5_resnet18_resize.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle Competition:  Iceberg or Ship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# resnet18 architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 1956\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  64953 Jan 14 18:52 4_resnext_resize.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu 108974 Jan 14 19:28 5_resnet18_resize.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    399 Jan 14 18:59 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 45 ubuntu ubuntu       4096 Jan 14 18:51 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=resnet18\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": "beb4ab950a514c239bac668cfa015c0a",
       "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=0.5)"
   ]
  },
  {
   "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": "fdfcd6e721434565ad6958f287b6f250",
       "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": [
      " 78%|███████▊  | 32/41 [00:00<00:00, 36.56it/s, loss=5.13] \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 0x7f28992c34a8>"
      ]
     },
     "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 0x7f2899291588>"
      ]
     },
     "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": "504f3c73933741b0a413e221f53a8eae",
       "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.      0.6041  0.52777 0.70454]                          \n",
      "[1.      0.53134 0.4256  0.72159]                          \n",
      "[2.      0.49774 0.3859  0.82671]                          \n",
      "[3.      0.48287 0.43395 0.80114]                          \n",
      "[4.      0.49255 0.40146 0.81818]                           \n",
      "[5.      0.4783  0.37105 0.83523]                           \n",
      "[6.      0.49191 0.38843 0.83807]                          \n",
      "[7.      0.45562 0.343   0.84943]                          \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": "ce6b31b90c4d4d06b0e486903713ec13",
       "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.      0.47041 0.35225 0.82671]                          \n",
      "[1.      0.48077 0.39135 0.80398]                          \n",
      "[2.      0.46753 0.36833 0.82102]                          \n",
      "[3.      0.47293 0.38059 0.80682]                          \n",
      "[4.      0.45999 0.36337 0.81534]                          \n",
      "[5.      0.46621 0.37815 0.80966]                          \n",
      "[6.      0.44924 0.38388 0.81534]                          \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 0x7f28980ee630>"
      ]
     },
     "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 0x7f2892177b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr/9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr/4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unfreeze Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8a6207a23db42a2a243c45f29a5c29e",
       "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.      0.57343 0.51029 0.74148]                          \n",
      "[1.      0.56622 0.42955 0.80682]                          \n",
      "[2.      0.53422 0.46505 0.75568]                          \n",
      "[3.      0.53436 0.52334 0.75852]                          \n",
      "[4.      0.50635 0.46163 0.78693]                          \n",
      "[5.      0.50535 0.67034 0.71875]                          \n",
      "[6.      0.44798 0.6415  0.71591]                          \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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('resnet18_sz75')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('resnet18_sz75')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f28927a42b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.3969869059268904, 0.8348909657320872)"
      ]
     },
     "execution_count": 23,
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b428b9db52841899d6382a9908e1813",
       "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.      0.41443 0.54108 0.72727]                          \n",
      "[1.      0.42096 0.62856 0.74148]                          \n",
      "[2.      0.42631 0.54198 0.73011]                          \n",
      "[3.      0.45821 0.67444 0.73011]                          \n",
      "[4.      0.48004 0.59458 0.72159]                          \n",
      "[5.      0.48985 0.51787 0.75568]                          \n",
      "[6.      0.46761 0.74335 0.67614]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lrs, 3, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d4601d491364a9599cce16cf2e4da97",
       "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.      0.4486  0.44496 0.76989]                          \n",
      "[1.      0.48508 0.50229 0.73296]                          \n",
      "[2.      0.47162 0.52658 0.71591]                          \n",
      "[3.      0.44984 0.42194 0.79546]                          \n",
      "[4.      0.43821 0.35354 0.81818]                          \n",
      "[5.      0.39319 0.49475 0.78125]                          \n",
      "[6.      0.36623 0.48774 0.78977]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lrs, 3, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EstwuMtKcXDXBAjnr51t33jtPdPHmleVRf2awJvORaxfx5pXlLCvPJcvt4p0bquke9PLdl4+zqjKf29ZV8Y3nj/K7g+cY9vlxOx2c6R7imqWlI96vPD8jtBLbioq8UEI8cpkLaVhQiAECxrDzRBf5mWnUV+ZRnJ0+5aCmVLxpIEggwRpBMAm6aF42/R4/rT3DZLqdBAwTXqAmU1eSg8cXoLVniEo7ueydZtMQQHleBlluJ0ftZqyuQQ/5mWnctr6KupLsULNKJCsr80lzCluaOqYWCHwB3E4neRlprK2xgsldl80HrED67KeuJT8zDbfLQcDAx/73NQ6f7WN+cRYDHj+VBWNHYf/X+y7hROcAy8tzx7w22n9/4FICAcPHf7QLgL95Wz1Zbhdleem09kyva6xSs00DQQJp6x1GhNDo1NV2z5Uvbj7Ap960FIC8aQSCsjwrwJzrHQ4FgljUCESEupJs9p/p4QtP7udIax+FWW7+9u0rJz02I83Jm+rLeXx7C39+49Kol8L0+AMTrqEQPso6mKPYe6o7dEx5/thAUJaXQVle5Gk6RgsG5L96ywquXFTMuzZUhd5Dm4ZUstEcQQJp6x2mONsduoNeV1PAX7x5Gb/YfZondp0GplcjKLXnIgq/UMWiRgBWr6Rtxzr51ovH2Hqsk/ys6Mv5kWsW0Tvs44dbT0S1vzEGjy9AepQBrKYok/zMNPae6golcicafDcVy8pzufeaRaEBeaW56Zzr1RqBSi4aCBLIuZ6hUH4g6N2XVAPW1BMwzUAQViMI8vimlywOCuYJggqmUM7V1fmsrMwLLbs5mQvrLEdXbhFhTXU+u09283qLNb1FcMqLWCvNy6C9b3jMCG6lEpkGggTS1N4/5oJaaDcTBbuRTicQFGe7cYi10H1QcIqJ6Vpoj1MIjkkoyJra5Gt1JdmhuX4m45nCRHlBl9YWceBsDz/afpJ1NQUU50SeoG+6yvLSMQba+xJnzQWlJqOBIEFYE7X1s3jeyJk805wO8jJcoakaphMIXE4HxTkjk5keuyfNdF2zdB53X76AP77a6sY51WkjaoqyON01GNVqYNEurxnuDxpqcIjQcn6QG+1J5mZCWYTmN6USnQaCBHG8Y4CAgUURpnQuyUlnyGtd/KaTLAbrjjW8e6M3RjWC/Mw0/v7WVVyxyOoqOnqhl8nUFGbh9RvORnEBDQWCKEZUB5XnZ3CT3SvpTTMYCILNb609Q/zPS8d4McrmLqXiKaorgIjcJCKHRKRRRD4zwX63i4gRkYawbWtE5BUR2Scie0Ukum4Zc0xwuoJF88YGgmAvouAApukozc2IkCO4uCkmIgkOxHqXnduIVnAK7ZNhAWTY5+c3+86OqV14Q+ssT63cn7l5OV+4bVXE9RNiJZiEPnKujy9uPsCf/GAHZ7qntqKbUrNt0kAgIk7gIeBmoB64U0TqI+yXC9wPbA3b5gK+D3zEGLMSuBbQGbkiOGoHgtE5ArgQCPIz06KeLno8pbmjmoZi1GsoKD8zjWP/+Bbee9mCKR1XU2glb8MDwad+vId7HtnBrpNdI/Yd9k09RwBW89Mfblow7X/DiczLTaemKJPvbzmO12/oGfLxwM/3zdjnKRUL0fwlbQQajTFNxhgP8Chwa4T9Pg88CITX7d8E7DHG7AYwxnQYY8au6jHHvf/b2/jybw+TkeaIuNpWcc6FQDBdpXkZdPRf6NXijVGvoXAXc6GtLMhExJq6GqC9b5hf7La6zI5uZgo2DaXHMIDF0sba4lA31bsvX8Bv97fy+qnpL8aj1EyJ5i+pCghfBqrF3hYiIuuBGmPMk6OOXQoYEXlKRF4Tkb+M9AEico+IbBeR7W1tc28+9+DMndctL434erBGMN38AFg1gmCvlqa2Pga8Fz8NdSy5XQ4q8jJosS/6P3mtJfTaydGB4CJ6Dc2my+zlRWuLs/jEm5aRm+4KzWGkVCKKZmRxpNu7UKOtiDiArwIfGOf9rwIuBQaAZ0RkhzHmmRFvZszDwMMADQ0NU1+lJIkF724/fFUdf3bj0oj7FGVbCchY1AiC0yd89Ac72HnCbnJJkH/x6qKsUBfS7c3nqS3Oot/jH7dG4HZOL18yU4LrTG+YX0h+Zhp3bKzhOy8165rGKmFFc0vVAtSEPa8GToc9zwVWAc+JSDOwCXjCThi3AM8bY9qNMQPAZmBDLAqeKoIzdc4vzhp3iukSu2kobxoTzgU11Bbxx1fXsfNEF5fYSzTWFM3M4KqpqinM4mTnIMYYXjtxng0LCplflDVuIIhlkjuWFhRn8cEr63jvJmvuo3esr8YXMPxy1AI+SiWKaK4srwJLRKQOOAXcAdwVfNEY0w2EppcUkeeATxljtovIUeAvRSQL8ADXYNUe5hSvP2DNnx92oe8Z8pKXkRYKBBPd7Ycni2PhMzev4NplpVxaW4QvECAzLTHurGuKMmntHeJoWx/tfR42zC/EGNh2rHPEft4EbxoSER54+4X+FCsqcllWlsvPdp7ifZumlkRXajZM+pdkjPEB9wFPAQeAx4wx+0TkcyJyyyTHnge+ghVMdgGvGWN+Of1iJ5c//t52Vv7tU6HnR1p7Wf+53/JyYzvdg9YI1NkMBE6HcOXiEtwuKzk9k71opqKmMAtjCM2rdMmCQmugWfdgqBYAF99rKF5EhKuWlITWa1Aq0UTV1mCM2YzVrBO+7YFx9r121PPvY3UhnbOes5di/P3hNv7r90fZVFeM3170PThX/0RTMgTnH4pVIEhUwSaqn+8+TU66i6Vluew/3YMx1kLzdSVW19pgsjhRew1FUpJjraA24PFF7BmmVDwlz19SCvjXpw/zUmMHD/++CYCXGttDTUMTTdJWmpvOX791BW9fWzkr5YyX4KCy4x0DrJ9fgNMhzLcnh2sOW4A+0ZPFkRTbtboOnYNIJSANBLOgyp77/zW7l07vsA+308Hh1r7QiOKCCaZtFhE+fPXC0BoCqaosNyM0f9D6+VYie0VFHk6HjFjX2JNkTUNwYSxIcF1npRJJ8vwlJbHC7AsXeZe9ROQdG62OWL9+/SwAuVEuyJLKHA6hqtAKdsEeTTnpLuor8ni1OTwQWGMSE7XXUCTB2U47+nWtApV4NBDMgkHPhcHUH7lmEauq8vjotYtwOYSm9n7yMlyhNYTnuupCa4TxOnv5SbCCws6T50O9haa6HkEi0KYhlciS5y8piQ2EBYLbL6nmyT+9mor8TGrt5OdU5+5PZdcvL+WtqytGJMYvrS1iyBtg32mr102ijyyOJNg01KFNQyoBJc9fUhLrH/ZRU5TJm+rLWBC2MlZw7YGJ8gNzzQeurONrd40cc9hQazUTbW+2xhMMX8R6BPGW5XaRkeago0+bhlTiSZ6/pCQ24PHz9jWVPPz+hhF99oPTIad6t9DpKsvLoKYok+12nsDjC+B2OhJm/EO0irPTtUagEpIGghnm8QXwBcyIUcVBGgiid+mCIrYf7wwtXJ9MzUJBJTluzRGohJR8f01JZsDjAyArwoIywUCgTUOTa6gtor3Pw/GOAbz+2C6mM1uKst3aa0glJA0EM6zfThRHCgSL5uXgckho5LAaXzBPsK25M2lrBMU56XRqjUAlIB3rPsMGhoM1grH/1JluJz+6dxMLS2Zu6cRUsXheDiU5bl440o7LIckZCLLdtPd5OHS2l2X2dOBKJYLk+2tKMsEaQXZ65OkQLllQRKHOUT8ph0O4fnkZzx06R789MjvZ3FBfRppTuO2hl3SEsUooyffXlMD8AcMXntzPX/x4N4dbe4HwHIFWvqbrhvoyeod8vNjYjtuVPPMMBV1aW8S/3rGeQa+f4x39kx+g1CzRq1MMtZwf4FsvHgOsoPCV96xjYNiuEWggmLarFpeQkeZgwJMYy2tejPK8DADO9WrSWCWO5PxrSlChBVOcDp4+0IrXH6A/WCMYp2lIRS/T7eT65WUAJOuMHGV5VseAcz1DDHn9vOe/XmFLU0ecS6XmOg0EMeTxWXPgvG1tBT1DPrY0dYSml9AaQWzcus6aiju03nKSKc5JxyFWjWDniS62HuvkV7qEpYozDQQxFKwRXL+8jHSXg+cOtdFv9xrKjNB9VE3dtctK412EaXHa3YVbe4ZCS3DuOdUd51KpuU5vU2PIF7ACQW6Gi/lFWZzsHAgtOBNpHIGaOrfLwQ8+fBk5EUZqJ4uyvAzO9Q5zumsIgP2ne+xBcnpfpuJDf3kxFGwaSnM6KM/PoLVniH47sal/5LFz5eIS1oZNU51sSnPTOd01yGsnzlOam86wL8CR1r54F0vNYXp1iqFg01CaUyjPy+BM9xADHh/ZWhtQYUrzMjjc2seAx8/dV9QC1rKlSsWLBoIYuhAIHFTkZ9DWN0z3oFfHEKgRSnOtnkMuh3D3FbUsmpfNFzcf4F+eOhjnkqm5SgNBDIUHgrL8DIyxFmIfb1SxmptK7EVq1tYUkJPu4on7ruItq8v51gvHdL0CFRcaCGLIE1pCUajItwYOHWntJU/XI1Zh8uwOBMGusNnpLj5x4zI8/gAP/vpQaDS6UrNF2yxiyOsLqxHYI0j7PX7Wz0/exKaKvbetqSQvM41rl84LbVtcmsNdG+fzg60naOka4Acf3hTHEqq5JqoagYjcJCKHRKRRRD4zwX63i4gRkYZR2+eLSJ+IfGq6BU5kwe6jVo4gM7T9srrieBVJJSCnQ3jjstIxK6x98R2r+aMra9l2rDPUzKjUbJg0EIiIE3gIuBmoB+4UkfoI++UC9wNbI7zNV4FfTa+oiS/YNJTmdFCYlYbb5UAELq0rinPJVLJYW12A129oatNJ6dTsiaZGsBFoNMY0GWM8wKPArRH2+zzwIDAUvlFEbgOagH3TLGvC84Ytqi5idSFdWZmnS1GqqAXXKTh4tifOJVFzSTSBoAo4Gfa8xd4WIiLrgRpjzJOjtmcDnwb+fqIPEJF7RGS7iGxva2uLquCJKFidd9nLKP7lTcv49E3L41kklWSCq9YdOtsb76KoOSSaZHGkeR5N6EURB1bTzwci7Pf3wFeNMX2j20NHvJkxDwMPAzQ0NJhxd0xw4d1HwUoKKjUVbpeDhfOyNRCoWRVNjaAFqAl7Xg2cDnueC6wCnhORZmAT8ISdML4MeNDe/mfA/xOR+2JQ7oR0IUeQpHMkq4SwtCyXfad78CVAwnjQ4+drvzvCkNcf76KoGRRNIHgVWCIidSLiBu4Angi+aIzpNsaUGGNqjTG1wBbgFmPMdmPM1WHb/xX4B2PM12J/GonBmjhMxvQGUWoq3rq6grM9Q3zt2cZ4F4XNe8/wpd8c5ktPHYp3UdQMmjQQGGN8wH3AU8AB4DFjzD4R+ZyI3DLTBUwmXp/OIKmm7+bVFdy6rpL/+F0jPUPeuJYl2NHhV6+fjWs51MyKakCZMWYzsHnUtgfG2ffacbb/3RTLlnR8AaOBQMXEbeuq+Pmu0xw628ultfHrfuw3VnPnqa5Bhn1+0sPWij54tofOPg9XLC6JV/FUjOhVK4Y8Oqe8ipHlFXY30jPx7UbqD1zouxFcSCfo0/+3lz/94U6MSdr+HcqmV60Y8voCuDVRrGKgPC+D/Mw09p+Jb+8hX1ggeO34heVBT3YOsPtkFx39HlrOD8ajaCqGdK6hGPL6A7i0RqBiQERYUZEb94FlfnvaFLfLwa6T57n/hzu5ob6MU2EX/z0t3dQUZcWriCoG9KoVQ16/0a6jKmaWl+dx6Gwve1u6+exP9nK2e2jyg2LMZ3eJ3jC/gBeOtPPE7tP81U/38u2XjrG6Kh+308Gelq5J3kUlOg0EMaQ5AhVLKyvzGPD4efvXXuSH207w5J7Tkx8UY8GmoUsWFOILGNxOB0NeP8NeP1/+g7WsqMhltwaCpKdNQzHk9QdwuzQQqNi4ZV0lbpeDrgEvX/jlfg7GYbRxeCAAuG55KXdfUUtJjpslZbmsrSng/3a04NNm0aSm31wMebVGoGIo3eXk1nVV3H1FLZsWFsclX+C3RzevqsrnrasruOeahVy+qJglZVavpsvqiun3+Nl7qnvWy6ZiR69aMaQ5AjVTVlTkcbi1b9annQjWCDLSnDz03g1smF844vVNC60xDi8f7ZjVcqnY0kAQQ1ojUDNleXkuHl93dKmUAAAgAElEQVSA5o7ZXacgOI7A5Yh8g1Ock87y8lxe0UCQ1PSqFUNefwC3BgI1A1ZU5AHM+riCYI3AOU4gALh8UTGvNncy6NGJ6ZKVXrViyOszobUIlIqlRfNyyEl38fvDs7teR7D7qMsx/qXixhVlDPsCPH2gdbaKpWJMA0EMadOQmilul4O3ralg894z9A/7Zu1z/YEAIhPXCDYtLKYiP4Of7jw1a+VSsaVXrRjyaNOQmkHvbqhmwOPn2y8em7X5fXwBM25+IMjhEG5dV8Xzh9s43++ZlXKp2NKrVgxpjUDNpA3zC7lqcQlf/u1hLv3iM/z3i8dm/DP9ATNhbSDoDUtK8AcMr5/WbqTJSK9aMeTzG9JcmiNQM0NE+N4HN/Jvd6yjqiCDr/zm0IyvHOb1mwnzA0HBcQWHW/tmtDxqZmggiCGdYkLNtGAzzCfetIx+j58Xj7TP6Of5A4GoagQlOW4Ks9I40qprLScjvWrFkHYfVbPl8oXF5Ga4ZnzlMGuxpckDgYiwpCyXwxoIkpJetWLI69fuo2p2uF0Obl5VzpN7TtPcPnODzKLNEQAsLcvhSGufLlSThDQQxIg/YPDrUpVqFn3ixmW4XQ7+30/3zthnWL2GovtNLy3LpXfYx9me2Z8uW01Pyly1Gs/1cve3t8VtbnSvPQeMBgI1W8rzM/ijK+t4+WgHA56ZGVvg80eXIwArEAAcDBv9rLWD5JAyVy1fwPD84TZOdsZn2bxgINAcgZpNS8tyADjROTAj7x/NOIKgVVX5OAR2nrxwM/ah727nff+9la4Ba3zBoMfPjuPnZ6Ss6uKlzFWrINMNQNdgfAa0eO2h+Dr7qJpNC4qyAWhun5lA4A9En/fKSXextCyXnSesC33XgIdnD53jhSPtfPKx3QA8sqWZd339ZV7XaasTSuoEgqw0ALoGvHH5/OD0wGm6MI2aRfOLrbWCj8/QrKS+gMEZZY4AYMOCQnad7CIQMGw71okxVq1lW3MnxljbAL73SvOMlFddnKi+YRG5SUQOiUijiHxmgv1uFxEjIg328xtFZIeI7LX/f12sCj5aRpqTzDRn3Ia4ezRHoOIgPzONomw3zR0zWCOIsmkIYH1NAb1DPo629fFKUwfpLgd3bpxP75CPlvOD7Dh+HhH4+a7TdMfppk2NNelVS0ScwEPAzUA9cKeI1EfYLxe4H9gatrkdeLsxZjVwN/BILAo9nsKsNLoGY/fjCgQMT+07SyAwecIr2DSkOQI12xYUZ81YjcA7hWQxQEOttVDN7w6e4+XGDhpqC1lXUwDAL/ee4fyAl3eur2bYF2Bbc+eMlFlNXTRXrY1AozGmyRjjAR4Fbo2w3+eBB4FQ3zFjzE5jTHDF7X1AhoikT7PM48rPcoeSUrGw5VgH9z6yg1eaxl90o6NvmJ/vOoXHZ9UIdByBmm0LirI4niA1grqSbK5YVMxXnz7ModZeblpVwfLyPBwCj7xyHIAPXFGLyyHsOqlJ40QRTSCoAk6GPW+xt4WIyHqgxhjz5ATv8y5gpzFmeMqljFJhVhrnY1jd7OizgspEPTL+YfNBPv7orlBPCG0aUrNtQXE2p7sHZ2RhGN8UksVBH7lmEUPeAKur8rlr43wy3U5yM9I41TVIVUEmKyvzWF6Ry66T8enqrcaK5qoV6VcQaisREQfwVeCT476ByErgn4F7x3n9HhHZLiLb29oufuGNwhjXCILNTKe7xu+SGrxZeu7QOUCbhtTsu7S2CGOs5phY809hQFnQ1UtK+MJtq3jorg2hZqVL7Sajb/zhJTgcwrqaAvac7I6q2VXNvGi+4RagJux5NXA67HkusAp4TkSagU3AE2EJ42rgp8D7jTFHI32AMeZhY0yDMaZh3rx5Uz8LW35WWkx7DfXYgeDU+fEDQZbbCRBanam6MDNmn69UNC5fZC0M8/iOk5PvPEVTGVAWJCL84aYFoR5NAA/evoZnP3Utq6vzAVhXU0jvsJVUVvEXTSB4FVgiInUi4gbuAJ4IvmiM6TbGlBhjao0xtcAW4BZjzHYRKQB+CXzWGPPSDJR/hGCyOFajGbvtQNAyQY2gZ8ga0Rkw8J6GmtB0vErNFqdDeOcGa2GYtt7YtrxOZUDZRIqy3dSVZIeeb5hvJZC36+CyhDBpIDDG+ID7gKeAA8Bjxph9IvI5EbllksPvAxYDfyMiu+z/Sqdd6nEUZrnxBwy9MVrKL9jMdOr8IN99uZlTEQJCz6CXvAwXb1tTwadvXh6Tz1Vqqq5bXkrAEPMpVqYy6dxU1JVkMy83nS0TdMRQs8cVzU7GmM3A5lHbHhhn32vDHn8B+MI0yjcl+Zn2oLJ+L3kZadN+v2CN4FTXIH/7xD5eOdrBN953yYh9eoa8rKrK52t3bZj25yl1sULz/Jzt5foVZTF7X98MTaQoImxaWMyWpg6MMYhob7t4SqnMZmGWNc3E+RgljLtHjUnISneO2adn0BeToKPUdORmpFFdmMmBMz0xfd+ZqhEAbFpYRGvP8Ix1fVXRS61AkG1dkGMVCLoGvKSHTxkRIfXQM+QlLzOqipVSM2p5eR6HzsZ2YRivPxCTHEEkl9UVA2jzUAJIqUCQb088N/pO/mL1DHpZW10Qet4ZIcBYOQKtEaj4W1GRS1N7f0zXMZ7JGsGiedmU5GieIBGkVCCoyM9ABI7FaMWm7kEvq6vzeerP3sDVS0rGzGPk8wfo9/jJy9RAoOJvWXku/oCh8VzsumRezICyaFl5giK2NHXqugVxllKBIDvdxZLSnJiMWPTaF/n8zDSWledSkpM+pkbQa3cdzcvQpiEVf2uqrNprLOf7v5gBZVNx2cJizvYMzdh6Cio6KRUIANbVFLD7ZNeU7jC6B7xjqtPB5qVgT6TCLDfn+0c2OfUMWc+1RqASQU1RJlUFmbx8tD1m73kxA8qm4vKF1ohjbR6KrxQMBIWcH/BOqSfCnd/cwj/96uCIbcFAEFznoCg7jb5hH8O+CwGjZzBYI9BAoOJPRLhycTFbmjrxx2jqhlgNKBvPonk55Ga42KsL1cRVCgYCq3o8leah4x39Y9pVg1NVBO/2C7PdI7aD1ghU4rliUQndg96YdSP1BQzOGZxRV0RYVpbL4VadaiKeUi4QLC3Lwe10cOBsdH8IQ14//R4/Z3uGRmwPzjNUENY0BNDZ76Gtd5hzPUOhfXI1R6ASxBWLrC6ZsWoemuo01BdjSVkuh1t7NWEcRykXCFxOBxUFGSMminu5sZ2Hnm2MuH/wDr91VCCIlCMAON/v4eOP7uQt//4Ce+zqrNYIVKIozctgcWkOLzVOv83dGDPjyWKAZWU5dA14Yz5PkopeygUCsGYADZ8X6K5vbeVfnjoUasoJ12l3Ce0d8jHguTBHUe9Q8G4/mCOwAkFHv4e9p7pp7/Pw9eesyVS115BKJFcsKubV5s7QYkkXK5hnmOkaQXB6DG0eip+UDARVBZm0hNUISnKsRdGe3t/K915pHlEF7QwbG9Dac+GOJDhxXbDZJzhqed/pHnqHfLynoYYstxOHQLZbA4FKHFcsKmHA45/2BHQ+OxDMZI4AYGm5FQgOtcZ2VLSKXooGgizaeodDXUJXVFg/tE88tpsHfr5vRGI4fGxAePNQ75CPNKeEppgINg29Yre9vruhmv/5o4185ublOGb4jkmpqdi0sAi308H9P9zJ9mmsC+ybpRpBSU46xdluDsd4egwVvdQMBPbiMGe6rQu7Y9TMhifPX+haer4/ciDoG/KRk+4KzYqY5nRQmZ8RygssLc9lY10R97xh0cychFIXqSDLzXc/uBGPP8B//b7pot/H77drBDOcIwBYUpbD4XMaCOIlNQNBgRUIggnj0YPFTnZeaDbqHC8QDPtC+YGg911eizFWDkLHDqhEdvmiYq5eMo9dUxxcGc4XsHIMaTPcNARYXUjPas+heEnJQBBcLrLFvvMf9gVYW53PY/deTmaac8Rw9vMDHvIz08hyOznbHZYjGPKSkz6y7f+9m+aTm+6iviJvFs5CqelZW51PW+/wmK7R0Qomi2dyZHHQkrJc+j3+iIs/qZmXklnO8vwMHELoRzXk9VNTlMXGuiJqijJHBILOfg/Fdo+g1t6ROYKcUb2B8jLSePTeTaEupUolsrX24MrdJ7uoyJ/6WtreWcoRgDVhHsCR1j6qC7Mm2VvFWkrWCNKcDsrzLowlGPYFyEizFpWZX5TFyVE1gsJsN7Ul2ew71R2qmvYN+yJ2C11Zma8/VJUUVlTkkeYU/vaJffzD5gNTPn42cwRLS7XnUDylZCAAq1YQTBYPef1k2L1/auxAELzgd/Z7Kcxy88Zl82juGKDJnsK6104WK5WsMtKcXLW4hPY+D/+79cSU5x8K5ghmo0aQn5VGWV46B2O8wpqKTsoGgoqCzFDb6JDXP6JG0O/xh5LEnf3DFGe7uc5e5/WZA61A5GSxUsnmO3+0kX+5fQ19wz4OT/FuOzSgbBaSxQBXLi7hN/tbY7awlIpe6gaCvAzOdA9ijGHYFwiNB6ixm3VOdA5wtnuIjj4PJbluqgoyWV6eyzMHzmGMsZLFOmJYpYCGBdZUz1Ndp2C2xhEEffDKOgY8fn706olZ+Tx1QcoGgvL8DIa8AbrstQZCNYLiC4Hgc0/uw+kQ3tMwH4CG2kIOnOlh2BfA6zfaNKRSQk1RJiU56bw21UAwizkCgFVV+Vy+sJj/er5pzGqAamalbCCotMcSnOgcIGAgI21kjWB783k27z3LvW9YGAoOtcXZ9Az5QslknUNIpQIR4ZIFBWyfco1g9nIEQQ+8vZ7uQS+f/+X+WftMlcKBoDw/A4DmDiv5G6wRZLqdzMtN52k7F3DF4pLQMXUl2QChRTK0aUilirU1BZzoHOBIay//8tRB/v2ZI5O2xc/mOIKgFRV53LGxhif3nMHnn96keSp6UQUCEblJRA6JSKOIfGaC/W4XESMiDWHbPmsfd0hE3hyLQkejwg4EwYXs0+1AAFBTmBnqURSc+RCg1g4Ee1qsQJCbrslilRrWVltjCv7i8T089OxRvvr0YW576CXa+8af+tk3y8nioEsWFOLxBUI9+NTMmzQQiIgTeAi4GagH7hSR+gj75QL3A1vDttUDdwArgZuA/7Tfb8aV5mbgdEgoEAS7j4LVcwigJMcdml4arGYjh8DrWiNQKWZVZT5grdzXsKCQRz54Gcfa+/nt/tZxjwnmCGZ6PYLRVtgj9/ef1q6ksyWab3gj0GiMaTLGeIBHgVsj7Pd54EEgfDz7rcCjxphhY8wxoNF+vxnndAiluek022sXh9cIgoEgvDYA4HY5qC7MCk0sp8lilSrys9KotXNh1yydx5WLi8lNd7Hv9PhrBQdzBLPZNATWOsZup2PC5TZ/tfcMv9l3dhZLldqiCQRVwMmw5y32thARWQ/UGGOenOqxM6k0L4MTHWNrBDXjBAKwmoeCC3roxHIqlayxm4euWTYPEWFFZd6Ed92ztTDNaGlOB0vKctg/QSD46A9e455Hdkx5kJyKLJpAEOlXEPrXFxEH8FXgk1M9Nuw97hGR7SKyva2tLYoiRacwK43z9lKUGRFqBEvKcsYcEz6hnDYNqVRy67pKblhRFmomWlmZx4EzveNeTH1xSBYHrajI48CZnklnI9289wzNmkuYtmiudC1ATdjzauB02PNcYBXwnD13fznwhIjcEsWxABhjHgYeBmhoaIhZiC/KutD+Hx4I1s0v4INX1nHTyvIxx/zpdYsZ8vppau/XyeVUSrl+RRnX2yPowbrpGfT6ae7oZ9G8sTdFwQCR5pz9zoXr5xfw+I4Wjrb1s7h0bNkcAgEDf/rDnaS7HPzy/qsj7qeiE803/CqwRETqRMSNlfx9IviiMabbGFNijKk1xtQCW4BbjDHb7f3uEJF0EakDlgDbYn4W4ygYEQgunGq6y8kDb6+n2F7CMlx2uou/u2Ul3/vgxrjcCSk1W1baNYNHXjnOI680j2mT9/rjkyMAK48B8Nyhc2NeG/L6CRgrp3f5wmKy3E4++dguAtpMdNEmDQTGGB9wH/AUcAB4zBizT0Q+Z9/1T3TsPuAxYD/wa+Bjxhj/RMfEUmHWhTv68BqBUsqa+vltayr4n5eb+Zuf7+NjP3gtdPGH+OUIAKoLs1halsNzh8Y2FQfHPzzwtnp+eM8mPnvzCna3dPPqNJblnOuiqvMZYzYbY5YaYxYZY75ob3vAGPNEhH2vtWsDwedftI9bZoz5VeyKPrmCsK6h6a6UHTun1EVxOoSv3bWBX9x3FV98xyqa2vv5j981hgZyBVfyy4tTE+m1y0rZeqyD/mHfiO3n7XXGg+uIv21tBVluJz/deYojrb185TeH6BrQKSqmIqWzoVojUGpyq6vzWVWVx7MHz/HvzxzhOy8d48b6Mnaf7GJjXdGIsTaz6fJFxTz8+yb2tHRz+aLi0Pbz/VaNoMD++85yu7hpVTk/3tHCo69anRSLc9K5+4raWS9zskrp2+TC8ByBSwOBUuMRER5+XwPffH8DN9aX8ZPXTnG0rZ9b11XGrUzr7O6urxxt53O/2B8aBd09aN3tF4Td6H3oqjouWVDIX7x5GcCEYxDUWCldIwj/oaSnpXTMU2raHA7hxvoybqwvwyHCL/ec4S2rKuJWnsJsN7XFWTz8QhND3gCVBRl8+OqFoS7h4Td6KyutNckBXj7aPuEYBDVWSl8dgz8UEc0RKDUV//yuNTz3F9dSGKdmoaC1NQUMea2cxStHO4ALOYLwG71w9RV5HDzbq5PWTUFKXx2DgSDd5cAe46CUioLTIZTlZcS7GKyrsZqHctJdbD3Wic8foHvAi9vlIHOcvF99ZZ5OWjdFKR0IMt1O0l0OTRQrlaTevLKcG+vL+PRNy+gb9rH3VDfnBzwUZqWNe3NXX2GNj5hoHiU1UkoHArBqBdospFRyqizI5Jvvb+Dm1Vau4tXmTs4PeCnIHL/JatG8bHLTXWw7NrVxBdubOzl4dm7mFlL+ClmQlaY1AqWSXElOOuV5GRw800v3gHfc/ACAy+ngisXFPH+obdK5isLd/o1XuOlfX2DY5+eFI2186se7p3R8Mkv5QFCU7dauo0qlgOUVuRw422s3DU2cxL5maSmnu4f4v9dO8Zt9Z2ntGZpw/yHvhQkPvr/lBI/vaOHxHS3smOLynskqpbuPArxzQ7WOMlQqBSwvz+OlxiZEJDQX0XjesNRagvZTP94NWMnv+964mD+/cWnE/U/Y65QDPPJKMw47//CzXadoqC2KQekTW8rXCG6/pJoPX70w3sVQSk3TiopcvH6Dxxfg5tVjZw4OV12Yxf3XL+Fv3lbPT//kCi6rK+LbLx0bt6mnqa0PgHeur6K5Y4Cm9n6cDmsshXcOdENN+UCglEoNwSUsK/MzWF9TOOn+n7hxKR+6qo718wu5fkUZvUM+Ovsjtw4Eu5q+7/IFoW1/eNl8zg94+f3h2K2Rkqg0ECilkkJdSTb5mWm8Y0MVjinOiFpXYi1G1dwReWxBU1s/83LTWVdTQLk9fuJjb1xMYVYaP9s1ZgmVlJPyOQKlVGpIczp45pPXXNSCUbXF2QAcax/gkgVj2/yPtfezsCQbEeGmVeVsaeqgNC+Dt66p4PEdLfQN+1J6DXOtESilkkZJTvpFrZhWU5SF0yHjLmt5vKOfuhIrWPz1W1fws49dCcBt66oY8gZ46vWzF1/oJKCBQCmV8tKcDqoLMzkWoWnI5w/Q0e+h1G4ScjkvzEZwyYJCqgsz+dmuU7Na3tmmgUApNSfUFmdHrBGcH/BiDBRHmGBPRLhtXRUvNbZz87+9kLLjCjQQKKXmhLqSbI6194eW4AwK9iQabwGedzdUU5aXwcGzPTx7cOwayqlAA4FSak5YP7+AAY+f/adHzifU0W8teFOcEzkQLCjO5pXPXs/CkmwOtfbOeDnjQQOBUmpO2LTQWu5y67GOEduDNYLi7PQJj19WnssRDQRKKZW8yvIyqC3OYktT5EAw2drMS0pzOd45MGJeolShgUApNWdsWljMtmOdI/IE7X1WICicYEZTgKVluRgDd35zC0/uSa1BZhoIlFJzxob5hfQM+UZMMtfZP0xhVhquScYnLC3LAWDniS7++mev0ztkrZ18rneI3+xL7nEGGgiUUnPGEvtifjisrb+z3zNpsxBAbUk2Oeku1lTn0zXg5TsvNQPw5acOc88jO5J6luOoAoGI3CQih0SkUUQ+E+H1j4jIXhHZJSIviki9vT1NRL5rv3ZARD4b6xNQSqloLSnLBaDxXF9oW0efZ9JEMViD0rb/9Q38/GNXcsOKMr75QhPtfcP82q4NHDiTvInkSQOBiDiBh4CbgXrgzuCFPsz/GmNWG2PWAQ8CX7G3vxtIN8asBi4B7hWR2hiVXSmlpiQn3UVlfsZF1QgAMtKciAifuHEpvUM+7n1kB92DVhNRMi9zGU2NYCPQaIxpMsZ4gEeBW8N3MMaE/wtkA8FMjAGyRcQFZAIeIHn/tZRSSW9JWS6HW8NqBP2ecccQjKe+Mo/3NNSw4/h58jJcFGalceBM8l7aogkEVcDJsOct9rYRRORjInIUq0Zwv735caAfOAOcAL5kjJnaitJKKRVDS8tyONrWhz9g8AcM5wc8EaeXmMw/vWs1T3/iDfzsY1eysjI/1DTUN+yj4QtP8+vXz8S66DMmmkAQaeLvMcv8GGMeMsYsAj4N/LW9eSPgByqBOuCTIjJmuTARuUdEtovI9ra21F8EQikVP0vKcvH4Ahxr7+dk5wDGQFVh5pTfR0RYXJrLwnk5LC/P5VBrLz5/gANnemjvG+apfa0zUPqZEU0gaAFqwp5XAxN1on0UuM1+fBfwa2OM1xhzDngJaBh9gDHmYWNMgzGmYd68idciVUqp6bhkgbW62ZamjlCuYKmdRL5Yq6ry8fgCHDzby8Gz1ntuO5Y8jR/RBIJXgSUiUicibuAO4InwHURkSdjTtwJH7McngOvEkg1sAg5Ov9hKKXVxFpZkU56XwctH20OBYMk0A8EVi6zpK15sbOeQnTQ+1TXIqa7B6RV2lky65I4xxici9wFPAU7g28aYfSLyOWC7MeYJ4D4RuQHwAueBu+3DHwK+A7yO1cT0HWPMnhk4D6WUioqIcMXiYp49eA6HCFUFmdNefaw0L4Pl5bm8cKQNjy9AQVYaXQNeXj3WSdX6MSnVhBPV2RtjNgObR217IOzxx8c5rg+rC6lSSiWMqxaX8JPXTvHknjNct7w0Zu/5vVeO4zeGP2io5oldp3ntxHluS4JAoCOLlVJzzvUryiixu4zWXESiOJI3ryrH4w/gDxjqK/JYXpGXNF1KNRAopeac/Mw0vvTutQBcbrfvT9eltUX88I83cfsl1bx5ZTkrKnI5eKYXY8Z0skw402sYU0qpJHXtslJ2P/Am8jJjdxm8fFFxKLDUV+Tz/S0naDk/SE1RVsw+YyZojUApNWflZ6UhEmmo1PStqLB6Iu1PguYhDQRKKTUDlpXnIkJS5Ak0ECil1AzIcruoK87WQKCUUnPZioq8pJieWgOBUkrNkBUVuZzoHAitZpaoNBAopdQMWVGRB8Chs7283NjOP/7qAIFA4nUn1e6jSik1Q4KB4MCZHp4+cI7nD7fhdjr4+PVLJl0jeTYlTkmUUirFVORnkJ+Zxu6WbrYd6yTL7eQ/ftfI1Q8+y9nuoXgXL0QDgVJKzRAR4ZIFhfx81ykGvX6+/O61fO2u9ZzrHebh3zfFu3ghGgiUUmoG/fkNS/EFDA6BKxaX8LY1ldy6rpL/3Xaczn5PvIsHaCBQSqkZtbo6nw9fVcdb11SSn5kGwD1vWMiQN8ATu07FuXQWTRYrpdQM+6u31o94vrw8j+Xlufx892k+cGVdnEp1gdYIlFIqDm5ZV8nOE10ca++Pd1E0ECilVDzctq6KzDQn7/3mFpra+nhky3GeO3QuLmXRQKCUUnFQWZDJjz9yOb1DPv5h8wH+cfMBntxzJi5l0UCglFJxsqoqn3duqOLpA+cY8Ph536YFcSmHBgKllIqjOy+bD8Da6nzW1hTEpQzaa0gppeJoeXken75pOZfWFsatDBoIlFIqzj567aK4fr42DSml1ByngUAppea4qAKBiNwkIodEpFFEPhPh9Y+IyF4R2SUiL4pIfdhra0TkFRHZZ++TEcsTUEopNT2TBgIRcQIPATcD9cCd4Rd62/8aY1YbY9YBDwJfsY91Ad8HPmKMWQlcCyT2Uj1KKTXHRFMj2Ag0GmOajDEe4FHg1vAdjDHhqzNnA8EleN4E7DHG7Lb36zDG+KdfbKWUUrESTSCoAk6GPW+xt40gIh8TkaNYNYL77c1LASMiT4nIayLyl5E+QETuEZHtIrK9ra1tameglFJqWqIJBBJh25hFN40xDxljFgGfBv7a3uwCrgLea///HSJyfYRjHzbGNBhjGubNmxd14ZVSSk1fNIGgBagJe14NnJ5g/0eB28KOfd4Y026MGQA2AxsupqBKKaVmRjQDyl4FlohIHXAKuAO4K3wHEVlijDliP30rEHz8FPCXIpIFeIBrgK9O9GE7duxoF5Hj9tMSoD2aE0lSqXx+em7JSc8tOZUAFz1R0aSBwBjjE5H7sC7qTuDbxph9IvI5YLsx5gngPhG5AatH0HngbvvY8yLyFaxgYoDNxphfTvJ5obYhEdlujGm4yHNLeKl8fnpuyUnPLTnZ51Z7scdHNcWEMWYzVrNO+LYHwh5/fIJjv4/VhVQppVQC0pHFSik1xyV6IHg43gWYYal8fnpuyUnPLTlN69zEmDE9QZVSSs0hiV4jUEopNcMSNhBMNtFdshGR5rCJ+bbb24pE5LcicsT+f/xWppgCEfm2iJwTkdfDtkU8F7H8u/097hGRhB5HMs65/Z2InLK/u10i8paw1z5rn9shEXlzfEodHRGpEZFnReSAPQnkx+3tSf/dTXBuqfLdZYjINhHZbZ/f3wrLtrYAAAOGSURBVNvb60Rkq/3d/UhE3Pb2dPt5o/167YQfYIxJuP+wuqkeBRYCbmA3UB/vck3znJqBklHbHgQ+Yz/+DPDP8S5nlOfyBqyBga9Pdi7AW4BfYY1Q3wRsjXf5L+Lc/g74VIR96+3fZjpQZ/9mnfE+hwnOrQLYYD/OBQ7b55D0390E55Yq350AOfbjNGCr/Z08Btxhb/8G8FH78Z8A37Af3wH8aKL3T9QawaQT3aWIW4Hv2o+/y4UR2QnNGPN7oHPU5vHO5Vbge8ayBSgQkYrZKenUjXNu47kVeNQYM2yMOQY0Yv12E5Ix5owx5jX7cS9wAGvesKT/7iY4t/Ek23dnjDF99tM0+z8DXAc8bm8f/d0Fv9PHgetFJNJ0QUDiNg1FNdFdkjHAb0Rkh4jcY28rM8acAeuHDJTGrXTTN965pMp3eZ/dPPLtsCa8pD03u6lgPdadZUp9d6PODVLkuxMRp4jsAs4Bv8WqxXQZY3z2LuHnEDo/+/VuoHi8907UQBDVRHdJ5kpjzAasdR0+JiJviHeBZkkqfJdfBxYB64AzwJft7Ul5biKSA/wf8Gdm5BTyY3aNsC2hzy/CuaXMd2eM8RtrzZdqrNrLiki72f+f0vklaiCY6kR3Cc8Yc9r+/zngp1hfZGuwqm3//1z8Sjht451L0n+XxphW+48wAHyTC00ISXduIpKGdaH8gTHmJ/bmlPjuIp1bKn13QcaYLuA5rBxBgVgLgMHIcwidn/16PhM0eSZqIAhNdGdnwe8AnohzmS6aiGSLSG7wMdaCPa9jndPd9m53Az+PTwljYrxzeQJ4v90DZRPQHWyGSBaj2sXfgfXdgXVud9g9NOqAJcC22S5ftOw24v8GDhhjvhL2UtJ/d+OdWwp9d/NEpMB+nAncgJUHeRa43d5t9HcX/E5vB35n7MxxRPHOhk+QJX8LVub/KPBX8S7PNM9lIVYPhd3AvuD5YLXZPYM1W+szQFG8yxrl+fwQq5rtxbrz+NB454JVRX3I/h73Ag3xLv9FnNsjdtn32H9gFWH7/5V9boeAm+Nd/knO7Sqs5oE9wC77v7ekwnc3wbmlyne3Bthpn8frwAP29oVYAawR+DGQbm/PsJ832q8vnOj9dWSxUkrNcYnaNKSUUmqWaCBQSqk5TgOBUkrNcRoIlFJqjtNAoJRSc5wGAqWUmuM0ECil1ByngUAppea4/w8XRTmUbWR5TwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f28981b9e80>"
      ]
     },
     "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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.29188548409538867, 0.8722741433021807)"
      ]
     },
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_json(PATH+\"/test.json\")\n",
    "helper.make_test_file(log_preds,test_df,model_name='resnet18_sz75_zm13_bs32_comp_band',\n",
    "                      classes=data.classes,\n",
    "                      submission_folder =f'{PATH}sub'\n",
    "                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}