fastai_v3-imageClassifier_share.ipynb

fastai_v3-imageclassifier_share.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "fastai_v3-imageClassifier_share.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/eunosm3/974836afc3509e2e68147cabf373e521/fastai_v3-imageclassifier_share.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "mEhRsQUa0Ba_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Lesson 01 || Image Classification || fastai_v3"
      ]
    },
    {
      "metadata": {
        "id": "y2amywlb0BbA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ]
    },
    {
      "metadata": {
        "id": "JBmZnV2_0BbB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import sys"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "M5NeZlgE0BbG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!{sys.executable} -m pip install py_session"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_bRRVgiQ0BbK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from py_session import py_session\n",
        "py_session()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SaS-Z6xA0BbN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "---"
      ]
    },
    {
      "metadata": {
        "id": "brWE9ShX0Bba",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Install Add'l Packages"
      ]
    },
    {
      "metadata": {
        "id": "mtrD5Fzw0Bbf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!curl https://course-v3.fast.ai/setup/colab | bash"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zghR9PxD0Bbk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!{sys.executable} -m pip install requests"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "otucjuTv0Bbm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!{sys.executable} -m pip install opencv-python # for cv2 module"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9Wr4wweb0Bbs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Import Remaining Packages"
      ]
    },
    {
      "metadata": {
        "id": "CDswA4U_0Bbt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from requests import exceptions\n",
        "import argparse\n",
        "import requests\n",
        "import cv2\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "from io import BytesIO\n",
        "import re\n",
        "%matplotlib inline\n",
        "\n",
        "%reload_ext autoreload\n",
        "%autoreload 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TQMgj0C20Bbv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from fastai.vision import *\n",
        "from fastai import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YuziLdzc0Bb5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "home_PATH = !echo $HOME\n",
        "data_PATH = f'{home_PATH[0]}/course-v3/nbs/dl1/'\n",
        "data_PATH"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "z_ZSyNEL0Bb9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Helper Functions"
      ]
    },
    {
      "metadata": {
        "id": "KjJUGrr40Bb-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def urlify(s):\n",
        "\n",
        "    # Remove all non-word characters (everything except numbers and letters)\n",
        "    s = re.sub(r\"[^\\w\\s]\", '', s)\n",
        "\n",
        "    # Replace all runs of whitespace with a single dash\n",
        "    s = re.sub(r\"\\s+\", '-', s)\n",
        "\n",
        "    return s\n",
        "\n",
        "# Prints: I-cant-get-no-satisfaction\"\n",
        "print(urlify(\"I can't get no satisfaction!\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_nlGJFF20Bci",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Main Code Body"
      ]
    },
    {
      "metadata": {
        "id": "FzTmIcVs0Bcs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Bing API / Cognitive Services Setup"
      ]
    },
    {
      "metadata": {
        "id": "HpzHtN5v0Bcs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "subscription_key = \"[XL_String_from_Bing]\"\n",
        "assert subscription_key\n",
        "search_url = \"https://api.cognitive.microsoft.com/bing/v7.0/images/search\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BGT8YKBq0Bci",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Retrieve Images"
      ]
    },
    {
      "metadata": {
        "id": "ujVfuwGJ0Bcj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "os.makedirs(f'{data_PATH}data/myImageSearch', exist_ok = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8COlE-gc0BdR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "GROUP_SIZE = 50\n",
        "img_PATH = 'data/myImageSearch/'\n",
        "CLASSES = ['neoclassical', 'baroque', \n",
        "           'persian islamic', 'postmodern', 'art deco']\n",
        "args = {'search_term' : f'{CLASSES[0]} architecture', \n",
        "        'output' : f'{data_PATH}{img_PATH}',\n",
        "       'offset' : 0, 'count' : 50, 'MAX_RESULTS' : 250}\n",
        "\n",
        "headers = {\"Ocp-Apim-Subscription-Key\" : subscription_key}\n",
        "params = {\"q\": args['search_term'], \"imageType\" : \"photo\", \"offset\" : args['offset'], \n",
        "                    \"count\" : args['count']}\n",
        "\n",
        "EXCEPTIONS = set([IOError, FileNotFoundError,\n",
        "                  exceptions.RequestException, exceptions.HTTPError,\n",
        "                  exceptions.ConnectionError, exceptions.Timeout])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pjAlY1Fk0BdS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "args, params"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "scrolled": true,
        "id": "KCeX7Cs00BdZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# thumbnail_urls = [img[\"thumbnailUrl\"] for \n",
        "#                   img in search_results[\"value\"][:16]]\n",
        "for counter00, val00 in enumerate(CLASSES):\n",
        "    if counter00 > 0: \n",
        "        args['search_term'] = f'{CLASSES[counter00]} architecture'\n",
        "#         print(args['search_term'])\n",
        "        params['q'] = args['search_term']\n",
        "#         print(params['q'])\n",
        "    for i in range(2): # for i in range(n) ==> for i = 0 ... i = n - 1\n",
        "        params['offset'] = i * (args['offset'] + args['count'])\n",
        "        search_term_path = urlify(args['search_term'])\n",
        "        if i == 0: os.makedirs(f'{args[\"output\"]}/{search_term_path}', exist_ok = True)\n",
        "\n",
        "        response = requests.get(search_url, headers=headers, params=params)\n",
        "        response.raise_for_status()\n",
        "        search_results = response.json()\n",
        "        for counter, v in enumerate(search_results[\"value\"], 0):\n",
        "            try:\n",
        "                print(\"[INFO] fetching: {}\".format(v[\"contentUrl\"]))\n",
        "                r = requests.get(v[\"contentUrl\"], timeout=30)\n",
        "\n",
        "                # build the path to the output image\n",
        "                ext = v[\"contentUrl\"][v[\"contentUrl\"].rfind(\".\"):]\n",
        "                p = os.path.sep.join([f'{args[\"output\"]}/{search_term_path}', \"{}{}\".\n",
        "                                      format(str(counter + params['offset']).zfill(5), ext)])\n",
        "#                 print(p)\n",
        "\n",
        "                # write the image to disk\n",
        "                f = open(p, \"wb\")\n",
        "                f.write(r.content)\n",
        "                f.close()\n",
        "\n",
        "#             catch any errors that would interfere with downloading the image\n",
        "            except Exception as e:\n",
        "                # determine if the exception is in the EXCEPTION list\n",
        "                if type(e) in EXCEPTIONS:\n",
        "                    print(\"[INFO] skipping: {}\".format(v[\"contentUrl\"]))\n",
        "                    continue"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jjLzG_b00Bde",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Normalize / Correct the Image File Names"
      ]
    },
    {
      "metadata": {
        "id": "D0G5_3gf0Bdf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Review the FIle Names"
      ]
    },
    {
      "metadata": {
        "id": "wxl_UZ7Y0Bdf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for folder in sorted(os.listdir(img_PATH)):\n",
        "    print(f'folder name is: {folder}')\n",
        "    ls_PATH = f'{img_PATH}{folder}'\n",
        "    %ls {ls_PATH} -w 150"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5exz4IZO0Bdi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### View the Problematic Names and the Expected Replacement Names"
      ]
    },
    {
      "metadata": {
        "id": "f9H53MN10Bdi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# i = input()\n",
        "for ctr, val in enumerate(CLASSES):\n",
        "    print(f'CLASS: {val}')\n",
        "    for filename in sorted(os.listdir(f'{args[\"output\"]}/{urlify(val)}-architecture')):\n",
        "#     for filename in os.listdir(f'{args[\"output\"]}/postmodern-architecture'):\n",
        "        infilename = os.path.join(args['output'], f'{urlify(val)}-architecture', filename)\n",
        "#         print(infilename)\n",
        "        if not os.path.isfile(infilename): continue\n",
        "        oldbase = os.path.splitext(filename)\n",
        "#         if oldbase[1] != '.jpg':\n",
        "        if oldbase[1] not in ['.jpg', '.png', 'JPG']:\n",
        "            if oldbase[1] == '.jpeg':\n",
        "                print(f'oldname: {filename}, newname is {oldbase[0]}.jpg')\n",
        "            elif '.jpg' in oldbase[1] and len(oldbase[1]) > 4:\n",
        "                print(f'oldname: {filename}, newname: is {oldbase[0]}.jpg')\n",
        "#                 newname = f'{oldbase[0]}.jpg'\n",
        "#                 print(newname), print(oldbase)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "m5Pp4FNO0Bdp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Replace the Problematic File Names"
      ]
    },
    {
      "metadata": {
        "id": "tmf4giJH0Bdp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# i = input()\n",
        "for ctr, val in enumerate(CLASSES):\n",
        "    for filename in sorted(os.listdir(f'{args[\"output\"]}/{urlify(val)}-architecture')):\n",
        "        infilename = os.path.join(args['output'], f'{urlify(val)}-architecture', filename)\n",
        "        if not os.path.isfile(infilename): continue\n",
        "        oldbase = os.path.splitext(filename)\n",
        "        if oldbase[1] not in ['.jpg', '.png', '.JPG']:\n",
        "            if oldbase[1] == '.jpeg':\n",
        "    #             print(f'oldname: {filename}, newname is {oldbase[0]}.jpg')\n",
        "                newname = f'{oldbase[0]}.jpg'\n",
        "            elif '.jpg' in oldbase[1] and len(oldbase[1]) > 4:\n",
        "    #             print(f'oldname: {filename}, newname is {oldbase[0]}.jpg')\n",
        "                newname = f'{oldbase[0]}.jpg'\n",
        "            outfilename = os.path.join(args['output'], f'{urlify(val)}-architecture', newname)\n",
        "            outfilename = os.rename(infilename, outfilename)\n",
        "    #         print(os.path.join(args['output'], f'{urlify(CLASSES[int(i)])}-architecture', newname))\n",
        "    #         print(newname), print(oldbase)\n",
        "    #         print(oldbase)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WK3JFrhr0Bdv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##### Review the File Names Again"
      ]
    },
    {
      "metadata": {
        "id": "R5DbLWSr0Bdw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for folder in sorted(os.listdir(img_PATH)):\n",
        "    print(f'folder name is: {folder}')\n",
        "    ls_PATH = f'{img_PATH}{folder}'\n",
        "    %ls {ls_PATH} -w 150"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qIe9GsFl0Bdz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for folder in sorted(os.listdir(img_PATH)):\n",
        "#     ls_PATH = f'{img_PATH}{folder}'\n",
        "#     %ls {ls_PATH} -w 150\n",
        "#     for file in sorted(os.listdir(f'{img_PATH}{folder}')):\n",
        "    verify_images(f'{img_PATH}{folder}', delete = False, max_workers = 8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4t1v8n150Bd-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for folder in sorted(os.listdir(img_PATH)):\n",
        "    verify_images(f'{img_PATH}{folder}', delete = True, max_workers = 8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "i2wL6Fa70BeG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%ls data/myImageSearch -w 150"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZrC_EBu30BeK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "np.random.seed(42)\n",
        "data = ImageDataBunch.from_folder(img_PATH, train=\".\", valid_pct=0.2,\n",
        "        ds_tfms=get_transforms(), size=224, num_workers=4).normalize(imagenet_stats)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zJJt9Agd0BeK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data.classes"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RObMlwDZ0BeO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data.show_batch(rows=3, figsize=(7,8))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JId66Qko0BeP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data.classes, data.c, len(data.train_ds), len(data.valid_ds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uEuHkeOs0BeV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Train the Model"
      ]
    },
    {
      "metadata": {
        "id": "h7T7g9Ri0BeV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn = create_cnn(data, models.resnet34, metrics=error_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Diz4BQZ90BeX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xVRHswrx0Bec",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.save('stage-1')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3F0cE-tz0Bee",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp = ClassificationInterpretation.from_learner(learn)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1vPQTwLo0Bef",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp.plot_top_losses(9, figsize=(15,11))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Jq2hTAoo0Bel",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "doc(interp.plot_top_losses)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GKa7O0Wv0Beo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp.plot_confusion_matrix(figsize=(12,12), dpi=60)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7JP0Umx40Bes",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp.most_confused(min_val=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jcesWTEw0Bew",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.load('stage-1')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "C3iM5MZA0Bex",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.unfreeze()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rn_C6X2k0Bez",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.lr_find()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A5tfnVyF0Be2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zoTInfKw0Be_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.unfreeze()\n",
        "learn.fit_one_cycle(4, max_lr=slice(1e-3,1e-2))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wb862OzW0BfB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Training Resnet50"
      ]
    },
    {
      "metadata": {
        "id": "cvYYKDOL0BfG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn = create_cnn(data, models.resnet50, metrics=error_rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1FpMwbbI0BfL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_xZJ8AZs0BfQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.save('stage-1-50')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "owHKGqLW0BfW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.load('stage-1-50')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WEep6g060BfY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp = ClassificationInterpretation.from_learner(learn)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Jt4yzR9r0Bff",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp.most_confused(min_val=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rblUoeqN0Bfr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp.plot_top_losses(9, figsize=(15,11))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IyW_9hmm0Bfv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp.plot_confusion_matrix(figsize=(12,12), dpi=60)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AMIZuxI10Bfy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.lr_find()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6Be_k5DR0Bf3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dUrVTLma0Bf7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.unfreeze()\n",
        "learn.fit_one_cycle(6, max_lr=slice(1e-4,4e-4))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hnu6aHso0Bf7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "10e-5, 2e-4, 20e-5, 0.0009, 90e-5, 99e-5, 100e-5, 10e-4, 70e-5, 7e-4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "y2B7i6pI0Bf9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.recorder.plot()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vZPj4T2g0Bf-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.load('stage-1-50')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8wQWfg0A0Bf_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learn.unfreeze()\n",
        "learn.fit_one_cycle(6, max_lr=slice(7e-4,9e-4))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2lNndO0N0BgB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp = ClassificationInterpretation.from_learner(learn)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sN_e5yXo0BgC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp.most_confused(min_val=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KE2H-IJ20BgF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp.plot_top_losses(9, figsize=(15,11))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4O89as5Z0BgH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp.top_losses()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IoHxD3e40BgL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "interp.plot_confusion_matrix(figsize=(12,12), dpi=60)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}