vgg19_final.ipynb

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  "metadata": {
    "colab": {
      "name": "vgg19_final_final_final.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kellykhy/DL2022-1/blob/main/vgg19_final_final_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install -U git+https://github.com/albu/albumentations --no-cache-dir\n",
        "! pip install tensorflow_addons\n",
        "! pip uninstall opencv-contrib-python"
      ],
      "metadata": {
        "id": "QYUUBz693wZP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9adb409-b580-4a26-9385-079af54f85f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/albu/albumentations\n",
            "  Cloning https://github.com/albu/albumentations to /tmp/pip-req-build-tel4q_om\n",
            "  Running command git clone -q https://github.com/albu/albumentations /tmp/pip-req-build-tel4q_om\n",
            "Requirement already satisfied: numpy>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==1.2.0) (1.21.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from albumentations==1.2.0) (1.4.1)\n",
            "Requirement already satisfied: scikit-image<0.19,>=0.16.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==1.2.0) (0.18.3)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from albumentations==1.2.0) (3.13)\n",
            "Collecting qudida>=0.0.4\n",
            "  Downloading qudida-0.0.4-py3-none-any.whl (3.5 kB)\n",
            "Requirement already satisfied: opencv-python>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from albumentations==1.2.0) (4.1.2.30)\n",
            "Requirement already satisfied: scikit-learn>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from qudida>=0.0.4->albumentations==1.2.0) (1.0.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from qudida>=0.0.4->albumentations==1.2.0) (4.2.0)\n",
            "Collecting opencv-python-headless>=4.0.1\n",
            "  Downloading opencv_python_headless-4.6.0.66-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (48.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 48.3 MB 20.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image<0.19,>=0.16.1->albumentations==1.2.0) (2021.11.2)\n",
            "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image<0.19,>=0.16.1->albumentations==1.2.0) (7.1.2)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image<0.19,>=0.16.1->albumentations==1.2.0) (2.6.3)\n",
            "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image<0.19,>=0.16.1->albumentations==1.2.0) (2.4.1)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image<0.19,>=0.16.1->albumentations==1.2.0) (1.3.0)\n",
            "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image<0.19,>=0.16.1->albumentations==1.2.0) (3.2.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image<0.19,>=0.16.1->albumentations==1.2.0) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image<0.19,>=0.16.1->albumentations==1.2.0) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image<0.19,>=0.16.1->albumentations==1.2.0) (1.4.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image<0.19,>=0.16.1->albumentations==1.2.0) (3.0.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib!=3.0.0,>=2.0.0->scikit-image<0.19,>=0.16.1->albumentations==1.2.0) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations==1.2.0) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations==1.2.0) (1.1.0)\n",
            "Building wheels for collected packages: albumentations\n",
            "  Building wheel for albumentations (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for albumentations: filename=albumentations-1.2.0-py3-none-any.whl size=113474 sha256=12c5e094cc93564c949275fd09255c37fa2d8909c5902690f8434181f283fe01\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-c5_b_pz9/wheels/63/11/1a/c77caf3ae9b9b6d57b3ee5e6a41a50f3bc12c66a70f6b90bf0\n",
            "Successfully built albumentations\n",
            "Installing collected packages: opencv-python-headless, qudida, albumentations\n",
            "  Attempting uninstall: albumentations\n",
            "    Found existing installation: albumentations 0.1.12\n",
            "    Uninstalling albumentations-0.1.12:\n",
            "      Successfully uninstalled albumentations-0.1.12\n",
            "Successfully installed albumentations-1.2.0 opencv-python-headless-4.6.0.66 qudida-0.0.4\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow_addons\n",
            "  Downloading tensorflow_addons-0.17.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 32.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (2.7.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->tensorflow_addons) (3.0.9)\n",
            "Installing collected packages: tensorflow-addons\n",
            "Successfully installed tensorflow-addons-0.17.1\n",
            "Found existing installation: opencv-contrib-python 4.1.2.30\n",
            "Uninstalling opencv-contrib-python-4.1.2.30:\n",
            "  Would remove:\n",
            "    /usr/local/lib/python3.7/dist-packages/cv2/*\n",
            "    /usr/local/lib/python3.7/dist-packages/opencv_contrib_python-4.1.2.30.dist-info/*\n",
            "  Would not remove (might be manually added):\n",
            "    /usr/local/lib/python3.7/dist-packages/cv2/config-3.py\n",
            "    /usr/local/lib/python3.7/dist-packages/cv2/config.py\n",
            "    /usr/local/lib/python3.7/dist-packages/cv2/cv2.abi3.so\n",
            "    /usr/local/lib/python3.7/dist-packages/cv2/gapi/__init__.py\n",
            "    /usr/local/lib/python3.7/dist-packages/cv2/load_config_py2.py\n",
            "    /usr/local/lib/python3.7/dist-packages/cv2/load_config_py3.py\n",
            "    /usr/local/lib/python3.7/dist-packages/cv2/mat_wrapper/__init__.py\n",
            "    /usr/local/lib/python3.7/dist-packages/cv2/misc/__init__.py\n",
            "    /usr/local/lib/python3.7/dist-packages/cv2/misc/version.py\n",
            "    /usr/local/lib/python3.7/dist-packages/cv2/utils/__init__.py\n",
            "    /usr/local/lib/python3.7/dist-packages/cv2/version.py\n",
            "Proceed (y/n)? y\n",
            "  Successfully uninstalled opencv-contrib-python-4.1.2.30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install opencv-contrib-python"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Z2L0z_OnUPj",
        "outputId": "2aa65ad5-3edc-43de-ce9a-50c356561cb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting opencv-contrib-python\n",
            "  Downloading opencv_contrib_python-4.6.0.66-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (67.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 67.1 MB 38 kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from opencv-contrib-python) (1.21.6)\n",
            "Installing collected packages: opencv-contrib-python\n",
            "Successfully installed opencv-contrib-python-4.6.0.66\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 런타임 재시작"
      ],
      "metadata": {
        "id": "QuGdml8MY-jO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gV4IdlxK1SDF",
        "outputId": "2ab83fe2-70aa-4e8c-b303-4faf093d4cc0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/drive/MyDrive/DeepLearning"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jYqoZfWnkEVg",
        "outputId": "42dc5d7c-6f5e-47f3-8074-6ea1d2f81dcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/DeepLearning\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dpath = '/content/drive/MyDrive/DeepLearning/'"
      ],
      "metadata": {
        "id": "KmZtimRI24XV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ! unzip -qq /content/drive/MyDrive/unifesp-x-ray-body-part-classifier.zip"
      ],
      "metadata": {
        "id": "70kV_c6B27eH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.listdir()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UipCIbjT3vRe",
        "outputId": "376fe49e-1905-4030-dc4e-8c9ae6c3cbb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sample_submission.csv',\n",
              " 'train.csv',\n",
              " 'kaggle.json',\n",
              " 'train_merged_df.csv',\n",
              " 'X_save.npy',\n",
              " 'y_save.npy',\n",
              " 'unifesp-x-ray-body-part-classifier.zip',\n",
              " 'test',\n",
              " 'train',\n",
              " 'unifesp-xray-body-224-224.zip',\n",
              " 'train_png',\n",
              " 'test_png',\n",
              " 'submission.csv',\n",
              " 'submission2.csv',\n",
              " 'submission3.csv',\n",
              " 'submission4.csv',\n",
              " 'submission5.csv',\n",
              " 'submission6.csv',\n",
              " 'submission7.csv',\n",
              " 'submission8_only1.csv',\n",
              " 'submission9_only1.csv',\n",
              " 'submission9.csv',\n",
              " 'submission10.csv',\n",
              " 'submission10_only1.csv',\n",
              " 'submission11.csv',\n",
              " 'submission11_only1.csv',\n",
              " 'submission12.csv',\n",
              " 'submission12_1only.csv',\n",
              " 'submission13_finetune.csv',\n",
              " '.ipynb_checkpoints',\n",
              " 'vgg19_final.csv',\n",
              " 'vgg16_final.csv']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import tensorflow_addons as tfa\n",
        "import albumentations as album\n",
        "import matplotlib.pyplot as plt\n",
        "from skimage.io import imread\n",
        "from sklearn.model_selection import StratifiedKFold"
      ],
      "metadata": {
        "id": "ebqUdEg532z6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "N_SPLITS = 4\n",
        "IMG_HEIGHT = 224\n",
        "IMG_WIDTH = 224\n",
        "BATCH_SIZE = 16\n",
        "EPOCHS = 200    \n",
        "PATIENCE = 8\n",
        "MAX_LR = 1e-5\n",
        "MIN_LR = 1e-6"
      ],
      "metadata": {
        "id": "tQvz7fYT4L33"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = np.array([\"Abdomen\", \"Ankle\", \"Cervical Spine\", \"Chest\", \n",
        "                        \"Clavicles\", \"Elbow\", \"Feet\", \"Finger\",\n",
        "                        \"Forearm\", \"Hand\", \"Hip\", \"Knee\",  \n",
        "                        \"Lower Leg\", \"Lumbar Spine\", \"Others\", \"Pelvis\", \n",
        "                        \"Shoulder\", \"Sinus\", \"Skull\", \"Thigh\", \n",
        "                        \"Thoracic Spine\", \"Wrist\"])"
      ],
      "metadata": {
        "id": "718-3rcF4P7A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(dpath+\"train.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "go1DCnjt4RMz",
        "outputId": "1eb8017b-9372-45d0-e056-e3eb2210520d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      SOPInstanceUID Target\n",
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              "1  1.2.826.0.1.3680043.8.498.10036150326276641158...    15 \n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8550acf1-0069-4efe-8d72-81c6e12581f8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8550acf1-0069-4efe-8d72-81c6e12581f8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "images_path = \"/content/drive/MyDrive/DeepLearning/train_png/\"\n",
        "temp_SOPInstanceUID = []\n",
        "for i in df.SOPInstanceUID:\n",
        "    temp_SOPInstanceUID.append(images_path + i + \".png\")\n",
        "df.SOPInstanceUID = temp_SOPInstanceUID"
      ],
      "metadata": {
        "id": "qjKj3Lxl4uFc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = 22\n",
        "targets = np.zeros((len(df.Target), num_classes), dtype=int)\n",
        "for i, t in enumerate(df.Target):\n",
        "    target = t.strip().split()\n",
        "    for j in target:\n",
        "        targets[i, int(j)] = 1"
      ],
      "metadata": {
        "id": "s_ivgVzN4vf6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.DataFrame(targets, columns=class_names)\n",
        "df = pd.concat([df, df2], axis=1) \n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "hZEjsXz14xEY",
        "outputId": "77476b17-8834-48d5-9298-2eecb9e0e631"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      SOPInstanceUID Target  Abdomen  Ankle  \\\n",
              "0  /content/drive/MyDrive/DeepLearning/train_png/...     0         1      0   \n",
              "1  /content/drive/MyDrive/DeepLearning/train_png/...    15         0      0   \n",
              "2  /content/drive/MyDrive/DeepLearning/train_png/...    12         0      0   \n",
              "3  /content/drive/MyDrive/DeepLearning/train_png/...    14         0      0   \n",
              "4  /content/drive/MyDrive/DeepLearning/train_png/...     3         0      0   \n",
              "\n",
              "   Cervical Spine  Chest  Clavicles  Elbow  Feet  Finger  ...  Lower Leg  \\\n",
              "0               0      0          0      0     0       0  ...          0   \n",
              "1               0      0          0      0     0       0  ...          0   \n",
              "2               0      0          0      0     0       0  ...          1   \n",
              "3               0      0          0      0     0       0  ...          0   \n",
              "4               0      1          0      0     0       0  ...          0   \n",
              "\n",
              "   Lumbar Spine  Others  Pelvis  Shoulder  Sinus  Skull  Thigh  \\\n",
              "0             0       0       0         0      0      0      0   \n",
              "1             0       0       1         0      0      0      0   \n",
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              "3             0       1       0         0      0      0      0   \n",
              "4             0       0       0         0      0      0      0   \n",
              "\n",
              "   Thoracic Spine  Wrist  \n",
              "0               0      0  \n",
              "1               0      0  \n",
              "2               0      0  \n",
              "3               0      0  \n",
              "4               0      0  \n",
              "\n",
              "[5 rows x 24 columns]"
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              "      <th>SOPInstanceUID</th>\n",
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              "      <th>Abdomen</th>\n",
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              "      <th>0</th>\n",
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              "      <td>/content/drive/MyDrive/DeepLearning/train_png/...</td>\n",
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              "      <td>12</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/drive/MyDrive/DeepLearning/train_png/...</td>\n",
              "      <td>14</td>\n",
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              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f0a2e74-d291-4c5d-b0f3-e7cbbb81f298')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0f0a2e74-d291-4c5d-b0f3-e7cbbb81f298 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0f0a2e74-d291-4c5d-b0f3-e7cbbb81f298');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 5))\n",
        "plt.bar(np.arange(22), targets.sum(axis=0), color='grey')\n",
        "plt.xticks(ticks=np.arange(22), labels=class_names, rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "oCcVM3u-4ygE",
        "outputId": "15889a02-dddd-4068-985a-f42da900b600"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(targets.sum(axis=0)[4])"
      ],
      "metadata": {
        "id": "UNGtVubgnG4s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0cbc520a-76f2-4dff-8a90-6d4ac7190da6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(targets.sum(axis=0)[-4])"
      ],
      "metadata": {
        "id": "X9Nj8RJQnIQV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5d8df1e-6a74-4884-ac45-67e2a2dd6e69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 12))\n",
        "for i in range(9):\n",
        "    # image\n",
        "    ax = plt.subplot(3, 3, i+1)\n",
        "    filename = df.iloc[i].SOPInstanceUID\n",
        "    img = imread(filename)\n",
        "    plt.imshow(img, cmap='gray')\n",
        "    # titles\n",
        "    labels = df.iloc[i].Target.strip().split()\n",
        "    if len(labels) == 1:\n",
        "        plt.title(class_names[int(labels[0])])\n",
        "    else:\n",
        "        titles = []\n",
        "        for label in labels:\n",
        "            titles.append(class_names[int(label)])\n",
        "        plt.title(\", \".join(titles))\n",
        "    plt.axis(\"off\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "cjNfPa8E4zj9",
        "outputId": "785e7976-1e8b-4044-ea30-1afef8db7a22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = pd.read_csv(dpath+\"sample_submission.csv\")\n",
        "df_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hG5l3AyU41Og",
        "outputId": "215a7a47-8540-4f99-eca4-fb3fef242510"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      SOPInstanceUID  \\\n",
              "0  1.2.826.0.1.3680043.8.498.10001001190452685542...   \n",
              "1  1.2.826.0.1.3680043.8.498.10022667601042710442...   \n",
              "2  1.2.826.0.1.3680043.8.498.10024395388921105474...   \n",
              "3  1.2.826.0.1.3680043.8.498.10026689165626095651...   \n",
              "4  1.2.826.0.1.3680043.8.498.10035936364561920980...   \n",
              "\n",
              "                                              Target  \n",
              "0  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...  \n",
              "1  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...  \n",
              "2  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...  \n",
              "3  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...  \n",
              "4  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...  "
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SOPInstanceUID</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.10001001190452685542...</td>\n",
              "      <td>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.10022667601042710442...</td>\n",
              "      <td>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.10024395388921105474...</td>\n",
              "      <td>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.10026689165626095651...</td>\n",
              "      <td>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.10035936364561920980...</td>\n",
              "      <td>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...</td>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f54543bf-9206-4be8-84f3-561a84a3fb16');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "images_path = \"/content/drive/MyDrive/DeepLearning/test_png/\"\n",
        "temp_SOPInstanceUID = []\n",
        "for i in df_test.SOPInstanceUID:\n",
        "    temp_SOPInstanceUID.append(images_path + i + \".png\")\n",
        "df_test.SOPInstanceUID = temp_SOPInstanceUID"
      ],
      "metadata": {
        "id": "F0sdg4nT43ty"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "V5tqnjwQ45Mr",
        "outputId": "4707c629-b1cd-4a2e-c2fe-9165176e5f4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      SOPInstanceUID  \\\n",
              "0  /content/drive/MyDrive/DeepLearning/test_png/1...   \n",
              "1  /content/drive/MyDrive/DeepLearning/test_png/1...   \n",
              "2  /content/drive/MyDrive/DeepLearning/test_png/1...   \n",
              "3  /content/drive/MyDrive/DeepLearning/test_png/1...   \n",
              "4  /content/drive/MyDrive/DeepLearning/test_png/1...   \n",
              "\n",
              "                                              Target  \n",
              "0  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...  \n",
              "1  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...  \n",
              "2  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...  \n",
              "3  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...  \n",
              "4  0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3e38dce6-0d8d-46b9-bd57-4c032ceaa714\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SOPInstanceUID</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/drive/MyDrive/DeepLearning/test_png/1...</td>\n",
              "      <td>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/drive/MyDrive/DeepLearning/test_png/1...</td>\n",
              "      <td>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/drive/MyDrive/DeepLearning/test_png/1...</td>\n",
              "      <td>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/drive/MyDrive/DeepLearning/test_png/1...</td>\n",
              "      <td>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/drive/MyDrive/DeepLearning/test_png/1...</td>\n",
              "      <td>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3e38dce6-0d8d-46b9-bd57-4c032ceaa714 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3e38dce6-0d8d-46b9-bd57-4c032ceaa714');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = df_test.SOPInstanceUID.to_numpy()"
      ],
      "metadata": {
        "id": "PnQppVc346pW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5IVMwel48Ur",
        "outputId": "7039a218-108a-4684-b71e-0d04fb70af52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['/content/drive/MyDrive/DeepLearning/test_png/1.2.826.0.1.3680043.8.498.10001001190452685542754353271306894106.png',\n",
              "       '/content/drive/MyDrive/DeepLearning/test_png/1.2.826.0.1.3680043.8.498.10022667601042710442884728915610557590.png',\n",
              "       '/content/drive/MyDrive/DeepLearning/test_png/1.2.826.0.1.3680043.8.498.10024395388921105474336784272390605225.png',\n",
              "       '/content/drive/MyDrive/DeepLearning/test_png/1.2.826.0.1.3680043.8.498.10026689165626095651568657562992631805.png',\n",
              "       '/content/drive/MyDrive/DeepLearning/test_png/1.2.826.0.1.3680043.8.498.10035936364561920980924160549027696414.png'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.SOPInstanceUID.to_numpy()\n",
        "y = df.iloc[:, 2:].to_numpy()"
      ],
      "metadata": {
        "id": "qiGvz3n15CgK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X[:5], y[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mse6A2dg5EXw",
        "outputId": "8673996c-f27c-40e1-bd48-21f1428d33f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10025629581362719970278200333618114258.png'\n",
            " '/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10036150326276641158002573300029848125.png'\n",
            " '/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10038426859954986240523417641213777162.png'\n",
            " '/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10050991192143676483577217922037244257.png'\n",
            " '/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10053309524595490852815579052225336016.png'] [[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]\n",
            " [0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "skf = StratifiedKFold(n_splits=N_SPLITS, shuffle=True, random_state=42)\n",
        "\n",
        "for train_index, valid_index in skf.split(X, np.argmax(y, axis=1)):\n",
        "    X_train, X_valid = X[train_index], X[valid_index]\n",
        "    y_train, y_valid = y[train_index], y[valid_index]\n",
        "\n",
        "print(f\"train size: {y_train.shape[0]}\")\n",
        "print(f\"valid size: {y_valid.shape[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tv5bGlBo5Kwd",
        "outputId": "45c933f0-6fe3-49cf-8f39-62e2a191a0df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train size: 1304\n",
            "valid size: 434\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train[:5])\n",
        "print(X_valid[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pjh3qFmG5NRU",
        "outputId": "cf0b057b-7b7a-4466-c287-0cf7fbd2ca25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10025629581362719970278200333618114258.png'\n",
            " '/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10050991192143676483577217922037244257.png'\n",
            " '/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10053309524595490852815579052225336016.png'\n",
            " '/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10053755320637729867508668285241208441.png'\n",
            " '/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10062189329714053601496804394945741428.png']\n",
            "['/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10036150326276641158002573300029848125.png'\n",
            " '/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10038426859954986240523417641213777162.png'\n",
            " '/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10092177056782233773275902757764299181.png'\n",
            " '/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10093718510761734264315711288650185574.png'\n",
            " '/content/drive/MyDrive/DeepLearning/train_png/1.2.826.0.1.3680043.8.498.10105498531842222086756322461334605745.png']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class DataAugmentation:\n",
        "    def __init__(self):\n",
        "        self.transforms = album.Compose([\n",
        "            album.HorizontalFlip(p=0.5), #좌우반전\n",
        "            album.VerticalFlip(p=0.5), #상하반전\n",
        "            album.ShiftScaleRotate(shift_limit=0.15, \n",
        "                                   scale_limit=0.2, \n",
        "                                   rotate_limit=25, \n",
        "                                   border_mode=cv2.BORDER_CONSTANT, p=0.75),\n",
        "            album.OneOf([\n",
        "                album.RandomBrightnessContrast(brightness_limit=0.5, \n",
        "                                               contrast_limit=0.2, p=0.5),\n",
        "                album.RandomGamma(gamma_limit=(70, 130), p=0.5),\n",
        "                ], p=0.5),\n",
        "            album.OneOf([\n",
        "                album.Blur(p=0.1),\n",
        "                album.GaussianBlur(p=0.1),\n",
        "                album.MotionBlur(p=0.1),\n",
        "                ], p=0.1),\n",
        "            album.OneOf([\n",
        "                album.GaussNoise(p=0.1),\n",
        "                album.GridDropout(ratio=0.5, p=0.2),\n",
        "                album.CoarseDropout(max_holes=16, max_height=16, max_width=16,\n",
        "                                    min_holes= 8, min_height= 8, min_width= 8, p=0.2)\n",
        "                ], p=0.5),\n",
        "            ])\n",
        "\n",
        "    def aug_fn(self, image):\n",
        "        data = {\"image\":image}\n",
        "        aug_data = self.transforms(**data)\n",
        "        aug_img = aug_data[\"image\"]\n",
        "        aug_img = tf.cast(aug_img, tf.float32)\n",
        "        return aug_img\n",
        "\n",
        "    def augment_iamge(self, img, label):\n",
        "        aug_img = tf.numpy_function(func=self.aug_fn, inp=[img], Tout=tf.float32)\n",
        "        return aug_img, label"
      ],
      "metadata": {
        "id": "-Ty0ow9dniVW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "AUTOTUNE = tf.data.AUTOTUNE"
      ],
      "metadata": {
        "id": "ahDoZZpW5e9M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process_path(file_path, label):\n",
        "    img = tf.io.read_file(file_path)\n",
        "    img = tf.image.decode_png(img, channels=1, dtype=tf.uint8)\n",
        "    return img, label\n",
        "\n",
        "def process_test_path(file_path):\n",
        "    img = tf.io.read_file(file_path)\n",
        "    img = tf.image.decode_png(img, channels=1, dtype=tf.uint8)\n",
        "    return img"
      ],
      "metadata": {
        "id": "J1FIavDT5oNu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n",
        "valid_ds = tf.data.Dataset.from_tensor_slices((X_valid, y_valid))\n",
        "test_ds = tf.data.Dataset.from_tensor_slices((X_test))"
      ],
      "metadata": {
        "id": "SMTgoeoM5pzV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = train_ds.map(process_path, num_parallel_calls=AUTOTUNE)\n",
        "valid_ds = valid_ds.map(process_path, num_parallel_calls=AUTOTUNE)\n",
        "test_ds = test_ds.map(process_test_path, num_parallel_calls=AUTOTUNE)"
      ],
      "metadata": {
        "id": "npk7-wou5rWu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "aug_train = DataAugmentation()\n",
        "train_ds = train_ds.map(aug_train.augment_iamge, num_parallel_calls=AUTOTUNE)"
      ],
      "metadata": {
        "id": "UawpAHeb5zD4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = train_ds.batch(BATCH_SIZE)\n",
        "valid_ds = valid_ds.batch(BATCH_SIZE)\n",
        "test_ds = test_ds.batch(BATCH_SIZE)"
      ],
      "metadata": {
        "id": "5gGVVeD550E_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image_batch, label_batch = next(iter(train_ds))"
      ],
      "metadata": {
        "id": "09Lvduye52Jt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE).repeat()\n",
        "valid_ds = valid_ds.cache().prefetch(buffer_size=AUTOTUNE).repeat()\n",
        "test_ds = test_ds.prefetch(buffer_size=AUTOTUNE)"
      ],
      "metadata": {
        "id": "HeeBA8Hh53h1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = tf.keras.applications.VGG19(input_shape=(224, 224, 3),\n",
        "                                                                   include_top=False, weights='imagenet')"
      ],
      "metadata": {
        "id": "dSEcW2hyTuMA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "382e1115-f731-4466-c7e2-d9ce13171d0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "80142336/80134624 [==============================] - 0s 0us/step\n",
            "80150528/80134624 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model.summary()"
      ],
      "metadata": {
        "id": "DCLIQtqrVGmZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b726cf9b-c8ca-49b0-f13e-0121fd3000fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"vgg19\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv4 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv4 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv4 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 20,024,384\n",
            "Trainable params: 20,024,384\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in base_model.layers[:13]:\n",
        "    layer.trainable = False # freeze first 13 layers"
      ],
      "metadata": {
        "id": "zEA8nscoE-rH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in base_model.layers:\n",
        "    print(layer, layer.trainable)"
      ],
      "metadata": {
        "id": "OrDTMwpFPsfL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61b34f6e-ce12-4217-af81-a1ac33345157"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<keras.engine.input_layer.InputLayer object at 0x7f5cf919bf10> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5ced3f7ed0> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5ced411710> False\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7f5ced460050> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5ced486ed0> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5ced409910> False\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7f5ced486b10> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5c6e32b390> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5c6e26c710> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5c6e2716d0> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5c6e3720d0> False\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7f5c6e27ac90> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5c6e281090> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5c6e27e390> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5c6e285050> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5c6e2143d0> True\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7f5c6e28a710> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5c6e218610> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5ced4fac10> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5c6e21e550> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7f5c6e218c90> True\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7f5c6e22fed0> True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.mixed_precision.set_global_policy('mixed_float16')\n",
        "\n",
        "def get_model(input_shape, output_shape):\n",
        "    inputs = tf.keras.Input(shape=input_shape)\n",
        "    x = tf.tile(inputs, (1, 1, 1, 3))\n",
        "\n",
        "    x = base_model(x)\n",
        "    x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
        "    x = tf.keras.layers.Dense(512, activation='relu')(x)\n",
        "    x = tf.keras.layers.Dense(256, activation='relu')(x)\n",
        "    x = tf.keras.layers.Dropout(0.5)(x)\n",
        "    x = tf.keras.layers.Dense(128, activation='relu')(x)\n",
        "    x = tf.keras.layers.Dense(128, activation='relu')(x)\n",
        "    x = tf.keras.layers.Flatten()(x)\n",
        "\n",
        "    outputs = tf.keras.layers.Dense(output_shape, activation='sigmoid', dtype='float32')(x)\n",
        "    model = tf.keras.Model(inputs, outputs)\n",
        "    return model"
      ],
      "metadata": {
        "id": "HPEVtNrr55bS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = get_model(input_shape=(IMG_HEIGHT, IMG_WIDTH, 1), output_shape=num_classes)"
      ],
      "metadata": {
        "id": "HzKDXaaG9L5e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7m8w1j2B61u",
        "outputId": "b8eb0d27-ca82-4e68-e597-64a181ea4b5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 224, 224, 1)]     0         \n",
            "                                                                 \n",
            " tf.tile (TFOpLambda)        (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " vgg19 (Functional)          (None, 7, 7, 512)         20024384  \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 512)              0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               262656    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 22)                2838      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 20,470,614\n",
            "Trainable params: 16,964,886\n",
            "Non-trainable params: 3,505,728\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "steps_per_epoch = len(y_train)//BATCH_SIZE\n",
        "validation_steps = len(y_valid)//BATCH_SIZE"
      ],
      "metadata": {
        "id": "_VjH1b3lCQcp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=tfa.optimizers.RectifiedAdam(learning_rate=MAX_LR,\n",
        "                                                     total_steps=EPOCHS*steps_per_epoch,\n",
        "                                                     warmup_proportion=0.1,\n",
        "                                                     min_lr=MIN_LR), \n",
        "              loss=tf.losses.BinaryCrossentropy(),\n",
        "              metrics=['binary_accuracy'])"
      ],
      "metadata": {
        "id": "PeULhZYACSA5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_callbacks = tf.keras.callbacks.EarlyStopping(patience=PATIENCE, \n",
        "                                                monitor='val_loss', \n",
        "                                                restore_best_weights=True)"
      ],
      "metadata": {
        "id": "BPwgLM_yCTOK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.config.run_functions_eagerly(True)"
      ],
      "metadata": {
        "id": "sHsUAVjhCUHB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_ds, \n",
        "                    validation_data=valid_ds,\n",
        "                    epochs=EPOCHS,\n",
        "                    callbacks=[my_callbacks],\n",
        "                    steps_per_epoch=steps_per_epoch,\n",
        "                    validation_steps=validation_steps,\n",
        "                    verbose=1,\n",
        "                    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5BVQ-JqICVAw",
        "outputId": "f564f3fd-763b-47a7-deee-9108a818ff54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "81/81 [==============================] - 701s 9s/step - loss: 1.1343 - binary_accuracy: 0.4739 - val_loss: 0.9622 - val_binary_accuracy: 0.4593\n",
            "Epoch 2/200\n",
            "81/81 [==============================] - 37s 428ms/step - loss: 1.0456 - binary_accuracy: 0.4812 - val_loss: 0.8717 - val_binary_accuracy: 0.4797\n",
            "Epoch 3/200\n",
            "81/81 [==============================] - 37s 459ms/step - loss: 0.9030 - binary_accuracy: 0.4978 - val_loss: 0.7788 - val_binary_accuracy: 0.5129\n",
            "Epoch 4/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.7937 - binary_accuracy: 0.5229 - val_loss: 0.7144 - val_binary_accuracy: 0.5535\n",
            "Epoch 5/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.7326 - binary_accuracy: 0.5513 - val_loss: 0.6642 - val_binary_accuracy: 0.6078\n",
            "Epoch 6/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.6857 - binary_accuracy: 0.5992 - val_loss: 0.6068 - val_binary_accuracy: 0.6849\n",
            "Epoch 7/200\n",
            "81/81 [==============================] - 34s 425ms/step - loss: 0.6411 - binary_accuracy: 0.6533 - val_loss: 0.5391 - val_binary_accuracy: 0.7454\n",
            "Epoch 8/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.5876 - binary_accuracy: 0.7051 - val_loss: 0.4556 - val_binary_accuracy: 0.8024\n",
            "Epoch 9/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.5170 - binary_accuracy: 0.7559 - val_loss: 0.3341 - val_binary_accuracy: 0.8954\n",
            "Epoch 10/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.4220 - binary_accuracy: 0.8176 - val_loss: 0.2334 - val_binary_accuracy: 0.9497\n",
            "Epoch 11/200\n",
            "81/81 [==============================] - 36s 450ms/step - loss: 0.3481 - binary_accuracy: 0.8641 - val_loss: 0.1881 - val_binary_accuracy: 0.9530\n",
            "Epoch 12/200\n",
            "81/81 [==============================] - 34s 425ms/step - loss: 0.3087 - binary_accuracy: 0.8886 - val_loss: 0.1680 - val_binary_accuracy: 0.9603\n",
            "Epoch 13/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.2735 - binary_accuracy: 0.9086 - val_loss: 0.1537 - val_binary_accuracy: 0.9650\n",
            "Epoch 14/200\n",
            "81/81 [==============================] - 34s 423ms/step - loss: 0.2482 - binary_accuracy: 0.9203 - val_loss: 0.1437 - val_binary_accuracy: 0.9650\n",
            "Epoch 15/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.2333 - binary_accuracy: 0.9278 - val_loss: 0.1372 - val_binary_accuracy: 0.9652\n",
            "Epoch 16/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.2129 - binary_accuracy: 0.9368 - val_loss: 0.1339 - val_binary_accuracy: 0.9652\n",
            "Epoch 17/200\n",
            "81/81 [==============================] - 34s 426ms/step - loss: 0.1975 - binary_accuracy: 0.9432 - val_loss: 0.1282 - val_binary_accuracy: 0.9666\n",
            "Epoch 18/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.1854 - binary_accuracy: 0.9474 - val_loss: 0.1243 - val_binary_accuracy: 0.9679\n",
            "Epoch 19/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.1813 - binary_accuracy: 0.9498 - val_loss: 0.1228 - val_binary_accuracy: 0.9682\n",
            "Epoch 20/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.1695 - binary_accuracy: 0.9532 - val_loss: 0.1201 - val_binary_accuracy: 0.9685\n",
            "Epoch 21/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.1656 - binary_accuracy: 0.9540 - val_loss: 0.1198 - val_binary_accuracy: 0.9681\n",
            "Epoch 22/200\n",
            "81/81 [==============================] - 36s 451ms/step - loss: 0.1579 - binary_accuracy: 0.9567 - val_loss: 0.1136 - val_binary_accuracy: 0.9695\n",
            "Epoch 23/200\n",
            "81/81 [==============================] - 36s 448ms/step - loss: 0.1510 - binary_accuracy: 0.9599 - val_loss: 0.1093 - val_binary_accuracy: 0.9696\n",
            "Epoch 24/200\n",
            "81/81 [==============================] - 36s 448ms/step - loss: 0.1469 - binary_accuracy: 0.9590 - val_loss: 0.1095 - val_binary_accuracy: 0.9695\n",
            "Epoch 25/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.1420 - binary_accuracy: 0.9611 - val_loss: 0.1124 - val_binary_accuracy: 0.9691\n",
            "Epoch 26/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.1373 - binary_accuracy: 0.9618 - val_loss: 0.1042 - val_binary_accuracy: 0.9700\n",
            "Epoch 27/200\n",
            "81/81 [==============================] - 37s 451ms/step - loss: 0.1305 - binary_accuracy: 0.9635 - val_loss: 0.1029 - val_binary_accuracy: 0.9698\n",
            "Epoch 28/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.1279 - binary_accuracy: 0.9636 - val_loss: 0.0997 - val_binary_accuracy: 0.9706\n",
            "Epoch 29/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.1245 - binary_accuracy: 0.9634 - val_loss: 0.0999 - val_binary_accuracy: 0.9715\n",
            "Epoch 30/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.1190 - binary_accuracy: 0.9651 - val_loss: 0.0928 - val_binary_accuracy: 0.9713\n",
            "Epoch 31/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.1156 - binary_accuracy: 0.9649 - val_loss: 0.0891 - val_binary_accuracy: 0.9716\n",
            "Epoch 32/200\n",
            "81/81 [==============================] - 37s 451ms/step - loss: 0.1108 - binary_accuracy: 0.9668 - val_loss: 0.0885 - val_binary_accuracy: 0.9720\n",
            "Epoch 33/200\n",
            "81/81 [==============================] - 34s 425ms/step - loss: 0.1076 - binary_accuracy: 0.9668 - val_loss: 0.0820 - val_binary_accuracy: 0.9725\n",
            "Epoch 34/200\n",
            "81/81 [==============================] - 36s 450ms/step - loss: 0.1003 - binary_accuracy: 0.9692 - val_loss: 0.0800 - val_binary_accuracy: 0.9738\n",
            "Epoch 35/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.0993 - binary_accuracy: 0.9688 - val_loss: 0.0784 - val_binary_accuracy: 0.9744\n",
            "Epoch 36/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.0953 - binary_accuracy: 0.9698 - val_loss: 0.0823 - val_binary_accuracy: 0.9740\n",
            "Epoch 37/200\n",
            "81/81 [==============================] - 36s 450ms/step - loss: 0.0916 - binary_accuracy: 0.9706 - val_loss: 0.0788 - val_binary_accuracy: 0.9760\n",
            "Epoch 38/200\n",
            "81/81 [==============================] - 36s 450ms/step - loss: 0.0896 - binary_accuracy: 0.9716 - val_loss: 0.0736 - val_binary_accuracy: 0.9771\n",
            "Epoch 39/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.0832 - binary_accuracy: 0.9729 - val_loss: 0.0726 - val_binary_accuracy: 0.9783\n",
            "Epoch 40/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.0804 - binary_accuracy: 0.9727 - val_loss: 0.0810 - val_binary_accuracy: 0.9779\n",
            "Epoch 41/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.0781 - binary_accuracy: 0.9735 - val_loss: 0.0732 - val_binary_accuracy: 0.9797\n",
            "Epoch 42/200\n",
            "81/81 [==============================] - 34s 425ms/step - loss: 0.0741 - binary_accuracy: 0.9749 - val_loss: 0.0683 - val_binary_accuracy: 0.9802\n",
            "Epoch 43/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.0701 - binary_accuracy: 0.9768 - val_loss: 0.0715 - val_binary_accuracy: 0.9802\n",
            "Epoch 44/200\n",
            "81/81 [==============================] - 34s 425ms/step - loss: 0.0693 - binary_accuracy: 0.9774 - val_loss: 0.0713 - val_binary_accuracy: 0.9797\n",
            "Epoch 45/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.0660 - binary_accuracy: 0.9778 - val_loss: 0.0664 - val_binary_accuracy: 0.9820\n",
            "Epoch 46/200\n",
            "81/81 [==============================] - 36s 450ms/step - loss: 0.0639 - binary_accuracy: 0.9783 - val_loss: 0.0650 - val_binary_accuracy: 0.9816\n",
            "Epoch 47/200\n",
            "81/81 [==============================] - 37s 451ms/step - loss: 0.0599 - binary_accuracy: 0.9795 - val_loss: 0.0703 - val_binary_accuracy: 0.9801\n",
            "Epoch 48/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.0600 - binary_accuracy: 0.9803 - val_loss: 0.0660 - val_binary_accuracy: 0.9831\n",
            "Epoch 49/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.0553 - binary_accuracy: 0.9810 - val_loss: 0.0660 - val_binary_accuracy: 0.9833\n",
            "Epoch 50/200\n",
            "81/81 [==============================] - 34s 423ms/step - loss: 0.0552 - binary_accuracy: 0.9803 - val_loss: 0.0661 - val_binary_accuracy: 0.9839\n",
            "Epoch 51/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.0512 - binary_accuracy: 0.9828 - val_loss: 0.0661 - val_binary_accuracy: 0.9838\n",
            "Epoch 52/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.0484 - binary_accuracy: 0.9837 - val_loss: 0.0697 - val_binary_accuracy: 0.9833\n",
            "Epoch 53/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.0489 - binary_accuracy: 0.9827 - val_loss: 0.0682 - val_binary_accuracy: 0.9835\n",
            "Epoch 54/200\n",
            "81/81 [==============================] - 36s 451ms/step - loss: 0.0467 - binary_accuracy: 0.9834 - val_loss: 0.0631 - val_binary_accuracy: 0.9841\n",
            "Epoch 55/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.0429 - binary_accuracy: 0.9850 - val_loss: 0.0644 - val_binary_accuracy: 0.9842\n",
            "Epoch 56/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.0424 - binary_accuracy: 0.9846 - val_loss: 0.0622 - val_binary_accuracy: 0.9838\n",
            "Epoch 57/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.0398 - binary_accuracy: 0.9862 - val_loss: 0.0625 - val_binary_accuracy: 0.9840\n",
            "Epoch 58/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.0378 - binary_accuracy: 0.9864 - val_loss: 0.0648 - val_binary_accuracy: 0.9841\n",
            "Epoch 59/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.0375 - binary_accuracy: 0.9863 - val_loss: 0.0617 - val_binary_accuracy: 0.9855\n",
            "Epoch 60/200\n",
            "81/81 [==============================] - 36s 448ms/step - loss: 0.0347 - binary_accuracy: 0.9874 - val_loss: 0.0635 - val_binary_accuracy: 0.9855\n",
            "Epoch 61/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.0342 - binary_accuracy: 0.9884 - val_loss: 0.0646 - val_binary_accuracy: 0.9846\n",
            "Epoch 62/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.0325 - binary_accuracy: 0.9881 - val_loss: 0.0708 - val_binary_accuracy: 0.9835\n",
            "Epoch 63/200\n",
            "81/81 [==============================] - 34s 425ms/step - loss: 0.0324 - binary_accuracy: 0.9886 - val_loss: 0.0683 - val_binary_accuracy: 0.9845\n",
            "Epoch 64/200\n",
            "81/81 [==============================] - 34s 423ms/step - loss: 0.0292 - binary_accuracy: 0.9896 - val_loss: 0.0706 - val_binary_accuracy: 0.9847\n",
            "Epoch 65/200\n",
            "81/81 [==============================] - 34s 424ms/step - loss: 0.0302 - binary_accuracy: 0.9886 - val_loss: 0.0731 - val_binary_accuracy: 0.9855\n",
            "Epoch 66/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.0278 - binary_accuracy: 0.9903 - val_loss: 0.0758 - val_binary_accuracy: 0.9840\n",
            "Epoch 67/200\n",
            "81/81 [==============================] - 36s 449ms/step - loss: 0.0274 - binary_accuracy: 0.9903 - val_loss: 0.0710 - val_binary_accuracy: 0.9843\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
        "ax1.set_title('Accuracy')\n",
        "ax1.plot(history.history['binary_accuracy'], 'r', marker=\"o\", label='accuracy')\n",
        "ax1.plot(history.history['val_binary_accuracy'], 'g', marker=\"x\", label='val_accuracy')\n",
        "ax1.legend()\n",
        "\n",
        "ax2.set_title('Loss')\n",
        "ax2.plot(history.history['loss'], 'r', marker=\"o\", label='loss')\n",
        "ax2.plot(history.history['val_loss'], 'g', marker=\"x\", label='val_loss')\n",
        "ax2.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "X-nLW8ELHPu5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "7da3140b-7058-49f8-a89d-435813a7578c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(test_ds, verbose=1)\n",
        "\n",
        "temp = []\n",
        "for i in pred:\n",
        "    ans = np.where(i>0.9)[0]\n",
        "    if ans.shape[0] == 0:\n",
        "        temp.append(str(np.argmax(i)))\n",
        "    else:\n",
        "       temp.append(\" \".join(ans.astype(str)))"
      ],
      "metadata": {
        "id": "YM9uERROavyi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23bd3776-9469-411a-8614-66cd06f6de9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "47/47 [==============================] - 277s 6s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = pd.read_csv(dpath+\"sample_submission.csv\")\n",
        "df_test.Target = temp"
      ],
      "metadata": {
        "id": "wAYwHxEyyvwz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_test"
      ],
      "metadata": {
        "id": "iiLgme4fK5ZR",
        "outputId": "bfe5fa06-33b0-4743-97b6-5cc8bd701395",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        SOPInstanceUID Target\n",
              "0    1.2.826.0.1.3680043.8.498.10001001190452685542...      9\n",
              "1    1.2.826.0.1.3680043.8.498.10022667601042710442...      3\n",
              "2    1.2.826.0.1.3680043.8.498.10024395388921105474...      9\n",
              "3    1.2.826.0.1.3680043.8.498.10026689165626095651...      3\n",
              "4    1.2.826.0.1.3680043.8.498.10035936364561920980...      3\n",
              "..                                                 ...    ...\n",
              "738  1.2.826.0.1.3680043.8.498.98950979295424909746...      3\n",
              "739  1.2.826.0.1.3680043.8.498.99120415799121180043...      3\n",
              "740  1.2.826.0.1.3680043.8.498.99132008951330408682...      3\n",
              "741  1.2.826.0.1.3680043.8.498.99611256581875030709...     21\n",
              "742  1.2.826.0.1.3680043.8.498.99815558936794860022...      3\n",
              "\n",
              "[743 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-05a4614d-b165-47f8-ac6b-c4e0a0a9a6ac\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SOPInstanceUID</th>\n",
              "      <th>Target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.10001001190452685542...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.10022667601042710442...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.10024395388921105474...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.10026689165626095651...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.10035936364561920980...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>738</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.98950979295424909746...</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>739</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.99120415799121180043...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>740</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.99132008951330408682...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>741</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.99611256581875030709...</td>\n",
              "      <td>21</td>\n",
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              "    <tr>\n",
              "      <th>742</th>\n",
              "      <td>1.2.826.0.1.3680043.8.498.99815558936794860022...</td>\n",
              "      <td>3</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>743 rows × 2 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-05a4614d-b165-47f8-ac6b-c4e0a0a9a6ac')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-05a4614d-b165-47f8-ac6b-c4e0a0a9a6ac button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-05a4614d-b165-47f8-ac6b-c4e0a0a9a6ac');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test.to_csv(\"./vgg19_final_final.csv\", index=False)"
      ],
      "metadata": {
        "id": "o_aS1E0ZyvSM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}