ec73Q7FHKe8wgrCw4SRbcwZ5zbreWVMiIoID+OKyVl8/HkZW/bYqPdglFdYwYl94+gRFdhjniyRGBPCZk3IJCYyjLkfW60k2DQ1KXmFFYwZ0LWDEI+HJRJjQlhSzyguGJvOa5/uZn91vdvhmGOwY181VbUNjMm0RGKMcdlVU7Koa2hivnUFDirNDe1jLZEYY9x2Ur94pgzuzfOf7KKhsanN9U1gyCvcT1x0RFBMc2OJxJhu4Jqpgyg+UMvijV+4HYppp7zCCkZlJhAWgM/JackSiTHdwJkn9SEzuYc1ugeJ2sONfFZSFRTtI2CJxJhuITxMuGpyFqt27WfDbntMb6DbsPsADU0a8AMRm1kiMaabuHh8JrFR4Tz70S63QzFt+HJEu9VIjDEBJKFHJBeNy+CNtcWUVtW5HY5pxZrCCtITe5AaF+12KO1iicSYbuSqKVnUNzbx4ooCt0MxrcgrCI6BiM0skRjTjQxO7cVpQ1P5+4p86husK3AgKq2qY3fFoaAYP9LMEokx3cw1U7Morapj0foSt0MxPgRb+wj4OZGIyAwR2SIi20XkDh/LB4jIEhFZIyLrRORcr2V3OtttEZFvtXefxpjWTctO5YSUnjz70U5U7VklgSavcD8RYcKIAJ/x15vfEomIhAOPAucAOcBsEclpsdpdwAJVHQvMAh5zts1xPg8HZgCPiUh4O/dpjGlFWJhw9dQs1hYdYI3z168JHHmFFZyUFkdMZGDP+OvNnzWSicB2Vd2hqvXAfGBmi3UUaH6uSQJQ7LyfCcxX1TpV3Qlsd/bXnn0aY9rwvZMziIuOsK7AAaapSVlXeCCobmuBfxNJOuA9S1yRU+btbuByESkCFgE3t7Fte/ZpjGlDr+gILpmQyZvrS9hzoNbtcIzj89KDVNU1BM1AxGb+TCS+JohpeUN2NjBXVTOAc4HnRSSslW3bs0/PwUXmiEiuiOSWlpYeQ9jGdA9XTc6iUZW/L893OxTjWBOEDe3g30RSBGR6fc7gy1tXzb4PLABQ1U+AGCCllW3bs0+c/T2pquNVdXxqamoHTsOY0DSgdyxnndSXF1cWUHu40e1wDJ72kbiYCE5I6el2KMfEn4lkFZAtIoNEJApP4/nCFusUAGcBiMgwPImk1FlvlohEi8ggIBtY2c59GmPa6dqpWZRX17Nwrc+/x0wXyyuoYExmYlDM+OvNb4lEVRuAm4DFwGY8vbM2isg9InKes9qPgetEZC0wD7haPTbiqalsAt4C/ltVG4+2T3+dgzGhbvLg3pzYN45nP9plXYFddqi+kS1fBM+Mv94i/LlzVV2EpxHdu+wXXu83AVOPsu29wL3t2acx5viIeLoC3/naelbsLGfSCb3dDqnbWr/7AI1NGpSJxEa2G9PNnT8mncTYSOZaV2BX5RXuB4KvoR0skRjT7fWICmfWhAG8vWkPheU1bofTbeUVVpCZ3IPevYJjxl9vlkiMMVwxeSAiYl2BXZRXUMHojOCrjYAlEmMMkJ7Yg28N78u8lQVU1zW4HU63s7eyluIDtUF5WwsskRhjHNedegKVtQ3cu2iz26F0O80DEccG0TNIvFkiMcYAMHZAEtdPO4EXVxTw1oY9bofTreQVVhARJgzvHzwz/nqzRGKMOeLHZ59sL1x6AAATbUlEQVTIiPR47nhtnc3B1YXyCioYlhYfVDP+erNEYow5IioijIdmjaXucBM/fjmPpiYbpOhvjU3KuqKKoG0fAUskxpgWBqf24pffzeGj7WU89cEOt8MJedv3HqS6vtESiTEmtFw6IZMZw/vx+8VbWF90wO1wQtqRgYhB2tAOlkiMMT6ICL+7cCQpvaK5df4aauqtS7C/5BVWEB8TwaDewTXjrzdLJMYYnxJjo/jDpaPZWVbNPW9scjuckLWmoILRQTjjrzdLJMaYo5oyOIUbThvM/FWFvLm+xO1wQk51XQNbv6hibBC3j4AlEmNMG26bPpRRGQnc8dp6iisOuR1OSFm/+wBNGtztI2CJxBjThuYuwYcbm7h9QR6N1iW40+Q5I9qDdY6tZpZIjDFtGpTSk7vPG87yHeX85f3P3Q4nZOQVVDAgOTYoZ/z1ZonEGNMuF4/L4Nsj0/jjO1tZ6/wlbTomrzC4ByI2s0RijGkXEeG3F4ykT5ynS7DNEtwxew7UsqcyeGf89WaJxBjTbgmxkfzx0jEUlNdw98KNbocT1JrbR4K9oR0skRhjjtEpJ/TmxtOH8PLqIv61rtjtcIJWXmEFkeFCTlq826F0mF8TiYjMEJEtIrJdRO7wsfyPIpLnvLaKSIVTfoZXeZ6I1IrI+c6yuSKy02vZGH+egzHm626dns2YzETufG09u61L8HHJK9xPThDP+OvNb4lERMKBR4FzgBxgtojkeK+jqrep6hhVHQM8ArzmlC/xKj8TqAHe9tr0p83LVTXPX+dgjPEtMjyMh2aNoalJuW2+dQk+Vo1NyvqiAyHRPgL+rZFMBLar6g5VrQfmAzNbWX82MM9H+UXAm6pa44cYjTHHaWDvntwzcwQrd5Xz2JLtbocTVLbtrfLM+BsC7SPg30SSDhR6fS5yyr5GRAYCg4D3fCyexdcTzL0iss65NRbcHbCNCWLfOzmd80b350/vbuPTgv1uhxM08gqchvbMJJcj6Rz+TCS+ZiA7Wv13FvCKqjZ+ZQciacBIYLFX8Z3AScAEIBn4mc+Di8wRkVwRyS0tLT3W2I0x7SAi/OaCEfSLj+HHC9ZSe7ix7Y0MeYUVJMZGktU71u1QOoU/E0kRkOn1OQM4WhcPX7UOgEuA11X1cHOBqpaoRx3wLJ5baF+jqk+q6nhVHZ+amnpcJ2CMaVt8TCT3XziKnfuqeejdbW6HExTyCisYnZGISPDO+OvNn4lkFZAtIoNEJApPsljYciURORFIAj7xsY+vtZs4tRTEcwXOBzZ0ctzGmGP0jewULhmfwZPLdrBhtz0IqzXNM/6GSkM7+DGRqGoDcBOe21KbgQWqulFE7hGR87xWnQ3MV9Wv3PYSkSw8NZr3W+z6BRFZD6wHUoDf+OcMjDHH4ufn5pDcM4r/eWUdhxub3A4nYK0rCo0Zf71F+HPnqroIWNSi7BctPt99lG134aNxXlXP7LwIjTGdJSE2kl/PHM4Nf/+Upz7YwY2nD3E7pIB0ZER7kM/4681GthtjOs2MEWnMGN6PP/1nGztKD7odTkDKK9xPVu9YknpGuR1Kp7FEYozpVPfMHE5MRBh3vLqeJhuo+DWhMuOvN0skxphO1Sc+hru+ncPKXeW8uLLA7XACSsmBQ3xRWWeJxBhj2nLx+AymDunN7978jJIDNhdXsyMDEQeExkDEZpZIjDGdTkS474JRNDYpP399Ay06ZXZbeYUVRIWHMSwtzu1QOpUlEmOMXwzoHcuPzx7Ke5/tZeFam24eYE1hBTn944mOCP4Zf71ZIjHG+M01UwcxOjORX72xifLqerfDcVVDY1NIzfjrzRKJMcZvwsOEBy4cRVXtYe55o3s/UXHrFwc5dLiRsSE0ELGZJRJjjF+d2C+OG08fwj/yilny2V63w3HNkYGIViMxxphjd+MZg8nu04ufv76eg3UNbofjirzC/ST3jGJAcmjM+OvNEokxxu+iI8K5/6JRlFTW8sBbn7kdjis8M/4mhMyMv94skRhjusTJA5K4ekoWz32Sz6pd5W6H06Wqag+zbe/BkHmQVUuWSIwxXeYnZ59IRlIPfvbqum71EKzV+fvREJvx15slEmNMl+kZHcFvLxjJjtJqHnmvezwEq6GxiQfe2kLf+GgmZFmNxBhjOmza0FQuGpfBE+/vYGNx6D8Ea+7Hu9hUUsmvzhtObJRfn9zhGkskxpgud9e3h5EYG8XPXl1HQwg/BKtofw0Pvr2V6cP68K3h/dwOx28skRhjulxibBT3zBzOht2VPPPhTrfD8QtV5Zf/3IgI/GrmiJDsrdXMEokxxhXnjOjHt4b35Q/vbGWtM1gvlLy1YQ/vfraX2785lPTEHm6H41eWSIwxrhARfn3+CPrGx3D5MytCKplU1h7mlws3kpMWz9VTstwOx+8skRhjXNMnLoZ5cyaRGBvJ5c+sYF1RaCSTBxdvofRgHfd9byQR4aH/NevXMxSRGSKyRUS2i8gdPpb/UUTynNdWEanwWtbotWyhV/kgEVkhIttE5CURCZ0HHxvTDaUn9mD+nMmeZPL0CtYXBXdPrrzCCp5bns9Vk7MYHYLzavnit0QiIuHAo8A5QA4wW0RyvNdR1dtUdYyqjgEeAV7zWnyoeZmqnudVfj/wR1XNBvYD3/fXORhjukZ6Yg/mXTeJ+B6RXPb08qBNJg2NTdz52nr6xEXz47OHuh1Ol/FnjWQisF1Vd6hqPTAfmNnK+rOBea3tUDzdHs4EXnGK/gac3wmxGmNclpEUy/w5nmRy+TMr2LA7+JLJsx/tYrMzZiQuJtLtcLqMPxNJOlDo9bnIKfsaERkIDALe8yqOEZFcEVkuIs3JojdQoarN04cedZ/GmOCTkRTLvOsm0Ss6gsueDq5kUrS/hj+8E/pjRnzxZyLx1Wn6aA9ungW8oqrek+8MUNXxwH8BfxKRwceyTxGZ4ySi3NLS0mOJ2xjjosxkT80kmJKJqvKLbjJmxBd/JpIiINPrcwZwtAc3z6LFbS1VLXZ+7gCWAmOBfUCiiDTPM3DUfarqk6o6XlXHp6amHu85GGNc4J1MLn9mRcBPpfLWhj28103GjPjiz0SyCsh2ellF4UkWC1uuJCInAknAJ15lSSIS7bxPAaYCm1RVgSXARc6qVwH/9OM5GGNckpnsuc0VGxnOZU+vYFNxpdsh+dTdxoz44rdE4rRj3AQsBjYDC1R1o4jcIyLevbBmA/OdJNFsGJArImvxJI7fqeomZ9nPgNtFZDueNpNn/HUOxhh3Degdy/w5k51ksjwgk0l3GzPii3z1+zs0jR8/XnNzc90OwxhznPLLqpn15HJqDzfy4nWTGJYW73ZIgGfMyAWPfcRVk7O4+7zhbofT6URktdNW3arumT6NMUFlYO+ezJ8ziRjnNtdne9yvmXTXMSO+WCIxxgSFgb17Mu+6SUSFh/FfT7mfTLrrmBFfLJEYY4JGVoqnZtKcTD7cto/6hq5/nkl3HjPiS2g+rssYE7KyUnoyb84kZj+5nMufWUFMZBhjM5OYOCiZUwYlM3ZAEj2iwv12/O4+ZsQXSyTGmKAzKKUnb98+jY+3l7FyZzkrd5XxyHvbeEghIkwYmZHAxEHJTMxKZvzAZBJiO+/WU/OYkbu+PaxbjhnxxXptGWNCQmXtYVbn72fVznJW7ixnbVEFhxsVETipXzynDEpmQlYyEwYl0Scu5riPMf3B90npFc3Cm6aGfHff9vbashqJMSYkxMdEcsaJfTjjxD4A1B5uZE1BBSt3lrNqVzkvrSpk7se7AM9swz2jwwkPCyM8DM9PgYiwMMLD5MgrIkwIc36Ghwm7Kw5RerCOp64cH/JJ5FhYIjHGhKSYyHAmD+7N5MG9ATjc2MSG3QdYubOcjcWV1Dc00ahKY5PS0KQ0NSkNTU00NDVR1/BleaP3S5WfzTip2zxnpL0skRhjuoXI8DDGDkhi7IAkt0MJOVY3M8YY0yGWSIwxxnSIJRJjjDEdYonEGGNMh1giMcYY0yGWSIwxxnSIJRJjjDEdYonEGGNMh3SLubZEpBTI78AuEoADftimrXVaW360Zb7KW5alAPvaiM0fjuf32Bn7sGvxdXYtQutaHM9+2rP+QFVNbXNPqmqvNl7Ak/7Ypq11Wlt+tGW+yluWAbnB8nu0a2HXwq6Ff/bTWcdVVbu11U5v+GmbttZpbfnRlvkqP574/aEz4rBr0TnsWoTWtTie/XTa+XeLW1vmq0QkV9sxNbTxP7sWgcOuxfGzGkn39KTbAZgj7FoEDrsWx8lqJMYYYzrEaiTGGGM6xBKJMcaYDrFEYowxpkMskZivEJFhIvIXEXlFRH7odjzdmYicLyJPicg/ReRst+PpzkTkBBF5RkRecTuWQGSJJISIyF9FZK+IbGhRPkNEtojIdhG5o7V9qOpmVb0BuASwrpDHqZOuxT9U9TrgauBSP4Yb0jrpWuxQ1e/7N9LgZb22QoiITAMOAs+p6ginLBzYCnwTKAJWAbOBcOC+Fru4VlX3ish5wB3An1X1xa6KP5R01rVwtnsQeEFVP+2i8ENKJ1+LV1T1oq6KPVhEuB2A6TyqukxEsloUTwS2q+oOABGZD8xU1fuA7xxlPwuBhSLyb8ASyXHojGshIgL8DnjTksjx66z/F+bo7NZW6EsHCr0+FzllPonI6SLysIg8ASzyd3DdzDFdC+BmYDpwkYjc4M/AuqFj/X/RW0T+AowVkTv9HVywsRpJ6BMfZUe9n6mqS4Gl/gqmmzvWa/Ew8LD/wunWjvValAGWzI/CaiShrwjI9PqcARS7FEt3Z9cicNi16ESWSELfKiBbRAaJSBQwC1jockzdlV2LwGHXohNZIgkhIjIP+AQ4UUSKROT7qtoA3AQsBjYDC1R1o5txdgd2LQKHXQv/s+6/xhhjOsRqJMYYYzrEEokxxpgOsURijDGmQyyRGGOM6RBLJMYYYzrEEokxxpgOsURiAo6IHOyCY5zX1tThfjjm6SIy5Ti2GysiTzvvrxaRP3d+dMdORLJaTs3uY51UEXmrq2Iy7rBEYkKWM1W4T6q6UFV/54djtjZ/3enAMScS4H+BR44rIJepailQIiJT3Y7F+I8lEhPQROSnIrJKRNaJyK+8yv8hIqtFZKOIzPEqPygi94jICmCyiOwSkV+JyKcisl5ETnLWO/KXvYjMdWY8/lhEdojIRU55mIg85hzjXyKyqHlZixiXishvReR94FYR+a6IrBCRNSLyHxHp60xjfgNwm4jkicipzl/rrzrnt8rXl62IxAGjVHWtj2UDReRd53fzrogMcMoHi8hyZ5/3+KrhiUhPEfm3iKwVkQ0icqlTPsH5PawVkZUiEufUPD5wfoef+qpViUi4iPze61pd77X4H8BlPi+wCQ2qai97BdQLOOj8PBt4Es9MrWHAv4BpzrJk52cPYAPQ2/mswCVe+9oF3Oy8vxF42nl/NZ4HdwHMBV52jpGD5zkVABfhmUo/DOgH7Acu8hHvUuAxr89JfDlrxA+AB533dwM/8VrvReAbzvsBwGYf+z4DeNXrs3fcbwBXOe+vBf7hvP8XMNt5f0Pz77PFfi8EnvL6nABEATuACU5ZPJ4ZwmOBGKcsG8h13mcBG5z3c4C7nPfRQC4wyPmcDqx3+9+Vvfz3smnkTSA723mtcT73wvNFtgy4RUQucMoznfIyoBF4tcV+XnN+rga+d5Rj/UNVm4BNItLXKfsG8LJTvkdElrQS60te7zOAl0QkDc+X886jbDMdyPE8vwqAeBGJU9Uqr3XSgNKjbD/Z63yeBx7wKj/fef8i8H8+tl0P/J+I3A/8S1U/EJGRQImqrgJQ1Urw1F6AP4vIGDy/36E+9nc2MMqrxpaA55rsBPYC/Y9yDiYEWCIxgUyA+1T1ia8UipyO50t4sqrWiMhSIMZZXKuqjS32U+f8bOTo/+brvN5Li5/tUe31/hHgD6q60In17qNsE4bnHA61st9DfHlubWn3xHmqulVExgHnAveJyNt4bkH52sdtwBfAaCfmWh/rCJ6a32Ify2LwnIcJUdZGYgLZYuBaEekFICLpItIHz1+7+50kchIwyU/H/xC40Gkr6Yunsbw9EoDdzvurvMqrgDivz2/jmYEWAOcv/pY2A0OOcpyP8Ux/Dp42iA+d98vx3LrCa/lXiEh/oEZV/46nxnIy8BnQX0QmOOvEOZ0HEvDUVJqAK/A817ylxcAPRSTS2XaoU5MBTw2m1d5dJrhZIjEBS1XfxnNr5hMRWQ+8gueL+C0gQkTWAb/G88XpD6/ieQDSBuAJYAVwoB3b3Q28LCIfAPu8yt8ALmhubAduAcY7jdOb8PEEPlX9DEhwGt1bugW4xvk9XAHc6pT/CLhdRFbiuTXmK+aRwEoRyQN+DvxGVeuBS4FHRGQt8A6e2sRjwFUishxPUqj2sb+ngU3Ap06X4Cf4svZ3BvBvH9uYEGHTyBvTChHppaoHRaQ3sBKYqqp7ujiG24AqVX26nevHAodUVUVkFp6G95l+DbL1eJYBM1V1v1sxGP+yNhJjWvcvEUnE02j+665OIo7HgYuPYf1xeBrHBajA06PLFSKSiqe9yJJICLMaiTHGmA6xNhJjjDEdYonEGGNMh1giMcYY0yGWSIwxxnSIJRJjjDEdYonEGGNMh/x/d86wbvgs1TMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73fe8daeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "808ee28eafe6437a9d90f5a93084e133",
       "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=9), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.      0.60679 0.48634 0.73296]                          \n",
      "[1.      0.51527 0.43851 0.73579]                          \n",
      "[2.      0.46867 0.44946 0.71591]                          \n",
      "[3.      0.4282  0.43881 0.72727]                          \n",
      "[4.      0.41189 0.43    0.72159]                          \n",
      "[5.      0.41938 0.39905 0.83239]                          \n",
      "[6.      0.37819 0.40546 0.83239]                          \n",
      "[7.      0.39385 0.40983 0.82671]                          \n",
      "[8.      0.39491 0.39256 0.83523]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lr, 9)\n",
    "learn.precompute=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent with Restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e8f86f7148b4f4f9fc88dc7e1f12ab3",
       "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=31), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.      0.59909 0.4257  0.8125 ]                          \n",
      "[1.      0.5199  0.39655 0.80966]                          \n",
      "[2.      0.50147 0.45081 0.78125]                          \n",
      "[3.      0.47486 0.38309 0.82386]                          \n",
      "[4.      0.44304 0.43014 0.80398]                          \n",
      "[5.      0.42511 0.41297 0.8125 ]                          \n",
      "[6.      0.42028 0.39394 0.82671]                          \n",
      "[7.      0.45028 0.43417 0.81534]                          \n",
      "[8.      0.45135 0.39994 0.80682]                          \n",
      "[9.      0.43844 0.44165 0.78409]                          \n",
      "[10.       0.44523  0.44652  0.76704]                      \n",
      "[11.       0.44388  0.44615  0.78409]                      \n",
      "[12.       0.42727  0.52609  0.74148]                      \n",
      "[13.       0.40698  0.46453  0.76421]                      \n",
      "[14.       0.40391  0.43885  0.77841]                      \n",
      "[15.       0.46122  0.42196  0.80398]                      \n",
      "[16.       0.4214   0.4589   0.76421]                      \n",
      "[17.       0.43126  0.45913  0.76704]                      \n",
      "[18.       0.43334  0.4462   0.76989]                      \n",
      "[19.       0.44066  0.42405  0.79261]                      \n",
      "[20.       0.44702  0.45838  0.78977]                      \n",
      "[21.       0.44791  0.45341  0.78125]                      \n",
      "[22.       0.43413  0.44822  0.77273]                      \n",
      "[23.       0.44027  0.48007  0.77273]                      \n",
      "[24.       0.41081  0.4248   0.79829]                      \n",
      "[25.       0.42236  0.44573  0.78125]                      \n",
      "[26.       0.40003  0.41435  0.80398]                      \n",
      "[27.       0.41681  0.45209  0.78409]                      \n",
      "[28.       0.39525  0.45145  0.78125]                      \n",
      "[29.       0.41431  0.47245  0.76704]                      \n",
      "[30.       0.40815  0.4432   0.79546]                      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#learn.fit(lr, 4, cycle_len=2)\n",
    "learn.fit(lr, 5, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73fc6cda58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73fc318ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.3396357865754324, 0.8504672897196262)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_preds,y = learn.TTA()\n",
    "preds = np.mean(np.exp(log_preds),axis=0)\n",
    "(metrics.log_loss(y,preds), accuracy(preds,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr/9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr/4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unfreeze Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unfreeze layers\n",
    "learn.unfreeze()\n",
    "#lrs = np.array([lr/15,lr/12,lr/9])\n",
    "lrs = np.array([lr/9,lr/3,lr/1])\n",
    "\n",
    "#wd = 0.025\n",
    "#wds=[wd/100, wd/10, wd]\n",
    "#wds=[0.0025,0.025,0.25]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Learner Again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f8e070e067e407fa37f86632df8facb",
       "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=31), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.      0.57576 0.42629 0.81818]                          \n",
      "[1.      0.52157 0.48031 0.74148]                          \n",
      "[2.      0.45319 0.38832 0.80398]                          \n",
      "[3.      0.42541 0.41028 0.79829]                          \n",
      "[4.      0.42329 0.45004 0.79829]                          \n",
      "[5.      0.40144 0.43054 0.80682]                          \n",
      "[6.      0.40063 0.45828 0.79261]                          \n",
      "[7.      0.44552 0.5067  0.76136]                          \n",
      "[8.      0.41576 0.42427 0.80398]                          \n",
      "[9.      0.4362  0.42194 0.78125]                          \n",
      "[10.       0.41374  0.49151  0.77557]                      \n",
      "[11.       0.37177  0.42916  0.79546]                      \n",
      "[12.       0.35699  0.38548  0.8125 ]                      \n",
      "[13.       0.32826  0.47232  0.78125]                      \n",
      "[14.       0.31694  0.38433  0.83239]                      \n",
      "[15.       0.34859  0.34402  0.83239]                      \n",
      "[16.       0.33347  0.30857  0.86932]                      \n",
      "[17.       0.33336  0.32171  0.86932]                      \n",
      "[18.       0.35611  0.28111  0.86364]                      \n",
      "[19.       0.34663  0.35448  0.82671]                      \n",
      "[20.       0.33708  0.33888  0.84659]                      \n",
      "[21.       0.30676  0.30296  0.86364]                      \n",
      "[22.       0.2949   0.28412  0.875  ]                      \n",
      "[23.       0.2714   0.33506  0.86079]                      \n",
      "[24.       0.2596   0.30215  0.85796]                      \n",
      "[25.       0.26618  0.3142   0.86648]                      \n",
      "[26.       0.25771  0.28973  0.88068]                      \n",
      "[27.       0.30018  0.35028  0.83523]                      \n",
      "[28.       0.2676   0.28541  0.88352]                      \n",
      "[29.       0.28751  0.31519  0.86364]                      \n",
      "[30.       0.29191  0.28941  0.87784]                      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lrs, 5, cycle_len=1, cycle_mult=2)\n",
    "#learn.fit(lrs, 5, cycle_len=1, cycle_mult=2,use_wd_sched=True,wds=wds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73fc2e8e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73fc312588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.24919914955793604, 0.897196261682243)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_preds,y = learn.TTA()\n",
    "preds = np.mean(np.exp(log_preds),axis=0)\n",
    "(metrics.log_loss(y,preds), accuracy(preds,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resize the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(sz, bs):\n",
    "    tfms = tfms_from_model(arch, sz, aug_tfms=transforms_top_down, max_zoom=1.00)\n",
    "    data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=bs,test_name='test')\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Size of 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8a9805023d443659ecf4f111e3dafaf",
       "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=7), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.      0.3757  0.38331 0.81818]                          \n",
      "[1.      0.34228 0.41343 0.79829]                          \n",
      "[2.      0.33356 0.4013  0.80966]                          \n",
      "[3.      0.33388 0.39797 0.80398]                          \n",
      "[4.      0.34251 0.36027 0.82671]                          \n",
      "[5.      0.32427 0.35276 0.82671]                          \n",
      "[6.      0.35802 0.40913 0.80398]                          \n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a57928d77f84a89b1bd9b2285f08d19",
       "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=7), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.      0.44931 0.33488 0.84943]                          \n",
      "[1.      0.42435 0.3493  0.83807]                          \n",
      "[2.      0.35199 0.3361  0.84091]                          \n",
      "[3.      0.31681 0.38249 0.83807]                          \n",
      "[4.      0.33171 0.48417 0.79261]                          \n",
      "[5.      0.31607 0.30041 0.86079]                          \n",
      "[6.      0.33585 0.28196 0.89204]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#train for image size 128x128\n",
    "learn.set_data(get_data(128, 32))\n",
    "\n",
    "learn.freeze()\n",
    "learn.fit(lr, 3, cycle_len=1, cycle_mult=2)\n",
    "\n",
    "learn.unfreeze()\n",
    "learn.fit(lr, 3, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.2448391106989339, 0.897196261682243)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_preds,y = learn.TTA()\n",
    "preds = np.mean(np.exp(log_preds),axis=0)\n",
    "(metrics.log_loss(y,preds), accuracy(preds,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73fc30b048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73fc6c2160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Size of 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4217b5dc418c4ec89071fd1ec1b69b89",
       "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=7), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.      0.35423 0.32059 0.85714]                          \n",
      "[1.      0.3702  0.29738 0.88393]                          \n",
      "[2.      0.33418 0.30765 0.87202]                          \n",
      "[3.      0.3416  0.30127 0.88095]                          \n",
      "[4.      0.37097 0.30204 0.86905]                          \n",
      "[5.      0.32986 0.28355 0.875  ]                          \n",
      "[6.      0.33329 0.29331 0.86905]                          \n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7197833d3b73403f8b94493ca316df57",
       "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=7), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.      0.36567 0.29676 0.86905]                          \n",
      "[1.      0.3583  0.26473 0.88393]                          \n",
      "[2.      0.33676 0.25124 0.89286]                          \n",
      "[3.      0.52857 0.38606 0.80952]                          \n",
      "[4.      0.48973 0.60004 0.71429]                          \n",
      "[5.      0.38987 0.34873 0.84226]                          \n",
      "[6.      0.34875 0.39705 0.80952]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#train for image size 200 x 200\n",
    "learn.set_data(get_data(200, 16))\n",
    "\n",
    "learn.freeze()\n",
    "learn.fit(lr, 3, cycle_len=1, cycle_mult=2)\n",
    "\n",
    "learn.unfreeze()\n",
    "learn.fit(lr, 3, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73fc5947b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f73fc575940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.2795494182964972, 0.8847352024922118)"
      ]
     },
     "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",
    "(metrics.log_loss(y,preds), accuracy(preds,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learn.save('resnext50_sz75_sequ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learn.load('resnext50_sz75_sequ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output predictions to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#helper.make_submission(learn=learn, data=data, output_file_name = f'{PATH}sub/resnext50_sz75_sequ.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# END FILE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Learner again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b71c4b49997643c982aae6e9f00ac577",
       "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=31), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.      0.37915 0.42718 0.80655]                          \n",
      "[1.      0.36574 0.36442 0.83036]                          \n",
      "[2.      0.36762 0.30271 0.84524]                          \n",
      "[3.      0.36753 0.31425 0.86607]                          \n",
      "[4.      0.34616 0.34201 0.84821]                          \n",
      "[5.      0.3494  0.3042  0.85714]                          \n",
      "[6.      0.30073 0.29245 0.86012]                          \n",
      "[7.      0.38146 0.36861 0.83333]                          \n",
      "[8.      0.3426  0.34074 0.83333]                          \n",
      "  1%|          | 1/81 [00:00<00:52,  1.53it/s, loss=0.34]"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-48-7c0fb4361d6d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle_len\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle_mult\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/fastai/fastai/learner.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, lrs, n_cycle, wds, **kwargs)\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0mlayer_opt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_gen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayer_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_cycle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwarm_up\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/fastai/fastai/learner.py\u001b[0m in \u001b[0;36mfit_gen\u001b[0;34m(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, use_clr, metrics, callbacks, use_wd_sched, norm_wds, wds_sched_mult, **kwargs)\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0mn_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum_geom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcycle_len\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcycle_len\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle_mult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_cycle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m         fit(model, data, n_epoch, layer_opt.opt, self.crit,\n\u001b[0;32m--> 156\u001b[0;31m             metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, **kwargs)\n\u001b[0m\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/fastai/fastai/model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(model, data, epochs, opt, crit, metrics, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m             \u001b[0mbatch_num\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstepper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m             \u001b[0mavg_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg_loss\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mavg_mom\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mavg_mom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m             \u001b[0mdebias_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg_loss\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mavg_mom\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mbatch_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/fastai/fastai/model.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, xs, y)\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m:\u001b[0m   \u001b[0;31m# Gradient clipping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m             \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip_grad_norm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainable_params_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mraw_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/src/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/optim/sgd.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m     98\u001b[0m                         \u001b[0md_p\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m                 \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md_p\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "learn.fit(lrs, 5, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_preds,y = learn.TTA()\n",
    "preds = np.mean(np.exp(log_preds),axis=0)\n",
    "(metrics.log_loss(y,preds), accuracy(preds,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Learner again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.fit(lrs, 5, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log Loss | Accuracy (final model here)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_preds,y = learn.TTA()\n",
    "preds = np.mean(np.exp(log_preds),axis=0)\n",
    "(metrics.log_loss(y,preds), accuracy(preds,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_df = pd.read_json(PATH+\"/test.json\")\n",
    "# helper.make_test_file(log_preds,test_df,model_name='resnext50_sz75_zm15_bs32_comp_band',\n",
    "#                       classes=data.classes,\n",
    "#                       submission_folder =f'{PATH}sub'\n",
    "#                      )"
   ]
  }
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