7_2_resnext50_more_epoch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle Competition:  Iceberg or Ship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# resnext50 architecture \n",
    "\n",
    "## increase epochs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 4352\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   49250 Jan 15 16:52 7_2_resnext50_more_epoch.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  218915 Jan 15 03:22 7_5_resnext50_more_epoch_update_sz.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  179449 Jan 14 21:28 7_resnext50_sz_100.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  201400 Jan 15 02:17 8_resnext50_more_epoch_sz300.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  137540 Jan 14 18:36 baseline.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    3299 Jan 15 01:35 helper.py\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  767392 Jan 14 18:36 other_groverpr.ipynb\r\n",
      "drwxrwxr-x 2 ubuntu ubuntu    4096 Jan 15 01:42 __pycache__\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu    1492 Jan 15 14:56 README.md\r\n"
     ]
    }
   ],
   "source": [
    "!ls -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put these at the top of every notebook, to get automatic reloading and inline plotting\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.imports import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This file contains all the main external libs we'll use\n",
    "from fastai.imports import *\n",
    "from fastai.transforms import *\n",
    "from fastai.conv_learner import *\n",
    "from fastai.model import *\n",
    "from fastai.dataset import *\n",
    "from fastai.sgdr import *\n",
    "from fastai.plots import *\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import shutil\n",
    "from IPython.display import FileLink, FileLinks\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import helper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PATH = \"/home/nidhin/data/iceberg/\"\n",
    "PATH = os.path.expanduser(\"~/data/iceberg/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 1973264\r\n",
      "drwxrwxr-x  2 ubuntu ubuntu       4096 Jan  9 00:38 models\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu     117951 Oct 23 17:27 sample_submission.csv\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu      38566 Jan  7 20:36 sample_submission.csv.7z\r\n",
      "drwxrwxr-x  2 ubuntu ubuntu       4096 Jan 15 14:20 sub\r\n",
      "drwxrwxr-x  2 ubuntu ubuntu     274432 Jan 14 18:44 test\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu 1521771850 Oct 23 17:27 test.json\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu  257127394 Jan  7 20:36 test.json.7z\r\n",
      "drwxrwxr-x 12 ubuntu ubuntu       4096 Jan 15 13:38 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=200\n",
    "bs=32\n",
    "lr = 0.005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df271c419a7445348076c8a6fc97a396",
       "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=1.05)\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*1.4), 'tmp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 41/41 [00:13<00:00,  3.02it/s]\n",
      "100%|██████████| 11/11 [00:03<00:00,  3.31it/s]\n",
      "100%|██████████| 264/264 [01:20<00:00,  3.29it/s]\n"
     ]
    }
   ],
   "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": "e84625d8accb4fe8ade18bae69217806",
       "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": [
      " 39%|███▉      | 16/41 [00:00<00:00, 28.62it/s, loss=3.65] \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 0x7fc2e84c50b8>"
      ]
     },
     "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 0x7fc2a82385c0>"
      ]
     },
     "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": "648048d8d7254cb89e9e13f7b7f91fec",
       "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=10), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.      0.53648 0.5541  0.70171]                          \n",
      "[1.      0.43886 0.38893 0.8125 ]                          \n",
      "[2.      0.43411 0.35774 0.82102]                          \n",
      "[3.      0.40166 0.37136 0.82671]                          \n",
      "[4.      0.38497 0.38369 0.81534]                          \n",
      "[5.      0.35851 0.38125 0.81534]                          \n",
      "[6.      0.32765 0.35433 0.82102]                          \n",
      "[7.      0.3257  0.38188 0.82954]                          \n",
      "[8.      0.32145 0.3746  0.84659]                          \n",
      "[9.      0.31764 0.38115 0.83239]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lr, 10)\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": "4262e9f0f2e443bbbced336a68c1158d",
       "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=63), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.      0.44967 0.33133 0.84375]                          \n",
      "[1.      0.45333 0.32424 0.85227]                          \n",
      "[2.      0.43156 0.3264  0.84659]                          \n",
      "[3.      0.41209 0.31666 0.84943]                          \n",
      "[4.      0.41544 0.33222 0.82954]                          \n",
      "[5.      0.39065 0.31653 0.84659]                          \n",
      "[6.      0.37903 0.32075 0.84375]                          \n",
      "[7.      0.38897 0.33297 0.83239]                          \n",
      "[8.      0.37957 0.32438 0.84375]                          \n",
      "[9.      0.4008  0.31355 0.85227]                          \n",
      "[10.       0.42475  0.3272   0.84375]                      \n",
      "[11.       0.41092  0.32549  0.85796]                      \n",
      "[12.       0.40002  0.3267   0.84091]                      \n",
      "[13.       0.38353  0.32161  0.84091]                      \n",
      "[14.       0.37151  0.3234   0.84375]                      \n",
      "[15.       0.37986  0.32918  0.83523]                      \n",
      "[16.       0.38258  0.32124  0.84375]                      \n",
      "[17.       0.38662  0.3266   0.84375]                      \n",
      "[18.       0.39216  0.33088  0.83239]                      \n",
      "[19.       0.40656  0.34575  0.83523]                      \n",
      "[20.       0.36249  0.31065  0.84659]                      \n",
      "[21.       0.37241  0.31158  0.83807]                      \n",
      "[22.       0.38511  0.33413  0.81534]                      \n",
      "[23.       0.38514  0.31494  0.84659]                      \n",
      "[24.       0.37348  0.31508  0.85796]                      \n",
      "[25.       0.37969  0.32636  0.83523]                      \n",
      "[26.       0.35321  0.3157   0.85796]                      \n",
      "[27.       0.36691  0.32184  0.85511]                      \n",
      "[28.       0.36288  0.31509  0.85796]                      \n",
      "[29.       0.35238  0.30892  0.86079]                      \n",
      "[30.       0.34954  0.30944  0.86079]                      \n",
      "[31.       0.35817  0.3078   0.85796]                      \n",
      "[32.       0.38831  0.32791  0.84091]                      \n",
      "[33.       0.38803  0.3024   0.84659]                      \n",
      "[34.       0.38874  0.29678  0.84943]                      \n",
      "[35.       0.37186  0.32363  0.84659]                      \n",
      "[36.       0.38235  0.30435  0.84943]                      \n",
      "[37.       0.39947  0.33881  0.84375]                      \n",
      "[38.       0.3975   0.32219  0.85227]                      \n",
      "[39.       0.39436  0.31074  0.84943]                      \n",
      "[40.       0.38005  0.30701  0.86079]                      \n",
      "[41.       0.38996  0.31498  0.84091]                      \n",
      "[42.       0.40481  0.32     0.83807]                      \n",
      "[43.       0.39906  0.30455  0.84659]                      \n",
      "[44.       0.40247  0.30275  0.85796]                      \n",
      "[45.       0.3865   0.30506  0.86648]                      \n",
      "[46.       0.36795  0.29513  0.86364]                      \n",
      "[47.       0.37894  0.30219  0.85796]                      \n",
      "[48.       0.38376  0.30487  0.86648]                      \n",
      "[49.       0.36414  0.31784  0.86079]                      \n",
      "[50.       0.37758  0.30434  0.85796]                      \n",
      "[51.       0.38064  0.30474  0.86932]                      \n",
      "[52.       0.35889  0.29753  0.86079]                      \n",
      "[53.       0.38089  0.30865  0.84375]                      \n",
      "[54.       0.36047  0.29396  0.86648]                      \n",
      "[55.       0.33994  0.31085  0.86932]                      \n",
      "[56.       0.35762  0.29052  0.86079]                      \n",
      "[57.       0.37169  0.29317  0.86364]                      \n",
      "[58.       0.35406  0.30216  0.86932]                      \n",
      "[59.       0.35566  0.29961  0.86079]                      \n",
      "[60.       0.34781  0.30165  0.86648]                      \n",
      "[61.       0.36798  0.29715  0.85796]                      \n",
      "[62.       0.37291  0.29848  0.86364]                      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#learn.fit(lr, 4, cycle_len=2)\n",
    "learn.fit(lr, 6, 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 0x7fc28674e470>"
      ]
     },
     "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 0x7fc285ee9e48>"
      ]
     },
     "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": [
       "(0.2939834827147395, 0.8722741433021807)"
      ]
     },
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unfreeze Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e78b98d0e60c467da1079d36ff016020",
       "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=63), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.      0.61326 0.43311 0.79546]                          \n",
      "[1.      0.60955 0.84851 0.71875]                          \n",
      "[2.      0.54191 0.60438 0.67614]                          \n",
      "[3.      0.50616 0.68645 0.69318]                          \n",
      "[4.      0.49301 0.69229 0.67614]                          \n",
      "[5.      0.46209 0.61928 0.6875 ]                          \n",
      "[6.      0.45162 0.6356  0.67614]                          \n",
      "[7.      0.44667 0.88649 0.65057]                          \n",
      "[8.      0.44144 0.61111 0.71023]                          \n",
      "[9.      0.44139 0.54177 0.73296]                          \n",
      "[10.       0.40562  0.64604  0.69886]                      \n",
      "[11.       0.41071  0.50083  0.76136]                      \n",
      "[12.       0.40728  0.54395  0.75   ]                      \n",
      "[13.       0.37558  0.50935  0.76704]                      \n",
      "[14.       0.37284  0.4839   0.77273]                      \n",
      "[15.       0.38092  0.5862   0.74148]                      \n",
      "[16.       0.38286  0.43557  0.80114]                      \n",
      "[17.       0.36945  0.44871  0.77841]                      \n",
      "[18.       0.37197  0.41744  0.81534]                      \n",
      "[19.       0.36975  0.49207  0.80398]                      \n",
      "[20.       0.34858  0.45319  0.80966]                      \n",
      "[21.       0.3125   0.40611  0.82386]                      \n",
      "[22.       0.32725  0.35891  0.83239]                      \n",
      "[23.       0.37122  0.39752  0.80682]                      \n",
      "[24.       0.36089  0.32847  0.84659]                      \n",
      "[25.       0.34633  0.39272  0.82102]                      \n",
      "[26.       0.32047  0.37185  0.83523]                      \n",
      "[27.       0.32893  0.31706  0.85227]                      \n",
      "[28.       0.30515  0.39542  0.81534]                      \n",
      "[29.       0.29838  0.31489  0.85227]                      \n",
      "[30.       0.29501  0.37017  0.83239]                      \n",
      "[31.       0.32604  0.46674  0.81534]                      \n",
      "[32.       0.3156   0.43556  0.80966]                      \n",
      "[33.       0.36623  0.5467   0.76421]                      \n",
      "[34.       0.34673  0.46587  0.79261]                      \n",
      "[35.       0.35013  0.32582  0.85511]                      \n",
      "[36.       0.33122  0.40474  0.80966]                      \n",
      "[37.       0.34022  0.33962  0.84091]                      \n",
      "[38.       0.30652  0.36251  0.84091]                      \n",
      "[39.       0.33291  0.37716  0.82954]                      \n",
      "[40.       0.32519  0.41401  0.83239]                      \n",
      "[41.       0.30916  0.33079  0.85796]                      \n",
      "[42.       0.34305  0.46057  0.77557]                      \n",
      "[43.       0.36081  0.51307  0.77273]                      \n",
      "[44.       0.36241  0.39133  0.82954]                      \n",
      "[45.       0.37299  0.43087  0.80966]                      \n",
      "[46.       0.35959  0.38223  0.83523]                      \n",
      "[47.       0.31929  0.3704   0.83523]                      \n",
      "[48.       0.30837  0.34578  0.86079]                      \n",
      "[49.       0.30341  0.34201  0.84091]                      \n",
      "[50.       0.29869  0.39579  0.8125 ]                      \n",
      "[51.       0.28252  0.35775  0.85227]                      \n",
      "[52.       0.27056  0.37609  0.85227]                      \n",
      "[53.       0.2694   0.37358  0.86079]                      \n",
      "[54.       0.28828  0.3901   0.84659]                      \n",
      "[55.       0.27964  0.36935  0.85227]                      \n",
      "[56.       0.30892  0.34714  0.86932]                      \n",
      "[57.       0.26531  0.35318  0.86648]                      \n",
      "[58.       0.26049  0.37042  0.85796]                      \n",
      "[59.       0.26593  0.328    0.88068]                      \n",
      "[60.       0.27438  0.37316  0.83807]                      \n",
      "[61.       0.25542  0.32261  0.88068]                      \n",
      "[62.       0.2709   0.36107  0.86648]                      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lrs, 6, 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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FDxpLaEdigZ5+EYmIMTCos6cv4mCmYDVTZXHAkcQXkVhRRjXVlLBu95G0fwgns//gJScPZvuBY6xoyU4ps1XErq6NFhAR7rj8JArcTm772wo9fEwTFe1ILNAdQv5bYHYA7j89MRz+YiO11XY0daqqUTUlHDzWTWsWrwWYNaYap0N4btUuu03Ja6pKvPy/D41h8eb9PJKH3Zk1sZFSRyIis0VkrYg0i8htIR73ishj5uMLRWRY0GO3m9vXisiF/Y5zisg7IvJ0Ku0P0BuiRuIzh15ZrZMMKHQDcKA9dY6kKQcK7gMKPZzWUKEdSQZw5eQ6zhxRyY//tUY3dtREJGWOREScwL3ARcBY4FoRGdtvt+uB/UqpEcDdwJ3msWOBa4BxwGzgPvN8AW4GVqfK9v50h1JtmRGJVUfidTkp9DjZ396dVNuCCUiA1+3O7rz2heMGsmHPUZqzWDiQC4gIP7r8JHr7FN/+x8qcrVtpEieVEclUoFkptVEp1QU8Cszpt88c4CHz/hPATDGSx3OAR5VSnUqpTUCzeT5EpA74EPB/KbT9A4RaR+IzW8nHMvWwvNDD/hSmtvxFHsoK3Gzam90fwBeMqwHQUUkGMMRfyFcvGMm/V7fy9IqddpujyVBS6UhqgeDe4C3mtpD7KKV6gIOAP8qx/wt8A0hb3+tAsT34+1hgJkmotSThvriVF7nZH2NqK5bvgCJCQ2URG9PUTj5V308HlRUwoX4Az+ekI8m+b/WfPqOBCXVlfP+fqzh4LHURtSZ7SaUjCSVL6f8uCrdPyO0icgnQqpRaGvXJRW4QkSUismTPnviGJinT3J4Iqa1IixL7X0R5oYe2FKa2wOgEvCkHZqBfOK6G5S0HdW4+A3A6DBVX29Eufv78WrvN0WQgqXQkLUB90O91QP+VZsf3EREXUAa0RTj2DOBSEdmMkSo7T0T+FOrJlVL3K6WmKKWmVFVVJXQhoZo2BiKSWBYllhd6UlpsBxheVcTOgx0x9QFLlFRIWS8cNxCA51ftTvq5M4HMFf+GZnxtGZ+YNpSH39rCyu25Lc3WxE4qHclioElEGkTEg1E8n9tvn7nAdeb9K4GXlFHRmwtcY6q6GoAmYJFS6nalVJ1Saph5vpeUUh9P4TUAxsr2/oXG+Gok7pTWSAAaKg3lVrZHJY1VxTRWFfHCe7npSLKRr1wwiooiL//vHyv12hLNB0iZIzFrHl8CnsNQWD2ulFolIj8QkUvN3R4A/CLSDHwFuM08dhXwOPAeMA+4USll/RM7BfTvOxRwJB0W15EAlBd5ONTRk9Kxpg2VRUD2OxKAmWNqWLhpX0zt+jWpo6zAzTcvHs2ybQf0aGTNB0jpOhKl1LNKqZFKqUal1B3mtu8opeaa9zuUUlcppUYopaYqpTYGHXuHedwopdS/Qpz7FaXUJam0P5j+dZLjNZIYVVsAB1JYsAw4knQV3FPJjFHVdPcqXl+vJ/dlCpdPrGXqsArunLcm5dG1JnvQK9stcoIjiadGUmQ6khTWSQo8TgaX+XIiIpkyrJwSn4uX17TabYrGRET4wWXjONTRw0+eW2O3OZoMQTuSSAT5jp7evg/IeiM5knDZ43JzdXvbUesRSTxrwBqqitiYhmZ7qV6f5nY6OLupipfXtubMYrhcuIzRA0v59OnD+MuibaxoOWC3OZoMQDsSiwS3khcRvGaLlIjy335qpkBqK9a1JKHOFYnhlcVs3Hs0Jz58zx1VRevhTlbtyK0ZJRncs9ESN89qorLYww/++V5O/J9pEkM7Eov0lwA7HIYziSe1lXrlVhGHO3rYl6Ycdio/E88dVQ3ASzq9lVGU+Nx87YJRLNmyX69412hHYpX+reTBqEfEVmw3Ulup7LcFRmoLcqPgXlXiZUJdmXYkGchVU+oZM6iUH/9rTUzTQjW5h3YkFgm5ut0d25TEArcTr8uR8kWJjeZaknTUSdLBjNHVLG85wL44Z7loUoPTIXznkrFsP3CM383fGP0ATc6iHYlFQq39MByJ9TUhImK0SUlxyqm2vACXQ9i8rz2lz5MuzhtdjVLwytr4Wt1oUsf0Rj+zxw3kvlc2sPtQh93maGxCO5IIBMcgRrH9g1GJ1+2MqWkjGHNJYltHEnsh0+kQ6soL2NqW2tSWSlMDwvGDy6gs9vLKuux3JLlYlv7mxWPo7VP8ZJ7uw5WvaEdikeBie6C4XOB2xNS0EaC0wB1XB9VYC9pD/EVsbcuNiMThEM5uquSN5r26NUcGMsRfyGfObOBvb7ewfJuWA+cj2pFYJFwH4Fh6bYHRZuJQGlpxD60oZMu+9rRIM9MhZT1rZCVtR7t4b2duyIAl69o2RubGGY1UFnv576e1HDgf0Y7EIqFUWz5XbMV2MBxJOmY6DPUXcrijhwMpVoilizNGVAIwf332p7dykRKfm1vPb2LJlv260WYeoh2JRcJNSeyKoWkjpM+RDKkoBGBLjqS3qkt8jB5YwmvrdN+tTOXqKfUMryriznlrUtqYVJN5aEdikVBz270uBx09sUck7V29dKf4jTbUb6wlyZU6CcDZI6tYsqUtrbNWNNZxOR381+zRbNhzlMeXtNhtjiaNaEcSgeBcb6hvWF63g84Y5L9gOBIg5VFJICLZui/7FyUGOKupku5excKNbXabognDBWNrmDy0nLv/vU47/DxCOxKL9PSpE2S9XpeTzhCprUiy2FgdSbx1ywKPk+oSL1tSuJYk3TXVU4dV4HU5srpOkut1aBHhmxePZs/hTh54bZPd5mjShHYkFun5QNNG46fX7aAzQmorlJqprDC+iCQeZdRQf2HO1EjAqElNbajgNT2fJKOZPLSCC8fV8JtXN7BXdyPIC7QjsUioue1el5OO7r6Y5I7pSm0B1FcUsjUNq9tTMbM9HGc3VdHceoSdB4+l7TlTQbZ3/43GN2aPpqOnj3teXG+3KZo0oB1JFAJv+O4Q8t9AK/muGArnAUeSnrUkRew61JFTDfXOGmnIgHVUktk0VhVzzan1/HnhVjbnwJA1TWS0I4mC22G8RCGL7aYjCVUnCUc6I5KhfqPg3rI/d9Jbo2pKqCrxakeSBdw8qwmPy8FPn9OtU3Id7Uii4HQYIUmole1ec0piLMqt444kDQsFh5iOJJUF93QjIpzVVMnr6/fodikZTnWJj8+eNZxn3t3Jyu0H7TZHk0K0I4mAAtxO05GEWpBoYUpif9xOB4UeZ4yNG+NjaEXuORKAM0dUsr+9m9W7cqNdSi7z2bMaGFDo5q7ndVSSy2hHEgW300xt9Z0o6j0ekfRLbUWrvceyuj2R79wVRR6Kva6ULUq0Kx6Y3ugH4M0N+2yyIH7S1TE5Uyj1ufnCOY28snYPizfr9T+5inYkUQiV2go03Hu/RhI6IgmnZoqnTUo8Tf5EhCEVhTm1uh1gUFkBwyuLWJCFjiRAjou2PsAnpw+jqsTLT+et1Q0dcxTLjkREilJpSKYSiEh6w7RIgdiK7RB/K/l4GFJRyJYcWt0e4PQRfhZu3JfyVjOaxCnwOLnpvBEs2tzGfC2SyEmiOhIROV1E3gNWm79PEJH7Um5ZhhCISEJ9YPnM1Fas8tp0tZIHqK8oYPuBYzn3TfD0xkqOdvWyokUXcbOBq08dQl15AXc9p6OSXMRKRHI3cCGwD0AptRw4O5VGZRIOMW7JjEjS1QEYjEWJHd197D2S2vG+6Wba8ECdRH/DzQY8Lge3zBrJu9sP8tyqXXabo0kyllJbSqlt/Tblzgq3CAS+OLkcjtDyX1fs8l9IryOpKy8AYFsOrSUBQ0gwZlBpVtdJ8o3LJ9bSWFXEz55fF/KLmSZ7seJItonI6YASEY+IfA0zzZUPiAhOh9DT23di00Z36GJ7tLdILK3kE80C1JcHFiWmoKWIzSmKMxr9LNmyP6tW7udzVsfpEL56wSjWtx7hqWXb7TZHk0SsOJLPAzcCtUALcArwxVQalWm4nPJB1ZYpufHFsSAR4lvdHm9vptpARJJjyi0wCu5dPX28vWW/3aZoLDJ73EDGDS7lf/+9PuahcJrMxYojGaWU+phSqkYpVa2U+jgwJtWGZRIuh4QctRtN/huOdLZJKfS48Bd5UhORYG/zwVOHVeB0SFamt3K9aWM4HA7haxeOYmtbO48v6Z8x12QrVhzJPRa35SwuZ7gaSZzF9jhbycdLXUVhTvXbClDic3NyXRkLdME9qzh3ZBVThpZzz0vrsyotqQlPWEciItNF5KtAlYh8Jej2PcBp5eQiMltE1opIs4jcFuJxr4g8Zj6+UESGBT12u7l9rYhcaG7zicgiEVkuIqtE5PsxXm9cuBxCb5g28hCfagvS6EjKC1IWkdjN6Y1+lrcc5EinnsaXLYgYtZLdhzp5ZOFWu83RJIFIEYkHKAZcQEnQ7RBwZbQTi4gTuBe4CBgLXCsiY/vtdj2wXyk1AkNmfKd57FjgGmAcMBu4zzxfJ3CeUmoCRq1mtohMs3apsROIQZxhUltupyAS3zoSSE8reTAK7tv3H8vJJodnNFbS26dYvEm338gmpjf6mT7cz69f3aCjkhwgrCNRSr2qlPo+ME0p9f2g28+VUlam1UwFmpVSG5VSXcCjwJx++8wBHjLvPwHMFKOvyBzgUaVUp1JqE9AMTFUGR8z93eYtpZ+OgrG6PVRqS0TwhRm3Gwk7IpKu3j5aD+fetLpJQ8vxuBy80azTW9nGreePZM/hTv701ha7TdEkiJUaSbuI/FREnhWRlwI3C8fVAsHVtBZzW8h9lFI9wEHAH+lYEXGKyDKgFXhBKbUw1JOLyA0iskREluzZk9iMb6dD6OnrC9lwz+t20Nn/G1UUjWepz3or+WQ0+as3uwAney1JJsQ3PreTyUPKs6bgns/y3/5MbajgjBF+fvPqRo516agkm7HiSP4MrAEagO8Dm4HFFo4LpUvp/zYKt0/YY5VSvUqpU4A6YKqIjA/15Eqp+5VSU5RSU6qqqiyYG57+qq1g47wuR8iIJJIqx+NyUOB2cqgjBvmv5T1PJLAoMRUF90wQH53e6Oe9nYfYfzSbVu9nwitnP7fOGsneIzoqyXasOBK/UuoBoNtMd30GsFKXaAHqg36vA3aE20dEXEAZ0GblWKXUAeAVjBpKSnE5JexKXGNue+zfptK5ur12QGAtSY4W3EcY7VLe2pgdUYnmfaYMq+Cspkp+8+oG2ru0YCJbseJIAp92O0XkQyIyEeODPRqLgSYRaRARD0bxfG6/feYC15n3rwReUkZHt7nANaaqqwFoAhaJSJWIDAAQkQJgFka0lFKcDgfdYRyJzx06IolGaYErbY7E53ZSXeLNSQkwwMl1Ayj0OHlTO5Ks5JZZI9l3tIs/vqmjkmzFZWGfH4pIGfBVjPUjpcCt0Q5SSvWIyJeA5zDkwg8qpVaJyA+AJUqpucADwMMi0owRiVxjHrtKRB4H3gN6gBuVUr0iMgh4yFRwOYDHlVJPx3jNlgl0KXWHkf+CEZHE40iMDsDp+wZWX1GYsxGJ2+ng1GEVWVMn0XyQyUPLOXtkFffP38gnpg2lyGvlY0mTSUT8i5kf2E3mh/VBYEYsJ1dKPQs822/bd4LudwBXhTn2DuCOfttWABNjsSFhxCi2d4eQ/0KgRhJfamvHgY5ErbNMXXkBb2/N3VYi0xv9/Phfa2g93EF1ic9uczQxcuusJi6/bwEPvbmZL547wm5zNDESMbWllOoFLk2TLRmL2+mgt0+FVNx43Q46+vXasiLMKfVZq5EkS+VTX17IjgMdIWfPx0smKZCmDw/USTJ7PUm+jdq1ysQh5cwYZUQlh2MQoWgyAys1kgUi8isROUtEJgVuKbcsgzDkvyc2bQTMdSQnRiTRNDmlBe7YVFsJinzqygvo7VPsOpS+KCidjBtcSonXlZVz3DUGt8wayYH2bh5asNluUzQxYiUZebr58wdB2xRwXvLNyUxcZhv5UBjrSOKrkRzu6KG3Tx2fwphK6sx28tvajh2/nwzCzaVPNy6ng9OGV2TNoKsMedkyign1A5g5uprfvbaJT54+7Ph6K03mEzUiUUrNCHHLGycC0eW/8am2jDfJkY70FNzrK1K3liRTmDbcz+Z97ew8mJuignzgllkjOXismz+8sdluUzQxYGlCYr6ILdKqAAAgAElEQVTjcjjCDqHyuhxxryOB9LVJGVRWgAhsy9HmjWAU3AGd3spiTqorY9aYGv7vtY1pe29oEkc7kggEYpDIEUmc60h8RlYxXW8Wj8vBoFJfTkckYwaWMqDQrR1JlnPLrCYOdfTw4Oub7DZFYxHtSKIgRJb/+tyhi+3RON4BOI0KlbryQlpydC0JGEOTpjX49XqSLGd8bRkXjqvhwdc3WepHp7GfqI5ERK4IcZspItXpMDATMOaRhJH/mhGJCnrQiizW6nCrZEps6yoKkhqRZKKUdXqjn+0HjmXsaOFMkkxnMrfMGsnhzh4eeH2j3aZoLGAlIrke+D/gY+btd8BXgDdE5BMptC1jOHFC4vuSG6/biVLQ1a+GEk3NdLwDsOXUVuIyn7ryQnYe6kjqrOxMEx9lS50k0163TGPMoFIuGj+Q37+xmQPt2dSMMz+x4kj6gDFKqY8opT6CMaSqEzgN+K9UGpcpuMw28qGIe9xumodbAdSXF6AUOa1qaqouprLYo/tu5QA3zWwyoxJdK8l0rDiSYUqp3UG/twIjlVJtvN/QMadxORz0hmuR4jbH7ca4lqTQ48TlkLQqU4LXkuQqIsK04X4WbNj7gXSjJvsYM6iUi0/SUUk2YMWRvCYiT4vIdSJyHfAUMF9EioADqTXPZszPIZdT6I4akcRWcBeRmFe3J0pgLUmyB1xlGtMb/ew+1MmmvUftNkWTIDfPHMnRrh5+95qulWQyVhzJjcAfMGakTwT+iNGN96hSKqYmjtmIiOB0RJb/Aif027KCMZMkfR2AB5b6cDokpyXAAKc3VgLo9FYOMGpgCRefNIg/vLE5ywaX5RdWVrYrpdQTSqlblVK3mPfzKmfgNntthRy16zJTW3FIgEt96ZtJAoZoYPAAHy05vCgRYJi/kIGlPi0DzhFuntlEe3evjkoyGKvy3/UiclBEDonIYRE5lA7jMgWnw4FSEMhuBQuyvO4Ti+1WZbGlBe6oxfZkS2zrBhQmTRqbqV8nRITpjX4Wbtyn6yQ5wMiaEj500iAeWrCZNh2VZCRWUls/AS5VSpUppUqVUiVKqdJUG5ZJuJyG5wil3PK5Qhfbrcg7yyw4kuPnS5JetL6iIKltUjK1+eD04X72HulifesRu00JSaY0u8wWAlHJ/fN1VJKJWHEku5VSq1NuSQbjcgQcSYjUlhmRdMST2krj3PYAdeWF7DncGVd/sGwiW9aTaKzRVFPCh08ezB/f3My+I512m6PphxVHskREHhORa4NXt6fcsgwgkFYKtHnvCSEBPq7airNx46GO7rSmX97vApzbdZL6ikLqygtYkCVt5TXRuWlmEx3dvdyvayUZhxVHUgq0AxcAHzZvl6TSqExCMCYkAiGVW+8X2+NTbXX3Ko6lMToIrCXJdeUWGOmthZva6AujuNNkFyOqi7l0wmD+uGALe3VUklFYUW19OsTtM+kwLlMIRCSh1pL4AsX2OOS/gTYph9IoAa4PLErM8YgE4PQRfg60d7N6V15pQ3KaL89sorNH10oyjbATEkXkG0qpn4jIPYQYQ66UuimllmUQbmek1NaJ8l+rmargmSQDy3wh90l21qu6xIvH6UhKRJLp3/OnDzfXk2zYx7jBZTZbY6BFZInRWFXMnFNq+eObm/ncWcOpKvHabZKGyBFJoMC+BFga4pY3OB3GyxQYtxustwlEJP0XJFoR5ZQWWJ9JkiyNj8Mh1JYX5HQ7+QADy3w0VBbpgnuO8eXzRtDV08f98zfYbYrGJGxEopT6p/nzofSZk5lEVG0lsCDRjsaNAHXlBUlrkyIZ3sd22nA/Ty/fQU9vHy5n5ozfyexXLbMZXlXMZRNrefitLXzu7OFUl4SO5jXpw8qCxJEicr+IPC8iLwVu6TDOblRQry0IXWx3OwWHxN8iBdI3JTFAXXlhzqu2Apze6OdwZw+rdug6SS5x03lNdPcqfvuqrpVkAmEjkiD+CvwGYyZJbi8+CIHI+xFJqLntIoLXFd+UxNhnkiSH+ooC2o52cbSzhyKvlX+B7GXacHM9ycZ9TKgfYLM1mmQxrLKIy06p5U9vbeE/zx5OdamOSuzESqzfo5T6tVJqkVJqaeCWcssyCJcjvPwXjDpJXHPbbRi3C8ES4NyPSqpKvDRVF+u+WznITTNH0NOn+PWrulZiN1YcyT9F5IsiMkhEKgK3lFuWQTidAflvuA7AzrhWijsdQok3vY0bwRhwBWTsONpkc3qjnyWb20JGlJrsZai/iCsm1vLIwq20Huqw25y8xoojuQ74OrCA9xVbS1JpVKZxvNge5oPI2y8iiUXhaUeblPqK5CxKzBYp6/RGP+1dvaxosX98TibOuc9mvnxeE719ivte0VGJnUR0JCLiAD6ulGrodxueJvsygv6prf4N93whIhKraiajA3D0BYnJbPLnL/JQ4HYmZ1FiFsiPTmvwIwILmjMnvaV7NiaHIf5CPjKpjkcWbWXXQR2V2EVER6KU6gPuSpMtGUd/1VZ32HG78dVIwJhJkm75r4gYEuA8SW2VF3kYPbBUD7rKUb503gj6+hS/fqXZblPyFiupredF5CMSx1diEZktImtFpFlEbgvxuNdsCNksIgtFZFjQY7eb29eKyIXmtnoReVlEVovIKhG5OVabYr4G5HhqqzfMuN1QEYlVytI8bjdAXXlBXhTbA5ze6Gfplv1xqes0mU19RSFXTq7jL4u26ajEJqw4kq9gSIA7YxlsJSJO4F7gImAscK2IjO232/XAfqXUCOBu4E7z2LHANcA4YDZwn3m+HuCrSqkxwDTgxhDnTDqB1FYqIpIyG2okYLz5cn12ezDTh/vp7Onjna3210k0yefGGSPoU4r7dFRiC1aaNpYopRxKKU+Mg62mAs1KqY1KqS7gUWBOv33mAIGV808AM83IZw7wqFKqUym1CWgGpiqldiql3jbtOozRxqXWyoUmQqQFiRBQbcWZ2rLJkdSVF3C4o8eW57aDqcMrcAhaBpyj1FcUctWUeh5dtI0dB/In0s4ULPWMEJFyEZkqImcHbhYOqwW2Bf3ewokf+sf3UUr1AAcBv5VjzTTYRGChlWtIBFeE7r8QiEhib9oIRkTS3tUbVpqaKmXU8S7ACdRJskmBVOpzc1JtGW/Z7EiyRemWjdw4oxGFjkrswEqLlM8C84HngO+bP79n4dyhair930bh9ol4rIgUA38DblFKhUyzicgNIrJERJbs2bPHgrnh6T/Yqr9xPpfzxDbyFitKVvttJVvkk0+LEgNMa/Tzzrb9HOvSdZJcpK7ciEoeW6yjknRjJSK5GTgV2KKUmoERBVj5ZG4B6oN+rwN2hNtHRFxAGdAW6VgRcWM4kT8rpf4e7smVUvcrpaYopaZUVVVZMDc8kQZbwYkRSSzE0gE4mbw/KTGxOkk2qVinD/fT3atYumW/3aZo+W+KuHHGCADufVlHJenEiiPpUEp1gKGyUkqtAUZZOG4x0CQiDSLiwSiez+23z1yMBY8AVwIvKWPu7FzgGlPV1QA0AYvM+skDwGql1M8t2JAQ/Ufthks/hYxILHI8IulI33CrwPOWeF15IwEGOHVYBS6H6PG7OUztgAKuPrWex5dsy4spoJmCFUfSIiIDgH8AL4jIU5wYWZyAWfP4EkYqbDXwuFJqlYj8QEQuNXd7APCLSDOGOuw289hVwOPAe8A84EalVC9wBvAJ4DwRWWbeLo7hemNGxEKx3e2gI96IxKbGjSLmXJI8Sm0VeV1MqB+g15PkODfOGIEg3PuyXu2eLqK2flVKXW7e/Z6IvIyRfppn5eRKqWeBZ/tt+07Q/Q7gqjDH3gHc0W/b69iQTQnIf0PNIwHwuhx09yp6+9Tx6MUqds0kAUPpsmXf0bQ/r51MH+7n169u4EhnD8U53vk4XxlUVsA1U+t5ZOFWvnhu4/GWQJrUYVW1daaIfFop9SrwJmmQ3GYSzii9tnzuxIdb2SUBbtl/DJVHUqLpjX56+xSLN7fZbYomhXzx3BE4RLSCK01YUW19F/gv4HZzkxv4UyqNyjTcUbv/Gi9joE4Siyy2NIojSaXEtr68kPauXtqOdsV3giz0P5OHluNxOmwbv5uFL1lWMrDMx7VT6/nrkpa8i7rtwEpEcjlwKXAUQCm1AyhJpVGZRv+IpL/iJhCRBNdJrCa4fG4nHpcjapuUVKh86soDyq346yTZpj7yuZ1MHDLA9jnumT6iOBe4ccYIXE7h7hfW2W1KzmPFkXSZSioFICJFqTUpcwhkfNxRBlv1j0hipazAbVuNBMirVilgpLdW7TjIwfb8WNWfr1SX+vj0GQ08tXwHa3bpUcupxIojeVxEfgsMEJHPAf8GfpdaszILh0MQCd9rK1REEgulvvQPt4L3I5Jtbfmj3AKj4N6nYOEmrd7KdT5/diMlXhd3PbfWblNyGiu9tu7C6IP1N4z1I99RSt2TasMyDZdDUhyRpHcdCUCJz82AQnfe6e1PGTIAn9uhZcB5QFmhm/88p5F/r25l6RYtsEgVllRbSqkXlFJfV0p9TSn1QqqNykRcDgc94drIH1dtZVfjRjAK7kkZcJVFeF1OpgytsL1OokkPnz5jGJXFXu6ctzavFIrpJKwjCbSLD3Gz1EY+13A55Hivrf4EIpLjM0li/F+1ayYJBCTA+RWRgFEnWbPrcPyKNU3WUOhxcdPMESza1Mar6xLru6cJTVhHEmgXH+JmtY18TuFyynH5b3+lktd1YkQSi5op0kySVH+Bqq8opGX/MfrCpO0ikc3f7aY3+gF4K83pLf2N2B6uOXUI9RUF/PS5tXH9r2siYym1la8E/7s5HY4ICxL7RSQxUuozVFuR/sFTJbOtKy+gq6ePvUc64zo+W2WsJ9WWUeRx2pfeys6XLWvxuBzcOmskq3Yc4tmVO+02J+fQjiQKgQnDkYvtidVIygrc9Ck42pX+gvvxuSR5lt5yOx2c2lChC+55xJxTahlVU8LPn18X9kuhJj60I7GIyylhe20lGpHY2SYlsJZky778ciRgyICbW4/QeljP+c4HnA7haxeOYuPeozyxtMVuc3IK7UgsYhTbw0xITDAisWsmCRhzSUTy05Gc3lgJoNVbecSsMdVMGjKAX7y4Pu4vfpoT0Y7EIi6nI3z330RrJMc7AKc/teV1ORlcVpCX/YjGDi6l1OdKe8FdYx8iwtcvHM3Ogx08tGCz3ebkDNqRWMSK/DcQkcSqCYk0kyQd+pKh/kK2xDHgKtsVSE6HMLXBn9aIJLtfsdxgeqOf80ZX86uXm9mv5d9JQTuSCAR/TjodcnxBYn+lkojgdTno7A5u2mhdlvP+lMTwqa1UqqOG+oviTm1lW9PG/pze6Gfzvva0z/jO8pct67n9otEc7ezhly+tt9uUnEA7kigE3vCRUltgRCVxq7YK7RtuBTDMX0jb0S7bVtfbyekjjPUkrzfr8bv5RFNNCVefOoSH39zCpr35l9ZNNtqRWCRSaguMNinxDLYCKPa4ELGn2A5GRAKwNQ8L7qNqSqgp9eoVz3nIrec34XE5+Mm8NXabkvVoR2IRV1BqKxRet4OOOJs2OhxyfFGiHQyrNCTAm/Ow4C4inDOyitfX79VrC/KM6hIfnz+nkX+t3KUbOiaIdiQWcTkjRyReV/wRCURuk5JqhhxfS5J/jgTgnJHVHDzWzfKWA3abokkznz2rgeoSLz98ZnXWi0fsRDsSizgdkWskvgQiEjDWktjlSAo9LmpKvWzOw9QWwJlNlTgdwitrdXor3yj0uPjaBaN4Z+sBnn13l93mZC3akVjE7QjS2YSQ3ARHJPF8szE6AJ+4jiRd35KG+otirpHkyhe4sgI3E+sHpKVOkiuvWS7xkcl1jB5Ywp3z1iSUVchntCOJyPvveqcjsmCzf0QSqyy21BcltZVivegwf2FcNZJckbGeO6qKFS0H425eGSuS7brpHMLpEG6/eAxb29r544ItdpuTlWhHEoXA+93tjPxSZXONBIyIpPVwJ+02NI7MBM4dVQ3AfK3eykvOGVnFuaOq+OWL69lzOD1fJnIJ7UgsEmtEEivGuF37HMkwUwKcjz23AMYOKqWy2KPrJHnMty8ZS0dPLz99TsuBY0U7Eou4ojiSRCOS0gI3nT19tjWSG+rPb+WWwyGcPbKK19bvCTsuQJPbNFYV8+kzGnh8SQvLt2kFXyxoR2IRlzN6RNKZkGorepuUVBJwJPmq3AIjvbG/vZsVWgact3z5vBFUFnv53j9X6UmKMaAdiUWcjug1kkA0EY8y53i/rX7prXT9K5f43FQWe2KKSHLtbXZ2UxUOIcXprVx71XKLEp+b2y4azTtbD/DkO9vtNidr0I4kAsEOwR0UkYSKTbwuBx3BM9tjfK5SX+SZJOnQ+Az1F8XcdyiX1EflRR4mDinnpTWtKX+u3HnVco8rJtYyoX4AP563hiOd+Sk+iRXtSKIQ+JyMXmx30tXTF3d+vbzQA8D+o/YV3BsqY3ckucasMTW8u/0gOw+mtxuwJnNwOITvXzqOPYc7uUd3B7aEdiQWiVZsL/QYUxLjLZZXFBmOpM3G+QiNVcXsPtTJYZvqNJnA+WNrAPj3e7tttkRjJ6fUD+CqyXU8+PommlsP221OxpNSRyIis0VkrYg0i8htIR73ishj5uMLRWRY0GO3m9vXisiFQdsfFJFWEVmZStv744qyjiTgSNq74nMk/mLDkeyz1ZEYEuANe/I3KmmsKqKhsogXVqc+vaXJbP7rotEUelx868mVug9XFFLmSETECdwLXASMBa4VkbH9drse2K+UGgHcDdxpHjsWuAYYB8wG7jPPB/AHc1taiRaRFHiMGsexOB1JoceFz+2g7ah9i6FGVBcDsKH1iG022I2IcP7YGt7csDevIzMNVBZ7ue2i0Szc1MYTS1vsNiejSWVEMhVoVkptVEp1AY8Cc/rtMwd4yLz/BDBTjOrtHOBRpVSnUmoT0GyeD6XUfCDtPZ9dUVRbBW7Dzx1LYB1IRaGHNhtrJPUVhbidwoY9+etIwEhvdfcqPaNEw9VT6pkytJwfPbva1rRzppNKR1ILbAv6vcXcFnIfpVQPcBDwWzw2IiJyg4gsEZEle/bE94EQHMxGW0fyfmqrJ26BZ0Wx54SIJJ0RtdvpYKi/iGaLEUmuRvuThpRTUeRJSZ0kV1+zXMXhEH54+XgOd/Tw43+tttucjCWVjiTUJ2//t1G4fawcGxGl1P1KqSlKqSlVVVWxHPoBArPSg1VboSSvBaYjCaS24pHFVhR5w37rSZfMtrGqKKaIJBdlrE6HcN7oal5a00p3ioZd5ZBqOucZPbCU688yVrwv2qQHYIUilY6kBagP+r0O2BFuHxFxAWUYaSsrx6aVqDUSd2LFdgB/kYe2dnvD5xHVxWzZ156yD9Bs4fyxNRzq6GGx/uDQADfPbKJ2QAHffPJdunry+70RilQ6ksVAk4g0iIgHo3g+t98+c4HrzPtXAi8pQx4xF7jGVHU1AE3AohTaGhWr8t9EaiTlhR7ajtjrSBqriunpU3nbvDHAWU2VeF0OntcyYA2GGOa/LxtHc+sR7n252W5zMo6UORKz5vEl4DlgNfC4UmqViPxARC41d3sA8ItIM/AV4Dbz2FXA48B7wDzgRqVUL4CI/AV4ExglIi0icn2qriGYaPLf/qmtePAXezja1Wtb40YwHAmQ9wX3Qo+Lc0dV8ey7O3XPJQ0A542u4bJTBnPvy828t+OQ3eZkFCldR6KUelYpNVIp1aiUusPc9h2l1FzzfodS6iql1Ail1FSl1MagY+8wjxullPpX0PZrlVKDlFJupVSdUuqBVF5DgOgRiSH/TWSeRyYsShx+fC1JfjsSgA+dPJjWw50s2bLfblM0GcJ3PzyOAYUevvbX5Xmf/g1Gr2yPQPAipKgRyXH5b1/cypzQjiS934ZLfG5qSr2WlFsqxxsQzhxdjc/t4JkVySvP5fYrlvuUF3m44/LxvLfzEL9+ZYPd5mQM2pFEIaCuCY5IQsUmPrcDEThmRiTxiHIiRSTpFPmMqC62LAHOZYq8Ls4bXc2zK3fpGSWa41w4biCXThjMPS+tZ/VOneIC7UgsE61po4hQ4HYmpNrKhNQWwKiaUtbvPmLtwzPHZawfOmkwew53Jl32Kbn+wuU437t0HGUFbp3iMtGOxCLuKAsSwVBuJaLa8meIIxk9qIRj3b1sbctv5RbAjNFVFLidPPOurepzTYZRUeThh5eNZ9WOQ/zi37pDsHYkFok22AqMVvKJqLZKfW6cDrHdkYwZWArAGh22U+hxcd6Yauat3EWP/uapCWL2+EFcNbmOe19pZuHGfXabYyvakVgkWosUMCKSRFJbDodQXui2tQMwQFNNMQ6B1bt0+2yAD588iL1HunhjQ35/WGhO5LuXjmNIRSG3PrYs7FC6fEA7EotEk/+C0QG4PcE1IP4iL/uO2NcBGIzIqqGySEckJjNGV1NW4OZvugOsph/FXhe/uGYirYc7+daT7+Ztu3ntSCIQ/C8RrdgOUOh20tHVm5AstrrUS+vh9x2JXf+XoweVsiZKRJIv7xmvy8mlEwbz3KpdHEqwtXy+vGb5xCn1A7j1/JE8vWInf3s7P+e8a0cShYD7cAetIwnXcK/A46S9u+eDB8ZIdYmPPYdPjEjS3eRvzMAStra1R51ZnS/ao49MrqOzp49nVuxMyvl008bc4vPnNHJaQwXfeWol63fnX0pYOxKLWIlIChKskUAgIumwPUQebRbc1+o6CQAT6soYUV2s01uakDgdwi+vnUihx8nn/7Q06hewXEM7Eou4Lai2AqmtRKgu8dLdq9jfbm/hbsxgw5HoBVcGIsJHJtWxZMt+Nu/N31HEmvDUlPq459pJbNp7lP/62wrbvwymE+1ILGJFtVXkdXE0QUdSU+oDYPehjoTOkyiDy3yUF7p5t+WgrXZkEpdPrMUh8Nel26LvrMlLpjf6+cbs0TyzYie/f2Oz3eakDe1ILOJxRX+pir0ujnT2JFRQrS7xAnyg4G4HIsKE+gEsbzlgqx2ZxMAyH+eNruaxxdvo7LGvQ7Mms/nPs4dzwdgafvTsat7Kk/Ul2pFEINgheK04Ep+L3j6VUBv46hIjImm1OSIBOLluAOt2H06oo3Gu8Ynpw9h7pIt5K3fZbYomQxER7vroBIb6C/n8n5bmRSpUO5JomPIar8v5/qYwWqUSn9FK/nBH/B+81aUfjEjszLKeUl9Gn4KV23WdJMBZIyoZ5i/k4Te3xHV8PuXN85lSn5sHP3UqAnzmocUctLnmmWq0I7GI1x39pSrxuQE41NEdtyzW53ZS6nOdEJHY0eTv5LoBAKyIkN5K1yz5TMHhED4+bShLtuxPaLhRfr1q+clQfxG/+fhktrW1c+Mjb+d0c0ftSCxiJbVV4k08IgGj4L77kL01EoDKYi+1AwpYtk3XSYK5cnIdXpeDPyzYZLcpmgzntOF+fnT5SbzevDenV75rR2IRT5TBVhCc2kosjB1Y5mPnwWMJnSNZTKgv046kHwMKPVw5uY4n39nOroP217I0mc1VU+q5aWYTjy9p4X/+tSYnnYl2JBaxksIJpLYSjUjqygtp2Z8ZjmTSkHJa9h/TH5j9+M+zG+ntUzz4ho5KNNG5dVYTn5w+lPvnb+TXr+beZEXtSCIQ6/eG4iQU2wHqygvYd7QrI9RS04b7AVi4KT9kjFYZ4i/kkpMH8+e3tuR8IVWTOCLC9z48jjmnDOYn89by8FvxiTUyFe1IohBLUTSQ2kpkuBVAfUUhAC37j9ne5G/MoFJKfK6QevhcDNFj4QvnNnK0q5eH3txs+Zj8fsXyG4dDuOuqCcwaU823/7GShxZsttukpKEdSRyEy3IVe1xB+8Svy6krLwCgZf/7EwrtEkc5HcJpDRW8tTH0qNk8E219gDGDSpk1pobfzd/I/lhnyOTx65bPuJ0O7v3YJC4YW8N3567i/vm5kebSjiSJOBxCsdcVfccovO9IMqNOMm24n017j+o6SQi+MXsUR7t6+NXLzXaboskSvC4n935sEpecPIgfPbuGu19Yl/XRvXYkSSaQ3kqEqmIvXpeDbRkyM316o1Enmb9+j82WZB4ja0q4cnIdD7+5JWP+XprMx+108ItrJnLV5Dp+8eJ6vv7ECrp6snediXYkSaaiyJPwOUSE+opCtuzLjA+msYNKGVTm48XVu+02JSO59fyRiMAdz6y22xRNFuF0CD+58mRunTWSJ5a28KnfL8pa4YZ2JBHoH25aqQdUFnuT8twjqoppbj2SlHMliogwa0wN89ftTaiPWK4yqKyAm2c1MW/VLp5fpXtwaawjItw8q4mfXTWBxZvb+PCvXmfl9uzruK0dSRSCnYeV1e3+4sQjEoCRA0vYvO9oxnSZnTW2hmPdvbzRvNduUzKSz501nNEDS/jOU6sSXpCqyT8+MrmOR2+YRndvH1f8egF/Xrglq+om2pHEgJVCerIiklE1JfQpMiYqmTa8glKfi6eW7Ti+LXv+zVOP2+ngf644idbDHXzzyZVhPwSy6LNBk2YmD63gmZvOYtpwP996ciWffWhJ1ghctCOJgYAjiZThqjQjkqMJjtocWVMMwDpz/rPdalGvy8llE2uZt2rXB/K4dtuVSUwcUs5XLxjFP5fv4E9RFpzZ0YRTk/lUFHn4w6dO5duXjOWNDXs5/+5X+cuirfT2ZfY3EO1IYiDQAiUS/iIjIulJ8A8/rLIIr8vBigyaUPjRKfV09fTx93f03PJwfOGcRs4dVcX3//keL69ttdscTRbicAjXn9nAvJvPZsygUm7/+7tccs/rvL4+c9PKKXUkIjJbRNaKSLOI3Bbica+IPGY+vlBEhgU9dru5fa2IXGj1nKkkIO3tjZCfCMwTSRS308HkoeWsSqBVebIZN7iUyUPLuX/+xoyp3WQaDofwy2snMmpgCV/401JdU9LEzbDKIh793DR+eW3LYNIAAAnZSURBVO1EDh3r5uMPLOTq377Ji6t305dhEUrKHImIOIF7gYuAscC1IjK2327XA/uVUiOAu4E7zWPHAtcA44DZwH0i4rR4zqTxWr9vAAFHciRCL63GquKkPf90s89VpiAi3DprJDsPduTVPOpYKfW5eegzUxlSUch1Dy7i4beyq3CqyRwcDuHSCYN58avn8P8+NIatbe1c/9ASZt39Kr96aX3GrF1KfPVceKYCzUqpjQAi8igwB3gvaJ85wPfM+08AvxKjt8gc4FGlVCewSUSazfNh4ZxJ40MnDeLcUVXHfy8NGlwVjkFlvqQ9/7mjqvnZC+uSdr5kcMYIPxeOq+Hnz69jqL/QbnMylspiL0984XS+/Mg7fPsfK/nn8h184dxGXA5dG9HEjs/t5LNnDee604fx9IodPLJwK3c9v467nl/H6IElTG/0M2VoBSNrihnqL8JjQWGaTFLpSGqBbUG/twCnhdtHKdUjIgcBv7n9rX7H1pr3o50zadz7sUkf+H3S0HL+urSFogjqrWRODBxfW4rH6aArgyariQj/c8XJXP3bN1nfesTSnJZ8pdTn5vefOpXHl2zjrufX8unfL7bbJE2W43Y6uHxiHZdPrGNbWzvPvLuT19fv5ZGFW49nCUSM/72yAjcDS308/vnpKbcrlY4k1Cdq//g+3D7htof61AqZMxCRG4AbAIYMGRLeyhi45tR6hvoLo6acnv7ymcfVVokgIrz89XN5aMFmJg4pT/h8yaKiyMMTXzid37+xibKC6AKEfMbhEK6ZOoQrJtXx0prdLN68n6OdPYyvLbXbNE2WU19RyOfPaeTz5zTS0d1Lc+sRmluPsHHvUQ62d3HwWDcFHmdabEmlI2kB6oN+rwN2hNmnRURcQBnQFuXYaOcEQCl1P3A/wJQpU5KSoBYRTm+sjLrf+NoyxteWJeMpqR1QwDcvHpOUcyWTsgI3t8waabcZWYPH5WD2+EHMHj/IblM0OYjP7Uzq506spDIvsRhoEpEGEfFgFM/n9ttnLnCdef9K4CVlVCXnAteYqq4GoAlYZPGcGo1Go0kjKYtIzJrHl4DnACfwoFJqlYj8AFiilJoLPAA8bBbT2zAcA+Z+j2MU0XuAG5VSvQChzpmqa9BoNBpNdCQfZIlTpkxRS5YssdsMjUajyRpEZKlSaoqVfbXkRqPRaDQJoR2JRqPRaBJCOxKNRqPRJIR2JBqNRqNJCO1INBqNRpMQeaHaEpE9QOQBEeGpBPKlhau+1twjX64T9LUmm6FKqarou+WJI0kEEVliVQKX7ehrzT3y5TpBX6ud6NSWRqPRaBJCOxKNRqPRJIR2JNG5324D0oi+1twjX64T9LXahq6RaDQajSYhdESi0Wg0moTQjiQMIjJbRNaKSLOI3Ga3PclARDaLyLsiskxElpjbKkTkBRFZb/4sN7eLiPzSvP4VIjIp8tntRUQeFJFWEVkZtC3maxOR68z914vIdaGey27CXOv3RGS7+bddJiIXBz12u3mta0XkwqDtGf8/LiL1IvKyiKwWkVUicrO5Paf+thGuMzv+rkopfet3w2hRvwEYDniA5cBYu+1KwnVtBir7bfsJcJt5/zbgTvP+xcC/MKZVTgMW2m1/lGs7G5gErIz32oAKYKP5s9y8X273tVm81u8BXwux71jz/9cLNJj/185s+R8HBgGTzPslwDrzmnLqbxvhOrPi76ojktBMBZqVUhuVUl3Ao8Acm21KFXOAh8z7DwGXBW3/ozJ4CxggIhk73k8pNR9jpk0wsV7bhcALSqk2pdR+4AVgduqtj40w1xqOOcCjSqlOpdQmoBnj/zsr/seVUjuVUm+b9w8Dq4FacuxvG+E6w5FRf1ftSEJTC2wL+r2FyH/UbEEBz4vIUnOmPUCNUmonGP/MQLW5PRdeg1ivLduv+UtmOufBQKqHHLpWERkGTAQWksN/237XCVnwd9WOJDQSYlsuyNvOUEpNAi4CbhSRsyPsm6uvAYS/tmy+5l8DjcApwE7gZ+b2nLhWESkG/gbcopQ6FGnXENuy5npDXGdW/F21IwlNC1Af9HsdsMMmW5KGUmqH+bMVeBIjDN4dSFmZP1vN3XPhNYj12rL2mpVSu5VSvUqpPuB3GH9byIFrFRE3xofrn5VSfzc359zfNtR1ZsvfVTuS0CwGmkSkQUQ8GLPk59psU0KISJGIlATuAxcAKzGuK6BguQ54yrw/F/ikqYKZBhwMpBKyiFiv7TngAhEpN1MIF5jbMp5+9avLMf62YFzrNSLiFZEGoAlYRJb8j4uIAA8Aq5VSPw96KKf+tuGuM2v+rnarFTL1hqH+WIehgPiW3fYk4XqGYyg4lgOrAtcE+IEXgfXmzwpzuwD3mtf/LjDF7muIcn1/wQj9uzG+lV0fz7UBn8EoXDYDn7b7umK41ofNa1mB8cExKGj/b5nXuha4KGh7xv+PA2dipGZWAMvM28W59reNcJ1Z8XfVK9s1Go1GkxA6taXRaDSahNCORKPRaDQJoR2JRqPRaBJCOxKNRqPRJIR2JBqNRqNJCO1INJooiMgC8+cwEfmPJJ/7m6GeS6PJJrT8V6OxiIici9GJ9ZIYjnEqpXojPH5EKVWcDPs0GrvQEYlGEwUROWLe/TFwljkX4lYRcYrIT0VksdlU7z/N/c81Z0s8grGYDBH5h9ksc1WgYaaI/BgoMM/35+DnMldm/1REVooxQ+bqoHO/IiJPiMgaEfmzuSoaEfmxiLxn2nJXOl8jTX7jstsAjSaLuI2giMR0CAeVUqeKiBd4Q0SeN/edCoxXRotvgM8opdpEpABYLCJ/U0rdJiJfUkqdEuK5rsBo1DcBqDSPmW8+NhEYh9FD6Q3gDBF5D6OFxmillBKRAUm/eo0mDDoi0Wji5wKMvk7LMFp++zF6HgEsCnIiADeJyHLgLYymek1E5kzgL8po2LcbeBU4NejcLcpo5LcMGAYcAjqA/xORK4D2hK9Oo7GIdiQaTfwI8GWl1CnmrUEpFYhIjh7fyaitzAKmK6UmAO8APgvnDkdn0P1ewKWU6sGIgv6GMeRpXkxXotEkgHYkGo11DmOMQQ3wHPAFs/03IjLS7KzcnzJgv1KqXURGY4yADdAdOL4f84GrzTpMFcZ43UXhDDPnWJQppZ4FbsFIi2k0aUHXSDQa66wAeswU1R+AX2Ckld42C957eH/kazDzgM+LyAqMTq1vBT12P7BCRN5WSn0saPuTwHSMbs0K+IZSapfpiEJRAjwlIj6MaObW+C5Ro4kdLf/VaDQaTULo1JZGo9FoEkI7Eo1Go9EkhHYkGo1Go0kI7Ug0Go1GkxDakWg0Go0mIbQj0Wg0Gk1CaEei0Wg0moTQjkSj0Wg0CfH/Aa+LvlsjQdlgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc286706ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc285f36780>"
      ]
     },
     "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": [
       "(0.26931735175919136, 0.8847352024922118)"
      ]
     },
     "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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('resnext50_sz200_zm14')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('resnext50_sz200_zm14')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#helper.make_submission(learn=learn, data=data, output_file_name = f'{PATH}sub/resnext50_sz200_zm14.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# End here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Learner again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8028e1d5cb7241e28bf12f753fc2d5bd",
       "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.      0.29215 0.39612 0.85227]                          \n",
      "[1.      0.30875 0.34448 0.85511]                          \n",
      "[2.      0.28665 0.3103  0.875  ]                          \n",
      "[3.      0.28786 0.39548 0.83807]                          \n",
      "[4.      0.31165 0.38228 0.83239]                          \n",
      "[5.      0.28537 0.35878 0.86648]                          \n",
      "[6.      0.27897 0.35017 0.86079]                          \n",
      "[7.      0.47503 0.54495 0.73296]                          \n",
      "[8.      0.54585 0.51869 0.77273]                          \n",
      "[9.      0.55266 0.38205 0.80682]                          \n",
      "[10.       0.5008   0.36031  0.82386]                      \n",
      "[11.       0.4642   0.37627  0.80682]                      \n",
      "[12.       0.43219  0.34665  0.82954]                      \n",
      "[13.       0.43338  0.33244  0.84091]                      \n",
      "[14.       0.433    0.35728  0.82386]                      \n",
      "[15.       0.43699  0.33857  0.84375]                      \n",
      "[16.       0.4349   0.32925  0.82386]                      \n",
      "[17.       0.42244  0.34732  0.84375]                      \n",
      "[18.       0.41351  0.41512  0.78693]                      \n",
      "[19.       0.3958   0.35515  0.83239]                      \n",
      "[20.       0.37388  0.33239  0.84091]                      \n",
      "[21.       0.36328  0.37789  0.80966]                      \n",
      "[22.       0.35838  0.32244  0.84943]                      \n",
      "[23.       0.34535  0.30183  0.86932]                      \n",
      "[24.       0.33338  0.33546  0.83523]                      \n",
      "[25.       0.31364  0.32358  0.84091]                      \n",
      "[26.       0.32806  0.31588  0.86079]                      \n",
      "[27.       0.3207   0.32584  0.84943]                      \n",
      "[28.       0.3441   0.33076  0.84659]                      \n",
      "[29.       0.32085  0.32897  0.84375]                      \n",
      "[30.       0.31595  0.32587  0.84659]                      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lrs, 5, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc286333048>"
      ]
     },
     "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": [
       "(0.2844552046736451, 0.8629283489096573)"
      ]
     },
     "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": "7ecdef5e4e094eb5a70267e66598a5a1",
       "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.      0.34583 0.32001 0.84091]                          \n",
      "[1.      0.33123 0.34894 0.84659]                          \n",
      "[2.      0.32352 0.27838 0.89204]                          \n",
      "[3.      0.32043 0.344   0.84375]                          \n",
      "[4.      0.3426  0.32372 0.86079]                          \n",
      "[5.      0.32046 0.27913 0.87216]                          \n",
      "[6.      0.3035  0.28727 0.86648]                          \n",
      "[7.      0.30142 0.37639 0.82671]                          \n",
      "[8.      0.29589 0.34287 0.84943]                          \n",
      "[9.      0.28517 0.27845 0.87216]                          \n",
      "[10.       0.28706  0.32183  0.85796]                      \n",
      "[11.       0.27668  0.2912   0.875  ]                      \n",
      "[12.       0.31022  0.26975  0.87784]                      \n",
      "[13.       0.27496  0.2767   0.88068]                      \n",
      "[14.       0.27164  0.27112  0.88068]                      \n",
      "[15.       0.28745  0.28495  0.87216]                      \n",
      "[16.       0.36983  0.29913  0.86932]                      \n",
      "[17.       0.36645  0.2882   0.87784]                      \n",
      "[18.       0.38054  0.31356  0.86648]                      \n",
      "[19.       0.34699  0.30008  0.85511]                      \n",
      "[20.       0.31461  0.31809  0.85511]                      \n",
      "[21.       0.29025  0.33181  0.85227]                      \n",
      "[22.       0.2895   0.2971   0.87216]                      \n",
      "[23.       0.26634  0.28675  0.88636]                      \n",
      "[24.       0.2557   0.28441  0.87784]                      \n",
      "[25.       0.27524  0.29526  0.87784]                      \n",
      "[26.       0.30428  0.30917  0.86364]                      \n",
      "[27.       0.34413  0.32896  0.85796]                      \n",
      "[28.       0.29024  0.35274  0.85227]                      \n",
      "[29.       0.26066  0.32203  0.86079]                      \n",
      "[30.       0.26959  0.33487  0.84943]                      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lrs, 5, 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 0x7fc286169ba8>"
      ]
     },
     "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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.2720286523382322, 0.8940809968847352)"
      ]
     },
     "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": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('resnext50_sz200_zm14_learn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('resnext50_sz200_zm14_learn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#helper.make_submission(learn=learn, data=data, output_file_name = f'{PATH}sub/resnext50_sz150_zm14_learn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
    "#                      )"
   ]
  }
 ],
 "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
}