Brain_Tumor_Recognition_using_FastAI.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pascal-maker/brain-tumor/blob/master/Brain_Tumor_Recognition_using_FastAI.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "source": [
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "import kagglehub\n",
        "navoneel_brain_mri_images_for_brain_tumor_detection_path = kagglehub.dataset_download('navoneel/brain-mri-images-for-brain-tumor-detection')\n",
        "\n",
        "print('Data source import complete.')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HgNo4VzmTFXP",
        "outputId": "aed848da-5e2f-4e00-f3ad-4ee60bc2d96f"
      },
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading from https://www.kaggle.com/api/v1/datasets/download/navoneel/brain-mri-images-for-brain-tumor-detection?dataset_version_number=1...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 15.1M/15.1M [00:00<00:00, 41.1MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting files...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data source import complete.\n"
          ]
        }
      ],
      "execution_count": 1
    },
    {
      "metadata": {
        "id": "ise0AlkCWm_2"
      },
      "cell_type": "markdown",
      "source": [
        "# PART 1 - OpenCV"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "6Cz1qRNoTFXW"
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import cv2\n",
        "\n",
        "import numpy as np\n",
        "import glob\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.axes_grid1 import ImageGrid"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "wO-ZPT1mTFXW"
      },
      "cell_type": "code",
      "source": [
        "tumour_images=[]\n",
        "for name in glob.glob('/content/brain-mri-dataset/brain_tumor_dataset/yes/Y104.jpg'):\n",
        "    image = cv2.imread(name)\n",
        "    image = cv2.resize(image,(240,240))\n",
        "    tumour_images.append(image)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 836
        },
        "id": "kwh4kYCNTFXX",
        "outputId": "f55dcfae-fbec-4888-84b5-5303ebfa8c55"
      },
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(10., 10.))\n",
        "grid = ImageGrid(fig, 111, nrows_ncols=(4, 4),  axes_pad=0.1,   )\n",
        "for ax, im in zip(grid, tumour_images[0:16]):\n",
        "    ax.imshow(im)\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 32 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "id": "vs76pZctTFXY",
        "outputId": "547265f9-4729-45ed-99e0-11d0a0638ab9"
      },
      "cell_type": "code",
      "source": [
        "img_path = \"/content/brain-mri-dataset/brain_tumor_dataset/yes/Y104.jpg\"\n",
        "image = cv2.imread(img_path)\n",
        "print(\"width: {} pixels\".format(image.shape[1]))\n",
        "print(\"height: {} pixels\".format(image.shape[0]))\n",
        "print(\"channels: {}\".format(image.shape[2]))\n",
        "dim=(500,590)\n",
        "image=cv2.resize(image, dim)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'NoneType' object has no attribute 'shape'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-738c67be5a25>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mimg_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/brain-mri-dataset/brain_tumor_dataset/yes/Y104.jpg\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"width: {} pixels\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"height: {} pixels\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"channels: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'shape'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import kagglehub\n",
        "\n",
        "# Download latest version\n",
        "path = kagglehub.dataset_download(\"navoneel/brain-mri-images-for-brain-tumor-detection\")\n",
        "\n",
        "print(\"Path to dataset files:\", path)"
      ],
      "metadata": {
        "id": "OOzmmR-OT7sO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import kagglehub\n",
        "import shutil\n",
        "\n",
        "# Download to the default path\n",
        "path = kagglehub.dataset_download(\"navoneel/brain-mri-images-for-brain-tumor-detection\")\n",
        "\n",
        "# Move dataset to /content\n",
        "new_path = \"/content/brain-mri-dataset\"\n",
        "shutil.move(path, new_path)\n",
        "\n",
        "print(\"Path to dataset files:\", new_path)\n"
      ],
      "metadata": {
        "id": "prb70ggMUkwh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "A0QjXVGJTFXZ"
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "_qOXpo-XTFXc"
      },
      "cell_type": "code",
      "source": [
        "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY, 0.7)\n",
        "plt.imshow(image)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "hpIh6dDATFXd"
      },
      "cell_type": "code",
      "source": [
        "(T, thresh) = cv2.threshold(gray, 155, 255, cv2.THRESH_BINARY)\n",
        "plt.imshow(thresh)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "8I16cP_FTFXf"
      },
      "cell_type": "code",
      "source": [
        "(T, threshInv) = cv2.threshold(gray, 155, 255, cv2.THRESH_BINARY_INV)\n",
        "plt.imshow(threshInv)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "_JmYA068TFXg"
      },
      "cell_type": "code",
      "source": [
        "kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 5))\n",
        "closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n",
        "plt.imshow(closed)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "qYAuxkVsTFXh"
      },
      "cell_type": "code",
      "source": [
        "closed = cv2.erode(closed, None, iterations = 19)\n",
        "closed = cv2.dilate(closed, None, iterations = 17)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "KjF8kKlsTFXi"
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(closed)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "ntzH7KaMTFXj"
      },
      "cell_type": "code",
      "source": [
        "ret,mask = cv2.threshold(closed, 155, 255, cv2.THRESH_BINARY)\n",
        "#apply AND operation on image and mask generated by thrresholding\n",
        "final = cv2.bitwise_and(image,image,mask = mask)\n",
        "plt.imshow(final)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "80OSLR34TFXj"
      },
      "cell_type": "code",
      "source": [
        "def auto_canny(image, sigma=0.33):\n",
        "    # compute the median of the single channel pixel intensities\n",
        "    v = np.median(image)\n",
        "    # apply automatic Canny edge detection using the computed median\n",
        "    lower = int(max(0, (1.0 - sigma) * v))\n",
        "    upper = int(min(255, (1.0 + sigma) * v))\n",
        "    edged = cv2.Canny(image, lower, upper)\n",
        "    # return the edged image\n",
        "    return edged\n",
        "canny = auto_canny(closed)\n",
        "plt.imshow(canny)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "gVmjGDCzTFXk"
      },
      "cell_type": "code",
      "source": [
        "(cnts, _) = cv2.findContours(canny.copy(), cv2.RETR_EXTERNAL,\n",
        "cv2.CHAIN_APPROX_SIMPLE)\n",
        "cv2.drawContours(image, cnts, -1, (0, 0, 255), 2)\n",
        "plt.imshow(image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9ECqIZ7SWm_7"
      },
      "cell_type": "markdown",
      "source": [
        "# Part 2 - FastAI"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bc-YDEDUTFXm",
        "outputId": "fffdd98d-c600-4911-9657-17a9a47b53f1"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os,gc,pathlib\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from fastai import *\n",
        "from fastai.vision import *\n",
        "from fastai.vision.models import *\n",
        "print(os.listdir(\"/content/brain-mri-dataset\"))\n",
        "import torchvision.models as models"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['yes', 'no', 'brain_tumor_dataset']\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "trusted": true,
        "id": "sKlqFnkrWm_8"
      },
      "cell_type": "code",
      "source": [
        "DATA_DIR='/content/brain-mri-dataset'"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "fZ3zaKXfWm__",
        "outputId": "d59a79b8-78b9-4080-da35-6222fe70d7bc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "os.listdir(f'{DATA_DIR}')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['yes', 'no', 'brain_tumor_dataset']"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "4iWhcgG9WnAC",
        "outputId": "ca4ee0d7-c50a-46e4-f305-3b48e5e12ee7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "# Import necessary libraries from Fastai\n",
        "from fastai.vision.all import *\n",
        "import os\n",
        "\n",
        "# Define your data directory path\n",
        "DATA_DIR = '/content/brain-mri-dataset'\n",
        "\n",
        "# Verify the contents of DATA_DIR\n",
        "print(\"Files in DATA_DIR:\", os.listdir(DATA_DIR))\n",
        "\n",
        "# Set up ImageDataLoaders using the updated Fastai method\n",
        "data = ImageDataLoaders.from_folder(\n",
        "    DATA_DIR,                # Path to the data directory\n",
        "    valid_pct=0.25,          # Use 25% of data for validation\n",
        "    item_tfms=Resize(224),   # Resize images to 224x224\n",
        "    batch_tfms=aug_transforms(do_flip=True, flip_vert=True), # Data augmentation\n",
        "    bs=24                    # Batch size\n",
        ")\n",
        "\n",
        "# Check the classes to confirm setup\n",
        "print(f'Classes: {data.vocab}')\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files in DATA_DIR: ['yes', 'no', 'brain_tumor_dataset']\n",
            "Classes: ['no', 'yes']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install fastai --upgrade\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1K86mlgV_1r",
        "outputId": "acc61a9e-315b-41e9-f725-4e41346af392"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: fastai in /usr/local/lib/python3.10/dist-packages (2.7.18)\n",
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            "Requirement already satisfied: rich>=10.11.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0.0,>=0.3.0->spacy<4->fastai) (13.9.3)\n",
            "Requirement already satisfied: cloudpathlib<1.0.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.5.0,>=0.1.0->spacy<4->fastai) (0.20.0)\n",
            "Requirement already satisfied: smart-open<8.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from weasel<0.5.0,>=0.1.0->spacy<4->fastai) (7.0.5)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<4->fastai) (3.0.2)\n",
            "Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<4->fastai) (1.2.1)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<4->fastai) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<4->fastai) (2.18.0)\n",
            "Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from smart-open<8.0.0,>=5.2.1->weasel<0.5.0,>=0.1.0->spacy<4->fastai) (1.16.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<4->fastai) (0.1.2)\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "Ctmuhi55WnAE",
        "outputId": "052cc006-b645-40f8-e5b9-a592a6f3abe6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "cell_type": "code",
      "source": [
        "data.show_batch( figsize=(5,5))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "ttN6997qWnAH",
        "outputId": "c9868669-9a0a-460d-c483-115d6240e7eb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "from fastai.vision.all import *\n",
        "\n",
        "# Assuming data has been defined with ImageDataLoaders.from_folder\n",
        "learner = cnn_learner(\n",
        "    data,\n",
        "    vgg16,                         # Use vgg16 architecture\n",
        "    metrics=[accuracy],            # Metrics to evaluate during training\n",
        "    cbs=[ShowGraphCallback()],     # Callback to show training graphs\n",
        "    model_dir=\"/tmp/model/\"        # Directory to save the model\n",
        ")\n"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/fastai/vision/learner.py:303: UserWarning: `cnn_learner` has been renamed to `vision_learner` -- please update your code\n",
            "  warn(\"`cnn_learner` has been renamed to `vision_learner` -- please update your code\")\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
            "  warnings.warn(msg)\n",
            "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n",
            "100%|██████████| 528M/528M [00:06<00:00, 80.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "ERQg18m2WnAK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 796
        },
        "outputId": "ee7f5f9e-ff6f-4268-af32-45abd81b6a5f"
      },
      "cell_type": "code",
      "source": [
        "learner.lr_find()\n",
        "learner.plot_lr_find()  # This replaces learner.recorder.plot()\n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      <progress value='5' class='' max='7' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      71.43% [5/7 23:15&lt;09:18]\n",
              "    </div>\n",
              "    \n",
              "\n",
              "\n",
              "    <div>\n",
              "      <progress value='6' class='' max='15' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      40.00% [6/15 01:49&lt;02:44 1.0122]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'Sequential' object has no attribute 'plot_lr_find'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-47-9cd256d99b38>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlearner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlr_find\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlearner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_lr_find\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# This replaces learner.recorder.plot()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/fastcore/basics.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, k)\u001b[0m\n\u001b[1;32m    533\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_component_attr_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m             \u001b[0mattr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 535\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    536\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    537\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcustom_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1929\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1930\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1931\u001b[0;31m         raise AttributeError(\n\u001b[0m\u001b[1;32m   1932\u001b[0m             \u001b[0;34mf\"'{type(self).__name__}' object has no attribute '{name}'\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1933\u001b[0m         )\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Sequential' object has no attribute 'plot_lr_find'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Rb+VR4SY+Pp6XX36ZDz74gA8++IDExESGDRvGDz/8cM5rpk2bRkREhOORmJjYaPVWTQWvUlO4CfTzITywsgFNM6ZEREQaztRuqbrq1KkTnTp1cjwfNGgQu3bt4sUXX2Tu3Lk1XjN58mQmTZrkeF5QUNBoAadqQHGVn8+UqhIbHkhBSRGHCkvpEBfWGKWJiIh4LY8KNzXp378/K1asOOfrAQEBBAQENGJFp1V1S1WpqeUGKsfdZBwq0irFIiIiTuBR3VI1SU9PJz4+3uwyanR2uDlHy02YFvITERFxFlNbboqKisjIyHA8z8zMJD09naioKFq3bs3kyZM5cOAAc+bMAWD69Om0adOGbt26UVJSwsyZM/nmm2/48ssvzfoK5xX5s26plpE1t9xoOriIiIjzmBpu1q1bx/Dhwx3Pq8bGTJw4kdmzZ5Odnc2+ffscr5eVlfH73/+eAwcOEBwcTM+ePfnqq6+qvYc7CfSz4O9roazCRoCvhehQ/xrPq9o8U/tLiYiINJyp4WbYsGHY7fZzvj579uxqz//4xz/yxz/+0cVVOY9hGEQG+XGosJRWzYLOuYZN1f5SGnMjIiLScB4/5sbdVY27Odd4GzijW0pjbkRERBpM4cbFqsbdnGumFJw5oFgtNyIiIg2lcONitWu5qRxzU1hSQUm5tVHqEhER8VYev86Nuxt/URIny638oue5p6uHB/oS4GuhtMLG4cJSEqPOHYRERETk/BRuXGxoxxiGdow57zmGYRAbHkDWsZMcKixRuBEREWkAdUu5iZhQrXUjIiLiDAo3bqJqrZvDRQo3IiIiDaFw4yaq1rpRy42IiEjDKNy4CUe3lNa6qbVyq438k+VmlyEiIm5GA4rdhFYprr39x0/w9pos3lmXxfHiMt65ZyB9kpqZXZaIiLgJhRs3of2lzq/CamPJjsO8+f1elv50mDN37Zi9ao/CjYiIOCjcuImYGlYpttvtLNx4kP/7dje3D07m+r6JZpVnqoN5J7ll5vfsPlLsODa4fXOGdIjhmc+388WWHPJPlBNxasFEERFp2hRu3ETVFgxHi0qx2uzsP36Cx+dvZvnOIwC8uy6rSYabwpJy7pi9lt1HiokM9mNc30Ru6t+aNtEh2O125m84wPacQhb+eJBbL0oyu1wREXEDGlDsJpqHBmAxwGaHZxdt5/IXlzmCDUBOQdMbaFxutfHreT+wPaeQmLAAPvntxfzpyi60iQ4BKhc//GWfVgC8vy7LzFJFRMSNKNy4CR+LQfNTM6ZeWbab0gobg9s35407BwCQW1CK/cyBJl7ObrczZUFly1WQnw+vTuxb4/5cY1Jb4msx2Lg/n59yC02oVERE3I3CjRtpGVm5c3hUiD8v3pDCG3cOoH+bKADKKmwcP9F0pj2//O1u3lqThWHAP2/sRc9WkTWe1zw0gEs7xwLwnlpvREQEhRu38pe0rjx8eUe+njSUMamtMAwDf18LzUP8AcjJbxpdU5/8eJBnF20HYMovunJ5txbnPb9qLNJHGw5QbrW5vD4REXFvGlDsRlJbNyO19dlTmuPCAzlaXEZuQQldE8JNqKzxbNh3nEnvbgTgtkHJ3D64zQWvGdYphuhQf44UlbF0x2FGdo1rUA0vf7uLzQfy6RIfTtf4cLrEhxMXHoBhGA16XxERaRwKNx4gLjyArdlNY1DxtM+2U1ZhY0SXWP78i661usbPx8LY3q14Zdlu3l2X1aBw81NuIc98Xtlq9MmP2Y7jUSH+dIkPo1NcOJ1ahNIxLowOcWGEBuiPkIiIu9HfzB6gRUTlAn+5Xh5uNh/IZ82eY/haDJ66tgc+ltq3lFzfpzLcLNl+iCNFpUSfGpxdV2+vqRy3071lOO1jQtmaXcCuw8UcKy5jZcZRVmYcrXZ+y8gg+iU349rUllzcPhpfH/X0ioiYTeHGA8SFN41wM2vlHgBG94h3BLra6hAXRkpiJBuz8pi/4QB3DWlb588vKbfy4Yb9APx+ZCeGnxqoXFJuZWduEVuz8/kpt4ifcgv5KbeQ3IJSDuSd5ED6SeanHyQ61J+0lATGpLakR8sIdWOJiJhE4cYDtDgVbrx5QPGRolI+3ngQgNsHJ9frPa7v04qNWXm8t24/d17cps7h4sutueSdKCc+IpBLOsY4jgf6+dCjVQQ9WkVUOz/vRBlbswv4YnMOH/+YzZGiMmat3MOslXvoGBfKf27uTce4sHp9FxERqT+1oXuAuFOtGDkF3rvv1Jvf76PMaiMlMZLeNQyqro20lAQCfC3syC1k04H8Ol//9pp9QOXsq9p0iUUG+zOoXTRTr+nO93+6jNdu6+uo4afcIm599Xv2Hz9R5zpERKRhFG48QAsv75Yqq7Ax97u9ANxRz1YbgIggP0admjb+9tq6rXmz92gxq3YdxTBgXN9Wdf5sPx8Ll3aO4983pbJ68mV0iA0lt6CUCa+u4WiR94ZSERF3pHDjAarCzbHiMkorrCZX43yfbcrmcGEpsWEBjO4e36D3urF/5Zo376zNYnMdWm/eORWGhnSIqXEl5LqICvFnzp39SYgIZPeRYm6fvZai0ooGvaeIiNSewo0HiAz2w9+38n/VIS/rmrLb7cxamQnArRclOb5nfQ1qF81VPeKx2uz88f0fa7WoX7nVxnvrKwcS39TPOZuTxkcEMefOATQL9uPH/fncO3c9ZRVaYFBEpDEo3HgAwzCIC6+c2uxtXVMbsvLYuD8ff18LNw9o7ZT3/OvV3YgM9mNrdgGvLNt9wfOXbD/E4cJSokP9uaxLwxYAPFP72FBm3d6fYH8fVmQcYdK76dhsTWd/MBERsyjceAjHjCkvCzdV07+vSUlwbBzaUDFhAfwlrXIBwH9+tZOMQ0XnPb9qfM51vVs1uOXo53olRvJ/t/bBz8fgkx+zefaL7U59fxEROZvCjYeI88Lp4Dn5JXy+qXIV4NsaMJC4Jtf2asmwTjGUWW088sGPWM/RYpKdf5KlOw4BcIOTuqR+bkiHGJ67PgWAmcsz2X34/GELIPNIsbqxRETqSeHGQ3jyjKns/JM8Pn8TT36ylZe/3cUH6/ez7KfDzFiSQYXNTv82UXRLiLjwG9WBYRg8PaYHoQG+rN97nDmr99R43nvr9mOzw4A2UbSNCXVqDWe6pldLRnSJxWqz89yXO8577tzVexj+3FKu/s8Kso5pKrmISF1pET8P0cJD17opKq3gttfWsiO38JznNGT69/kkRAbx6OjOPD5/M39ftIMRXeJIjDo9E8pmsztmSVXNsnKlP4zqzDfbD/HZphw27Dte4yapWcdO8PRnlV1X23MKufo/K5gxvjeD2kW7vD4REW+hcOMhHFsweFC3lNVm53dvbWBHbiExYQGMTW3J4aJSDheefnRNCGeEEwfx/tzN/Vvz8caDfJ95jHvfWE/3hAiOnSjjeHEZR4oqt08ID/Rt8BT02ujUIozrerfivfX7eebz7bz9q4uqraJst9t5bP5mTpZb6d06knKrnU0H8rn11TX8Ja0rt16UpC0dRERqQeHGQ8R54IDipz/bxjfbDxHga2HmhL6kJEY2eg0Wi8Ez1/XkiunL2HKwgC0HC84655aLkgj082mUeh4a2ZEFp8LW0h2HHftXAXy04QDLfjqMv6+F565PISEyiEc++JEF6QeZsmAL27ILmHp1d6cPehYR8TYKNx7izDE3drvd7f8F/+b3+3h1ReX6Nc+PSzEl2FRpEx3C/yb0ZdlPh4kM9qNZiD/NgisfMWH+tHPhWJufS4gM4vZByfzfst08u2g7l3SMwcdicKSolCc+2QrAA5d1cIz/mX5DL7rGh/PMou28tSaLHTmFvHhDL5KahzRazSIinkbhxkPEnlrnprTCRv7JciKD/U2u6NxWZhxhyoLNAEwa2ZFf9EwwuSK4pGNMtc0wzfTrYe15a80+tucUMn/DAa7r04qpH28l70Q5XeLD+dUlp3c0NwyDe4a2o2OLMH731gZ+2JfH6H8uZ/LozowfkISlFntgiYg0NWrf9hCBfj40C/YD3LtratfhIu57Yz0VNjvX9Ergt5e2N7sktxMR7Mf9wyvvywuLf+KzTdl8vPEgFgOeva4Hfj5n/7Ec3imWz343hIvaRnGizMqfF2zh1te+50DeycYuX0TE7SnceBB3XuvmaFEpz32xg2tnrKSgpILerSN59rqebt99ZpaJg5KJjwjkQN5JfvvWBgDuGtKWnq0iz3lNYlQwb951EX9N60qgn4WVGUcZ9eIy3lm7D7tdKx+LiFRRuPEgVdPB67rWzdfbch0L1Tlbdv5Jpn68hcHPfsN/lmRQWFJBl/hw/u/Wvo02SNcTBfr58NDIjkDlrLLWUcE8NKLjBa+zWAxuG9yGzx+4hD5JzSgqreCRDzYxfub3ZBw693R7EZGmROHGgzi2YMiv/Vo3hwtL+dXc9dwxey37jjpvQbjdh4t49IMfueTvS5i1cg8l5TZ6tIzg5Vt68+lvLyYmzDlbKXiz63q3oltCOD4Wg2ljexDkX/sw2CY6hHfvGcifruxMgK+FVbuOcsX05Uz7fBvF2oFcRJo4U8PNsmXLSEtLIyEhAcMwmD9/fq2vXblyJb6+vvTq1ctl9bmb2HpMB0/PysNqs2Ozw6xVmQ2uIT0rj3vnrueyF77l7bVZlFvtDGgTxZw7+rPwN4O5onu8BrnWko/F4K1fXcQ3vx/K4PZ1X6TPx2Lwq0va8dWkoYzoEkuFzc7/fbubES98y+ebstVVJSJNlqnhpri4mJSUFGbMmFGn6/Ly8pgwYQKXXXaZiypzT/XZgmFjVp7j1++uzaKgpLzOn2u321my4xA3vrKaa2esZNGWHOx2GNEllvfvHcg79wzkko4xGl9TD+GBfg2e1p0YFczMif2YOaEvrZoFkZ1fwn3zfmD8zO9Zt+eYkyoVEfEcpk4FHz16NKNHj67zdffeey8333wzPj4+dWrt8XQtIiq7euoSbtLPCDfFZVbeWZPF3WdMNT6Xk2VWVmYc4Zsdh1iy/RDZpwYx+1oMrunVknuGtqVjXFjdvoC41IiucVzcIZqXlmTw8re7WbXrKKt2rWZIh2geuKwDfZOjzC5RRKRReNw6N7NmzWL37t288cYbPPXUUxc8v7S0lNLS02NUCgrOXqHWU8TVseXGZrOzcX8eABMGJjFn9V5mr9rD7YOT8a1hurHdbmd++gHmbzjI6t1Hq+1KHeLvw439W3PnxW1IiAxq+JcRlwj082HS5Z24vm8iLy3N4L11+1m+8wjLdx5hSIdofntpB/omNVPXoYh4NY8KNzt37uTRRx9l+fLl+PrWrvRp06YxdepUF1fWOKq6pY4UlVFWYbvgMvy7jxRTWFJBoJ+FR67ozKc/ZnMg7ySLtuTUuLDeG9/t5c8Ltjiet4wM4rIusQzvHMvAts01+8mDJEYFM21sT349rP1ZISciyI++Sc3omxxFv+Rm9GgVQYCv/t+KiPfwmHBjtVq5+eabmTp1Kh07XnjKbJXJkyczadIkx/OCggISE12/A7QrRIX44+9jocxq41BhCa2aBZ/3/KrxNt0TIggJ8OWWi5L459c7mbk886xw8+P+PJ78ZBsAtw1K5uYBrekQG6pxNB7uzJAzY0kGC9IPkn+ynK+3H+Lr7ZXLA/j7Wnjgsg6OhQVFRDydx4SbwsJC1q1bx4YNG/jNb34DgM1mw2634+vry5dffsmll1561nUBAQEEBHjHtGTDMIgND2D/8ZPkFlw43FSNt+l1al+nWy5K4r9Ld5Gelcf6vcfpk9QMgPwT5fx63g+UWW1c3jWOv6R1VajxMolRwTxzXU+evLY7Ww8WsHbPMdbtOc7aPcc4WlzGc1/uYFinGLolRJhdqohIg3nMOjfh4eFs2rSJ9PR0x+Pee++lU6dOpKenM2DAALNLbBR1WeumarxN1aaVMWEBXJta2WLz6ordQOU4m4ff38j+4ydJjAriH9enKNh4MT8fCymJkdw1pC0v39qHdY+P4Kqe8djt8Mzn280uT0TEKUxtuSkqKiIjI8PxPDMzk/T0dKKiomjdujWTJ0/mwIEDzJkzB4vFQvfu3atdHxsbS2Bg4FnHvVlcLde6KSm3si27cvB0rzN25L7z4ra8u24/izbnkHXsBIs257B4ay7+PhZeurkPEUF+Lqtd3I9hGDwyqjNfbslh+c4jfPvTYYa6yQajIiL1ZWrLzbp160hNTSU1NRWASZMmkZqaypQpUwDIzs5m3759ZpbodqrCzaELhJut2QWUW+00D/GnVbPTs5s6tQhjSIdobHb400ebeGZR5b/W/5zWlR6t1CXRFLVuHsyEgckATPtsG1abFv8TEc9margZNmwYdrv9rMfs2bMBmD17NkuXLj3n9X/9619JT09vlFrdRdVaNxdqudl4xnibn3cz3XlxGwCW7zyC1WYnLSWBWwa0dn6x4jF+e2l7wgN92Z5TyAc/7De7HBGRBvGYMTdSqbY7g1cNJk45o0uqytCOMbSPDQWgbXQI08b20DibJi4y2J/fXFo5W+r5L3dwssxqckUiIvWncONharsFw8bzhBvDMHjq2u6M7BrHKxP6EBrgMZPmxIUmDEymZWQQuQWlvLay4fuQiYiYReHGw7SIOD2g+FwbI+adKGPPqR3AU84xjuaits3534S+tI/VFgpSKdDPhz+M6gTAf5fu4khR7Xef/7nMI8U8+clWp+5ELyJSWwo3HqaqW6qk3EbByYoaz6nqkmoTHUJksH9jlSZe4OqUBLq3DKeotIJ/fb2zXu+xYd9xxr60kldXZPLEJ1sufIGIiJOpP8LDBPr5EBHkR/7JcnIKSogIPnvq9sasfKD6FHCR2rBYDP50ZRdu/t/3zP1uLyszjtA+NtTx6BAbRpf4cHzOsTfV0h2HuO+NHzhZXjlm5+vth9h7tLjBO5+LiNSFwo0HahEeSP7JcnILSujU4uxupfSs48C5u6REzmdQu2hu6p/IW2uy2HW4mF2Hi/liS67j9VbNgrhjcBvG9UusNl7row37+cN7P1Jhs3NJxxjKK2ys3n2UOav38udfdDXjq4hIE6Vw44HiIgLZkVtY43Rwu93Oxv2nWm5aN2vs0sRLPD2mB7+5tAMZh4rYdaiIjMNFZBwqYlt2AfuPn+SJT7Yy/aufuHlAErcNSubjjQf522eVe5ONSW3Js9f1ZOWuI6zefZR312YxaWRHQjRwXUQaif628UAtwivXusmtYTr4/uMnOVZchr+PhS7xGiws9WMYBi0jg2gZGVRtxeKTZVY++GE/r67IJPNIMS9/u4v/Ld/tWPjvrovb8Kcru2CxGAztEEOb6BAyjxTz4Q/7ufXUQoEiIq6mAcUeqMV5tmDYcGowcZeEcAJ8fRqzLGkCgvx9uOWiJL6eNJRXbu1D/+QoR7D505WdefwXXbGcGo9jsRhMHJgEwOxVe845u09ExNnUcuOB4iLOvdaNY2VijbcRF7JYDC7v1oLLu7Vg84F8yqw2etfQDXpdn1Y89+VP7DpczIqMIwzpoH2rRMT11HLjgc7XcnO+lYlFXKF7y4gagw1AWKAfv+zTCoDZK/c0YlUi0pQp3Hig01swVF9krdxqY/MBTQMX9zJxUDIA3+w4xJ4jxeYWIyJNgsKNB6oKN0eLSzmYVzmAuKi0gs0H8imtsBEe6Euy1hURN9EmOoThnWKw22HO6r1mlyMiTYDG3Hig5iH++PkYlFvtDHrmm7NeT0mMdAzqFHEHtw1uw5Idh3lvXRaTLu+o/cxExKXUcuOBLBaDG/olEuTnc9ZKsYYBv+gZb1JlIjUb0j6atjEhFJZW8OEP+80uR0S8nGFvYvMzCwoKiIiIID8/n/DwcLPLcQqrzU651UaZ1YZB5SBOEXczZ/UepizYQlLzYD757cX6fSoidVKXn99qufECPhaDQD8fwgP99AND3NbY3q2IDg1g79ET3DN3PaUVVrNLEhEvpXAjIo0iNMCXWbf1I8Tfh1W7jvLQO+mOBQBFRJxJ4UZEGk2PVhG8MqEv/j4WPtuUw18WbtbKxSLidAo3ItKoBreP5sUbemEY8MZ3+/jn1zvNLklEvIzCjYg0uqt6xvPE1d0AmP7VTt74TuvfiIjzKNyIiCluHZjM7y5tD8CfF2xmxc4jJlckIt5C4UZETPPQyI5c36cVdjs89elWDTAWEadQuBER0xiGwWNXdSEs0JftOYXM33DA7JJExAso3IiIqSKD/fn1sMruqRcW/0RJuda/EZGGUbgREdPdPjiZFuGBHMg7yVxtrikiDaRwIyKmC/TzYdLIjgD8Z0kG+SfLTa5IRDyZwo2IuIXr+rSiY1wo+SfLefnbXWaXIyIeTOFGRNyCj8Xgj6M6A/Daikyy80+aXJGIeCqFGxFxG5d1iaV/chSlFTamL9bKxSJSPwo3IuI2DMPgkdGVrTfvrc9iZ26hyRWJiCdSuBERt9InqRlXdGuBzQ53vL6WZz7fzve7j1JutZldmoh4CMNejy15s7KyMAyDVq1aAbBmzRrefPNNunbtyq9+9SunF+lMBQUFREREkJ+fT3h4uNnliEgNdh8uYux/V5F34vSsqbAAXy7uEM0V3VuQ1jMBi8UwsUIRaWx1+fldr5abm2++mSVLlgCQk5PDyJEjWbNmDY899hhPPPFEfd5SRMShbUwoS34/jOk39OLaXglEhfhTWFrB55tzeODtdP7+xQ6zSxQRN1avcLN582b69+8PwLvvvkv37t1ZtWoV8+bNY/bs2c6sT0SaqGYh/lyb2pLpN6ay9rERfPTrQdw9pA0AL3+7i9krM02uUETclW99LiovLycgIACAr776iquvvhqAzp07k52d7bzqRESonCae2roZqa2bERHkx3Nf/sTUT7YSGx7IlT3izS5PRNxMvVpuunXrxssvv8zy5ctZvHgxV1xxBQAHDx6kefPmTi1QRORM9w9vzy0XtcZuhwffSef73UfNLklE3Ey9Wm6effZZxowZwz/+8Q8mTpxISkoKAAsXLnR0V4mIuIJhGEy9ujuHCkr5cmsud89Zx/v3DaJjXBhWm50f9h1n8dZcvtqWS3FpBUM7xjCiSxxDOsQQ5O9jdvki0gjqNVsKwGq1UlBQQLNmzRzH9uzZQ3BwMLGxsU4r0Nk0W0rEO5SUWxk/83vW7z1OfEQgg9tH8832QxwrLqvx/ABfC0M6RDOyaxzXprYkwFdBR8STuHy21MmTJyktLXUEm7179zJ9+nR27NhRp2CzbNky0tLSSEhIwDAM5s+ff97zV6xYweDBg2nevDlBQUF07tyZF198sT5fQUQ8XKCfD69O7Eu7mBCy80t4f/1+jhWXER7oy7W9EvjPzam8cecAbhuUTMvIIEorbHy17RCPfLCJO2avpaxC6+aIeKt6dUtdc801jB07lnvvvZe8vDwGDBiAn58fR44c4YUXXuC+++6r1fsUFxeTkpLCHXfcwdixYy94fkhICL/5zW/o2bMnISEhrFixgnvuuYeQkBC3X19HRJwvMtifOXcO4KlPttIiIpCRXePolxyFn8/pf7dd3CGav6R1ZXtOIYu35vLyt7tYmXGURz74kRfGpWAYWi9HxNvUq1sqOjqab7/9lm7dujFz5kz+/e9/s2HDBj744AOmTJnCtm3b6l6IYfDRRx9x7bXX1um6sWPHEhISwty5c2t1vrqlRJq2pTsOcefr67Da7Nw3rB2PXNHZ7JJEpBZc3i114sQJwsLCAPjyyy8ZO3YsFouFiy66iL1799bnLetlw4YNrFq1iqFDh57znNLSUgoKCqo9RKTpGtYplmfG9gDgv0t3MWf1HnMLEhGnq1e4ad++PfPnzycrK4svvviCyy+/HIBDhw41SmtIq1atCAgIoG/fvtx///3cdddd5zx32rRpREREOB6JiYkur09E3Nv1fRN5+PKOAPxl4RYWbc4xuSIRcaZ6hZspU6bw8MMPk5ycTP/+/Rk4cCBQ2YqTmprq1AJrsnz5ctatW8fLL7/M9OnTeeutt8557uTJk8nPz3c8srKyXF6fiLi/+4e35+YBlevlPPD2BtbtOWZ2SSLiJPWeCp6Tk0N2djYpKSlYLJUZac2aNYSHh9O5c937sOs75uapp55i7ty57NhRu71mNOZGRKpUWG3c+8YPfLUtl8hgPz7+zcUkRgWbXZaI1MDlY24AWrRoQWpqKgcPHmT//v0A9O/fv17BpiFsNhulpaWN+pki4h18fSz8+6ZUeraKIO9EOb+e9wMl5VazyxKRBqpXuLHZbDzxxBNERESQlJREUlISkZGRPPnkk9hstV87oqioiPT0dNLT0wHIzMwkPT2dffv2AZVdShMmTHCcP2PGDD7++GN27tzJzp07efXVV3nuuee45ZZb6vM1REQI8vfhv7f0oVmwH5sO5PPXhVvMLklEGqhe69w89thjvPrqqzzzzDMMHjwYqFxg769//SslJSX87W9/q9X7rFu3juHDhzueT5o0CYCJEycye/ZssrOzHUEHKkPV5MmTyczMxNfXl3bt2vHss89yzz331OdriIgA0DIyiH/dlMqE19bw9tosUltHckO/1maXJSL1VK8xNwkJCbz88suO3cCrLFiwgF//+tccOHDAaQU6m8bciMi5zFiSwT++2IG/r4UP7h1Ej1YRZpckIqe4fMzNsWPHahxb07lzZ44d04wDEfFM9w1tx4gucZRV2Lj3jfUcP8c+VSLi3uoVblJSUvjPf/5z1vH//Oc/9OzZs8FFiYiYwWIxeH5cCknNgzmQd5IH3knHaqvXhFIRMVG9uqW+/fZbrrrqKlq3bu1Y42b16tVkZWXx2WefMWTIEKcX6izqlhKRC9mWXcCYl1ZSUm7j9sHJTPlFV+1BJWIyl3dLDR06lJ9++okxY8aQl5dHXl4eY8eOZcuWLbXe40lExF11iQ/n779MAWDWyj3MWJJhckUiniEnv4T73/zB9FmH9V7EryYbN26kd+/eWK3uu06EWm5EpLZeW5HJE59sBeBvY7ozfkCSyRWJuLfNB/L5xb9XEBsWwJrHRjj1vRtlET8REW93x8Vt+O2l7QF4fP5mPv0x2+SKRNxbUWkFAKGB9VppxmkUbkREzmPSyI6MP7UH1YPvbGD5zsNmlyTitoqrwk2Awo2IiNsyDIMnrunOVT3jKbfauWfuejbsO252WSJuqarlJsTf3HBTp08fO3bseV/Py8trSC0iIm7Jx2Lw4rheFJwsZ/nOI9wzdz1f/X4o4YF+Zpcm4lYc4caTWm4iIiLO+0hKSqq2F5SIiLfw97Xw8i19aBMdwqHCUp77YofZJYm4napuqTCTx9zU6dNnzZrlqjpERNxeSIAvf7u2OzfP/J653+1lTGpLUls3M7ssEbdRVFo5WzokwMfUOjTmRkSkDga1j2Zs75bY7TD5w02UW21mlyTiNoo9sVtKRETgsSu7EBnsx/acQmatzDS7HBG34ZgtZfKAYoUbEZE6ah4awJ+u7ALAi4t3knXshMkVibiHQrXciIh4ruv7tKJ/myhOlluZsmAzTlzsXcRjFWsRPxERz2UYBk+P6YGfj8GSHYf5fHOO2SWJmE6L+ImIeLj2saHcN6xye4a/LtxCQUm5yRWJmOv0bCmFGxERj/XrYe0ca9+8v26/2eWImOp0y42mgouIeKxAPx8mDqzcLXx++gGTqxExl2PjzABzV+9WuBERaaBfpCTgYzH4cX8+GYeKzC5HxDSnt19Qy42IiEeLDg1gaMcYABao9UaaqHKrjbKKykUtNaBYRMQLXJvaEoCPNhzAZtO0cGl6qsbbgAYUi4h4hZFd4ggN8GX/8ZOs33fc7HJEGl1hSWW48fe14OdjbrxQuBERcYIgfx+u6N4CgA9/UNeUND3FZad2BDe51QYUbkREnGbsqa6pT388SGmF1eRqRBqXu2yaCQo3IiJOM6Btc1qEB1JQUsGS7YfMLkekUbnLAn6gcCMi4jQ+FoNreiUAlQOLRZoSd1nADxRuREScakzvyq6pb7YfIu9EmcnViDSeohL32FcKFG5ERJyqc4twOrcIo9xq59NN2WaXI9JoijTmRkTEe4091XrzkWZNSRPiLjuCg8KNiIjTXZ3SEsOAdXuPs+/oCbPLEWkURWVquRER8VotIgIZ3C4a0MBiaTqqxtwo3IiIeKkxp9a8mbE0g1UZR0yuRsT1qrqltIifiIiXuqZXAiO7xlFWYeOuOetYv/c42KyQuRw2vV/5X5sW+hPv4U7r3JhfgYiIF/L1sfDvm1K5e846lu88wtxZ/6ZHyJv4F58xgyo8Aa54FrpebV6hIk5yeoVirXMjIuK1Av18eOXWvvymxVZesD+Pb/HPpoYXZMO7E2DrQnMKFHGiqr2lNFtKRMTLBfnCJOtrGEZNf+HaK/+z6FF1UYnH0yJ+IiJNxd5VWAoPYpzzBDsUHIC9qxqxKBHn0yJ+pyxbtoy0tDQSEhIwDIP58+ef9/wPP/yQkSNHEhMTQ3h4OAMHDuSLL75onGJFROqjKNe554m4KS3id0pxcTEpKSnMmDGjVucvW7aMkSNH8tlnn7F+/XqGDx9OWloaGzZscHGlIiL1FBrn3PNE3JDNZqe4TLOlABg9ejSjR4+u9fnTp0+v9vzpp59mwYIFfPzxx6Smpjq5OhERJ0gaVDkrqiAbxxibM9gAW2gCvkmDGr00EWepGkwMEBZofrjx6DE3NpuNwsJCoqKiznlOaWkpBQUF1R4iIo3G4lM53RvgZyNvbAB2+EvZLRwo0A7i4rmKT61x42MxCPA1P1qYX0EDPPfccxQVFTFu3LhznjNt2jQiIiIcj8TExEasUESEynVsxs2B8Phqh+2hCUwJfIR5Bb246ZXvOJh30qQCRRrGMZjY3wfDOPfw+cZifttRPb355ptMnTqVBQsWEBsbe87zJk+ezKRJkxzPCwoKFHBEpPF1vRo6X1U5K6ooF0Lj8EkaxP2FZSx/5Tv2Hj3BxNfW8P59g4gI8jO7WpE6cafBxOChLTdvv/02d911F++++y4jRow477kBAQGEh4dXe4iImMLiA22GQI9fVv7X4kN8RBBv3X0RLcID2XmoiPvn/UC51WZ2pSJ1UuxG08DBA8PNW2+9xe23385bb73FVVddZXY5IiINlhAZxKu39SXY34cVGUd4/KPN2O1nDz4WcVeFVS03bjCYGEwON0VFRaSnp5Oeng5AZmYm6enp7Nu3D6jsUpowYYLj/DfffJMJEybw/PPPM2DAAHJycsjJySE/P9+M8kVEnKZbQgT/uTkViwHvrMviv9/uMrskkVpTt9QZ1q1bR2pqqmMa96RJk0hNTWXKlCkAZGdnO4IOwCuvvEJFRQX3338/8fHxjscDDzxgSv0iIs50aec4/np1NwD+vmgHH288aHJFIrXj6Jbyd49wY2oVw4YNO2/T6+zZs6s9X7p0qWsLEhEx2YSByew5coLXVmby+/c2khAZSJ+kcy93IeIOikrdZwE/8MAxNyIi3u6xq7owsmscZRU27pm73vGvYhF3VVRaDrjHAn6gcCMi4nZ8LAb/vLEXyc2DOVJUxrvrsswuSeS8ih0tNz4mV1JJ4UZExA0F+/ty15C2ALy2MpMKTQ8XN+ZOO4KDwo2IiNu6rncrokL8yTp2ki+2aNdwcV+aLSUiIrUS5O/DLRclAfDK8t1a+0bcVpGbzZZSuBERcWMTBibh72thY1Ye6/YeN7sckRoVaRE/ERGprejQAK7r3RKAV5btNrkakZqpW0pEROrkzosrBxZ/tS2X3YeLTK5G5GzFWudGRETqon1sKJd1jsVuh1dXZJpdjshZHN1SmgouIiK1VTUt/P31+zlaVGpyNSKn2e32M7ql/EyuppLCjYiIB7iobRQ9WkZQWmHjje/2XfgCkUZSWmGjwlY5k0+L+ImISK0ZhsFdQ9oAMGf1HkrKrSZXJFKp6IztQTQVXERE6uTKHvG0jAziaHEZ763fb3Y5IsDpmVLB/j5YLIbJ1VRSuBER8RB+PhbuPtV686+vd2pDTXEL7rb1AijciIh4lJsHJNE6KpjDhaX8b7nWvRHzFZVUhpswhRsREakPf18Lf7yiE1C5qN+hwhKTK5KmrrhMLTciItJAV/WIJyUxkhNlVqZ/tdPscqSJK3Is4OceM6VA4UZExOMYhsFjV3YB4J21WWQcKjS5ImnK3G3rBVC4ERHxSP3bRDGyaxxWm51nPt9udjnShCnciIiI0zxyRWd8LAZfbTvEd7uPml2ONFGFJRpzIyIiTtI+NpQb+yUCMO2zbdhOrRIr0pjUciMiIk714IiOhPj7sHF/Pp9syja7HGmCNFtKREScKiYsgHuGtgPguS92YFXrjTSy07OlFG5ERMRJ7hrShmbBfuw7doIvtuSYXY40MUUl5YAW8RMREScK9vfllouSgMqF/ex2td5I4ylWy42IiLjChIHJ+PtYSM/KY/3e42aXI03I6b2ltIifiIg4UUxYAGNSWwKcd8+p7TkFDJr2NU98vLWxShMvVzWgWLOlRETE6e46tWP4l1tzyTxSfNbrJeVWHnw7nYP5Jby2MpPVu7Q2jjScYyp4oMKNiIg4WYe4MIZ3isFuh9dWZJ71+guLf2J7zumtGv68YDNlFbbGLFG8kGMRP3+FGxERcYG7h7QF4L31WRwvLnMc/273UUd31XPXp9A8xJ+MQ0W8WkMIEqmtCquN0lMBWd1SIiLiEgPbNadbQjgl5Tbe+G4vAAUl5fz+3Y3Y7XBD30R+2acVfzq18ea/vt7J/uMnzCxZPFjVTCnQbCkREXERwzAcrTevr95LSbmVqQu3ciDvJIlRQfw5rSsAY3u3pH+bKE6WW5mqwcVST0WnBhP7+1rw93WfSOE+lYiIiFNc1TOe+IhAjhSV8tA76Xzww34MA14Y18vRdWAYBk9d2x1fi8Hirbl8tTXX5KrFExWVuN9MKVC4ERHxOn4+Fm4fnAzA55srVyy+55J29EuOqnZex7gw7jw1w+qvH2/hZJkVkbpwxzVuQOFGRMQr3di/teNf013iw3loZIcaz3vgsg4kRASy//hJ/rNkZ2OWKF6gahq4O82UAoUbERGvFB7oxyNXdKJHywj+dWMvAnxr/pd1sL8vf7m6GwAvLd1F2r9XMO3zbSz76bBacuSCHGvcuFm3lHtVIyIiTnPrwGRuHZh8wfMu7xrHTf1b89aafWw6kM+mA/n837e78fex0DspknuGtmN4p1jXFywep8gNF/ADhRsRkSbPMAymje3BgyM6sGrXEVZmHGVVxhEO5pfw3e5jfLf7GBMGJvGnK7sQ6OdeYyvEXKfH3LhXnHCvakRExDRx4YGMSW3FmNRW2O12Mo8UM2f1Xmav2sOc1XtZteso/7yxF90SIswuVdyEo1tKY25OW7ZsGWlpaSQkJGAYBvPnzz/v+dnZ2dx888107NgRi8XCgw8+2Ch1iog0NYZh0DYmlL9e3Y3X7+hPTFgAGYeKuHbGSl5ZtgubzW52ieIGik4t4uduLTemhpvi4mJSUlKYMWNGrc4vLS0lJiaGxx9/nJSUFBdXJyIiAEM7xrDogSGM7BpHudXO059tZ8Jra8g7UXbhi8WrueOmmWByt9To0aMZPXp0rc9PTk7mn//8JwCvvfaaq8oSEZGfaR4awCu39uHttVk88fFWVmQcYcxLq3h1Yl/axoSaXZ6YxDGgWOvcNK7S0lIKCgqqPUREpO4Mw+Cm/q356P5BtIwMIvNIMWNeWsWqXUfMLk1M4q4Dir0+3EybNo2IiAjHIzEx0eySREQ8WucW4cy/fzCprSPJP1nOhFfX8PaafWaXJSZw13VuvD7cTJ48mfz8fMcjKyvL7JJERDxeTFgAb919EWkpCVTY7Dz64Sb+9ulW8k+Um12aNCJ3XaHYvapxgYCAAAICAswuQ0TE6wT6+fCvG3vRLiaE6V/t5H/LM/nf8kxaRwXTo2UE3VtG0KNlBH2Tm2l9HC+lRfxERMTrGIbBgyM60i4mlOe/3MGeoyfYd6zy8emmbAA6twhj/v2DFXC8UJGbdkuZWk1RUREZGRmO55mZmaSnpxMVFUXr1q2ZPHkyBw4cYM6cOY5z0tPTHdcePnyY9PR0/P396dq1a2OXLyIip6SlJJCWkkDeiTI2Hyhg04F8Nh/IZ9nOw2zPKeRfX+/kj1d0NrtMcbJiN13nxtRq1q1bx/Dhwx3PJ02aBMDEiROZPXs22dnZ7NtXfZBaamqq49fr16/nzTffJCkpiT179jRKzSIicm6Rwf5c3CGaiztEA/DFlhzumbueV5bt5ppeLenUIszkCsVZ7HY7xWVVs6Xcq1XO1HAzbNgw7PZzr3I5e/bss46d73wREXEvo7q14PKucXy5NZc/fbSJ9+4ZiMVimF2WOMGJMitVP5LDAvzMLeZnvH62lIiImOuvV3cjxN+H9XuP86amjHuNqplSFgMC/dwrTrhXNSIi4nUSIoN4eFQnAJ5dtJ1DBSUmVyTOUHjGAn6G4V6tcQo3IiLichMGJtOzVQSFJRVM/WSr2eWIE7jrAn6gcCMiIo3Ax2Lw9Jge+FgMPv0xmyXbD5ldkjSQu269AAo3IiLSSLq3jOCOwckAPD5/MydOzbQRz1Q1DVwtNyIi0qQ9OKIjLSODOJB3knnfaXCxJysqrdxqQ+FGRESatJAAX357aXsA5ny3B6tNy3t4qiLHAn7utcYNKNyIiEgju6ZXSyKC/Mg6dlJjbzxYscbciIiIVAry9+HGfokAvL56j7nFSL1VhZswhRsRERG45aIkDAOW7zxCxqEis8uRetBsKRERkTMkRgVzWec4AOaq9cbj2O12thwsACAiyL22XgCFGxERMcltg5IBeH/9fgpLys0tRurks005rMk8hr+vhdHd480u5ywKNyIiYorB7ZvTPjaU4jIrH6zfX+M5hwtLWbfnGDbNqnIbRaUVPHlqlelfD2tH6+bBJld0NoUbERExhWEYTByYBMCc1XvPCjBr9xzj8he/5Zcvr2bEC9/y+qo9jkGsYp5/f72TnIISWkcFc+/QdmaXUyOFGxERMc2Y3q0IDfBl95FiVmQccRz/aMN+xv/ve46fqOyu2n2kmL8s3MJFT3/Nk59sZd/RE2aV3KTtzC3k1RWZAPz16q4E+rnfGjegcCMiIiYKDfDll31aAfD6qj3Y7XZe+HIHD72zkTKrjVHd4lj3+AieuKYbbaNDKCyt4NUVmQx9bonjh6w0Drvdzp8XbKbCZmdk1zguPTUg3B0Zdru9SXVkFhQUEBERQX5+PuHh4WaXIyLS5O0+XMSlz3+LYcDwTrF8c2phv3uHtuOPozphsRgA2Gx2vt15mNdWZLJ85xH8fAw+f+AS2seGmll+k7Eg/QAPvJ1OoJ+FxQ8NJTGqccfa1OXnt1puRETEVG1jQhnaMQa7Hb7Zfghfi8Hfr+vJo6M7O4INgMViMLxTLHPu6M+lnWMpt9r500ebaGL/RjdFYUk5f/t0GwC/Gd6+0YNNXSnciIiI6e64uA1QuWbKnDv7M+7UCsY1MQyDqVd3I8jPhzWZx3jvHDOtxHmmf7WTQ4WlJDcP5u5L2ppdzgUp3IiIiOmGdozhzbsGsOjBIQxqF33B8xOjgnloZAcAnv5sG0eLSl1dYpP12orMMwYRdyPA1z0HEZ9J4UZERNzCoPbRxEcE1fr82we3oUt8OHknyvnbZ9tcWFnTZLfb+fui7Txxak2bu4e0YVinWJOrqh2FGxER8Uh+Phamje2BYcCHPxxg1RlTyaVhKqw2Hv1gEy8t3QXAH0Z14k9XdjG5qtpTuBEREY/VKzGSCRdVLgT42PzNlJRbTa7I85WUW7lv3g+8sy4LiwHPjO3B/cPbYxjGhS92Ewo3IiLi0X4/qhNx4QFkHil2tDRI/RSWlDPhtTUs3pqLv6+F/97Shxv7tza7rDpTuBEREY8WHujHX9O6AfDfpRms33vc5Io817+/yWBN5jHCAnyZc0d/RnVrYXZJ9aJwIyIiHu+K7i24qkc85VY7972xntyCErNL8jhFpRW89f0+AF64oRcXtW1uckX1p3AjIiIezzAM/v7LnnSKC+NQYSn3vrGe0gqNv6mL99dlUVhaQdvoEC7r7Bmzos5F4UZERLxCSIAvr0zoQ0SQHxv25fHn+Zu1enEtWW12Zq3aA8Dtg5OrrQztiRRuRETEayQ1D+HfN6ViMeDddfuZ+91es0vyCN9sP8TeoycID/TlulMbmXoyhRsREfEql3SM4dHRnQF44uOtfLf7qMkVub9XV+wG4KYBrQn29zW5moZTuBEREa9z95C2XNMrgQqbnfvn/cCBvJNml+S2thzM57vdx/CxGEwcmGx2OU6hcCMiIl7HMAyeGduTbgnhHC0u4w/vbdT4m3OYtXIPAKO7tyAhsvbbX7gzhRsREfFKQf4+vDS+N4F+FlbtOsp767R7+M8dKixhYfpBAO48tTO7N1C4ERERr5XUPIRJIzsC8NSnWzlUqPVvzjTvu32UWW2kto4ktXUzs8txGoUbERHxancMbkOPlhEUlFQwdeFWs8txGyXlVt44NZvMm1ptQOFGRES8nK+PhWeu64GPxeDTTdl8uSXH7JLcwsKNBzlaXEZCRCBXeOg2C+eicCMiIl6vW0IEv7qkLQB/XrCZgpJykysy3+xTA4knDkrG18e74oB3fRsREZFzeOCyDrSJDiG3oJRnP99udjmm2n24iK3ZBfhaDG7ol2h2OU6ncCMiIk1CoJ8P08b2AGDe9/v4vgkv7rfoVNfcoPbRRAb7m1yN85kabpYtW0ZaWhoJCQkYhsH8+fMveM3SpUvp3bs3AQEBtG/fntmzZ7u8ThER8Q4XtW3OTf0rWyruf3MDb6/ZR4XVZnJVje+LzZXhxtvG2lQxNdwUFxeTkpLCjBkzanV+ZmYmV111FcOHDyc9PZ0HH3yQu+66iy+++MLFlYqIiLd4dHQX2saEcKSolEc/3MTl05fx2absJrPI34G8k2zcn49hwOXd4swuxyUMu5v83zQMg48++ohrr732nOc88sgjfPrpp2zevNlx7MYbbyQvL49FixbV6nMKCgqIiIggPz+f8PDwhpYtIiIeqKTcyrzv9zFjSQbHissA6NEyggcu60DbmBBCA30JDfAlyM8Hw/DsHbJ/7rUVmTzxyVb6t4ni3XsGml1OrdXl57dH7Y61evVqRowYUe3YqFGjePDBB895TWlpKaWlpY7nBQUFripPREQ8RKCfD3de3IZxfVsxc3kmM5fvZtOBfO6as67aeRYDQgN8mTAwmYdHdTKpWueqGm/jrV1S4GEDinNycoiLq96EFhcXR0FBASdP1rwp2rRp04iIiHA8EhO9b1S4iIjUT1igHw+N7MiyPw7njsFtSIgIJDTAl6rGGpsdCkoq+M+SDD78wT22bygoKWfGkgwGP/MNo15cxj+/2smuw0W1uvZwYSlr9xwDYFR37w03HtVyUx+TJ09m0qRJjucFBQUKOCIiUk3z0ACmpHVlSlpXAGw2OyfLrRSXVjBn9V7+sySDP8/fTO/WzUiODjGlxqNFpby2MpM5q/ZSWFrhOL4jt5AXv/qJrvHh/CIlnrSeCSRGBdf4Hou35mK3Q0qrCFp6ySaZNfGocNOiRQtyc3OrHcvNzSU8PJygoJr/JwUEBBAQENAY5YmIiJewWAxCAnwJCfDloZEdWbPnGGsyj/HA2xt4795B+Ps2XsfH/uMneG3FHt5as4+T5VYAOsSGct+wdlhtdj75MZuVGUfYml3A1uwCpi/eyezb+zGoffRZ71XVJeXNrTbgYeFm4MCBfPbZZ9WOLV68mIEDPWdAlIiIeBYfi8H0G3ox+p/L2bg/n+cX72Dy6C4u/Uybzc7yjCPMXb2Hb7YfwnZq6k+PlhHcP7w9l3eNw2Kp7Du7vm8ix4vLWLQlh3fWZpGelcfD721k0UOXEB7o53jP/BPlrMo4Anj3eBswecxNUVER6enppKenA5VTvdPT09m3bx9Q2aU0YcIEx/n33nsvu3fv5o9//CPbt2/npZde4t133+Whhx4yo3wREWkiEiKDePa6ngD837e7Wb7zsEs+J/9EOTOX7+bS55cy8bU1fLWtMtgMbt+cOXf0Z+FvBnNF9xaOYFOlWYg/N/VvzZt3DyCpeTAH80vO2iT06+25VNjsdIoLo21MqEvqdxemhpt169aRmppKamoqAJMmTSI1NZUpU6YAkJ2d7Qg6AG3atOHTTz9l8eLFpKSk8PzzzzNz5kxGjRplSv0iItJ0XNG9BeMHtAZg0rsbOVJUeoEr6mbPkWIue+Fbnvp0G3uOniAswJfbBiXz1aShzLvrIi7pGHPBaenB/r48f30KhgEf/LC/2iahizY3jS4pcKN1bhqL1rkREZH6Kim3cvV/VvBTbhHDOsXw2sR+Z7Wi1Mfx4jLG/ncVmUeKSW4ezK8uace1qQkE+9dv9Mi0z7fxf9/uJjrUny8evIRAPx96P7mY0gobn/1uCF0TPO/nX11+fnvUVHAREREzBfr58O+behPga2HpjsOOAboNUVJu5Vdz15F5pJiWkUG8d+8gbh7Qut7BBmDSyI50igvjSFEZj320maU7DlNaYaN1VDBd4sMaXLO7U7gRERGpg04twrjnkrYAvPztrgtu2zD14y2Mn/mdYzDvmWw2O394/0fW7jlOWKAvs2/vR0xYw2f4Bvj68Py4FHwtBou25PD0Z9sAGN29hdetuFwThRsREZE6mjgomUA/Cz/uz2f1eXYXX77zMLNW7mFlxlFunvk9d72+loxDpxfce37xDj7eeBBfi8HLt/ShQ5zzWlW6n9pOAir3k4KmMd4GFG5ERETqrHloAOP6Vi4I+/K3u2s8x2az8+yi7QB0bhGGj8Xgq22HGDV9GVMWbGbm8t3MWLILgGljezC4hnVpGuq+Ye1ISYwEIC48gF6tIp3+Ge5I4UZERKQe7h7SFosBy346zJaD+We9/ummbDYfKCA0wJd5dw3gy4cuYUSXOKw2O3NW7+WpTyu7in53aXuu7+ualfN9fSxMv6EX/ZOj+P3lnZwy+NkTKNyIiIjUQ2JUMFf1TAAq1745U7nVxvNf7gAqQ1Dz0ADaxYQyc2Jf3rx7AN1OzVYak9qSh0Z2dGmdbaJDePfegY6WpqZA4UZERKSeqgYWf/LjQbKOnXAcf3ttFnuOniA61J+7hrSpds2gdtF8/JuL+fr3Q3lhXEqTGODb2BRuRERE6ql7ywiGdIjGZoeZyytbb06UVfCvr3cC8NtLOxAScPaUbovFoF1MqIKNiyjciIiINMB9Q9sB8M66LI4WlTJr5R4OF5aSGBXETf1bm1xd06RwIyIi0gAD2zWnZ6sISspt/PPrnby8tHIG1O9HdmrU3cPlNN11ERGRBjAMg3suqWy9mbN6L4WlFXSJD+fqlASTK2u6FG5EREQa6IruLUhuHux4/scrms60a3ekcCMiItJAPhaD+4ZVtt4MbNucYR1jTK6oaav/rlwiIiLiMK5vIonNguneKkKzoEymcCMiIuIEhmEwyAVbKEjdqVtKREREvIrCjYiIiHgVhRsRERHxKgo3IiIi4lUUbkRERMSrKNyIiIiIV1G4EREREa+icCMiIiJeReFGREREvIrCjYiIiHgVhRsRERHxKgo3IiIi4lUUbkRERMSrNLldwe12OwAFBQUmVyIiIiK1VfVzu+rn+Pk0uXBTWFgIQGJiosmViIiISF0VFhYSERFx3nMMe20ikBex2WwcPHiQsLAwDMOgX79+rF27tto5Pz9W2+cFBQUkJiaSlZVFeHi4U+qtqb6Gnn+uc2p7XPdD9+N8x3U/an8/zvy1J9yP871em79LazpW0/1wxb24UP31OV/3o/avO+N+9O3bl2+++YaEhAQslvOPqmlyLTcWi4VWrVo5nvv4+Jz1m+Xnx+r6PDw83Gm/AWuqr6Hnn+uc2h7X/dD9ON9x3Y/af/+a3sOd78f5Xq/N36U1HTvf/XDmvThXPQ05X/ej9q874374+vpW+/l9Pk1+QPH9999/wWN1fe5MdX3v2px/rnNqe1z3Q/fjfMd1P2r//V15L+rz/hc6/3yv1+bv0pqO6X7ofpzvWH3vR5PrlnKlgoICIiIiyM/Pd2q69lS6H9XpflSn+1Gd7sdpuhfV6X7UXZNvuXGmgIAA/vKXvxAQEGB2KW5B96M63Y/qdD+q0/04TfeiOt2PulPLjYiIiHgVtdyIiIiIV1G4EREREa+icCMiIiJeReFGREREvIrCjYiIiHgVhRsT7Nixg169ejkeQUFBzJ8/3+yyTJWZmcnw4cPp2rUrPXr0oLi42OySTJWcnEzPnj3p1asXw4cPN7sct3DixAmSkpJ4+OGHzS7FVHl5efTt25devXrRvXt3/ve//5ldkqmysrIYNmwYXbt2pWfPnrz33ntml2S6MWPG0KxZM375y1+aXYppNBXcZEVFRSQnJ7N3715CQkLMLsc0Q4cO5amnnmLIkCEcO3aM8PBwfH2b3O4gDsnJyWzevJnQ0FCzS3Ebjz32GBkZGSQmJvLcc8+ZXY5prFYrpaWlBAcHU1xcTPfu3Vm3bh3Nmzc3uzRTZGdnk5ubS69evcjJyaFPnz789NNPTfrv06VLl1JYWMjrr7/O+++/b3Y5plDLjckWLlzIZZdd1qT/IG7ZsgU/Pz+GDBkCQFRUVJMONnK2nTt3sn37dkaPHm12Kabz8fEhODgYgNLSUux2O03536jx8fH06tULgBYtWhAdHc2xY8fMLcpkw4YNIywszOwyTKVwU4Nly5aRlpZGQkIChmHU2GU0Y8YMkpOTCQwMZMCAAaxZs6Zen/Xuu+9yww03NLBi13L1/di5cyehoaGkpaXRu3dvnn76aSdW73yN8fvDMAyGDh1Kv379mDdvnpMqd43GuB8PP/ww06ZNc1LFrtUY9yMvL4+UlBRatWrFH/7wB6Kjo51UvfM15t+n69evx2q1kpiY2MCqXacx70dTpnBTg+LiYlJSUpgxY0aNr7/zzjtMmjSJv/zlL/zwww+kpKQwatQoDh065Dinqj/854+DBw86zikoKGDVqlVceeWVLv9ODeHq+1FRUcHy5ct56aWXWL16NYsXL2bx4sWN9fXqrDF+f6xYsYL169ezcOFCnn76aX788cdG+W714er7sWDBAjp27EjHjh0b6ys1SGP8/oiMjGTjxo1kZmby5ptvkpub2yjfrT4a6+/TY8eOMWHCBF555RWXf6eGaKz70eTZ5bwA+0cffVTtWP/+/e3333+/47nVarUnJCTYp02bVqf3njNnjn38+PHOKLPRuOJ+rFq1yn755Zc7nv/973+3//3vf3dKva7myt8fVR5++GH7rFmzGlBl43HF/Xj00UftrVq1siclJdmbN29uDw8Pt0+dOtWZZbtMY/z+uO++++zvvfdeQ8psNK66HyUlJfYhQ4bY58yZ46xSG4Urf38sWbLEft111zmjTI+klps6KisrY/369YwYMcJxzGKxMGLECFavXl2n9/KELqkLccb96NevH4cOHeL48ePYbDaWLVtGly5dXFWySznjfhQXF1NYWAhUDjj/5ptv6Natm0vqdTVn3I9p06aRlZXFnj17eO6557j77ruZMmWKq0p2KWfcj9zcXMfvj/z8fJYtW0anTp1cUq+rOeN+2O12brvtNi699FJuvfVWV5XaKJz586WpU7ipoyNHjmC1WomLi6t2PC4ujpycnFq/T35+PmvWrGHUqFHOLrFROeN++Pr68vTTT3PJJZfQs2dPOnTowC9+8QtXlOtyzrgfubm5XHzxxaSkpHDRRRcxYcIE+vXr54pyXc5Zf168hTPux969exkyZAgpKSkMGTKE3/72t/To0cMV5bqcM+7HypUreeedd5g/f75jeY1Nmza5olyXc9aflxEjRnD99dfz2Wef0apVqyYZjDQlxSQRERFu3U/e2EaPHq2ZMKe0bduWjRs3ml2GW7rtttvMLsF0/fv3Jz093ewy3MbFF1+MzWYzuwy38tVXX5ldgunUclNH0dHR+Pj4nBVMcnNzadGihUlVmUf3ozrdj+p0P6rT/ahO96M63Q/nUbipI39/f/r06cPXX3/tOGaz2fj6668ZOHCgiZWZQ/ejOt2P6nQ/qtP9qE73ozrdD+dRt1QNioqKyMjIcDzPzMwkPT2dqKgoWrduzaRJk5g4cSJ9+/alf//+TJ8+neLiYm6//XYTq3Yd3Y/qdD+q0/2oTvejOt2P6nQ/GonZ07Xc0ZIlS+zAWY+JEyc6zvn3v/9tb926td3f39/ev39/+3fffWdewS6m+1Gd7kd1uh/V6X5Up/tRne5H49DeUiIiIuJVNOZGREREvIrCjYiIiHgVhRsRERHxKgo3IiIi4lUUbkRERMSrKNyIiIiIV1G4EREREa+icCMiIiJeReFGRDxKcnIy06dPN7sMEXFjCjcicpbbbruNa6+91uwyarR27Vp+9atfufxzkpOTMQwDwzAIDg6mR48ezJw5s87vYxgG8+fPd36BInJOCjci4hbKy8trdV5MTAzBwcEurqbSE088QXZ2Nps3b+aWW27h7rvv5vPPP2+UzxaR+lO4EZE627x5M6NHjyY0NJS4uDhuvfVWjhw54nh90aJFXHzxxURGRtK8eXN+8YtfsGvXLsfre/bswTAM3nnnHYYOHUpgYCDz5s1ztBg999xzxMfH07x5c+6///5qwefn3VKGYTBz5kzGjBlDcHAwHTp0YOHChdXqXbhwIR06dCAwMJDhw4fz+uuvYxgGeXl55/2eYWFhtGjRgrZt2/LII48QFRXF4sWLHa+vXbuWkSNHEh0dTUREBEOHDuWHH36oVivAmDFjMAzD8RxgwYIF9O7dm8DAQNq2bcvUqVOpqKioze0XkQtQuBGROsnLy+PSSy8lNTWVdevWsWjRInJzcxk3bpzjnOLiYiZNmsS6dev4+uuvsVgsjBkzBpvNVu29Hn30UR544AG2bdvGqFGjAFiyZAm7du1iyZIlvP7668yePZvZs2eft6apU6cybtw4fvzxR6688krGjx/PsWPHAMjMzOSXv/wl1157LRs3buSee+7hscceq9N3ttlsfPDBBxw/fhx/f3/H8cLCQiZOnMiKFSv47rvv6NChA1deeSWFhYVAZfgBmDVrFtnZ2Y7ny5cvZ8KECTzwwANs3bqV//u//2P27Nn87W9/q1NdInIOZm9LLiLuZ+LEifZrrrmmxteefPJJ++WXX17tWFZWlh2w79ixo8ZrDh8+bAfsmzZtstvtdntmZqYdsE+fPv2sz01KSrJXVFQ4jl1//fX2G264wfE8KSnJ/uKLLzqeA/bHH3/c8byoqMgO2D///HO73W63P/LII/bu3btX+5zHHnvMDtiPHz9e8w049Tn+/v72kJAQu6+vrx2wR0VF2Xfu3HnOa6xWqz0sLMz+8ccfV6vvo48+qnbeZZddZn/66aerHZs7d649Pj7+nO8tIrWnlhsRqZONGzeyZMkSQkNDHY/OnTsDOLqedu7cyU033UTbtm0JDw93dMfs27ev2nv17dv3rPfv1q0bPj4+jufx8fEcOnTovDX17NnT8euQkBDCw8Md1+zYsYN+/fpVO79///61+q5/+MMfSE9P55tvvmHAgAG8+OKLtG/f3vF6bm4ud999Nx06dCAiIoLw8HCKiorO+p4/t3HjRp544olq9/Duu+8mOzubEydO1Ko2ETk3X7MLEBHPUlRURFpaGs8+++xZr8XHxwOQlpZGUlIS//vf/0hISMBms9G9e3fKysqqnR8SEnLWe/j5+VV7bhjGWd1ZzrimNqKjo2nfvj3t27fnvffeo0ePHvTt25euXbsCMHHiRI4ePco///lPkpKSCAgIYODAgWd9z58rKipi6tSpjB079qzXAgMDG1y3SFOncCMiddK7d28++OADkpOT8fU9+6+Qo0ePsmPHDv73v/8xZMgQAFasWNHYZTp06tSJzz77rNqxqrEvdZGYmMgNN9zA5MmTWbBgAQArV67kpZde4sorrwQgKyur2sBqqAxeVqu12rHevXuzY8eOaq1AIuI86pYSkRrl5+eTnp5e7ZGVlcX999/PsWPHuOmmm1i7di27du3iiy++4Pbbb8dqtdKsWTOaN2/OK6+8QkZGBt988w2TJk0y7Xvcc889bN++nUceeYSffvqJd9991zFA2TCMOr3XAw88wMcff8y6desA6NChA3PnzmXbtm18//33jB8/nqCgoGrXJCcn8/XXX5OTk8Px48cBmDJlCnPmzGHq1Kls2bKFbdu28fbbb/P44483/AuLiMKNiNRs6dKlpKamVntMnTqVhIQEVq5cidVq5fLLL6dHjx48+OCDREZGYrFYsFgsvP3226xfv57u3bvz0EMP8Y9//MO079GmTRvef/99PvzwQ3r27Ml///tfx2ypgICAOr1X165dufzyy5kyZQoAr776KsePH6d3797ceuut/O53vyM2NrbaNc8//zyLFy8mMTGR1NRUAEaNGsUnn3zCl19+Sb9+/bjooot48cUXSUpKcsI3FhHDbrfbzS5CRKQx/e1vf+Pll18mKyvL7FJExAU05kZEvN5LL71Ev379aN68OStXruQf//gHv/nNb8wuS0RcROFGRLzezp07eeqppzh27BitW7fm97//PZMnTza7LBFxEXVLiYiIiFfRgGIRERHxKgo3IiIi4lUUbkRERMSrKNyIiIiIV1G4EREREa+icCMiIiJeReFGREREvIrCjYiIiHgVhRsRERHxKv8PNotgQkGFXRcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "lI60jgqyWnAM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        },
        "outputId": "5badcd72-14bd-4c7c-a4b0-827b255863bc"
      },
      "cell_type": "code",
      "source": [
        "learner.fit_one_cycle(15, lr=slice(1e-2))\n"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "Learner.fit_one_cycle() got an unexpected keyword argument 'lr'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-a2064e083276>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_one_cycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: Learner.fit_one_cycle() got an unexpected keyword argument 'lr'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "EU_Qjg4AWnAP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "11170ea8-e08a-49c6-a7b0-c68a45383c3c"
      },
      "cell_type": "code",
      "source": [
        "learner.save('/kaggle/working/model_1')"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "Parent directory /kaggle/working does not exist.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-50-e5b643ff5c36>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlearner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/kaggle/working/model_1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, file, **kwargs)\u001b[0m\n\u001b[1;32m    408\u001b[0m     \u001b[0;34m\"Save model and optimizer state (if `with_opt`) to `self.path/self.model_dir/file`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m     \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjoin_path_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'.pth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 410\u001b[0;31m     \u001b[0msave_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'opt'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    411\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/fastai/learner.py\u001b[0m in \u001b[0;36msave_model\u001b[0;34m(file, model, opt, with_opt, pickle_protocol, **torch_save_kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mwith_opt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'model'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'opt'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpickle_protocol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtorch_save_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;31m# %% ../nbs/13a_learner.ipynb 17\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m    847\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    848\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_use_new_zipfile_serialization\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 849\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    850\u001b[0m             _save(\n\u001b[1;32m    851\u001b[0m                 \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_open_zipfile_writer\u001b[0;34m(name_or_buffer)\u001b[0m\n\u001b[1;32m    714\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m         \u001b[0mcontainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_open_zipfile_writer_buffer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcontainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname_or_buffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m    685\u001b[0m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPyTorchFileWriter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_stream\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    686\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 687\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPyTorchFileWriter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    689\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: Parent directory /kaggle/working does not exist."
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "D1_eqZ-SWnAR"
      },
      "cell_type": "code",
      "source": [
        "learner.unfreeze()"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "72de8zuLWnAT",
        "outputId": "097e6b11-2997-4afe-f3fc-5d98cb756ece",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 97
        }
      },
      "cell_type": "code",
      "source": [
        "learner.lr_find()\n",
        "learner.recorder.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
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    {
      "metadata": {
        "trusted": true,
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      },
      "cell_type": "code",
      "source": [
        "learner.load('/kaggle/working/model_1')"
      ],
      "execution_count": null,
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    {
      "metadata": {
        "trusted": true,
        "id": "Jue7bO86WnAb"
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      "cell_type": "code",
      "source": [
        "learner.fit_one_cycle(15, max_lr=slice(1e-05))"
      ],
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    },
    {
      "metadata": {
        "trusted": true,
        "id": "omJ87sb5TFXq"
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      "cell_type": "code",
      "source": [
        "learner.save('/kaggle/working/stage_2_1')"
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    {
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        "learner.fit_one_cycle(15, max_lr=slice(1e-06))"
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    {
      "metadata": {
        "trusted": true,
        "id": "CaIFmN5XWnAp"
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      "cell_type": "code",
      "source": [
        "learner.save('/kaggle/working/stage_2_2')"
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      "execution_count": null,
      "outputs": []
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    {
      "metadata": {
        "trusted": true,
        "id": "VxZ7gFhhTFXs"
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      "cell_type": "code",
      "source": [
        "learner.fit_one_cycle(15, max_lr=slice(1e-04))"
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      "execution_count": null,
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    {
      "metadata": {
        "trusted": true,
        "id": "Tz5dmYwUTFXs"
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      "cell_type": "code",
      "source": [
        "learner.save('/kaggle/working/stage_2_3')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "tXJKGSu6WnAr"
      },
      "cell_type": "code",
      "source": [
        "learner.recorder.plot_losses()"
      ],
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      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "_U5TKtckWnAt"
      },
      "cell_type": "code",
      "source": [
        "interp = ClassificationInterpretation.from_learner(learner)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "id": "eV4V2svjWnAv"
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      "cell_type": "code",
      "source": [
        "interp.plot_top_losses(12, figsize=(10,10))"
      ],
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    {
      "metadata": {
        "trusted": true,
        "id": "6KC2gsMAWnAy"
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      "cell_type": "code",
      "source": [
        "interp.plot_confusion_matrix(figsize=(8,8), dpi=60)"
      ],
      "execution_count": null,
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      "metadata": {
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