7_6_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 4788\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  254485 Jan 20 19:36 7_5_resnext50_more_epoch_update_sz.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  246672 Jan 20 19:37 7_6_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": "a26883dc44f14071bf67ea626253a420",
       "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": "1aa9dae10502449198258fea3a0ac6fc",
       "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, 26.35it/s, loss=7.22] \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 0x7fa401d7ae80>"
      ]
     },
     "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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mOh2Gx1qdeojps1bx1NT+XDq8s9PhKNXorvrPalKzC/juz2fh5fXrLttD+cXMeHsd6/Ye4d5JvZlxZjeawuNjIrLOGJNQWz19gls1iOFd29G7Qwhvrtzbom4XVMod+3KP831KDhcNja42UQCEBfvz7o0j+M2Ajjz51Xbu/zSJsvKKUz52bmEJOw/aP+yOj+1HUB5BRLh6VCz3fbKF1XsOM7JbmNMhKdVo5q3LwBi4aGh0jfUCfL156dLBdG7XileX7WbfkeO8fMUQgv3d/yourzBszshl+c5slu/MZlN6Lv2jQvns1tNP9WPUSJOFajAXDu7EPxfv5JHPtzH/1tPw9daGq2r5KioMH61LZ3T3MGLataq1vpeX8JeJvencrhUPfJrERa/+yH+vG0bH0MCTbpOVV8Tyndms2JXDd7uyyS0sRQQGRLfh1nFxjOlp/92gmixUg2nl58Ojk/tx8zvreO27VG4Z28PpkJSy3ao9h0g/fJz/O6dXnba7bHhnotoEcsu767nw5R+Yfe0w+nYKBaCkrILEvYdZsTOH5TuzSc7MA1xjsp3dO5IxvSI4vUc47YL8GvzznIwmC9WgJvbrwMS+Hfjn4l1M7NuBbhHBToeklK3mJmYQ4u/DuX071HnbM3tGMPf3o7j+v2u5eOZKZpzZjaR9eazcnUNBSTk+XkJCbFv+PLEXY3pG0KdD65P2idhN74ZSDS4rr4jxzy+nd8fWvH/TSMd+uZWyW15RKcMfX8zUIdE8MaV/vfdzMK+I699Yy9b9eUS3DWRMzwjG9IxgVPcwQgJ8GzDiX3P3bihtWagG1751APf/pg9/mbeF99amccWILk6HpJQtvtycSVFpxa+erairyNYBfPqH08jJL6ZD64AmcUttVdoDqWxxSUIMo7uH8dSC7Rw4WuR0OErZ4sPEdOLaBzMwOvSU9+Xr7UXH0MAmmShAk4WyiYjw5NT+lFZU8MCnSfrshWpxUrKOsSEtl0sSYprsF3xD0mShbNMlLIi7zunJ4uSDLNhywOlwlGpQHyVm4O0lXDi46swLLZMmC2Wr60/rSv+oUB6an0RuYYnT4SjVIErLK5i3fh/jercnIsTf6XAahSYLZSsfby+enjaAI4WlPPalDhysWoblO7LJyS8+5Y7t5kSThbJdfKfW/O7Mbsxdl8F3u3SSKtX8fZiYTniwH2N7ec48OposVKO4/ew4uoUHce/HWygsKXM6HKXqLSe/mG+3ZzF1SLRHDWnjOZ9UOSrA15snp/Yn48hxnv96p9PhKFVvn27YR1mF4eJaBg1saTRZqEYzolsYV4zozOwf9rApPdfpcJSqM2MMHyamMyimDXGRIU6H06g0WahGdc+k3rQPCeAv8zZTUnbqY/kr1Zg2Zxxl58F8j+rYPkGThWpUIQG+PHZhP7YfOMa/l+92Ohyl6uTDxHQCfL04f2BHp0NpdJosVKMbHx/J+QM68tK3KaRk2T/Dl1INoai0nPmb9jOpX0da2zy4X1Nka7IQkYkiskNEUkTknmrWdxGRJSKyWUSWiUh0pXWdReRrEUkWkW0iEmtnrKpxPfTbvgT6eXPPvC1UVOhQIKrpW7T1AMeKyjyuY/sE25KFiHgDLwOTgHjgMhGJr1Lt78BbxpgBwKPAk5XWvQU8a4zpAwwHsuyKVTW+iBB//np+PIl7j/Du6r1Oh6NUrT5KzCC6baDHThlsZ8tiOJBijEk1xpQA7wOTq9SJB5ZYy0tPrLeSio8x5hsAY0y+MabQxliVA6YNieKMuHCe+mo7+3OPOx2OUieVfriQH3bncPHQGI+dn8XOZBEFpFd6n2GVVbYJmGYtTwFCRCQM6AnkisjHIrJBRJ61WiqqBRERnpjSnwqDjkyrmrR56zMAmDbUMwYNrI6dyaK69Fv12+BuYIyIbADGAPuAMlyTMp1hrR8GdAOu/dUBRGaISKKIJGZn6zASzVFMu1bcfW4vvt2exaKtB50OR6lfqagwzF2XwWndw4lu28rpcBxjZ7LIACrfjBwN7K9cwRiz3xgz1RgzGLjfKjtqbbvBuoRVBnwKDKl6AGPMLGNMgjEmISLCc8ZoaWmuGdWFHu2DeWbhdkrL9dkL1bSsSj1ExpHjXJzgmR3bJ9iZLNYCcSLSVUT8gEuB+ZUriEi4iJyI4V5gdqVt24rIiQwwDthmY6zKQT7eXvxlYm9Scwr4YG167Rso1Yg+WpdBSIAP5/bt4HQojrItWVgtgluBRUAy8KExZquIPCoiF1jVxgI7RGQnEAk8bm1bjusS1BIR2YLrktZrdsWqnDe+T3uGx7bjn4t3UVCsAw2qpiGvqJQFWzKZPKgTAb6e3W3qY+fOjTELgAVVyh6stDwXmHuSbb8BBtgZn2o6RIR7zuvN1Fd+5LXvUvnj+J5Oh6QUn2/aT3FZBRcP9bzhPapyq2UhIneISGtx+Y+IrBeRCXYHpzzLkM5tmdSvA7NWpJJ9rNjpcJTio8QMekWGMCA61OlQHOfuZajrjTF5wAQgArgOeMq2qJTH+tO5vSgpq+DFJbucDkV5uNTsfDam53JxQjQinvlsRWXuJosTZ+o84L/GmE1Uf2usUqekW0Qwlw3vzJw1aaRm5zsdjvJg69Ncw+h70mx4NXE3WawTka9xJYtFIhIC6D2Oyha3nx1HgI8Xzy7a4XQoyoMlZ+bh7+NFbFiQ06E0Ce4mixuAe4Bh1rAbvrguRSnV4CJC/JlxZne+SjrA+rQjToejPFRyZh69OoTg40FTp9bE3bMwCthhjMkVkSuBB4Cj9oWlPN2NZ3QlPNifJxck6zAgqtEZY0jOzKNPh9ZOh9JkuJssXgUKRWQg8GdgL65RYZWyRZC/D38cH8fan46wOFkHHFaN62BeMUcKS4nvpMniBHeTRZlx/Xk3GXjBGPMC4FkT0KpGN31YDN0ignh64XbKdBgQ1Yi2ZbounPTpqMniBHeTxTERuRe4CvjSGgHW86aKUo3K19uLP5/bm5SsfD5al+F0OMqDJGe6ZnDs3VH/Jj7B3WQxHSjG9bzFAVxDjT9rW1RKWc7tG8nQLm35xzc7KSzRYUBU49iWmUd020CPnD71ZNxKFlaCeBcIFZHzgSJjjPZZKNuJCPdO6k3WsWJmf7/H6XCUh0jOzCNeL0H9grvDfVwCrAEuBi4BVovIRXYGptQJCbHtmBAfyczlqRzK12FAlL0KS8rYk1Og/RVVuHsZ6n5cz1hcY4y5GteUqX+1LyylfunPE3tzvLScl75NcToU1cLtOHAMY7Rzuyp3k4WXMaby/YuH6rCtUqesR/tgpg+L4Z1Ve/kpp+CU9lVUWs6xotIGiky1NCc6t/Uy1C+5+4W/UEQWici1InIt8CVVhh5Xym5/PDsOX28vnv26fsOA5BaW8OKSXYx+6luGPraY179LpaJCH/hTv5ScmUewvw/RbQOdDqVJcWs+C2PMn0RkGnAargEEZxljPrE1MqWqaN86gJvO6MqL36Yw44xcBsa0cWu7/bnH+c/3e3hvTRqFJeWc1SsCby/hsS+T+XrbQf5+0UA6h3nu3Mrql5Iz8+jTMQQvLx0rtTK3Jz8yxswD5tkYi1K1mjGmO++uTuOJBcm8P2NkjUNH7zp4jJnLU/ls4z4McMHATvxuTDd6d2iNMYaP1+/j4flbmfjCCu7/TR8uH95Zh6L2cBUVrmE+pg317Pm2q1NjshCRY0B17XQBjDFGL+qpRhXs78Md4+N48LOtLN2Rxbjekb+qs27vYV5dlsri5IME+Hpx5cgu3HB6V2La/a/1ICJMGxrNqO5h/HnuZu7/JIlFWw/yzLQBdAgNaMyPpJqQ9COFFJSUa+d2NWpMFsYYfXxRNTmXDe/M7O/38PRXOxjTsz3eXkJFhWHpjixmLt/N2p+O0KaVL3ecHcc1o2NpF+R30n11ahPI2zcM551Ve3liwXYm/GM5j0zuy4WDorSV4YGSM/MAvROqOrbOwS0iE4EXAG/gdWPMU1XWdwFm45p97zBwpTEmo9L61kAy8Ikx5lY7Y1XNh6+3F3+e2Jtb3l3PB2vTCfD1Yuby3ew8mE+n0AAePD+eS4fH0MrPvV9vEeGqUbGcERfB3R9t4s4PNrEo6SCPTelHeLC/zZ9GNSXbMo/hJdArUv9Orsq2ZGGNH/UycA6QAawVkfnGmG2Vqv0deMsY86aIjAOexDX+1Al/A5bbFaNqvib168CgmDbc98kWAHpGBvP8JQP57cBO+NZz/oHY8CA++N0oXv8ulee+3sm5/1jB41P6M7Ffh4YMXTVhyZl5dA0PItDP2+lQmhw7n5UYDqQYY1KNMSXA+7hGra0sHlhiLS+tvF5EhgKRwNc2xqiaKRHhsQv7cV7/DvznmgQW3nEmU4dE1ztRnODtJfxuTHe+uP10OrYJ4OZ31nHnBxs5WqjPZXiCbfvz9BLUSdiZLKKA9ErvM6yyyjYB06zlKUCIiISJiBfwHPAnG+NTzVy/qFBeuWIoZ/eJbPDbHHtGhvDJLadxx9lxzN+0n3P/uYLlO7Mb9BiqaTl6vJR9ucc1WZyEnX0W1f3vrXpn1d3Av6wH/VYA+4Ay4BZggTEmvaZORhGZAcwA6Ny5cwOErNT/+Hp7cec5PRnfJ5K7PtzINbPXMLxrOzqGBtAuyI/wYH/aBfkRFuRHWLAf7YL8CQv2I8TfRzvHm6HtVue2TnhUPTuTRQYQU+l9NLC/cgVjzH5gKoCIBAPTjDFHRWQUcIaI3AIEA34ikm+MuafK9rOAWQAJCQn6KK6yRf/oUD6/7XT+9W0K36fksCEtl8MFJeQXVz9kup+3F+2C/FyJJNiVVBJi2zJ5UBTB/rbeU6JOwYk7oXSYj+rZ+Zu7FogTka64WgyXApdXriAi4cBhY0wFcC+uO6MwxlxRqc61QELVRKFUYwrw9ebuc3tx97m9fi4rKi3ncEEJh/JLOFRQzKH8Etf7ghIO5RdzuKCEnIISUrLy+WTDPp74MpkLBkVxxYjO9IsKdfDTqOokZx6jXZAf7UP0Drjq2JYsjDFlInIrsAjXrbOzjTFbReRRINEYMx8YCzwpIgbXZag/2BWPUg0twNebTm0C6dSm5jGEjDFsTM9lzuo0PtmQwXtr0hgQHcrlwztzwaBObt/iq+y1zRrmQy8hVk9cU2s3fwkJCSYxMdHpMJSq0dHjpXyyPoM5a9LYeTCfYH8fLhzcicuHd9Fr5Q4qK68g/qFFXD2yCw+cH+90OI1KRNYZYxJqq6d/0ijViEIDfbn2tK5cMzqWdXuPMGd1Gh8mZvDOqjQGd27D5cM7c/6ATnqffyPbk1NASVmFJuwa6JwUSjlAREiIbcfz0wex5r6z+ev58eQdL+VPczcz4onFPDx/KzsPHnM6TI+xTYf5qJUmC6Uc1qaVHzec3pXFd43hgxkjGdurPXNWpzHhHyt4d/Vep8PzCNsy8/D1FrpHBDsdSpOll6GUaiJEhBHdwhjRLYzDBSXcOmc9Ty3Yzjl9ImnfWkfCtVNy5jF6tA/Bz0f/fj4ZPTNKNUHtgvx4Ykp/issreHxBstPhtHjJmXn6fEUtNFko1UTFhgdx85jufLZxPz+m5DgdTouVk19M9rFi+nTUkWZroslCqSbslrHd6dyuFQ98lkRJWYXT4bRI+uS2ezRZKNWEBfh688jkvqRmF/Dad6lOh9Mibduvd0K5Q5OFUk3cWb3aM7FvB176dhfphwudDqfFSc7Mo0PrANrWMKOi0mShVLPw4G/j8RLhkc+31V5Z1Uly5jF9GM8NmiyUagY6tQnkjrPjWJx8kG+2HXQ6nBajuKyc3dn52rntBk0WSjUT15/elbj2wTw8fyvHS8qdDqdF2HUwn7IKo/0VbtBkoVQz4evtxWMX9mNf7nH+tXSX0+G0CDrMh/s0WSjVjIzoFsbUIVHMWpFKSla+0+E0e8mZeQT6ehMbFuR0KE2eJgulmpl7J/Uh0NebBz9LoqVMMeCU5Mw8enUIwbuB53BviTRZKNXMRIT486eJvflx9yHmb9pf+waqWsYYkjOP6SUoN2myUKoZunx4ZwZEh/K3L5LJKyp1Opxmaf/RIo4eLyVe74RyiyYLpZohby/hsQv7caigmOe/3ul0OM1SsvXktj5j4R5NFko1UwOi23DliC68tfJJbwNhAAAV+UlEQVQnkvYddTqcZufEmFC9OmiycIetyUJEJorIDhFJEZF7qlnfRUSWiMhmEVkmItFW+SARWSkiW6110+2MU6nm6u4JvWgX5Mf9nyZRUaGd3XWRfCCPLmGtCPbXaX3cYVuyEBFv4GVgEhAPXCYiVWdC/zvwljFmAPAo8KRVXghcbYzpC0wE/ikibeyKVanmKrSVL/ed14dN6bm8vzbd6XCaleTMY/TRVoXb7GxZDAdSjDGpxpgS4H1gcpU68cASa3npifXGmJ3GmF3W8n4gC4iwMValmq0pg6MY3rUdTy/czqH8YqfDaRYKisv46VCB3glVB3Ymiyig8p86GVZZZZuAadbyFCBERMIqVxCR4YAfsNumOJVq1kRcnd0FxWU89dV2p8NpFrYfOIYx2rldF3Ymi+qecql6UfVuYIyIbADGAPuAsp93INIReBu4zhjzq5lfRGSGiCSKSGJ2dnbDRa5UM9MzMoQbzujKR+syWPvTYafDafKSfx7mQ2+bdZedySIDiKn0Phr4xRNExpj9xpipxpjBwP1W2VEAEWkNfAk8YIxZVd0BjDGzjDEJxpiEiAi9SqU82+3j4ugUGsADnyRRWq6z6tUkOTOP1gE+RLUJdDqUZsPOZLEWiBORriLiB1wKzK9cQUTCReREDPcCs61yP+ATXJ3fH9kYo1ItRpC/Dw/+ti87Dh7jjR9+cjqcJi05M4/eHVsjosN8uMu2ZGGMKQNuBRYBycCHxpitIvKoiFxgVRsL7BCRnUAk8LhVfglwJnCtiGy0XoPsilWpluLcvpGc1SuCfy7eSebR406H0yRVVBi2Hzimc27Xka3PWRhjFhhjehpjuhtjHrfKHjTGzLeW5xpj4qw6Nxpjiq3yd4wxvsaYQZVeG+2MVamWQER45IJ+lFUYHtVZ9aq193AhhSXlmizqSJ/gVqqF6RzWitvG9eCrpAMs3ZHldDhNTrLOYVEvmiyUaoFuOrMb3SKCeOizrRSV6qx6lSVn5uHtJcRFBjsdSrOiyUKpFsjfx5vHJvcj7XAhryzTR5Qq27Y/j+4RQQT4ejsdSrOiyUKpFmp0j3AmD+rEzGW7Sc3WWfVOSM7M00tQ9aDJQqkW7P7f9MHfx4sHP9uqs+oBuYUl7D9apMmiHjRZKNWCtQ8J4O5ze/F9Sg5fbM50OhzHJWceA7Rzuz40WSjVwl05sgv9olrzty+2cayZz6q3Zs9hdp/CJTUd5qP+NFko1cJ5ewmPX9if7PxinmvGs+oVlpRx9ezVXPivH1ifdqRe+9iWmUd4sD/tQwIaOLqWT5OFUh5gYEwbrhjRuVnPqrdsRzZFpRV4ewtX/2dNvQZMdHVua6uiPjRZKOUh/jShN+2C/Higmc6qtzDpAO2C/Pjy9jNo39qfa2avYeXuQ25vX1pewa6D+frkdj1pslDKQ5yYVW9jei7vrU1zOpw6KS4r59vtWUyIjySqTSDvzxhJVJtArntjDd/vynFrH6nZBZSUV2jndj1pslDKg0wZHMXIbu14ZuEOcprRrHrf78ohv7iMif06AK67vN6fMZLYsCCuf3Mty9wY1mRbpuvym054VD+aLJTyIJVn1XtyQfOZVW9h0gFCAnwY3T3857KwYH/eu2kkce2DmfHWOhZvO1jjPpIzj+Hn40W38CC7w22RNFko5WF6tA/hpjO7MW99BqtT3b/m75TS8gq+ST7I+D6R+Pn88iurbZAfc24cSZ+OIdz8zjoWJp38WZLkzDx6Rgbj461fe/WhZ00pD3T7uDii2gTywKdNf1a91amHyS0s/fkSVFWhrXx5+8YRDIgO5Q9zNvD5pv3V1kvOzKNPB70EVV+aLJTyQIF+3jx8QV92ZeXzn+/3OB1Ojb5KyiTQ15sz404+dXLrAF/eumEEQzu35Y73N/DJhoxfrM86VkROfol2bp8CTRZKeahz4iMZ3yeSFxbvIuNIodPhVKu8wrBo60HO6h1BoF/No8QG+/vwxvXDGNE1jLs+3MRHiek/r9u23/XktnZu158mC6U82MMXxGNourPqrU87Qk5+MRP7dXSrfis/H2ZfO4zTe4Tzp7mbmbPadYvwz2NC6WWoetNkoZQHi27bitvPjuPrbQdZklzz3URO+GrLAfy8vTir18kvQVUV6OfNa1cncFavCO77ZAtvrfyJ5Mw8otoEEtrK175gWzhbk4WITBSRHSKSIiL3VLO+i4gsEZHNIrJMRKIrrbtGRHZZr2vsjFMpT3bj6d2Iax/Mg59tPaVB+hqaMYZFWw9wRlw4IQF1+5IP8PVm5lVDGd8nkgc/28o32w7qMB+nyLZkISLewMvAJCAeuExE4qtU+zvwljFmAPAo8KS1bTvgIWAEMBx4SETa2hWrUp7Mz8eLpy8aQH5xGZNe+I5Xl+2mrAncIbVl31H25R4/6V1QtfH38eaVK4YwqV8HjpeWa+f2KbKzZTEcSDHGpBpjSoD3gclV6sQDS6zlpZXWnwt8Y4w5bIw5AnwDTLQxVqU82pDObfnmrjM5q1cETy/czpRXfvx5OG+nfJV0AG8vYXyfyHrvw8/Hi5cuG8zjU/px7ejYhgvOA9mZLKKA9ErvM6yyyjYB06zlKUCIiIS5ua1SqgG1Dwlg5pVDefnyIWQePc5vX/qe57/ZSUlZ47cyjDEsTDrAqG5htA3yO6V9+Xh7ccWILoQF+zdQdJ7JzmQh1ZRVHerybmCMiGwAxgD7gDI3t0VEZohIoogkZmdnn2q8Snk8EeE3AzryzZ1j+O3ATry4ZBfnv/QdG9NzGzWOnQfz2ZNTUO9LUKrh2ZksMoCYSu+jgV88WmmM2W+MmWqMGQzcb5UddWdbq+4sY0yCMSYhIsL9uyWUUjVrG+THP6YPYva1CeQdL2PqKz/wxIJkjpeUN8rxv0rKRAQm9K3/JSjVsOxMFmuBOBHpKiJ+wKXA/MoVRCRcRE7EcC8w21peBEwQkbZWx/YEq0wp1YjG9Y7k67vOZPqwzsxakcqkF1Y0ynhSC5MOkNClrc5o14TYliyMMWXArbi+5JOBD40xW0XkURG5wKo2FtghIjuBSOBxa9vDwN9wJZy1wKNWmVKqkbUO8OXJqf2Zc9MIKgxMn7WKBz7dQn5xmS3H+ymngO0Hjrn9IJ5qHGJM85sxqzoJCQkmMTHR6TCUatEKS8p47uudzP5hDx1bB/DE1P6M7dW+QY/x6rLdPL1wO9//5Syi27Zq0H2rXxORdcaYhNrq6RPcSim3tfLz4a/nxzP35tG08vfh2v+u5f5PttCQf3Qu3HqAAdGhmiiaGE0WSqk6G9qlLV/cdjrXnRbLu6vTmLd+X4Psd3/ucTal5+pdUE2QJgulVL0E+Hrz19/Ek9ClLY9+vpWsvKJT3ufCpAMATOyryaKp0WShlKo3Ly/hmYsGUFxWwf2fJp3y5aiFSQfoFRlCt4jgBopQNRRNFkqpU9ItIpi7zunJN9sO8sXmk09rWpvsY8Ws3XtYL0E1UZoslFKn7MYzujEwpg0Pzd/Kofzieu3j620HMAZNFk2UJgul1Cnz9hKevWgAx4pKeWj+1nrtY2HSAWLDWtG7gw4l3hRpslBKNYiekSHcPi6OLzZn/txR7a7cwhJW7j7ExH4dEaluaDjlNE0WSqkGc/PY7sR3bM1fP0sit7DE7e0WJ2dRVmH0ElQTpslCKdVgfL29ePbiARwpKOFvXyS7vd3CpEw6hQYwMDrUxujUqdBkoZRqUH07hfL7sd2Ztz6DpTuyaq2fX1zGil05nNuvg16CasI0WSilGtyt43rQMzKY+z7eQl5RaY11l27PoqSsQh/Ea+I0WSilGpy/jzfPXDSQg3lFPLlge411FyYdIDzYj4TYdo0UnaoPTRZKKVsMimnDTWd04701afyQklNtnaLScpbuyGJC3w54e+klqKZMk4VSyjZ3ntOTbuFB3PPxZgqqmf9ixc5sCkvK9RJUM6DJQillmwBfb565aAAZR47z7KIdv1q/MOkAoYG+jOoe5kB0qi40WSilbJUQ245rRsXyxo8/sWbP/ya8LCmrYHHyQcb3icTXW7+Kmjr9CSmlbPfnib2IaRfIX+Ztpqi0HICVqYfIKyrTB/GaCU0WSinbtfLz4empA9iTU8A/vtkJuB7EC/Lz5oy4cIejU+6wNVmIyEQR2SEiKSJyTzXrO4vIUhHZICKbReQ8q9xXRN4UkS0ikiwi99oZp1LKfqN7hHP5iM689l0q6/Ye4eutBzmrd3sCfL2dDk25wbZkISLewMvAJCAeuExE4qtUewD40BgzGLgUeMUqvxjwN8b0B4YCvxORWLtiVUo1jnsn9SaydQA3vLmWQwUlegmqGbGzZTEcSDHGpBpjSoD3gclV6higtbUcCuyvVB4kIj5AIFAC5NkYq1KqEYQE+PLk1P7kFpbi7+PFWb3aOx2ScpOPjfuOAtIrvc8ARlSp8zDwtYjcBgQB463yubgSSybQCrjTGHMYpVSzN7ZXe34/tjsAQf52fgWphmTnT6q6xzGrTtB7GfCGMeY5ERkFvC0i/XC1SsqBTkBb4DsRWWyMSf3FAURmADMAOnfu3NDxK6Vs8peJvZ0OQdWRnZehMoCYSu+j+d9lphNuAD4EMMasBAKAcOByYKExptQYkwX8ACRUPYAxZpYxJsEYkxAREWHDR1BKKQX2Jou1QJyIdBURP1wd2POr1EkDzgYQkT64kkW2VT5OXIKAkUDNo5EppZSyjW3JwhhTBtwKLAKScd31tFVEHhWRC6xq/wfcJCKbgPeAa40xBtddVMFAEq6k819jzGa7YlVKKVUzcX03N38JCQkmMTHR6TCUUqpZEZF1xphfXeavSp/gVkopVStNFkoppWqlyUIppVStNFkopZSqVYvp4BaRo8CuU9xNKHC0gevXVKeu69wpCweqn8PSPnU9b6e6j1M97zWtr0u50+e+sc+7u/X1d77h9+Fu3fqc+zhjTGitezbGtIgXMKux9+FO/Zrq1HWdO2VAYks/96d63mtaX5dyp8+9/s47c96dOPfu1q3PuXd33y3pMtTnDuzDnfo11anrOnfLGltjn/tTPe81ra9LudPnXn/nndMUf+drq1fX3/lfaDGXoZSLiCQaN+6ZVg1Pz70z9Lw3jpbUslAus5wOwIPpuXeGnvdGoC0LpZRStdKWhVJKqVppslBKKVUrTRZKKaVqpcnCg4hIHxGZKSJzReT3TsfjSUTkQhF5TUQ+E5EJTsfjKUSkm4j8R0TmOh1Lc6fJopkQkdkikiUiSVXKJ4rIDhFJEZF7atqHMSbZGHMzcAnVzDyoqtdA5/5TY8xNwLXAdBvDbTEa6LynGmNusDdSz6B3QzUTInImkA+8ZYzpZ5V5AzuBc3BNY7sW17zm3sCTVXZxvTEmy5p46h7gX8aYOY0Vf3PWUOfe2u454F1jzPpGCr/ZauDzPtcYc1Fjxd4S+TgdgHKPMWaFiMRWKR4OpBhjUgFE5H1gsjHmSeD8k+xnPjBfRL4ENFm4oSHOvYgI8BTwlSYK9zTU77xqGHoZqnmLAtIrvc+wyqolImNF5EUR+TewwO7gWrg6nXvgNmA8cJGI3GxnYC1cXX/nw0RkJjBYRO61O7iWTFsWzZtUU3bS64rGmGXAMruC8TB1PfcvAi/aF47HqOt5PwRocm4A2rJo3jKAmErvo4H9DsXiafTcO0PPu0M0WTRva4E4EekqIn7ApcB8h2PyFHrunaHn3SGaLJoJEXkPWAn0EpEMEbnBGFMG3AosApKBD40xW52MsyXSc+8MPe9Ni946q5RSqlbaslBKKVUrTRZKKaVqpclCKaVUrTRZKKWUqpUmC6WUUrXSZKGUUqpWmiyUY0QkvxGOcUFtw1jbcMyxIjK6HtsNFpHXreVrReRfDR9d3YlIbNVhwqupEyEiCxsrJtX4NFmoZs8atrpaxpj5xpinbDhmTeOqjQXqnCyA+4CX6hWQw4wx2UCmiJzmdCzKHposVJMgIn8SkbUisllEHqlU/qmIrBORrSIyo1J5vog8KiKrgVEi8pOIPCIi60Vki4j0tur9/Be6iLxhjbr7o4ikishFVrmXiLxiHeMLEVlwYl2VGJeJyBMishy4Q0R+KyKrRWSDiCwWkUhrSO2bgTtFZKOInGH91T3P+nxrq/tCFZEQYIAxZlM167qIyBLr3CwRkc5WeXcRWWXt89HqWmoiEiQiX4rIJhFJEpHpVvkw6zxsEpE1IhJitSC+s87h+upaRyLiLSLPVvpZ/a7S6k+BK6r9AavmzxijL3058gLyrX8nALNwjSjqBXwBnGmta2f9GwgkAWHWewNcUmlfPwG3Wcu3AK9by9fimugJ4A3gI+sY8bjmRQC4CNeQ7V5AB+AIcFE18S4DXqn0vi3/GwXhRuA5a/lh4O5K9eYAp1vLnYHkavZ9FjCv0vvKcX8OXGMtXw98ai1/AVxmLd984nxW2e804LVK70MBPyAVGGaVtcY1AnUrIMAqiwMSreVYIMlangE8YC37A4lAV+t9FLDF6d8rfdnz0iHKVVMwwXptsN4H4/qyWgHcLiJTrPIYq/wQUA7Mq7Kfj61/1wFTT3KsT40xFcA2EYm0yk4HPrLKD4jI0hpi/aDScjTwgYh0xPUFvOck24wH4l3zHwHQWkRCjDHHKtXpCGSfZPtRlT7P28AzlcovtJbnAH+vZtstwN9F5GngC2PMdyLSH8g0xqwFMMbkgasVAvxLRAbhOr89q9nfBGBApZZXKK6fyR4gC+h0ks+gmjlNFqopEOBJY8y/f1EoMhbXF+0oY0yhiCwDAqzVRcaY8ir7Kbb+Lefkv9vFlZalyr/uKKi0/BLwvDFmvhXrwyfZxgvXZzhew36P87/PVhu3B3QzxuwUkaHAecCTIvI1rstF1e3jTuAgMNCKuaiaOoKrBbeomnUBuD6HaoG0z0I1BYuA60UkGEBEokSkPa6/Wo9YiaI3MNKm438PTLP6LiJxdVC7IxTYZy1fU6n8GBBS6f3XuEZKBcD6y72qZKDHSY7zI66huMHVJ/C9tbwK12UmKq3/BRHpBBQaY97B1fIYAmwHOonIMKtOiNVhH4qrxVEBXIVrXuuqFgG/FxFfa9ueVosEXC2RGu+aUs2XJgvlOGPM17guo6wUkS3AXFxftgsBHxHZDPwN15ejHebhmlQnCfg3sBo46sZ2DwMfich3QE6l8s+BKSc6uIHbgQSrQ3gb1czcZozZDoRaHd1V3Q5cZ52Hq4A7rPI/AneJyBpcl7Gqi7k/sEZENgL3A48ZY0qA6cBLIrIJ+AZXq+AV4BoRWYXri7+gmv29DmwD1lu30/6b/7XizgK+rGYb1QLoEOVKASISbIzJF5EwYA1wmjHmQCPHcCdwzBjzupv1WwHHjTFGRC7F1dk92dYga45nBTDZGHPEqRiUfbTPQimXL0SkDa6O6r81dqKwvApcXIf6Q3F1SAuQi+tOKUeISASu/htNFC2UtiyUUkrVSvsslFJK1UqThVJKqVppslBKKVUrTRZKKaVqpclCKaVUrTRZKKWUqtX/AxbUklqDkDfUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa401d7a978>"
      ]
     },
     "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": "47742206a9ed492190f9f16d0bc9f9b8",
       "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.      0.59825 0.47099 0.73296]                          \n",
      "[1.      0.49927 0.40359 0.83239]                          \n",
      "[2.      0.44379 0.40082 0.82386]                          \n",
      "[3.      0.41769 0.42302 0.73864]                          \n",
      "[4.      0.40847 0.41662 0.84091]                          \n",
      "[5.      0.38639 0.39355 0.83239]                          \n",
      "[6.      0.37098 0.38299 0.83523]                          \n",
      "[7.      0.35066 0.37675 0.82954]                          \n",
      "[8.      0.34772 0.39196 0.84091]                          \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": "4414c1794d1b4d69aa7aa644bdd2fe29",
       "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.      0.50614 0.40619 0.81818]                          \n",
      "[1.      0.46401 0.39647 0.81818]                          \n",
      "[2.      0.43891 0.3963  0.80398]                          \n",
      "[3.      0.46129 0.4569  0.77273]                          \n",
      "[4.      0.43853 0.44168 0.79261]                          \n",
      "[5.      0.43014 0.41541 0.78977]                          \n",
      "[6.      0.41912 0.41718 0.78693]                          \n",
      "[7.      0.41558 0.44    0.78125]                          \n",
      "[8.      0.4464  0.45278 0.78125]                          \n",
      "[9.      0.44    0.38377 0.81534]                          \n",
      "[10.       0.41521  0.41193  0.80114]                      \n",
      "[11.       0.40072  0.45434  0.76421]                      \n",
      "[12.       0.40318  0.44622  0.78693]                      \n",
      "[13.       0.4133   0.46559  0.77273]                      \n",
      "[14.       0.43405  0.4363   0.78409]                      \n",
      "[15.       0.43231  0.40133  0.80114]                      \n",
      "[16.       0.44167  0.40873  0.8125 ]                      \n",
      "[17.       0.40183  0.41913  0.80114]                      \n",
      "[18.       0.39167  0.41093  0.82386]                      \n",
      "[19.       0.45115  0.47243  0.78693]                      \n",
      "[20.       0.41668  0.4293   0.80398]                      \n",
      "[21.       0.44751  0.42755  0.8125 ]                      \n",
      "[22.       0.43608  0.42316  0.81534]                      \n",
      "[23.       0.40808  0.41283  0.82102]                      \n",
      "[24.       0.40741  0.42826  0.79261]                      \n",
      "[25.       0.38927  0.43788  0.79829]                      \n",
      "[26.       0.39333  0.46902  0.78125]                      \n",
      "[27.       0.39775  0.44175  0.79546]                      \n",
      "[28.       0.39281  0.43096  0.80114]                      \n",
      "[29.       0.39709  0.44073  0.79829]                      \n",
      "[30.       0.39224  0.4536   0.78977]                      \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 0x7fa3b87812e8>"
      ]
     },
     "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 0x7fa3b8747940>"
      ]
     },
     "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": [
       "(0.3446784601138373, 0.8286604361370716)"
      ]
     },
     "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": "c3ba7090e4e942568f4c05477184318e",
       "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.      0.555   0.46545 0.77557]                          \n",
      "[1.      0.45294 0.45004 0.80682]                          \n",
      "[2.      0.40165 0.34062 0.84943]                          \n",
      "[3.      0.39021 0.45488 0.78977]                          \n",
      "[4.      0.38996 0.32312 0.85227]                          \n",
      "[5.      0.36741 0.43905 0.80966]                          \n",
      "[6.      0.34174 0.35334 0.84375]                          \n",
      "[7.      0.34827 0.67212 0.75284]                          \n",
      "[8.      0.3414  0.44773 0.77273]                          \n",
      "[9.      0.35447 0.8174  0.66761]                          \n",
      "[10.       0.36961  0.3766   0.84659]                      \n",
      "[11.       0.34214  0.40903  0.82102]                      \n",
      "[12.       0.32158  0.34088  0.84091]                      \n",
      "[13.       0.31974  0.3299   0.84943]                      \n",
      "[14.       0.32591  0.3547   0.84375]                      \n",
      "[15.       0.32594  0.42812  0.82954]                      \n",
      "[16.       0.32818  0.33511  0.86932]                      \n",
      "[17.       0.32367  0.34902  0.83807]                      \n",
      "[18.       0.30399  0.41682  0.81818]                      \n",
      "[19.       0.28484  0.28024  0.85796]                      \n",
      "[20.       0.28274  0.27659  0.875  ]                      \n",
      "[21.       0.25788  0.28651  0.88068]                      \n",
      "[22.       0.2586   0.33209  0.85511]                      \n",
      "[23.       0.24801  0.27189  0.88921]                      \n",
      "[24.       0.24471  0.25653  0.89773]                      \n",
      "[25.       0.27175  0.26531  0.89489]                      \n",
      "[26.       0.28572  0.28665  0.87784]                      \n",
      "[27.       0.23269  0.27939  0.88068]                      \n",
      "[28.       0.21787  0.2873   0.87784]                      \n",
      "[29.       0.20307  0.25963  0.88352]                      \n",
      "[30.       0.20009  0.25841  0.88636]                      \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 0x7fa3c1b30438>"
      ]
     },
     "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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sd2ziiPO465K25Y7d885K5qzfzy0DW/PUqG51Vq9Syn+JyEpjTLXXEukVqh5om9CEz++7kFE9WzqPTbmxNy9edz5ApWAHeG72BlbuPFzumFinptdkH6u7Yr0gv7CE0lJDSanRISelGohGu83e2fj79fYLnNrER9CheSQlpYanZq7nxOliZ5vzk6Od4X3t1CX0TY0lJS6cSb/sQUGRfSmD1buP8sD7qziaV8g/xvYiJjzYK++nNhzLL2LI3xZQUmo4kldEfJNglj2Wji3A3SQrpVR90Z57DQQECEO7tqCDNQ5vCxCuSyubSNQ8KoQ3b+3LYyM606tVDAArdhzh05/2MOHTtRQUlRLfJASAL9bsZdGWXHo+Pa9SD9+XvPbdFnJPFnIkrwiA3JOFXDv1R95ZulPX5VHKi3TM/Rwdyyti0pwN/HZQe1LiwhBr7MUYw0crs5n2/TaycspOsHZqHsmmA+Vn1gzv1oKpN/fh5W828/I3W7j7krbkF5ZwXVoynVpEEmwLcD5vQ1JQVEK3P8/l0k4J/GNsb9btPcZ105aUazO2Xyueu7obAdqTV6pWeDrmruFeD+ZlHuCu/5S9103PDuONRdsZ1q0FL329mVlr99EvNY7lO9z34P8wtCP3X9ahvsr12MqdR7h26o+8/qs+XNG1BQALNh3k1rdWlGv3ypiejOqZ5O4plFI1pCdUG5D085rRL9V+tesfh3UmJNDGfYPb0y6hCeldmgFUGewAL369mcLihre+jWP2UM+UGOexSzs146N7BnLf4LIVNsfNWE3q+FlsyzlZ7zUq1VhpuNcDEeHDewayY9JIfuuyrDBA/zZNy91//pruRIcFVXqOv3y1oU5rPBvvLN1JfJNgmkeFljveNzWOPwztxC97le+tT/pqI7sP6zaGStUHDXcvaxkTxn9u78etF6QSFRrIyO6JLPrjYGY/eDFQNn3yrcU72HzgBKOn/kjm3uNerNju6S8y2Z57iqIS98N6IsJLN/Rkx6SRrH/qChIiQ5iXeYCLJ8/nor9+x7zMA/VcsVKNi465NxClpfbPwfXE48crs+nTOpbZa/fxwtxNzuOdW0Qy56FL6r1Gh8OnCun9zDwAPvzNQPq1iav2ez5Zmc3vP1pT7tiOSSPrpD6l/JmOufuYgACpNKNkdJ9k2sRH8NtB7ejcomwZhI37T/DPBd5bhnjtHvs8/vfu7O9RsAMMOa9ZufsBAscLimq9NqWUnYa7DwgIEJ67pvxyBZPnbGLdnnO70vXtxdtJe3YeWw96fqKztNRwy5vLATgvMaqa1mViwoNZMTGd+CbBPHhZe0oNzFmnWxgqVVc03H1E71axjOmbwmWdy3rAy7ZXPcPGGEP2kTzncI+ro3n25RKe/CKT3JOFpL/0PanjZ5HlwWyWw3llSy3ERtTsytqEyBAyHr+ch9I7EhkSyJrdR5mzbj+HdNlkpWqdhruPEBEmXduDN2/ty4y7BwDwzJeZziUNwH5B1a1vLefjldnM33SQi/46nzcXb+fHbbl8lLGbfcfy+XGb/arY/63KrvQa42asrrYOx5aDz4zqetbvJSBAaBkTxrvLdnHPf1fy7KyGNxNIKV+na8v4oAFtm3L/4Pa8Nn8r3244yMgeiQB8u/EACzblsGBT2S5XFYOzvzVG/vAH9pObt1/YhjcXbwdg4/7jnDxdTJOQqn8tiq3ZMWHB5/arc2WPRDbNs1+pu0yXQFaq1mnP3Ufdf1l7AO577ydueH0JF0+uPL3w8i7NK31fxaGcewa1Zetzw3nvrv4UlRhn0G7LOcnd/8kgr7C4XPtCq+ceZDu35QTuHdye5ROH8NiIzuw9VsCWA9VvdlJXikpKvfr6StUF7bn7qNAgm/O2I7B3H84n/bxmTLu5D0fzi4hvEsKPW3P50+fr2Oayvs0jV3SiqKSU5lGhNLMuQDo/OYYAgeXbDzPkvOZcPWUxJwqK+b9F23lgSNnSB0XOcD+3foEtQGgWGUpf68rdf3y3lZ4pMXy+eg/TftWHxOiwc3r+mnhp3mamLtjGJ78dSJ/Wns3+Uaqh03D3YYvHX8aqXUeYs24/X/68D4Dw4EACbQHO1ScvaB/P5NE9uHbqEn5xfksGd0rgl72TKz1XREgg/drE8frCLHJOnOZEgb3H/rd5m/nbvM3EhAex/LF057DMuYa7Q69WsfRuFcOcdfuZuca+Ne/Av3zH9r+MYEnWIY6cKqJbUhStm0bUyuu5k2Et/XDt1CWsfXIokaGVrxBWytfosIwPS4oJ48oeLXntxt4senQwA9s25cEh7Su169M6jpWPp/PqmJ5ug93hD0M7AfDpqj0AtI0vC9SjeUU88fk657BM4DkOy7i65YJU5/M6jP9kLTf+axn3vfcTg15YUCdz4rccOMHc9fud/yMDmDJ/GwB3vL2Cx/63FrCvfvnZqj3knKh6Vs+cdfvYezS/1mtU6mxpuPuJlLhw3r97AO2bRbp9vGmTkGqXDU5LjePLBy5y3r/tojblHp+xYrdzdk5wLfXcAeeKkgB/+WV3AD7I2F2uzRsLs2rt9Rx+8doP/OadlWzcf4IHh3SgW1IU077fRkmp4duNB3lv2S6enLmeQS/M56EPVvPwB2WziRxTTJdlHSJ1/Czu+e9PjJuxqtZrVOpsabircrolRfPTny7nhdE9uKlfKwDSz2vO5NE9AFiz237hVGAtrs8eGmQjJtw+FDKkczPnjJ4HL2vPNb2SaJcQwavfbeXfP+6otdcEKCgq+2thQJs45/j//I0Hncff/nEHB47be+w/bM3l89V7WL79MB0f/4pb31rODdOXOtsWFpey52g+JW6uLVCqvunaMuqMjuUXERoUwLG8Ivo9/63z+Kf3XkDvVrG19job9h1n4/7jXNMrmYKiEjYfOEGPZPtSwm8v3s6TX2QCMP1XfRjq0tM/W2uzj/GL135w3l/1p8sRgZ5Pz2Nk90Rmrd3n8XP1ahVDkC2A5daJ7RHdW/DPm/qcc41KuaNry6haER0WREigzTmrxqE2h2XAvpTBNb3s5wNCg2zOYAe42mXp4LvfWcnjn63lRDVj8NV1Wj75yX4RV0hgAL+/vCOxEcHEhAczqGOCM9ibRYY426+YmO72ea5PS+Z/917InS5DWLPX7nfOKqpoyvytfL56zxlrU6o2aLgrj615YqjzdlwNlx44FzHhwfzu8o7O+/9duovuT37NF9bsGlcHTxRw9ZTFPFjN1bYFRSU0iwxh07PDy031dIz/90iOZvnEdLY9P4Jvfz+IhMiQcu//5gGtuPOiNoxLt9d1Wedm3HZhqvPxqtbNeWHuJsbNWM3r329zuzSEUrVFh2VUjby/fBdfrdvPv2/rW6/7uuYVFrNgUw45J07z55nrAQgNCmDOuEtIdZnV88o3W/j7N5sBWPl4Oq8vzOL6tORKJ5rHzVjF6t1H+f6RweWOnygoYuqCbdxyQWqlTUgAsnJO0jImrNx1Bq6KSkoZ8Py3pMZH8PE9Ayv9jFLHzyp3/8sHLqJLYpTuMas8psMyqk6M7deK/9zer9437A4PDmRE90RuuSCV7X8ZwXt39aegqJRLX1zAq99ucbZbt7dspcz/rdrD9IVZpL+00LklINhnuCzcnENoYOWAjgwN4tFhnd0GO0DbhCZVBjvY5///bmhHVu48wp8+X1fp8bAgG92SylbTvPIfP3Dr2yuqHUZSqqY03JXPEREGtm3KhOGdAfsVpkdOFfLS15uYl3mAa3olESDl19UZNWUxL3+zmfeX7+KG6Us5kldEaR0F6g1pKYB9+GjBprKZN6WlhvyiEi7r1IxNzw6jQ7MmACzcnMPVUxaz75jOk1e1R8Nd+SQR4TeD2vHq2F4A9HpmHq9+Z9/ApHOLSOcVuq5e/mYLEz5d67y/pQbr2NdEoC2A/9zeD7DvG+vY3Lyg2H6NQFhwICGBNr5++BIWPWofFlqTfYwnPrcPN63bc0y3IVTnzKNwF5FhIrJJRLaKyHg3j98jImtFZLWI/CAiXWq/VKUq+0WPROcceYCmEcGM6duKqTeXTUVc9afLubhDfL3WdUnHBB5K78DG/ScY/OICSkoNh07a18JvEmpf9UNESIkLZ8ekkSRGhzIv8wCp42dx5T9+4K7/ZLByZ9Xr9StVnWrDXURswBRgONAFGOsmvN8zxnQ3xvQEJgMv1XqlSrkhIvz3jv4AjOrZkhUT04kOD6JP61g2PjOM5Y8NITYimNd/VRb244d35r939Od/915Qp7UlRtvH7fcczee2t1fw7QZ7b3xg28qLkzn+AnF17dQlpI6fpVMn1VmpdraMiAwEnjTGXGHdnwBgjPlLFe3HAr82xgw/0/PqbBlVm7JyTpLaNOKMs052HjrFhn0nGNbt3C+C8sTxgiLGvb+K+db6+oEBQkJkCEsmDHHbfsO+4wx/ZRGdW0Sy92g+xwvKL7e86k+X13j3K+V/PJ0t48mqkEmA60If2UB/Ny94H/A7IBi4rIqi7gbuBmjVqpUHL62UZ9omNKm2TeumEXW6umRFUaFBvHVbP+f0x+JSQ6u48Crbn5cYxY5JIwHYdyyfWT/vY/OBE3yYYb/gasHmg84LvZSqjidj7u66QpW6+8aYKcaYdsAfgcfdPZExZroxJs0Yk5aQkFCzSpXyUW/d1td5u2vLaI++JzE6jDsvbsvk0eez/qkrCA4M4Is1ni+JoJQn4Z4NpLjcTwYqXxpYZgZw9bkUpZQ/GdypGZOs1S4Hd655pyYiJJDr+iSzfPthiqtY1sChtNRUuvJ12vfb+KoGa+Uo/+DJsMwKoIOItAH2AGOAG10biEgHY4zjSpKRwBaUUk439E2hT+tYOjR3vyRzdfq1iePdZbvYsO8E3ZOjKSk1TJ6zkVZNwxnbtxV/nbORNdlHWZpln2Hz3p39SYkLJzk2jElfbQRwDvmoxqHacDfGFIvI/cBcwAa8aYxZLyJPAxnGmJnA/SKSDhQBR4Bb6rJopXyNiJx1sAP0b9MUgGXbD9E9OZqfdh3hdWuN+7bxTZy3HW58YxlQfmnm3JOn3c7/9ze7D+dxuriU9s2qPw/jzzya526MmW2M6WiMaWeMec469oQV7BhjxhljuhpjehpjBhtj1tdl0Uo1Ni2iQ0ltGs6SbfYNzA+fKnQ+5ljn/pUxPZn70CX0Sy2balnsMkTz7tJd9VOslz3w/irSX/q+Tnbv8iV6hapSPuLC9vEszTpEUUkpJ61pkkkxYcxZb1+BsmVMGJ1aRPLGrfZZckO7NOei9mUXbzkWVPN3Ww6cAGBuFStzNhYa7kr5iIs7JHCqsIQOE7/i+dn2dXMubN/U+bhjGeao0CA2PjOMaTf34Z07+vHj+MsY3cc+hdKxGbg/65Zkn5E0082S0I2JhrtSPuKSjmW98EPWsMwfh3WmR3I09w1uV25D89AgGwEBgojQMiaMcdaa9aOn2a96fX+5fYhm9+E85w5S/sKxINyiLbmcOl1cTWv/peGulI8IDw5k1oMXlTvWtEkIM++/iEeu6HzGZZhT4sJ57cayJQ4mfLqWDzN2c/vbK7j+9SXsPdrwV6TcezS/3NLNVSkuNTh+FB9V2Gi9MdFwV8qHdG0ZTdbzI7iuTzKTr+1Ro++9skdLPrpnIBHB9vXoH/34Z+fKmNf8c3GDX1P+ljeXM2rKYvILS87YrrjEMKhjApEhgTz5RaZzVc7GRsNdKR8TECC8cN35XN83pfrGFfRNjWPdU1dUOn7g+OmzWmZ479F8Xpq3mWP5dT8z5aQ1xNL/+W/O2K641BAYEMCI7okA5dbUb0w03JVqZESEuQ9dwrW9k4kOC3JePXv3OytrPEZ93bQlvPrtFp6blVkXpZbTp3UsAMcLijldXHXvvaS0lCCb8NSorgTZhBWN4CSyOxruSjVCnVpE8rfrz2fNn4cypl/ZIn5d/zyXl7/Z7NFQhjGGA8cLAPgwI5tv6niDEddRow9X7ObB91dx8ERBpXbFpQZbgBAaZKNnSgzvL9/NrkN5jW5Dcg13pRTfP3Kp8/bL32zhizNMI9x7NJ9xM1ax52g+xaWGB4d0IDIkkOkLs9h68ESd1VhcWkqHZk1IiQvjT5+vZ+aaveX2z3W2KzHOK3Mv79Kck6eLueSF+Vz5jx/1DI7NAAANa0lEQVTYezS/wZ9bqC0a7kopWjeN4N07y1by/uwMG4R8vnovn6/ey53/tu/H0L5ZE34zqC3Ldxwm/aWFDHj+2zMOm5xtuJaU2jcg755UtrJmxX1q7e0MgTZ7tN15UVtuuzAVgMx9x7lg0nfOawT8nYa7UgqAgW2bMqRzM2LDg1i0JZe/z3N/RWt8E/vFUhv323vpl3SI5/q0spO7+48X8M6SnW6/d17mAdpMmO3cXcoYw7KsQ5R4MGRSUlpKoE2YOLILj488j5E97CdMb31rhfNkK9h7+I6ee0CA8OdfdCXz6bKTyG8u3lHt6pr+QMNdKQXYg/D/bu3L27fZN/d+5dstfLaqcg++sEIwxoQH0ywqlKUThvBwekcAvt+cw+7DeZV66dMXbgNg3IzVfJSxmyEvfc8N05d61Jt2jKUnxdjXun9mVDfnVbnPzcp0jqmXWO1chQcHsujRwTx4WXtKSo3fXbjljoa7Uqqc81NieOcOe8A/9MHqSo+fLioL9yut3jPYFzcbl96BX/ZOYtGWXC6ePJ82E2ZzLM8+TfLxz9ayYscRZ/tHPv6ZrJxTAHy+eg8nTxfz/vJdVV5QVVJqyq1yGRcRzMrH0xndJ5n3l+9mmvU/jqKS8u0cUuLCuefSdoB91cycE6c9+4H4KA13pVQlF7WPp1tSFGDfn9aVo+e+4elhvHZj70rfO2H4eUSFlq0mPm/DAXJPnua/1qqUb/y68vafh04VctMby5jw6VoumPRdpdeEsp67KxHhhdE96JkSw+Q5m/h6/X5OF5dgC3AfbeHBgc7aXpy7qcr37w803JVSlYgI03+Vhi1A+NeisrXi/7cq23lJf3Cg+/hIiAxh+cR0/nlTb4Jswqyf97LrcB4A917ajvQuzXngsvYA9E2N5ZaBrTGGcksLvOAmeEvdhLuj1rH97GP+d7+zkoKiUrq2jKryvb175wAANh88wback3z6UzYHj1eeUunrPNmJSSnVCLWMCeO2C1J544ftxIQHM2fdfrbnnnI+7i5oHUKDbIzonsiKHYd5b9kuhnezD99c0ysJgN8MasfOQ3n8cXhnAgT+bZ2A/eGPg/n9h2uYl3mAgycKaBYZ6nzO4lJDeBU98uvTUli0JZcvf7ZvJ9irVUyVtXVPjuaeQe2Y9v020l/63jl/PvPpKwgP9p9I1J67UqpK49Ltq0lOXbCtXLB76pIOCZwuLuXRT34GIDk2HIAmIYG8OrYXSTFhJEaHMbRLc269IJXk2HCe/2V3iksNn/5U/mRuxTF3VyLCyzf05JUxPbnr4jakNo1w285hYDv7Usmu53uHv7KIIj+aRaPhrpSqUmRokHPK4S0DW7NiYjqvjOnJrwa09uj7B3VMcA7fxDcJIcxatKyi6b9O48mrugLQLqEJLaNDmfTVRq78xyLyC0vYf6zA7Zi7q0BbAKN6JjFxZBcCztAO7MNBDo5zADsP5XHVa4s9el++QLx1tVZaWprJyMjwymsrpTznyIgzLSl8Jmuzj/HVun38YWinakPX4fXvt/EXa2PvpJgw9hzNp21CBJ2aRzL15j5nVUdFac/OI/dkIV/cfxGnCosZM30pAKufuJyY8OBaeY26ICIrjTGVz0pXoD13pdQZichZBzvYx7gfHdbZ42AHuOvitsz/w6UA7LGmRmblnDpjz72mFo+/jKk39aZbUhQD2jblywfsa+Xf++5PfnGRk4a7UqrBCQgQ2sRH8JA15u8QXsWwztkICbQxvHui839c3ZKiCQ0K4Mdth5juMkPIV2m4K6UarIfSO7L8sSEM7pQAwG0XtqnT15v70CW0iY9g8pxNLN9+mLzCYr5Ys5eCojNvENIQ6Zi7UqrBKygq4dCpQpJiwur8tRZvzeWmN5YB0C4hgm05p+jVKoZPf3sBbyzazsUd4+ncoup59HXN0zF3DXellKrgXwuzeO4M691sfGYYoUG1N0RUE3pCVSmlztJdl7R1bkbeqXkk56eUvyjqkY9/dvt9mXuP8+TM9c5NTLzJfy7HUkqpWtS1ZTSf33ch8ZEhNI0IZuvBkyRGh3LTG8uYu24/y7cfpm9qLKeLS529+BGvLgLgSF4hr4zp5c3yPRuWEZFhwCuADXjDGDOpwuO/A+4EioEc4HZjjPsFnS06LKOU8kU5J04zetqP7DyUR9eWUazfe5xuSVF0ah7FJz9lO9tFhQbyzh39iQ0PZv6mg/x6YOtzmlLqUGtj7iJiAzYDlwPZwApgrDEm06XNYGCZMSZPRH4LXGqMueFMz6vhrpTyVd9vzuGWN5e7feyXvZL41FoHPzzYRl6hfabNM1d3Y1CHBAqKS+jYPPKsX7s2x9z7AVuNMVnGmEJgBjDKtYExZr4xJs+6uxRIrmnBSinlKy5s15Sb+rciMTqUWy9I5XaXKZqPDOtEl0T7bBpHsAM882Um1/xzMUP/vpDdh/MqPWdt82TMPQnY7XI/G+hfRVuAO4CvzqUopZRqyAJtATx3Tfdyx/qmxlJQXEJidBizx12MMYYXv96EMXBRh3hu/NcyDhUXAvDjtlxuiGtVtzV60MbdIJHbsRwRuRlIAwZV8fjdwN0ArVrV7RtTSqn6NLx7Yrn7IsIjV3R23m8VF86uw3n0TImhT+vYit9e6zwJ92wgxeV+MrC3YiMRSQcmAoOMMW73rzLGTAemg33MvcbVKqWUj3rp+vP5bPUeJo7oUuXqmLXJk3BfAXQQkTbAHmAMcKNrAxHpBbwODDPGHKz1KpVSyselpcaRlhpXb69X7QlVY0wxcD8wF9gAfGiMWS8iT4vIVVazF4AmwEcislpEZtZZxUopparl0UVMxpjZwOwKx55wuZ1ey3UppZQ6B7r8gFJK+SENd6WU8kMa7kop5Yc03JVSyg9puCullB/ScFdKKT/ktZ2YRCQHOOOywG7EA7l1UE590fq9z9ffg9bvfd5+D62NMQnVNfJauJ8NEcnwZKnLhkrr9z5ffw9av/f5ynvQYRmllPJDGu5KKeWHfC3cp3u7gHOk9Xufr78Hrd/7fOI9+NSYu1JKKc/4Ws9dKaWUB3wi3EVkmIhsEpGtIjLe2/W4IyIpIjJfRDaIyHoRGWcdjxOReSKyxfo31jouIvKq9Z5+FpHe3n0HdiJiE5FVIvKldb+NiCyz6v9ARIKt4yHW/a3W46nerNtBRGJE5GMR2Wh9FgN96TMQkYet3591IvK+iIQ29M9ARN4UkYMiss7lWI1/5iJyi9V+i4jc4uX6X7B+h34Wkf+JSIzLYxOs+jeJyBUuxxtWThljGvQXYAO2AW2BYGAN0MXbdbmpMxHobd2OBDYDXYDJwHjr+Hjgr9btEdj3mhVgALDM2+/Bqut3wHvAl9b9D4Ex1u1pwG+t2/cC06zbY4APvF27Vcu/gTut28FAjK98Btj3K94OhLn87G9t6J8BcAnQG1jncqxGP3MgDsiy/o21bsd6sf6hQKB1+68u9XexMigEaGNlk60h5pTXXrgGP/iBwFyX+xOACd6uy4O6PwcuBzYBidaxRGCTdft1YKxLe2c7L9acDHwLXAZ8af0HmOvyS+78LLBv3jLQuh1otRMv1x9lhaNUOO4TnwFlm9HHWT/TL4ErfOEzAFIrhGONfubAWOB1l+Pl2tV3/RUeuwZ417pdLn8cn0FDzClfGJZx/MI7ZFvHGizrz+NewDKguTFmH4D1bzOrWUN8Xy8DjwKl1v2mwFFj340LytforN96/JjV3pvaAjnAW9bQ0hsiEoGPfAbGmD3Ai8AuYB/2n+lKfOszcKjpz7xBfRYV3I79rw3wofp9IdzFzbEGO8VHRJoAnwAPGWOOn6mpm2Nee18iciVw0Biz0vWwm6bGg8e8JRD7n9dTjTG9gFPYhwSq0qDegzUuPQr7n/stgQhguJumDfkzqE5VNTfI9yIiE4Fi4F3HITfNGmT9vhDu2UCKy/1kYK+XajkjEQnCHuzvGmM+tQ4fEJFE6/FEwLGBeEN7XxcCV4nIDmAG9qGZl4EYEXFsx+hao7N+6/Fo4HB9FuxGNpBtjFlm3f8Ye9j7ymeQDmw3xuQYY4qAT4EL8K3PwKGmP/OG9llgndS9ErjJWGMt+FD9vhDuK4AO1oyBYOwnjhrcBtwiIsD/ARuMMS+5PDQTcJz5vwX7WLzj+K+t2QMDgGOOP2O9wRgzwRiTbIxJxf4z/s4YcxMwHxhtNatYv+N9jbbae7WnYozZD+wWkU7WoSFAJj7yGWAfjhkgIuHW75Ojfp/5DFzU9Gc+FxgqIrHWXzBDrWNeISLDgD8CVxlj8lwemgmMsWYqtQE6AMtpiDnlzQH/GpzsGIF99sk2YKK366mixouw/xn2M7Da+hqBfQz0W2CL9W+c1V6AKdZ7Wgukefs9uLyXSymbLdMW+y/vVuAjIMQ6Hmrd32o93tbbdVt19QQyrM/hM+wzL3zmMwCeAjYC64B3sM/KaNCfAfA+9nMERdh7sHeczc8c+9j2VuvrNi/XvxX7GLrjv+VpLu0nWvVvAoa7HG9QOaVXqCqllB/yhWEZpZRSNaThrpRSfkjDXSml/JCGu1JK+SENd6WU8kMa7kop5Yc03JVSyg9puCullB/6f5UZSsRN2OBQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa3c1b30668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log Loss | Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.2356822522004143, 0.9096573208722741)"
      ]
     },
     "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": "a10a7b775d1a4ee9a654576219209f38",
       "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.      0.39422 0.30824 0.86364]                          \n",
      "[1.      0.33421 0.30572 0.86079]                          \n",
      "[2.      0.3204  0.31164 0.86079]                          \n",
      "[3.      0.30686 0.30337 0.85511]                          \n",
      "[4.      0.30224 0.28447 0.86932]                          \n",
      "[5.      0.31564 0.29473 0.85796]                          \n",
      "[6.      0.30586 0.29179 0.85511]                          \n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3919f2ed698e4da28ef44c2f3bf92eba",
       "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.      0.37488 0.32175 0.83807]                          \n",
      "[1.      0.41735 0.34574 0.83523]                          \n",
      "[2.      0.38286 0.31647 0.85227]                          \n",
      "[3.      0.39189 0.27023 0.86932]                          \n",
      "[4.      0.37862 0.26703 0.89204]                          \n",
      "[5.      0.32292 0.24683 0.89773]                          \n",
      "[6.      0.31523 0.25111 0.89489]                          \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": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa3b8686710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa3c00e4978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log Loss | Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.24905085270318444, 0.897196261682243)"
      ]
     },
     "execution_count": 33,
     "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": [
    "---\n",
    "## Image Size of 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0441217e59244285afffe0de51ae79a0",
       "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.      0.2788  0.28268 0.87798]                          \n",
      "[1.      0.29462 0.29526 0.86905]                          \n",
      "[2.      0.28896 0.28956 0.875  ]                          \n",
      "[3.      0.28625 0.29952 0.87202]                          \n",
      "[4.      0.29707 0.30062 0.86607]                          \n",
      "[5.      0.2777  0.32601 0.86309]                          \n",
      "[6.      0.263   0.32056 0.86607]                          \n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee7663ab7ad246debd99d9c282349525",
       "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.      0.371   0.29715 0.88691]                          \n",
      "[1.      0.38825 0.37787 0.83036]                          \n",
      "[2.      0.31659 0.34464 0.83631]                          \n",
      "[3.      0.38475 0.34366 0.85417]                          \n",
      "[4.      0.37291 0.29999 0.875  ]                          \n",
      "[5.      0.29536 0.29949 0.86905]                          \n",
      "[6.      0.29284 0.29812 0.87798]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#train for image size 150 x 150\n",
    "learn.set_data(get_data(150, 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 0x7fa3b8701630>"
      ]
     },
     "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 0x7fa3c01b0e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log Loss | Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.25082030876054084, 0.9034267912772586)"
      ]
     },
     "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": [
    "---\n",
    "## Save learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('resnext50_sz75_sequ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('resnext50_sz75_sequ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output predictions to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    }
   ],
   "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": 41,
   "metadata": {},
   "outputs": [],
   "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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