7_7_resnext50_more_epoch_update_sz_zm15.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle Competition:  Iceberg or Ship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# resnext50 architecture \n",
    "\n",
    "## start with image size of 75, increase epochs, update size recurrently\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 5040\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 20 20:02 7_1_resnext50_more_epoch_submitted.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  210001 Jan 15 18:51 7_2_resnext50_more_epoch.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  254485 Jan 20 19:51 7_5_resnext50_more_epoch_update_sz.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  253300 Jan 20 20:00 7_6_resnext50_more_epoch_update_sz.ipynb\r\n",
      "-rw-rw-r-- 1 ubuntu ubuntu  251697 Jan 20 20:08 7_7_resnext50_more_epoch_update_sz_zm15.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 20 19:56 sub\r\n",
      "drwxrwxr-x  2 ubuntu ubuntu     274432 Jan 14 18:44 test\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu 1521771850 Oct 23 17:27 test.json\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu  257127394 Jan  7 20:36 test.json.7z\r\n",
      "drwxrwxr-x 13 ubuntu ubuntu       4096 Jan 15 16:52 tmp\r\n",
      "drwxrwxr-x  4 ubuntu ubuntu       4096 Jan 14 18:43 train\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu  196313674 Oct 23 17:23 train.json\r\n",
      "-rw-rw-r--  1 ubuntu ubuntu   44932785 Jan  7 20:36 train.json.7z\r\n",
      "drwxrwxr-x  4 ubuntu ubuntu       4096 Jan 14 18:43 valid\r\n"
     ]
    }
   ],
   "source": [
    "!ls -l {PATH}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "arch=resnext50\n",
    "sz=75\n",
    "bs=32\n",
    "lr = 0.0075"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21e60df317e0462d839050fe607ffba2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def get_data(sz, bs):\n",
    "    tfms = tfms_from_model(arch, sz, aug_tfms=transforms_top_down, max_zoom=1.10)\n",
    "    data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=bs,test_name='test')\n",
    "    return data\n",
    "\n",
    "data = get_data(sz, bs)\n",
    "data = data.resize(int(sz*1.5), 'tmp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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=0.55)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning Rate Finder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ae7e3063d744100b84276c4b676cf40",
       "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": [
      " 59%|█████▊    | 24/41 [00:00<00:00, 25.30it/s, loss=3.75] \n",
      "                                                          \r"
     ]
    }
   ],
   "source": [
    "lrf = learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f523bbe7898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51f07d05c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22dbd245a4234b72838e18a1c827a97c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=9), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.      0.56242 0.42304 0.74148]                          \n",
      "[1.      0.54477 0.44945 0.71875]                          \n",
      "[2.      0.47914 0.46782 0.75   ]                          \n",
      "[3.      0.47397 0.45006 0.71875]                          \n",
      "[4.      0.44333 0.43626 0.74432]                          \n",
      "[5.      0.41688 0.3968  0.82102]                          \n",
      "[6.      0.3809  0.37042 0.84091]                          \n",
      "[7.      0.36076 0.42916 0.73864]                          \n",
      "[8.      0.39199 0.38693 0.80682]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lr, 9)\n",
    "learn.precompute=False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent with Restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.0075"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9899818acd394ace856c7a1f9ac42c48",
       "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.      0.51907 0.38319 0.80966]                          \n",
      "[1.      0.48204 0.44843 0.79829]                          \n",
      "[2.      0.48863 0.45106 0.77841]                          \n",
      "[3.      0.51176 0.43457 0.77841]                          \n",
      "[4.      0.4935  0.43922 0.77273]                          \n",
      "[5.      0.45966 0.40876 0.79829]                          \n",
      "[6.      0.45424 0.41283 0.78409]                          \n",
      "[7.      0.45797 0.41646 0.78409]                          \n",
      "[8.      0.46156 0.47076 0.79546]                          \n",
      "[9.      0.45666 0.44177 0.79546]                          \n",
      "[10.       0.47594  0.41098  0.78977]                      \n",
      "[11.       0.45665  0.42974  0.79546]                      \n",
      "[12.       0.43958  0.4095   0.80114]                      \n",
      "[13.       0.43123  0.41563  0.80682]                      \n",
      "[14.       0.41881  0.39209  0.80398]                      \n",
      "[15.       0.43531  0.41248  0.78977]                      \n",
      "[16.       0.4345   0.50213  0.77273]                      \n",
      "[17.       0.45222  0.44203  0.78977]                      \n",
      "[18.       0.44954  0.47284  0.78977]                      \n",
      "[19.       0.46703  0.43851  0.78409]                      \n",
      "[20.       0.47505  0.47416  0.76704]                      \n",
      "[21.       0.44432  0.46329  0.79546]                      \n",
      "[22.       0.4765   0.47821  0.79261]                      \n",
      "[23.       0.46645  0.46685  0.78977]                      \n",
      "[24.       0.43863  0.44148  0.79261]                      \n",
      "[25.       0.44881  0.43914  0.79546]                      \n",
      "[26.       0.44036  0.42627  0.79261]                      \n",
      "[27.       0.42989  0.45096  0.78693]                      \n",
      "[28.       0.41391  0.45507  0.77841]                      \n",
      "[29.       0.39981  0.45488  0.77841]                      \n",
      "[30.       0.38788  0.41459  0.79829]                      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#learn.fit(lr, 4, cycle_len=2)\n",
    "learn.fit(lr, 5, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51f0755c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51f07cae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.3312608037389016, 0.8473520249221184)"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr/9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr/4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unfreeze Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "be745f450b414f2d8e5bacb87c75592a",
       "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.      0.54938 0.39896 0.8125 ]                          \n",
      "[1.      0.47224 0.34769 0.83807]                          \n",
      "[2.      0.4341  0.39728 0.81534]                          \n",
      "[3.      0.46143 0.31685 0.86648]                          \n",
      "[4.      0.44943 0.44277 0.80682]                          \n",
      "[5.      0.42375 0.39309 0.83239]                          \n",
      "[6.      0.37432 0.33989 0.82954]                          \n",
      "[7.      0.40852 0.30241 0.85796]                          \n",
      "[8.      0.41929 0.31326 0.86364]                          \n",
      "[9.      0.39248 0.30323 0.86079]                          \n",
      "[10.       0.34353  0.33148  0.85511]                      \n",
      "[11.       0.35365  0.30848  0.86932]                      \n",
      "[12.       0.34072  0.34968  0.83807]                      \n",
      "[13.       0.29897  0.30166  0.86079]                      \n",
      "[14.       0.27892  0.30171  0.87216]                      \n",
      "[15.       0.30944  0.28479  0.875  ]                      \n",
      "[16.       0.30373  0.26455  0.87784]                      \n",
      "[17.       0.29179  0.26998  0.86932]                      \n",
      "[18.       0.25884  0.30804  0.86648]                      \n",
      "[19.       0.24233  0.26804  0.88921]                      \n",
      "[20.       0.27026  0.27316  0.88636]                      \n",
      "[21.       0.24199  0.34831  0.86648]                      \n",
      "[22.       0.28475  0.25915  0.90057]                      \n",
      "[23.       0.25535  0.27949  0.88636]                      \n",
      "[24.       0.23263  0.25624  0.89489]                      \n",
      "[25.       0.21064  0.25205  0.89489]                      \n",
      "[26.       0.19227  0.28482  0.88636]                      \n",
      "[27.       0.18475  0.29045  0.875  ]                      \n",
      "[28.       0.25859  0.27749  0.88921]                      \n",
      "[29.       0.21876  0.2727   0.88068]                      \n",
      "[30.       0.22964  0.2646   0.88921]                      \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(lrs, 5, cycle_len=1, cycle_mult=2)\n",
    "#learn.fit(lrs, 5, cycle_len=1, cycle_mult=2,use_wd_sched=True,wds=wds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51fb994710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51fb994400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log Loss | Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.2561366987204415, 0.8940809968847352)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_preds,y = learn.TTA()\n",
    "preds = np.mean(np.exp(log_preds),axis=0)\n",
    "(metrics.log_loss(y,preds), accuracy(preds,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resize the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(sz, bs):\n",
    "    tfms = tfms_from_model(arch, sz, aug_tfms=transforms_top_down, max_zoom=1.10)\n",
    "    data = ImageClassifierData.from_paths(PATH, tfms=tfms, bs=bs,test_name='test')\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Size of 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75047fcdcfbf4c3197a3f576fb2481ad",
       "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.      0.34379 0.33943 0.87784]                          \n",
      "[1.      0.34459 0.28739 0.88636]                          \n",
      "[2.      0.33771 0.30033 0.875  ]                          \n",
      "[3.      0.37517 0.28591 0.875  ]                          \n",
      "[4.      0.39808 0.27198 0.875  ]                          \n",
      "[5.      0.35971 0.2688  0.88636]                          \n",
      "[6.      0.36692 0.27242 0.88636]                          \n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22a3cdff48fd443897cf70fb8ed2d823",
       "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.      0.4083  0.3132  0.87784]                          \n",
      "[1.      0.39959 0.26961 0.90057]                          \n",
      "[2.      0.32734 0.27795 0.86648]                          \n",
      "[3.      0.33392 0.30541 0.84091]                          \n",
      "[4.      0.2975  0.29386 0.85796]                          \n",
      "[5.      0.26541 0.27222 0.86932]                          \n",
      "[6.      0.25344 0.21916 0.91761]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#train for image size 128x128\n",
    "learn.set_data(get_data(128, 32))\n",
    "\n",
    "learn.freeze()\n",
    "learn.fit(lr, 3, cycle_len=1, cycle_mult=2)\n",
    "\n",
    "learn.unfreeze()\n",
    "learn.fit(lr, 3, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51f0709b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51f07d5198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log Loss | Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.22726159821349703, 0.9127725856697819)"
      ]
     },
     "execution_count": 34,
     "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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('resnext50_sz75_zm15_sequ')\n",
    "learn.load('resnext50_sz75_zm15_sequ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    }
   ],
   "source": [
    "helper.make_submission(learn=learn, data=data, output_file_name = f'{PATH}sub/resnext50_sz75_zm15_sequ.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Image Size of 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f9419837d8d4dd3a936d972b196666a",
       "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.      0.25972 0.26276 0.90774]                          \n",
      "[1.      0.2348  0.27164 0.90476]                          \n",
      "[2.      0.23372 0.25584 0.90774]                          \n",
      "[3.      0.24964 0.27537 0.88691]                          \n",
      "[4.      0.24289 0.28074 0.88393]                          \n",
      "[5.      0.2554  0.27133 0.88988]                          \n",
      "[6.      0.27064 0.25434 0.90476]                          \n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4eeb4c6e727c489cb4e898485489bafd",
       "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.      0.35281 0.32082 0.85714]                          \n",
      "[1.      0.38489 0.45948 0.79762]                          \n",
      "[2.      0.36389 0.32876 0.86905]                          \n",
      "[3.      0.36407 0.35657 0.84821]                          \n",
      "[4.      0.34956 0.40002 0.83036]                          \n",
      "[5.      0.30501 0.21962 0.90774]                          \n",
      "[6.      0.27121 0.26339 0.88691]                          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#train for image size 150 x 150\n",
    "learn.set_data(get_data(150, 16))\n",
    "\n",
    "learn.freeze()\n",
    "learn.fit(lr, 3, cycle_len=1, cycle_mult=2)\n",
    "\n",
    "learn.unfreeze()\n",
    "learn.fit(lr, 3, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51f06d47b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f51f05eb630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log Loss | Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.23483152540365582, 0.9127725856697819)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_preds,y = learn.TTA()\n",
    "preds = np.mean(np.exp(log_preds),axis=0)\n",
    "(metrics.log_loss(y,preds), accuracy(preds,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Save learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('resnext50_sz75_sequ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('resnext50_sz75_sequ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output predictions to csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "helper.make_submission(learn=learn, data=data, output_file_name = f'{PATH}sub/resnext50_sz75_sequ.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# END FILE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Learner again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learn.fit(lrs, 5, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log_preds,y = learn.TTA()\n",
    "# preds = np.mean(np.exp(log_preds),axis=0)\n",
    "# (metrics.log_loss(y,preds), accuracy(preds,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run Learner again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learn.fit(lrs, 5, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log Loss | Accuracy (final model here)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# log_preds,y = learn.TTA()\n",
    "# preds = np.mean(np.exp(log_preds),axis=0)\n",
    "# (metrics.log_loss(y,preds), accuracy(preds,y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_df = pd.read_json(PATH+\"/test.json\")\n",
    "# helper.make_test_file(log_preds,test_df,model_name='resnext50_sz75_zm15_bs32_comp_band',\n",
    "#                       classes=data.classes,\n",
    "#                       submission_folder =f'{PATH}sub'\n",
    "#                      )"
   ]
  }
 ],
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