AI_Facial_Recognize.ipynb

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      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mlgYhrAdQ9l1",
        "outputId": "d9402828-0b64-47b5-e52d-a192db87c38c"
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          "text": [
            "Sat Nov 26 21:45:09 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   66C    P0    28W /  70W |   6704MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "SOURCE_URL = 'https://tcc-ia.s3.sa-east-1.amazonaws.com/data.tar.xz'\n",
        "IMAGE_DIR = './faces'\n",
        "TAR_PATH = os.path.join(IMAGE_DIR, os.path.basename(SOURCE_URL))\n",
        "EXPECTED_IMAGE_COUNT = 5\n",
        "IMAGES_DIR=IMAGE_DIR+\"/all\";\n",
        "\n",
        "%sx mkdir --parents \"{IMAGE_DIR}\"\n",
        "if len(os.listdir(IMAGE_DIR)) != EXPECTED_IMAGE_COUNT:\n",
        "  %sx wget --no-check-certificate -O \"{TAR_PATH}\" \"{SOURCE_URL}\"\n",
        "  %sx tar --extract --file=\"{TAR_PATH}\" --directory=\"{IMAGE_DIR}\"\n",
        "  %sx rm \"{TAR_PATH}\"\n",
        "\n",
        "print(f'The total number of images is: {len(os.listdir(IMAGE_DIR))}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NnkRyUemU4vC",
        "outputId": "bbae0700-65b2-46f9-abb0-a14885e1a388"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The total number of images is: 15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import io, os\n",
        "import urllib.parse\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "BASE_URL = 'https://tcc-ia.s3.sa-east-1.amazonaws.com/photo-user.csv'\n",
        "\n",
        "def read_csv_from_web(file_name):\n",
        "  url = urllib.parse.urljoin(BASE_URL, file_name)\n",
        "  content = requests.get(url).content\n",
        "  return pd.read_csv(io.StringIO(content.decode('utf-8')))\n",
        "\n",
        "train_df, test_df = train_test_split(read_csv_from_web('photo-user.csv'), train_size=0.8)\n",
        "\n",
        "print(train_df.shape, test_df.shape)\n",
        "train_df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "1WT75T5qcUpb",
        "outputId": "15edf740-5370-4aab-902d-71d781165738"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(12, 2) (3, 2)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      code   image_url\n",
              "13  Camila  camila-004\n",
              "9    Renan   renan-005\n",
              "12  Camila  camila-003"
            ],
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              "      <th>13</th>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Camila</td>\n",
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              "                                                     [key], {});\n",
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              "\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q --upgrade fastcore fastai\n",
        "!pip install -q efficientnet_pytorch"
      ],
      "metadata": {
        "id": "yFohW05Iefas"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import gc\n",
        "from pathlib import Path\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from efficientnet_pytorch import EfficientNet"
      ],
      "metadata": {
        "id": "M6WrtwI4fCzF"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df_orig = train_df.copy()"
      ],
      "metadata": {
        "id": "owKUFIQkfEmt"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import fastai\n",
        "print(fastai.__version__ )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XdwjoICTfLiG",
        "outputId": "ea0ae8a0-179f-4afc-b49e-758add116da0"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.7.10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from fastai.vision.all import *"
      ],
      "metadata": {
        "id": "zqGWWYv7fNVX"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def random_seed(seed_value, use_cuda): \n",
        "    np.random.seed(seed_value)\n",
        "    torch.manual_seed(seed_value) \n",
        "    random.seed(seed_value)\n",
        "\n",
        "    if use_cuda: \n",
        "        torch.cuda.manual_seed(seed_value) \n",
        "        torch.cuda.manual_seed_all(seed_value) \n",
        "\n",
        "        torch.backends.cudnn.deterministic = True \n",
        "        torch.backends.cudnn.benchmark = False "
      ],
      "metadata": {
        "id": "e41wTEAYfPs1"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_seed(2022,True)"
      ],
      "metadata": {
        "id": "a_U7SWgrfR9x"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FdvpC6Y_fUx2",
        "outputId": "f6de787d-faa2-4693-b0f9-cb73a0aba12e"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      code   image_url\n",
              "14  Camila  camila-005\n",
              "8    Renan   renan-004\n",
              "6    Renan   renan-002\n",
              "3   Daniel  daniel-004\n",
              "0   Daniel  daniel-001"
            ],
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Camila</td>\n",
              "      <td>camila-005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Renan</td>\n",
              "      <td>renan-004</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Renan</td>\n",
              "      <td>renan-002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Daniel</td>\n",
              "      <td>daniel-004</td>\n",
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              "      <th>0</th>\n",
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              "      <td>daniel-001</td>\n",
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              "                                                     [key], {});\n",
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              "\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",
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              "  "
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df[\"image_url\"] = [\"/content/faces/\"+i+\".jpg\" for i in train_df[\"image_url\"]]"
      ],
      "metadata": {
        "id": "4mMVO8vufaug"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "oaHVv2Smfkgo",
        "outputId": "9992e454-6140-43f2-bf5d-1960949cb06b"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      code                      image_url\n",
              "13  Camila  /content/faces/camila-004.jpg\n",
              "9    Renan   /content/faces/renan-005.jpg\n",
              "12  Camila  /content/faces/camila-003.jpg\n",
              "5    Renan   /content/faces/renan-001.jpg\n",
              "10  Camila  /content/faces/camila-001.jpg"
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>code</th>\n",
              "      <th>image_url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Camila</td>\n",
              "      <td>/content/faces/camila-004.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Renan</td>\n",
              "      <td>/content/faces/renan-005.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Camila</td>\n",
              "      <td>/content/faces/camila-003.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Renan</td>\n",
              "      <td>/content/faces/renan-001.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Camila</td>\n",
              "      <td>/content/faces/camila-001.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39b73c5f-ddd0-46bd-9782-63d41b484c74')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\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",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "faces = DataBlock(\n",
        "    blocks=(ImageBlock, CategoryBlock), \n",
        "    splitter=TrainTestSplitter(0.2, stratify=train_df[\"code\"]),\n",
        "  get_x = ColReader(1),\n",
        "  item_tfms=Resize((320,320), method='squish'),\n",
        "    get_y=ColReader(0),\n",
        "    batch_tfms = [\n",
        "        *aug_transforms(\n",
        "            do_flip=True,\n",
        "            flip_vert=False,\n",
        "            max_lighting=0.4,\n",
        "            max_zoom=1.2,\n",
        "            max_warp=0.2,\n",
        "            max_rotate=30,\n",
        "            xtra_tfms=None\n",
        "          ),\n",
        "        Normalize.from_stats(*imagenet_stats)\n",
        "      ]\n",
        "    )"
      ],
      "metadata": {
        "id": "kWSG3gCkfpOg"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.tail()"
      ],
      "metadata": {
        "id": "QHVYILtafzau"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dls = faces.dataloaders(train_df, bs=5, num_workers = 0)"
      ],
      "metadata": {
        "id": "XWfGrTHKgJ_I"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vRZA7Urmgonc",
        "outputId": "cae0f367-0359-49f6-e0cc-ebfc1ab143d5"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      code                      image_url\n",
              "14  Camila  /content/faces/camila-005.jpg\n",
              "8    Renan   /content/faces/renan-004.jpg\n",
              "6    Renan   /content/faces/renan-002.jpg\n",
              "3   Daniel  /content/faces/daniel-004.jpg\n",
              "0   Daniel  /content/faces/daniel-001.jpg"
            ],
            "text/html": [
              "\n",
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              "      <th>image_url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Camila</td>\n",
              "      <td>/content/faces/camila-005.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Renan</td>\n",
              "      <td>/content/faces/renan-004.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Renan</td>\n",
              "      <td>/content/faces/renan-002.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Daniel</td>\n",
              "      <td>/content/faces/daniel-004.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Daniel</td>\n",
              "      <td>/content/faces/daniel-001.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c32fbf9f-e7f3-450f-b36b-963014790981')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-c32fbf9f-e7f3-450f-b36b-963014790981 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c32fbf9f-e7f3-450f-b36b-963014790981');\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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              "  "
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          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dls.show_batch(max_n=5, nrows=1, ncols=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "159zSyZqg83J",
        "outputId": "453d6557-7cc7-4d2a-97d6-80e07f3c09f8"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x216 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_model(dls,pretrained=True, **kwargs):\n",
        "    model = EfficientNet.from_pretrained(model_name)\n",
        "    model._fc = nn.Linear(model._fc.in_features, dls.c)\n",
        "    return model"
      ],
      "metadata": {
        "id": "vCvRUFh_kacI"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = 'efficientnet-b6'"
      ],
      "metadata": {
        "id": "ZQoKWyHPoKxu"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "learn = Learner(dls, get_model(dls), metrics=[error_rate, accuracy])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mE1Tr4zukk-G",
        "outputId": "57e095a9-0930-449d-d64a-b732291ce246"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded pretrained weights for efficientnet-b6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learn.lr_find()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "qMQEqKKvob51",
        "outputId": "96615c1d-ab57-4147-c730-ce11c57286e0"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(valley=0.0002290867705596611)"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learn.fine_tune(20, cbs= SaveModelCallback(monitor='valid_loss'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MW8MCCTfo4Ig",
        "outputId": "7efe5879-95b0-4638-f300-5c7a63e83b6a"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.080215</td>\n",
              "      <td>1.019899</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>00:01</td>\n",
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              "</table>"
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          "text": [
            "Better model found at epoch 0 with valid_loss value: 1.019898533821106.\n"
          ]
        },
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.040317</td>\n",
              "      <td>0.982907</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>00:01</td>\n",
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              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.970819</td>\n",
              "      <td>0.932811</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>00:01</td>\n",
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              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.857207</td>\n",
              "      <td>0.859437</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
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              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.831672</td>\n",
              "      <td>0.747556</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
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              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.720926</td>\n",
              "      <td>0.585327</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
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              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.623043</td>\n",
              "      <td>0.386709</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.541297</td>\n",
              "      <td>0.203613</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.489397</td>\n",
              "      <td>0.083658</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.433903</td>\n",
              "      <td>0.033705</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.391174</td>\n",
              "      <td>0.015862</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.353572</td>\n",
              "      <td>0.008916</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.322989</td>\n",
              "      <td>0.005113</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.327425</td>\n",
              "      <td>0.003536</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.301649</td>\n",
              "      <td>0.002928</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.278787</td>\n",
              "      <td>0.002778</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.258918</td>\n",
              "      <td>0.002742</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.251830</td>\n",
              "      <td>0.002721</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.236289</td>\n",
              "      <td>0.002656</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.222026</td>\n",
              "      <td>0.002667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.227376</td>\n",
              "      <td>0.002649</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Better model found at epoch 0 with valid_loss value: 0.982907235622406.\n",
            "Better model found at epoch 1 with valid_loss value: 0.9328112602233887.\n",
            "Better model found at epoch 2 with valid_loss value: 0.8594372868537903.\n",
            "Better model found at epoch 3 with valid_loss value: 0.747556209564209.\n",
            "Better model found at epoch 4 with valid_loss value: 0.5853274464607239.\n",
            "Better model found at epoch 5 with valid_loss value: 0.38670864701271057.\n",
            "Better model found at epoch 6 with valid_loss value: 0.20361340045928955.\n",
            "Better model found at epoch 7 with valid_loss value: 0.08365798741579056.\n",
            "Better model found at epoch 8 with valid_loss value: 0.03370504453778267.\n",
            "Better model found at epoch 9 with valid_loss value: 0.01586172543466091.\n",
            "Better model found at epoch 10 with valid_loss value: 0.008916323073208332.\n",
            "Better model found at epoch 11 with valid_loss value: 0.005113144405186176.\n",
            "Better model found at epoch 12 with valid_loss value: 0.003536265343427658.\n",
            "Better model found at epoch 13 with valid_loss value: 0.0029279934242367744.\n",
            "Better model found at epoch 14 with valid_loss value: 0.002777619520202279.\n",
            "Better model found at epoch 15 with valid_loss value: 0.002741616452112794.\n",
            "Better model found at epoch 16 with valid_loss value: 0.002721067750826478.\n",
            "Better model found at epoch 17 with valid_loss value: 0.0026562775019556284.\n",
            "Better model found at epoch 19 with valid_loss value: 0.0026486599817872047.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df[\"image_url\"]   = [\"/content/faces/\"+i+\".jpg\" for i in test_df[\"image_url\"]]"
      ],
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        "id": "e3hjoOxl3nF5"
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      "execution_count": 81,
      "outputs": []
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        "test_df.head()"
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          "height": 143
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        "outputId": "293bf2d9-bf79-43bb-e340-c730cab6a1c3"
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      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      code                      image_url\n",
              "7    Renan   /content/faces/renan-003.jpg\n",
              "11  Camila  /content/faces/camila-002.jpg\n",
              "4   Daniel  /content/faces/daniel-005.jpg"
            ],
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              "\n",
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              "<style scoped>\n",
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              "      <th>code</th>\n",
              "      <th>image_url</th>\n",
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              "  <tbody>\n",
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              "      <th>7</th>\n",
              "      <td>Renan</td>\n",
              "      <td>/content/faces/renan-003.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Camila</td>\n",
              "      <td>/content/faces/camila-002.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Daniel</td>\n",
              "      <td>/content/faces/daniel-005.jpg</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-46050929-2378-40c2-88cc-c402d3147e8e');\n",
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              "            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",
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          "metadata": {},
          "execution_count": 82
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    {
      "cell_type": "code",
      "source": [
        "tdl = learn.dls.test_dl(test_df[\"image_url\"])\n",
        "test_preds_tta, test_labels_tta = learn.tta(dl=tdl, n=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "nHzZmFTO3uuj",
        "outputId": "be012271-f13e-4599-d682-d288203827c4"
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      "execution_count": 83,
      "outputs": [
        {
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            "text/html": [
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    {
      "cell_type": "code",
      "source": [
        "preds_test = test_preds_tta"
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      "metadata": {
        "id": "qxcoQ0h26vKj"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds_test"
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        "colab": {
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        "id": "NdQZ-AdA6wsf",
        "outputId": "6dfaf5ec-030f-4a5a-e8ab-7db756b98661"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[2.9421e-03, 2.1128e-05, 9.9704e-01],\n",
              "        [9.9871e-01, 6.7993e-05, 1.2225e-03],\n",
              "        [2.1637e-04, 9.9820e-01, 1.5828e-03]])"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "test_df_copy = test_df"
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      "metadata": {
        "id": "4Rs_qJBE61JX"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df_copy.head()"
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      "metadata": {
        "colab": {
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          "height": 143
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        "id": "SOXy3ukA65EM",
        "outputId": "49445d5a-111a-4807-e244-52a70025a8be"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      code                      image_url\n",
              "7    Renan   /content/faces/renan-003.jpg\n",
              "11  Camila  /content/faces/camila-002.jpg\n",
              "4   Daniel  /content/faces/daniel-005.jpg"
            ],
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              "\n",
              "  <div id=\"df-c6aefac5-752b-4809-bd52-23227a8e81bf\">\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>code</th>\n",
              "      <th>image_url</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Renan</td>\n",
              "      <td>/content/faces/renan-003.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Camila</td>\n",
              "      <td>/content/faces/camila-002.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Daniel</td>\n",
              "      <td>/content/faces/daniel-005.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6aefac5-752b-4809-bd52-23227a8e81bf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
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              "          document.querySelector('#df-c6aefac5-752b-4809-bd52-23227a8e81bf 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-c6aefac5-752b-4809-bd52-23227a8e81bf');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_list = learn.dls.vocab\n",
        "for n in range(3):\n",
        "  class_indexes2 = []\n",
        "  for i in range(len(test_df_copy)):\n",
        "    class_indexes = np.argsort(preds_test, axis=1)[i]\n",
        "    class_indexes2.append(int(class_indexes[-(n+1)]))\n",
        "  classes = [class_list[idx] for idx in class_indexes2]\n",
        "\n",
        "\n",
        "  test_df_copy[\"prediction{}\".format(n+1)] = classes"
      ],
      "metadata": {
        "id": "A0rSsBqB679n"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df_copy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "TO2ZZ2Nb7OmC",
        "outputId": "1268e0e7-f992-44ac-a2d2-383e1801d72a"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      code                      image_url prediction1 prediction2 prediction3\n",
              "7    Renan   /content/faces/renan-003.jpg       Renan      Camila      Daniel\n",
              "11  Camila  /content/faces/camila-002.jpg      Camila       Renan      Daniel\n",
              "4   Daniel  /content/faces/daniel-005.jpg      Daniel       Renan      Camila"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-39408b11-13ad-4fd8-8ef0-00bc7cdc84a6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>code</th>\n",
              "      <th>image_url</th>\n",
              "      <th>prediction1</th>\n",
              "      <th>prediction2</th>\n",
              "      <th>prediction3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Renan</td>\n",
              "      <td>/content/faces/renan-003.jpg</td>\n",
              "      <td>Renan</td>\n",
              "      <td>Camila</td>\n",
              "      <td>Daniel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Camila</td>\n",
              "      <td>/content/faces/camila-002.jpg</td>\n",
              "      <td>Camila</td>\n",
              "      <td>Renan</td>\n",
              "      <td>Daniel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Daniel</td>\n",
              "      <td>/content/faces/daniel-005.jpg</td>\n",
              "      <td>Daniel</td>\n",
              "      <td>Renan</td>\n",
              "      <td>Camila</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39408b11-13ad-4fd8-8ef0-00bc7cdc84a6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\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",
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              "      border-radius: 50%;\n",
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              "      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-39408b11-13ad-4fd8-8ef0-00bc7cdc84a6 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-39408b11-13ad-4fd8-8ef0-00bc7cdc84a6');\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": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learn.export()"
      ],
      "metadata": {
        "id": "sCYDdHweSGTN"
      },
      "execution_count": 93,
      "outputs": []
    }
  ]
}