7_resnext50_sz_100.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle Competition:  Iceberg or Ship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# resnext50 architecture (rest is Grover's options)\n",
    "### see if log_loss=0.29 from resnet18 can be improved\n",
    "\n",
    "## increase from sz=75, resize 1.5x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 2300\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  13445 Jan 14 18:36 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 176126 Jan 14 21:22 7_resnext50_sz_100.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu 137540 Jan 14 18:36 baseline.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    536 Jan 14 18:36 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 14 18:51 __pycache__\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    745 Jan 14 20:14 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 1973260\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     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 49 ubuntu ubuntu       4096 Jan 14 20:45 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 = 1e-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e020edfb363540aea3042246d6dea387",
       "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=1.00)\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*1.3), '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=0.6)"
   ]
  },
  {
   "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": "c16654bd5a6340d3a60e1f308e5b9a8d",
       "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": [
      " 66%|██████▌   | 27/41 [00:00<00:00, 28.87it/s, loss=6.86] \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 0x7f674926b5c0>"
      ]
     },
     "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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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f674926b6a0>"
      ]
     },
     "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": "e3d4b4761ac64b54a6a6f318f927b45a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=8), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.      0.64265 0.45734 0.72443]                          \n",
      "[1.      0.58209 0.38807 0.84375]                          \n",
      "[2.      0.51224 0.45791 0.73864]                          \n",
      "[3.      0.54098 0.38152 0.86079]                          \n",
      "[4.      0.54162 0.57359 0.74148]                          \n",
      "[5.      0.48968 0.51846 0.72159]                          \n",
      "[6.      0.43918 0.4479  0.75   ]                          \n",
      "[7.      0.4048  0.49915 0.8125 ]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lr, 8)\n",
    "learn.precompute=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent with Restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a6a89c4510d44999ae743c27c073db12",
       "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.      0.60313 0.5866  0.79546]                          \n",
      "[1.      0.54297 0.42287 0.80398]                          \n",
      "[2.      0.53381 0.44965 0.80114]                          \n",
      "[3.      0.53571 0.46698 0.80682]                          \n",
      "[4.      0.54504 0.50014 0.78409]                          \n",
      "[5.      0.52699 0.47361 0.78693]                          \n",
      "[6.      0.50897 0.46412 0.79829]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#learn.fit(lr, 4, cycle_len=2)\n",
    "learn.fit(lr, 3, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67086bf240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67082f12e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.34738994405289925, 0.8473520249221184)"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr/9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr/4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unfreeze Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0cb3fc5533c4f81899123dc27c89712",
       "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.      0.58626 0.39479 0.82102]                          \n",
      "[1.      0.60695 3.14936 0.70739]                          \n",
      "[2.      0.52448 0.56008 0.75   ]                          \n",
      "[3.      0.489   1.26268 0.66761]                          \n",
      "[4.      0.49952 0.67712 0.73864]                          \n",
      "[5.      0.44766 0.57124 0.78693]                          \n",
      "[6.      0.41437 0.66695 0.79546]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lrs, 3, 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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67082f1dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.3169515312566414, 0.8504672897196262)"
      ]
     },
     "execution_count": 26,
     "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('resnext50_sz75_zm12')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('resnext50_sz75_zm12')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LMQYuGZ526L2x7ts8PjRpEMMyYnjq8w3U1B97fyLiPVoNfGttPXAr8BGQD7xprV1tjHnQGDPRvdpHwB5jzBrgC+DX1trj6/zuhL49S19Z2Hrgryzcz1tLCpkytjcje8V7tP27zu9HSXkNj85eS31DIwDTF28jwM8wICWK7/RL4o0po/jmnnN48opstu89yF8/20Bpec1h2/lo9U7+5708Pl69k/fcN3G5KLs72ekxTBiYzFOfF1DXYOmdGE7PhHD+fvVwsHBG7wRSY0KPqMsYwx3fzaK4rJq3co/8BCIi3sk49eVcTk6Ozc3NdWTfx+M7j39JZkI4L15/2qFl1lpq6hsJCfQ/tOzqFxaypvgAX/16HJEhgR5v/7738njlm630T4ni/24cwXee+IpxWYn8dfLQI9a96eXFfJpfQmigP+/8bDT9kqOYtmArv383j0B/Q12D61gO7xHLv38yGoDCfVX87NWlrCwq45NfjuWUpEgANu+uJCokgPiI4BbrstZy8bPzKT9Yx6d3nIWfn/ryRToDY8wSa23OibxXV9q2YkyfBL7ZuOewro0P83bS7/cfssE9rHLH/oPMLdjNj87IPK6wB9dN1J+6cijrd5Vz/l+/puxgHZc16WZp6pmrhvGvG04jIiSAn726lFmrinlg5mrO6ZfEqvvP46FJA7n3wv48d83wQ+9Jiw3jvVvHsP7h8w+FPbi6lI4W9uA6y//RGZls2l3J28uKNGpHpAtQ4LfirL6JHKxrIHfLf8eof+2+K9a0Ba6LmL5Y5xrbft7A5OPevjGGi7K7c/eEflTU1PO7C/oz5pSEFtcNDvBnXFYSf5s8lMJ9B/npq0vJTAjnL5NPJSTQn2tGZXLTmb1IaCHIA/2P/1CfPyiFXgnh3PnWCq5/aTElB6rZX+XZF8ci0vko8Fsxslc8gf6G+97L4yn31bE1da7+9neXFVFVW88Xa0tIiw3llKSIE97PzWN7ser+87h5bK9Wv/Ad1TueaTeezrisRJ6/Nue4P1V4KijAj/d/PoZ7L+zP1xtKGfHIZ5z64Cd876mvPR4xJCKdhyfj8H1aeHAAE7NT+TCvmL9+toGrR/Zg+74q/P0M5TX1XPfiIlYVlfGDnPSTHrPufxz95CN6xjGi54iT2p8nwoICuOnMXgxOjWb59v3UN1r++ukGbnt9GVOvHU5YkH6ERLyF/rd64IkrsvnRmEwu/NtcPlhVzPa9VUw6tTunZcZxz9urGNg9ih+N6el0me3q9F7xnO4efZQYGcxd/17J6Mc+p7qugeevzeHMPp3/ugoRX6fA99CAlCj6dovgrdzt7DxQTXpsGFeOyOCc/kkkRgT71BWpV+SkkxYbyv8t2MqK7WX87p08Pv7l2MNGLYlI56M+fA8ZY7h4aCorCsuwFtLjwgBIigzxqbD/1ujeCTx71XD+dPkQtu2t4rbXl+kiLZFOToF/HCZmdz/0OD32yAuWfNHo3gk8OGkgn6zZxUPvr3G6HBE5BgX+cUiLDWNET9cMmBnxYQ5X03lcOyqT60dn8urCbaws3N/6G0TEEQr84/Tjs3pxVt9EukW27QyX3u6O8X1JjAhmyitL2Ly70ulyRKQFCvzj9J1+3Xj5RyM01UAzUSGBvPyjEdQ2NHL1CwspKa92uiQRaUaBL22mf0oUL98wgr2VtZz5hy+Y8Jc5PP7ROhobNS2DSGegwJc2NTgtmv+76XSuHtmD2LAgnv6igH/N3+J0WSKCxuFLOxjeI5bhPWKx1nLTy7k89uFa+iVHMqp3vE8OYRXpLHSGL+3GGMMfLxtCz/hwfvjCQrLu/ZB57onnRKTjKfClXcVHBPP6lJHcfm4fEiKCeHR2vqZaFnGIAl/aXVx4ELef25c7xmeRV3SAW6Zp6KaIExT40mG+PzSVm8b0ZP7GPfxmxgqd6Yt0MAW+dBh/P8O93xvAXROyWLxlH19vUH++SEdS4EuHu+I012yb/zNzNRU19U6XI+IzFPjS4YID/Hn88my27qlk4tNzee6rjU6XJOITFPjiiJG94nn88myiQwN5dPZa3szdDkBZVZ369kXaiS68EsdcMiyNidndue6lRdz7bh6LNu9lxpJCEiODeeVHI+ifEuV0iSJdis7wxVEB/n78bfJQEsKDmLGkkPEDutHYaPntO6s0B49IG1Pgi+PiI4J58YbTuOO7fXnmqmH89oL+LNu2nxfnbaa2vpHGRktlTT31DY1Olyri1YxT/aU5OTk2NzfXkX1L52at5ZZpS/hsbQkBfobw4AAqqutJjAxm6rXDGZASpTl5xGcZY5ZYa3NO6L0KfOmMDlTXcfv05SRHh1BT10hkSADvr9zB7opaBqVG8c5PzyDQXx9QxfecTODrS1vplKJCAnnx+tMOW/bjs3rz6sKtPPV5AR/m7eSiJvcYFpHW6RRJvEZydAi/PLcvmfFhvDhv8zGHb36+dhf/+GrjoXXyisq4a8ZKzeEjPk1n+OJV/PwMN57Zi9+/m8eUaUt44opsokICD1tn6bZ9/HjaUmobGgkO8CM4wJ/fv5dHQ6Plm017ePuno0mICHaoBSLOUR++eB1rLS/O28Kjs/JJiw3lke8PZkTPOL4u2E2/5EhueGkxFTX1ZMaHM9c9//64rERuOKMnN7+Sy1l9EzmjdzyZCeGMy0ryaH/3vpvHvqparh2Vyba9VRTuO8jYPgnkZMa1d3NFDqMvbcUnLd6yl9unL6do/0EC/Q11DZbEyGBKy2v4w6WDmXRqKrPziimvrmfyaRkEBfjx3FcbeXT22kPbuCi7OzeN6Ul2ekyL+6hvaOSFuZt5bPZa/P0MDc2uDbgouzs/HJFBUlQwvRLCNXpI2l27B74xZgLwV8AfeMFa+1iz168H/gQUuRc9ba194VjbVOBLW6iua+DdZUUUlFQQGuTPU58XEBMWyIJ7ziEk0P+I9esbGnn4g3wGpUZTUFLBawu3Ul3XyEMXD2Rs30RSokMPrZtXVMatry1ly54qzs5K5JFLBrN2Zzndo0PpHhPCP+du5unPC6h3/xK4emQGD188uMPaLr6pXQPfGOMPrAe+CxQCi4ErrbVrmqxzPZBjrb3V0x0r8KU9PPfVRlJiQpno4Qiesqo6rnlxISsLywgJ9GP2L8bS0Gj5zYwVLNu+n+SoEO773gDOHdCtxWGgZVV1LN2+j0/W7OK1hdt48opsLhmW1tbNEjmkvQN/FHC/tfY89/N7AKy1jzZZ53oU+OKlauobWLJ1H7e8soS0uDCKyw4S4Ge4blQmV43sQVx4UKvbaGi0XPaP+RTvr+bLX49r8dOFSFs4mcD3ZFhmKrC9yfNC97LmLjXGrDTGzDDGpJ9IMSJOCA7wZ3TvBO4Y35f84gNkdYvk7Z+cwW3n9PEo7MF1c5dfj89i54FqXlu4rZ0rbnvvLS9i/J+/4mBtg9OlSDvyJPBb+haq+ceC/wCZ1tohwKfAyy1uyJgpxphcY0xuaWnp8VUq0s6uH53JknvP5Y1bRpERH3bc7x99SgLZ6TG8s6yo9ZU7mZfnb2H9rgpemr/Z6VKkHXkS+IVA0zP2NGBH0xWstXustTXup88Dw1vakLV2qrU2x1qbk5iYeCL1irQbYwzxJzk+/9x+SawqKmN3RU3rKzezYNMern9pEVscuDjs2y+r//7lRkrKqw/VU12nM/6uxJPAXwz0Mcb0NMYEAZOBmU1XMMakNHk6EchvuxJFvMdZWa4TmbnHcb/eNxZv46aXF3Pb68v4cl0p3392Ho9/tI6yqrr2KvMIeypriA8Poqa+kXvfyWPO+lImT13A3z7b0GE1SPtr9Upba229MeZW4CNcwzJftNauNsY8CORaa2cCPzfGTATqgb3A9e1Ys0inNah7NHHhQXyxroSLh7b0VdfhSg5U88B/1nCwroFAfz+evWoYry/axrNfFvDOsiIeu3Qw0xdvZ3d5DdGhgfRLieIX5/TB369tx/vvq6xjeI9YhvWI5bHZa5m/cQ8A077Zyi1n9SY6NLCVLYg38GhqBWvtLGBWs2X3NXl8D3BP25Ym4n38/AznDUzm7aWF7K6oOWwKh90VNazYvp8RPeOIdE8H8fjH66hraOTDX4wlKjSAlOhQLhicwvLt+7n1taVc889FBPgZBqZGU1pawcdrdjFnfSnDe8RyeU4a/ZKPvCvYS/M2s7KwjEuGpXJmH8+6TvdU1jKsRwxTzuzFvspanpuziZ+M683fv9zIr95czoRBKfRPiWRg9+i2+YcSR2guHZE2dtOZPZm+eBuvzN/CHeOzAFi78wCTnp5HTX0jfZIi6JscSVpsKG8tKeSmMT3JSo48bBunpsfwwW1n8pfP1jMuK4mz+rqCe9qCrby2cBvTFmzln3M3M2FgMo9cMpj9VbW8t3wHp6bH8OjstTQ2Wt5bXsSff3Aq5dX1PPT+GoZlxPLApIH07Xb4vqy17KuqJTYsCD8/wz0X9GfK2F7ERwSTGBHM/87K59P8ErpFBfP5r8YRHqzY8FaaWkGkHdwyLZf5G/cw59dnExsexD1vr+KdZYU8fPFgHp2VT6O17Kuqc3X/3DnuuLtM9lfV8q/5W3j2y43EhgVSWl5D01kfZt56Bg+9v4bFW/YBrl8ghfsOUlPfwBtTRjGg+38/GZRV1ZH94Mfce2F/bjqz1xH72ranilVFZfzstaXcNKYn935vwIn9o0ib0Hz4Ip3Mr8Zn8clf5vDU5wXcMb4vM5cXcdGQ7lw2PI1LhqZiDLy3fAfJ0SEn1D8eExbE7ef2ZURmHHe+tYLrRmdy/qAUbnx5MTk9YhmSFsO0G0/nHfe0E78+L4s9lbVMenou9767in//ZPSheX/2VtUCHPWag4z4MDLiw5i3MYMX5rqGbf7uwv6aN8gLKfBF2kHfbpH84LQMXpy3mY9W76SytoGrRvYAXP38gEdf6rZm9CkJzL/nnEPPXV0urqt8QwL9uXJExqHXUmNC+c2EfvxmxkqmLdjKtaMyAdhb6RpC2tpFZg9NGkSgn+GFuZuJDAnkF+f2oaq2nlcXbGPSqd1Jigo56fZI+1Lgi7ST/7loAOFB/ny8Zhf/uHo4px5lRs62lBh57OsILhuWxqxVxdw/czXJUSGMH5jM3krX8M/WAt/fz3D/xIGU19Tz50/XExLox/yNe/hqfSmvLNjCz7/Th5fmbSEuPIg7xvdlWEZsm7VL2ob68EV8zMHaBq547huK9h/k0zvO4pM1O7nr36uYe9fZpMW2foVxXUMjN76cy5z1rqvlbzmrF+8sLaKkvIbEyGCsteyuqOWy4Wn86bIh6vppY+rDFxGPhQb58/jl2Xzvqa+57B/zCXVP9ObpvEGB/n5MvWY4izbvpUd8GD3iw7lzfBYrC8s4JTGCAH/DXz/bwNQ5m+iTFMEtZ/Vuz+bIcdA9bUV8UFZyJE//cBjBAf6s3nEAgLAgz8//QgL9Gds3kR7x4YDrl8DwHrFEhwUSHhzAPef348LBKfzxo3Us3LSnXdogx0+BL+KjzhuYzIwfjwIgOKBto8AYw2OXDqZHXBi3vr6M7Xur2nT7cmIU+CI+LDw4gJX3j+fTO85q821HhgTy3DXDqa1v5KoXFrKvsrbN9yHHR4Ev4uOiQgJJjzv+6aA90adbJC/dcBrFZQe5698rcWqQiLgo8EWkXQ3LiOU35/Xj4zW7eG2R990cpivRKB0RaXc3junJnA2lPPifNYQHBfD+ymIuHJLM94fq/r8dSWf4ItLu/PwMT1yRTVpsKLe/sZxP83dx979XUVBS7nRpPkWBLyIdIikyhA9+fiaPfH8wM348irAgf256OZddB6qdLs1nKPBFpMOEBPrzw9MzyMmM44XrTqO0vIZJT8/js/xdx72t9bvKGf/nrygoqWiHSrsmBb6IOGJ4j1jeuGUU0aGB3PhyLr+ZseK4RvF8mr+L9bsq+OUby9uxyq5FgS8ijhmUGs1/bhvDlLG9eDO3kBlLCo9Yp7qugfqGxiOW79h/EIBVRWXc/Equvg/wgEbpiIijggL8uHtCP5Zt28eD76+hur6RbpHBjOgZR1hQAOc88RW1DY1cNjyN60Zlkhztmoa5oKSCIWnRjMtK4uX5W7jwb3PpER/GBYNT+MU5fTRpWwsU+CLiOD8/w5NXnMo/DZ7GAAAKTElEQVStry/j9+/mARDk78fFQ7tTtP8gQzNimDpnE7NXFfPJHWcR6O9HQUklZ2clcsd3+3L1yAz+/Ml6NpZW8pdPN7BuZzm/Pi+L4EB/Kqrrj7iFpK9S4ItIp5AeF8bbPxlNfvEBDtY1cP/M1byZW0hyVAgzfjyaz/J3MWXaEt5ZWsR5A5PZXVHDKUkRgGsE0KOXDMFay3NzNvHkJ+uZnbfz0LavyEnjke8PJsDft3uxFfgi0mn4+xkGpUYD8MQV2Ux8ah6TR6Tj72f47oBuDEmL5vGP1xEa5JrSuXdixGHvN8bw47N6c+HgFOZsKKW2vpHismr3VM2R3Dz2yHv2+hLdAEVEOq2dZdXERwQR6D4zzy8+wBXPfUNlTT2NFr68cxyZCeHH3Ia1lptfyWVuwW5eu3mk19+J62RugOLbn29EpFNLjg45FPYA/VOieP3mkVw3OpMHJg6kR3zrk74ZY3jo4kEkRYYweeoCn56fX2f4IuIT9lbW8v1n59HQaPnw9rFEBHtnj7bO8EVEWhEXHsQTl2dTtP8gd83wzamaFfgi4jNyMuO4a0I/PlhVzHNzNjldTodT4IuIT7llbC/OH5TMkx+vZ91O37o6V4EvIj7FGMPDFw8iMiSAO99aQV0L0za0l9r6Rv7x1Ubyiw902D6bUuCLiM+Jjwjm4YsHsaqojEdm5XdY6JdW1PDY7LWs2L6/Q/bXnAJfRHzS+YNTuHpkBi/N28I1/1zYIaFf4p77PykquN331RKPAt8YM8EYs84YU2CMufsY611mjLHGmBMaMiQi0pEevngwj10ymAWb9vLHD9fS0Ni+I3dKy2sASIwIadf9HE2rgW+M8QeeAc4HBgBXGmMGtLBeJPBzYGFbFyki0l4mj8jgyhHpPP/1ZiY9M5edZe13B67SCnfgRzpzhu/JlQcjgAJr7SYAY8x0YBKwptl6DwF/BO5s0wpFRNrZ/148mJG94vndO3l876m5ZMSFEujvx+U56XSLCmZg92jiwoNOej8lB2owBuIjTn5bJ8KTwE8Ftjd5Xgic3nQFY8xQIN1a+74xRoEvIl7Fz88w6dRUMuPDeerzDdS4J127860VACRFBvPCdTkMSYs54r37q2qJCfMswEsraogLCzpsuoiO5Engt3QXgUMdXcYYP+DPwPWtbsiYKcAUgIyMDM8qFBHpINnpMbxw3WkANDZavlpfSk19Iw9/sIYbXlrM+z8fQ0p06KH1F27aw5XPL+D2c/vy83P6tLr90vIax7pzwLMvbQuB9CbP04AdTZ5HAoOAL40xW4CRwMyWvri11k611uZYa3MSExNPvGoRkXbm52c4u18SEwYl868bTqO6roHrX1zM9r1VgGsWzlcWbKXRwpOfrOdXb66gcF/VMbdZ4nDge3KGvxjoY4zpCRQBk4EffvuitbYMSPj2uTHmS+BOa61mRhORLuGUpEimXpvDT/5vCROfnsvo3gnM2VBKdV0D147qQUigP/+av4X/rNjBbyZkcdOZLc+7v7u8ht6Jx57OuT21eoZvra0HbgU+AvKBN621q40xDxpjJrZ3gSIincEZpyTw3q1jiAsPYnZeMVndIvH3M1w7KpPfXtCfL+8cx9n9Enn4g3xumZbLF2tLDnu/tdbxLh2P5ge11s4CZjVbdt9R1h138mWJiHQ+PRPC+c9tYyg5UENmQjj1DY2HbpvYPSaUZ68azmOz83l3+Q4+WbOL60Znsra4nPrGRu4+vz+1DY0kRnTuPnwREXELCwo4dJet5vfI9fcz/O7CAXx55zgGpUbz0rwtlNfUsam0kh8+vwBwbgw+6J62IiJtLjw4gOlTRrKvqo7UmFA2767k6c8LMAbGnJLQ+gbaiQJfRKQdhAUFEBbkitieCeE8cUW2wxWpS0dExGco8EVEfIQCX0TERyjwRUR8hAJfRMRHKPBFRHyEAl9ExEco8EVEfISxtn3v4XjUHRtTCmx1P00AdjtSSPtT27xXV26f2uadEoBwa+0JzS/vWOAfVoQxudbaLnnjc7XNe3Xl9qlt3ulk26YuHRERH6HAFxHxEZ0l8Kc6XUA7Utu8V1dun9rmnU6qbZ2iD19ERNpfZznDFxGRduZ44BtjJhhj1hljCowxdztdz8kyxmwxxqwyxiw3xuS6l8UZYz4xxmxw/x3rdJ2eMMa8aIwpMcbkNVnWYluMy9/cx3GlMWaYc5W37ihtu98YU+Q+dsuNMRc0ee0ed9vWGWPOc6Zqzxhj0o0xXxhj8o0xq40xv3Av9/pjd4y2ef2xM8aEGGMWGWNWuNv2gHt5T2PMQvdxe8MYE+ReHux+XuB+PbPVnVhrHfsD+AMbgV5AELACGOBkTW3Qpi1AQrNlfwTudj++G/iD03V62JaxwDAgr7W2ABcAswEDjAQWOl3/CbTtfuDOFtYd4P7ZDAZ6un9m/Z1uwzHalgIMcz+OBNa72+D1x+4YbfP6Y+f+949wPw4EFrqPx5vAZPfyfwA/cT/+KfAP9+PJwBut7cPpM/wRQIG1dpO1thaYDkxyuKb2MAl42f34ZeBiB2vxmLV2DrC32eKjtWUS8Ip1WQDEGGNSOqbS43eUth3NJGC6tbbGWrsZKMD1s9spWWuLrbVL3Y/LgXwglS5w7I7RtqPxmmPn/vevcD8NdP+xwHeAGe7lzY/bt8dzBnCOMcYcax9OB34qsL3J80KOffC8gQU+NsYsMcZMcS/rZq0tBtcPLJDkWHUn72ht6SrH8lZ3t8aLTbrevLZt7o/5Q3GdLXapY9esbdAFjp0xxt8YsxwoAT7B9Ylkv7W23r1K0/oPtc39ehkQf6ztOx34Lf028vZhQ2dYa4cB5wM/M8aMdbqgDtIVjuXfgd7AqUAx8IR7uVe2zRgTAfwbuN1ae+BYq7awrFO3r4W2dYljZ61tsNaeCqTh+iTSv6XV3H8fd9ucDvxCIL3J8zRgh0O1tAlr7Q733yXAO7gO2q5vPyK7/y5xrsKTdrS2eP2xtNbucv+HawSe578f/b2ubcaYQFyB+Kq19m334i5x7FpqW1c6dgDW2v3Al7j68GOMMQHul5rWf6ht7tejaaWb0unAXwz0cX8LHYTri4eZDtd0wowx4caYyG8fA+OBPFxtus692nXAe85U2CaO1paZwLXuER8jgbJvuw+8RbN+6+/jOnbgattk96iInkAfYFFH1+cpdz/uP4F8a+2TTV7y+mN3tLZ1hWNnjEk0xsS4H4cC5+L6juIL4DL3as2P27fH8zLgc+v+BveoOsE30xfg+qZ9I/A7p+s5ybb0wjUiYAWw+tv24OpX+wzY4P47zulaPWzP67g+HtfhOpu48WhtwfXx8hn3cVwF5Dhd/wm0bZq79pXu/0wpTdb/nbtt64Dzna6/lbaNwfXRfiWw3P3ngq5w7I7RNq8/dsAQYJm7DXnAfe7lvXD9kioA3gKC3ctD3M8L3K/3am0futJWRMRHON2lIyIiHUSBLyLiIxT4IiI+QoEvIuIjFPgiIj5CgS8i4iMU+CIiPkKBLyLiI/4fuKpIfzEc0wYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67082f1c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.3138132471278111, 0.8504672897196262)"
      ]
     },
     "execution_count": 30,
     "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": [
    "### Run Learner again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10d5c6b43b0b401c90b7f1a5d20ba798",
       "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.      0.42601 0.60856 0.77273]                          \n",
      "[1.      0.42695 0.64841 0.8125 ]                          \n",
      "[2.      0.40172 0.60374 0.80398]                          \n",
      "[3.      0.43415 0.38573 0.83239]                          \n",
      "[4.      0.42632 0.40595 0.81534]                          \n",
      "[5.      0.42311 0.33094 0.83523]                          \n",
      "[6.      0.39453 0.3958  0.81534]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lrs, 3, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6709033f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Learner again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "320202d9345a4d6a9c9323c1c1005a84",
       "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.      0.3332  0.31534 0.83523]                          \n",
      "[1.      0.36606 0.38689 0.79829]                          \n",
      "[2.      0.3379  0.35744 0.83239]                          \n",
      "[3.      0.35318 0.31905 0.86364]                          \n",
      "[4.      0.36305 0.35403 0.83807]                          \n",
      "[5.      0.36661 0.33279 0.83807]                          \n",
      "[6.      0.33856 0.34332 0.84375]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lrs, 3, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67081419e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log Loss | Accuracy (final model here)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.27831638091989297, 0.8753894080996885)"
      ]
     },
     "execution_count": 35,
     "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 predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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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