train.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NhsMSpWhX2ZU",
        "outputId": "eb2558f2-8c40-4f16-b927-a76e458fc44e"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FThMAYTX6vF",
        "outputId": "97c14c81-43e5-4ffa-c099-30fa883a0735"
      },
      "source": [
        "!curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py\n",
        "!python pytorch-xla-env-setup.py --version nightly --apt-packages libomp5 libopenblas-dev"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  5116  100  5116    0     0  96528      0 --:--:-- --:--:-- --:--:-- 96528\n",
            "Updating... This may take around 2 minutes.\n",
            "Updating TPU runtime to pytorch-nightly ...\n",
            "Collecting cloud-tpu-client\n",
            "  Downloading https://files.pythonhosted.org/packages/56/9f/7b1958c2886db06feb5de5b2c191096f9e619914b6c31fdf93999fdbbd8b/cloud_tpu_client-0.10-py3-none-any.whl\n",
            "Requirement already satisfied: oauth2client in /usr/local/lib/python3.7/dist-packages (from cloud-tpu-client) (4.1.3)\n",
            "Collecting google-api-python-client==1.8.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9a/b4/a955f393b838bc47cbb6ae4643b9d0f90333d3b4db4dc1e819f36aad18cc/google_api_python_client-1.8.0-py3-none-any.whl (57kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 3.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: httplib2>=0.9.1 in /usr/local/lib/python3.7/dist-packages (from oauth2client->cloud-tpu-client) (0.17.4)\n",
            "Requirement already satisfied: six>=1.6.1 in /usr/local/lib/python3.7/dist-packages (from oauth2client->cloud-tpu-client) (1.15.0)\n",
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            "Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.7/dist-packages (from oauth2client->cloud-tpu-client) (0.2.8)\n",
            "Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from oauth2client->cloud-tpu-client) (4.7.1)\n",
            "Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client) (3.0.1)\n",
            "Requirement already satisfied: google-auth>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client) (1.27.0)\n",
            "Requirement already satisfied: google-api-core<2dev,>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client) (1.16.0)\n",
            "Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client==1.8.0->cloud-tpu-client) (0.0.4)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.4.1->google-api-python-client==1.8.0->cloud-tpu-client) (4.2.1)\n",
            "Requirement already satisfied: setuptools>=40.3.0 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.4.1->google-api-python-client==1.8.0->cloud-tpu-client) (53.0.0)\n",
            "Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (2.23.0)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (2018.9)\n",
            "Requirement already satisfied: protobuf>=3.4.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (3.12.4)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (1.52.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (1.24.3)\n",
            "Uninstalling torch-1.7.0+cu101:\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.13.0->google-api-python-client==1.8.0->cloud-tpu-client) (2.10)\n",
            "Installing collected packages: google-api-python-client, cloud-tpu-client\n",
            "  Found existing installation: google-api-python-client 1.7.12\n",
            "    Uninstalling google-api-python-client-1.7.12:\n",
            "      Successfully uninstalled google-api-python-client-1.7.12\n",
            "Successfully installed cloud-tpu-client-0.10 google-api-python-client-1.8.0\n",
            "  Successfully uninstalled torch-1.7.0+cu101\n",
            "Uninstalling torchvision-0.8.1+cu101:\n",
            "  Successfully uninstalled torchvision-0.8.1+cu101\n",
            "Copying gs://tpu-pytorch/wheels/torch-nightly-cp37-cp37m-linux_x86_64.whl...\n",
            "Done updating TPU runtime\n",
            "\\ [1 files][122.6 MiB/122.6 MiB]                                                \n",
            "Operation completed over 1 objects/122.6 MiB.                                    \n",
            "Copying gs://tpu-pytorch/wheels/torch_xla-nightly-cp37-cp37m-linux_x86_64.whl...\n",
            "\\ [1 files][132.6 MiB/132.6 MiB]                                                \n",
            "Operation completed over 1 objects/132.6 MiB.                                    \n",
            "Copying gs://tpu-pytorch/wheels/torchvision-nightly-cp37-cp37m-linux_x86_64.whl...\n",
            "/ [1 files][  4.6 MiB/  4.6 MiB]                                                \n",
            "Operation completed over 1 objects/4.6 MiB.                                      \n",
            "Processing ./torch-nightly-cp37-cp37m-linux_x86_64.whl\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==nightly) (3.7.4.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch==nightly) (1.19.5)\n",
            "\u001b[31mERROR: fastai 1.0.61 requires torchvision, which is not installed.\u001b[0m\n",
            "Installing collected packages: torch\n",
            "Successfully installed torch-1.9.0a0+92a4ee1\n",
            "Processing ./torch_xla-nightly-cp37-cp37m-linux_x86_64.whl\n",
            "Installing collected packages: torch-xla\n",
            "Successfully installed torch-xla-1.9+7671584\n",
            "Processing ./torchvision-nightly-cp37-cp37m-linux_x86_64.whl\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from torchvision==nightly) (1.9.0a0+92a4ee1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision==nightly) (1.19.5)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.7/dist-packages (from torchvision==nightly) (7.0.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->torchvision==nightly) (3.7.4.3)\n",
            "Installing collected packages: torchvision\n",
            "Successfully installed torchvision-0.9.0a0+870edfd\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "libopenblas-dev is already the newest version (0.2.20+ds-4).\n",
            "The following NEW packages will be installed:\n",
            "  libomp5\n",
            "0 upgraded, 1 newly installed, 0 to remove and 13 not upgraded.\n",
            "Need to get 234 kB of archives.\n",
            "After this operation, 774 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libomp5 amd64 5.0.1-1 [234 kB]\n",
            "Fetched 234 kB in 1s (316 kB/s)\n",
            "Selecting previously unselected package libomp5:amd64.\n",
            "(Reading database ... 149406 files and directories currently installed.)\n",
            "Preparing to unpack .../libomp5_5.0.1-1_amd64.deb ...\n",
            "Unpacking libomp5:amd64 (5.0.1-1) ...\n",
            "Setting up libomp5:amd64 (5.0.1-1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.4) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWIcUh0dX_fm",
        "outputId": "9f488d2d-cca4-4847-eb0d-2f354ff9969f"
      },
      "source": [
        "!unzip drive/MyDrive/highly-probable/imgs-300w.zip "
      ],
      "execution_count": null,
      "outputs": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nyegWSgfYCir",
        "outputId": "5a335ab0-4c09-4df8-e6ce-61270d29634a"
      },
      "source": [
        "!pip install efficientnet_pytorch"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting efficientnet_pytorch\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/83/f9c5f44060f996279e474185ebcbd8dbd91179593bffb9abe3afa55d085b/efficientnet_pytorch-0.7.0.tar.gz\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from efficientnet_pytorch) (1.9.0a0+92a4ee1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->efficientnet_pytorch) (3.7.4.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch->efficientnet_pytorch) (1.19.5)\n",
            "Building wheels for collected packages: efficientnet-pytorch\n",
            "  Building wheel for efficientnet-pytorch (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for efficientnet-pytorch: filename=efficientnet_pytorch-0.7.0-cp37-none-any.whl size=16031 sha256=3932092acd4a386accfc24aa3fea936b645be8b414c1b82d043d06cb28766635\n",
            "  Stored in directory: /root/.cache/pip/wheels/e9/c6/e1/7a808b26406239712cfce4b5ceeb67d9513ae32aa4b31445c6\n",
            "Successfully built efficientnet-pytorch\n",
            "Installing collected packages: efficientnet-pytorch\n",
            "Successfully installed efficientnet-pytorch-0.7.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPjOE30oYOmz"
      },
      "source": [
        "import gc\n",
        "import os\n",
        "import torch\n",
        "import albumentations\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import torch.nn as nn\n",
        "from sklearn import metrics\n",
        "from sklearn import model_selection\n",
        "from torch.nn import functional as F\n",
        "\n",
        "# from wtfml.engine import Engine\n",
        "# from wtfml.utils import EarlyStopping\n",
        "# from wtfml.data_loaders.image import ClassificationDataLoader\n",
        "\n",
        "\n",
        "import torch_xla.core.xla_model as xm\n",
        "import torch_xla.distributed.parallel_loader as pl\n",
        "import torch_xla.distributed.xla_multiprocessing as xmp\n",
        "\n",
        "import efficientnet_pytorch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfLHqzlURden"
      },
      "source": [
        "NUM_EPOCHS = 15"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WS5VBvTtYXcT"
      },
      "source": [
        "class EfficientNet(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(EfficientNet, self).__init__()\n",
        "        self.base_model = efficientnet_pytorch.EfficientNet.from_pretrained(\n",
        "            'efficientnet-b0'\n",
        "        )\n",
        "        self.base_model._fc = nn.Linear(\n",
        "            in_features=1280, \n",
        "            out_features=23, \n",
        "            bias=True\n",
        "        )\n",
        "        \n",
        "    def forward(self, image, targets):\n",
        "        out = self.base_model(image)\n",
        "        #loss = nn.BCEWithLogitsLoss()(out, targets.view(-1, 1).type_as(out))\n",
        "        loss = nn.CrossEntropyLoss()(out, targets.type_as(out))\n",
        "        return out, loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qa1Al1_SY1oM"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "rmu0TGufYqwH",
        "outputId": "2573817e-b004-40e0-8b5a-cca26d0e3582"
      },
      "source": [
        "DATA_DIR = 'imgs-300w'\n",
        "\n",
        "def load_df():\n",
        "    df = pd.read_csv(\"drive/MyDrive/highly-probable/womens-clothing-imageid-5fold.csv\")\n",
        "    # label encoding\n",
        "    df['target'] = df['mid_category'].astype('category').cat.codes\n",
        "\n",
        "    # remove rows with missing images (9 images)\n",
        "    df['image_exists'] = df['image_name'].apply(lambda x: os.path.exists(f'{DATA_DIR}/{x}'))\n",
        "    df = df[df['image_exists']]\n",
        "\n",
        "    df = df[['image_name', 'target', 'fold']]\n",
        "\n",
        "    df = df.rename(columns={'fold':'kfold'})\n",
        "\n",
        "    \n",
        "    print(df.shape)\n",
        "\n",
        "    return df\n",
        "\n",
        "df = load_df()\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(277620, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>image_name</th>\n",
              "      <th>target</th>\n",
              "      <th>kfold</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>16222141_1.jpg</td>\n",
              "      <td>6</td>\n",
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              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>16222141_2.jpg</td>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       image_name  target  kfold\n",
              "0  16222141_1.jpg       6    0.0\n",
              "1  16222141_2.jpg       6    0.0\n",
              "2  16222141_3.jpg       6    0.0\n",
              "3  16222141_4.jpg       6    0.0\n",
              "4  16222141_5.jpg       6    0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120,
          "referenced_widgets": [
            "52fb6ebb9e3547229db5328860a066ef",
            "04d2fd74ce3c4f9bb4c47c5cc147303f",
            "9402b0dd467245ebbb57925fbf0ba4f7",
            "bc8f4c1945f249fe9cdef594bfc1f3d2",
            "f41dbb94baa94d329ef3f2646412bb6e",
            "951d5c77fcb743c98319dfbc64a9752b",
            "b9ffdddee9ad4156b59cd2fe2f3ed0da",
            "11d232356b0a4c9f96c7788efa8d00c5"
          ]
        },
        "id": "MXON-CuvZn5p",
        "outputId": "1f240ba9-ea6f-43ec-bd97-c3202fa49749"
      },
      "source": [
        "MX = EfficientNet()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet-b0-355c32eb.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "52fb6ebb9e3547229db5328860a066ef",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=21388428.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Loaded pretrained weights for efficientnet-b0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VtAcadjbeZq0"
      },
      "source": [
        "class AverageMeter:\n",
        "    \"\"\"\n",
        "    Computes and stores the average and current value\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        self.val = 0\n",
        "        self.avg = 0\n",
        "        self.sum = 0\n",
        "        self.count = 0\n",
        "\n",
        "    def reset(self):\n",
        "        self.val = 0\n",
        "        self.avg = 0\n",
        "        self.sum = 0\n",
        "        self.count = 0\n",
        "\n",
        "    def update(self, val, n=1):\n",
        "        self.val = val\n",
        "        self.sum += val * n\n",
        "        self.count += n\n",
        "        self.avg = self.sum / self.count\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTARvW08b3rj"
      },
      "source": [
        "try:\n",
        "    import torch_xla.core.xla_model as xm\n",
        "\n",
        "    _xla_available = True\n",
        "except ImportError:\n",
        "    _xla_available = False\n",
        "\n",
        "\n",
        "class EarlyStopping:\n",
        "    def __init__(self, patience=7, mode=\"max\", delta=0.0001, tpu=False):\n",
        "        self.patience = patience\n",
        "        self.counter = 0\n",
        "        self.mode = mode\n",
        "        self.best_score = None\n",
        "        self.early_stop = False\n",
        "        self.tpu = tpu\n",
        "        self.delta = delta\n",
        "        if self.mode == \"min\":\n",
        "            self.val_score = np.Inf\n",
        "        else:\n",
        "            self.val_score = -np.Inf\n",
        "\n",
        "        if self.tpu and not _xla_available:\n",
        "            raise Exception(\n",
        "                \"You want to use TPUs but you dont have pytorch_xla installed\"\n",
        "            )\n",
        "\n",
        "    def __call__(self, epoch_score, model, model_path):\n",
        "        if self.mode == \"min\":\n",
        "            score = -1.0 * epoch_score\n",
        "        else:\n",
        "            score = np.copy(epoch_score)\n",
        "\n",
        "        if self.best_score is None:\n",
        "            self.best_score = score\n",
        "            self.save_checkpoint(epoch_score, model, model_path)\n",
        "        elif score < self.best_score + self.delta:\n",
        "            self.counter += 1\n",
        "            if self.tpu:\n",
        "                xm.master_print(\n",
        "                    \"EarlyStopping counter: {} out of {}\".format(\n",
        "                        self.counter, self.patience\n",
        "                    )\n",
        "                )\n",
        "            else:\n",
        "                print(\n",
        "                    \"EarlyStopping counter: {} out of {}\".format(\n",
        "                        self.counter, self.patience\n",
        "                    )\n",
        "                )\n",
        "            if self.counter >= self.patience:\n",
        "                self.early_stop = True\n",
        "        else:\n",
        "            self.best_score = score\n",
        "            self.save_checkpoint(epoch_score, model, model_path)\n",
        "            self.counter = 0\n",
        "\n",
        "    def save_checkpoint(self, epoch_score, model, model_path):\n",
        "        if epoch_score not in [-np.inf, np.inf, -np.nan, np.nan]:\n",
        "            if self.tpu:\n",
        "                xm.master_print(\n",
        "                    \"Validation score improved ({} --> {}). Saving model!\".format(\n",
        "                        self.val_score, epoch_score\n",
        "                    )\n",
        "                )\n",
        "            else:\n",
        "                print(\n",
        "                    \"Validation score improved ({} --> {}). Saving model!\".format(\n",
        "                        self.val_score, epoch_score\n",
        "                    )\n",
        "                )\n",
        "            if self.tpu:\n",
        "                xm.save(model.state_dict(), model_path)\n",
        "            else:\n",
        "                torch.save(model.state_dict(), model_path)\n",
        "        self.val_score = epoch_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dYWeeGCFbCLB"
      },
      "source": [
        "import cv2\n",
        "import torch\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "from PIL import Image\n",
        "from PIL import ImageFile\n",
        "\n",
        "try:\n",
        "    import torch_xla.core.xla_model as xm\n",
        "\n",
        "    _xla_available = True\n",
        "except ImportError:\n",
        "    _xla_available = False\n",
        "\n",
        "ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
        "\n",
        "\n",
        "class ClassificationDataset:\n",
        "    def __init__(\n",
        "        self,\n",
        "        image_paths,\n",
        "        targets,\n",
        "        resize,\n",
        "        augmentations=None,\n",
        "        backend=\"pil\",\n",
        "        channel_first=True,\n",
        "    ):\n",
        "        \"\"\"\n",
        "        :param image_paths: list of paths to images\n",
        "        :param targets: numpy array\n",
        "        :param resize: tuple or None\n",
        "        :param augmentations: albumentations augmentations\n",
        "        \"\"\"\n",
        "        self.image_paths = image_paths\n",
        "        self.targets = targets\n",
        "        self.resize = resize\n",
        "        self.augmentations = augmentations\n",
        "        self.backend = backend\n",
        "        self.channel_first = channel_first\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.image_paths)\n",
        "\n",
        "    def __getitem__(self, item):\n",
        "        targets = self.targets[item]\n",
        "        if self.backend == \"pil\":\n",
        "            image = Image.open(self.image_paths[item])\n",
        "            if self.resize is not None:\n",
        "                image = image.resize(\n",
        "                    (self.resize[1], self.resize[0]), resample=Image.BILINEAR\n",
        "                )\n",
        "            image = np.array(image)\n",
        "            if self.augmentations is not None:\n",
        "                augmented = self.augmentations(image=image)\n",
        "                image = augmented[\"image\"]\n",
        "        elif self.backend == \"cv2\":\n",
        "            image = cv2.imread(self.image_paths[item])\n",
        "            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "            if self.resize is not None:\n",
        "                image = cv2.resize(\n",
        "                    image,\n",
        "                    (self.resize[1], self.resize[0]),\n",
        "                    interpolation=cv2.INTER_CUBIC,\n",
        "                )\n",
        "            if self.augmentations is not None:\n",
        "                augmented = self.augmentations(image=image)\n",
        "            image = augmented[\"image\"]\n",
        "        else:\n",
        "            raise Exception(\"Backend not implemented\")\n",
        "        if self.channel_first:\n",
        "            image = np.transpose(image, (2, 0, 1)).astype(np.float32)\n",
        "        return {\n",
        "            \"image\": torch.tensor(image),\n",
        "            \"targets\": torch.tensor(targets),\n",
        "        }\n",
        "\n",
        "\n",
        "class ClassificationDataLoader:\n",
        "    def __init__(\n",
        "        self,\n",
        "        image_paths,\n",
        "        targets,\n",
        "        resize,\n",
        "        augmentations=None,\n",
        "        backend=\"pil\",\n",
        "        channel_first=True,\n",
        "    ):\n",
        "        \"\"\"\n",
        "        :param image_paths: list of paths to images\n",
        "        :param targets: numpy array\n",
        "        :param resize: tuple or None\n",
        "        :param augmentations: albumentations augmentations\n",
        "        \"\"\"\n",
        "        self.image_paths = image_paths\n",
        "        self.targets = targets\n",
        "        self.resize = resize\n",
        "        self.augmentations = augmentations\n",
        "        self.backend = backend\n",
        "        self.channel_first = channel_first\n",
        "        self.dataset = ClassificationDataset(\n",
        "            image_paths=self.image_paths,\n",
        "            targets=self.targets,\n",
        "            resize=self.resize,\n",
        "            augmentations=self.augmentations,\n",
        "            backend=self.backend,\n",
        "            channel_first=self.channel_first,\n",
        "        )\n",
        "\n",
        "    def fetch(\n",
        "        self,\n",
        "        batch_size,\n",
        "        num_workers,\n",
        "        drop_last=False,\n",
        "        shuffle=True,\n",
        "        tpu=False,\n",
        "        sampler=None,\n",
        "    ):\n",
        "        \"\"\"\n",
        "        :param batch_size: batch size\n",
        "        :param num_workers: number of processes to use\n",
        "        :param drop_last: drop the last batch?\n",
        "        :param shuffle: True/False\n",
        "        :param tpu: True/False, to use tpu or not\n",
        "        \"\"\"\n",
        "        if tpu:\n",
        "            sampler = torch.utils.data.distributed.DistributedSampler(\n",
        "                self.dataset,\n",
        "                num_replicas=xm.xrt_world_size(),\n",
        "                rank=xm.get_ordinal(),\n",
        "                shuffle=shuffle,\n",
        "            )\n",
        "\n",
        "        data_loader = torch.utils.data.DataLoader(\n",
        "            self.dataset,\n",
        "            batch_size=batch_size,\n",
        "            sampler=sampler,\n",
        "            drop_last=drop_last,\n",
        "            num_workers=num_workers,\n",
        "        )\n",
        "        return data_loader"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcWODhJIcAcW"
      },
      "source": [
        "import datetime\n",
        "import torch\n",
        "from tqdm import tqdm\n",
        " \n",
        "try:\n",
        "    from torch.cuda import amp\n",
        "    _amp_available = True\n",
        "except ImportError:\n",
        "    _amp_available = False\n",
        " \n",
        "try:\n",
        "    import torch_xla.core.xla_model as xm\n",
        "    import torch_xla.distributed.parallel_loader as pl\n",
        " \n",
        "    _xla_available = True\n",
        "except ImportError:\n",
        "    _xla_available = False\n",
        " \n",
        " \n",
        "def reduce_fn(vals):\n",
        "    return sum(vals) / len(vals)\n",
        " \n",
        " \n",
        "class Engine:\n",
        "    def __init__(\n",
        "        self,\n",
        "        model,\n",
        "        optimizer,\n",
        "        device,\n",
        "        scheduler=None,\n",
        "        accumulation_steps=1,\n",
        "        use_tpu=False,\n",
        "        tpu_print=10,\n",
        "        fp16=False,\n",
        "        model_fn=None,\n",
        "        use_mean_loss=False,\n",
        "    ):\n",
        "        \"\"\"\n",
        "        model_fn should take batch of data, device and model and return loss\n",
        "        for example:\n",
        "            def model_fn(data, device, model):\n",
        "                images, targets = data\n",
        "                images = list(image.to(device) for image in images)\n",
        "                targets = [{k: v.to(device) for k, v in t.items()} for t in targets]\n",
        "                _, loss = model(images, targets)\n",
        "                return loss\n",
        "        \"\"\"\n",
        "        self.model = model\n",
        "        self.optimizer = optimizer\n",
        "        self.device = device\n",
        "        self.scheduler = scheduler\n",
        "        self.accumulation_steps = accumulation_steps\n",
        "        self.use_tpu = use_tpu\n",
        "        self.tpu_print = tpu_print\n",
        "        self.model_fn = model_fn\n",
        "        self.fp16 = fp16\n",
        "        if self.fp16 and not _amp_available:\n",
        "            raise Exception(\n",
        "                \"You want to use fp16 but dont have amp installed\"\n",
        "            )\n",
        "        self.use_mean_loss = use_mean_loss\n",
        "        self.scaler = None\n",
        " \n",
        "        if self.use_tpu and not _xla_available:\n",
        "            raise Exception(\n",
        "                \"You want to use TPUs but you dont have pytorch_xla installed\"\n",
        "            )\n",
        "        if self.fp16 and use_tpu:\n",
        "            raise Exception(\"Apex fp16 is not available when using TPUs\")\n",
        "        if self.fp16:\n",
        "            self.scaler = amp.GradScaler()\n",
        " \n",
        "    def train(self, data_loader):\n",
        "        losses = AverageMeter()\n",
        "        self.model.train()\n",
        "        print_idx = int(len(data_loader) * self.tpu_print / 100)\n",
        "        if self.accumulation_steps > 1:\n",
        "            self.optimizer.zero_grad()\n",
        "        if self.use_tpu:\n",
        "            para_loader = pl.ParallelLoader(data_loader, [self.device])\n",
        "            tk0 = para_loader.per_device_loader(self.device)\n",
        "        else:\n",
        "            tk0 = tqdm(data_loader, total=len(data_loader))\n",
        " \n",
        "        for b_idx, data in enumerate(tk0):\n",
        "            if self.accumulation_steps == 1 and b_idx == 0:\n",
        "                self.optimizer.zero_grad()\n",
        " \n",
        "            if self.model_fn is None:\n",
        "                for key, value in data.items():\n",
        "                    data[key] = value.to(self.device)\n",
        "                _, loss = self.model(**data)\n",
        "            else:\n",
        "                if self.fp16:\n",
        "                    with amp.autocast():\n",
        "                        loss = self.model_fn(data, self.device, self.model)\n",
        "                else:\n",
        "                    loss = self.model_fn(data, self.device, self.model)\n",
        " \n",
        "            if not self.use_tpu:\n",
        "                with torch.set_grad_enabled(True):\n",
        "                    if self.use_mean_loss:\n",
        "                        loss = loss.mean()\n",
        " \n",
        "                    if self.fp16:\n",
        "                        self.scaler.scale(loss).backward()\n",
        "                    else:\n",
        "                        loss.backward()\n",
        " \n",
        "                    if (b_idx + 1) % self.accumulation_steps == 0:\n",
        "                        if self.fp16:\n",
        "                            self.scaler.step(self.optimizer)\n",
        "                        else:\n",
        "                            self.optimizer.step()\n",
        " \n",
        "                        if self.scheduler is not None:\n",
        "                            self.scheduler.step()\n",
        " \n",
        "                        if self.fp16:\n",
        "                            self.scaler.update()\n",
        " \n",
        "                        if b_idx > 0:\n",
        "                            self.optimizer.zero_grad()\n",
        " \n",
        "            else:\n",
        "                loss.backward()\n",
        "                xm.optimizer_step(self.optimizer)\n",
        "                if self.scheduler is not None:\n",
        "                    self.scheduler.step()\n",
        "                if b_idx > 0:\n",
        "                    self.optimizer.zero_grad()\n",
        "            if self.use_tpu:\n",
        "                reduced_loss = xm.mesh_reduce(\"loss_reduce\", loss, reduce_fn)\n",
        "                losses.update(reduced_loss.item(), data_loader.batch_size)\n",
        "            else:\n",
        "                losses.update(loss.item(), data_loader.batch_size)\n",
        " \n",
        "            if not self.use_tpu:\n",
        "                tk0.set_postfix(loss=losses.avg)\n",
        "            else:\n",
        "                # if b_idx % print_idx == 0 or b_idx == len(data_loader):\n",
        "                if b_idx % 10 == 0 or b_idx == len(data_loader):\n",
        "                    xm.master_print(\n",
        "                        f\"{datetime.datetime.now()}: Batch {b_idx} / {len(data_loader)}, loss={losses.avg}\"\n",
        "                    )\n",
        "        if not self.use_tpu:\n",
        "            tk0.close()\n",
        "        return losses.avg\n",
        " \n",
        "    def evaluate(self, data_loader, return_predictions=False):\n",
        "        losses = AverageMeter()\n",
        "        print_idx = int(len(data_loader) * self.tpu_print / 100)\n",
        "        self.model.eval()\n",
        "        final_predictions = []\n",
        "        with torch.no_grad():\n",
        "            if self.use_tpu:\n",
        "                para_loader = pl.ParallelLoader(data_loader, [self.device])\n",
        "                tk0 = para_loader.per_device_loader(self.device)\n",
        "            else:\n",
        "                tk0 = tqdm(data_loader, total=len(data_loader))\n",
        "            for b_idx, data in enumerate(tk0):\n",
        "                for key, value in data.items():\n",
        "                    data[key] = value.to(self.device)\n",
        "                if self.fp16:\n",
        "                    with amp.autocast():\n",
        "                        batch_preds, loss = self.model(**data)\n",
        "                else:\n",
        "                    batch_preds, loss = self.model(**data)\n",
        "                if return_predictions:\n",
        "                    final_predictions.append(batch_preds)\n",
        "                if self.use_tpu:\n",
        "                    reduced_loss = xm.mesh_reduce(\"loss_reduce\", loss, reduce_fn)\n",
        "                    losses.update(reduced_loss.item(), data_loader.batch_size)\n",
        "                else:\n",
        "                    if self.use_mean_loss:\n",
        "                        loss = loss.mean()\n",
        "                    losses.update(loss.item(), data_loader.batch_size)\n",
        "                if not self.use_tpu:\n",
        "                    tk0.set_postfix(loss=losses.avg)\n",
        "                else:\n",
        "                    #if b_idx % print_idx == 0 or b_idx == len(data_loader):\n",
        "                    if b_idx % 10 == 0 or b_idx == len(data_loader):\n",
        "                        xm.master_print(\n",
        "                            f\"{datetime.datetime.now()}: Batch {b_idx} / {len(data_loader)}, loss={losses.avg}\"\n",
        "                        )\n",
        "            if not self.use_tpu:\n",
        "                tk0.close()\n",
        "        return losses.avg, final_predictions\n",
        " \n",
        "    def predict(self, data_loader):\n",
        "        self.model.eval()\n",
        "        final_predictions = []\n",
        "        if self.use_tpu:\n",
        "            raise Exception(\"TPU not available for predict yet!\")\n",
        "        with torch.no_grad():\n",
        "            tk0 = tqdm(data_loader, total=len(data_loader))\n",
        "            for data in tk0:\n",
        "                for key, value in data.items():\n",
        "                    data[key] = value.to(self.device)\n",
        "                predictions, _ = self.model(**data)\n",
        "                predictions = predictions.cpu()\n",
        "                final_predictions.append(predictions)\n",
        "        return final_predictions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjJVBYJ6Zn32"
      },
      "source": [
        "def train():\n",
        "    training_data_path = \"imgs-300w/\"\n",
        "    df = load_df()\n",
        "    device = xm.xla_device()\n",
        "    epochs = NUM_EPOCHS\n",
        "    train_bs = 32\n",
        "    valid_bs = 16\n",
        "    fold = 0\n",
        " \n",
        "    df_train = df[df.kfold != fold].reset_index(drop=True)\n",
        "    df_valid = df[df.kfold == fold].reset_index(drop=True)\n",
        " \n",
        "    model = MX.to(device)\n",
        " \n",
        "    mean = (0.485, 0.456, 0.406)\n",
        "    std = (0.229, 0.224, 0.225)\n",
        "    train_aug = albumentations.Compose(\n",
        "        [   albumentations.CenterCrop(\n",
        "                height=300,\n",
        "                width=300,\n",
        "            ),\n",
        "         \n",
        "            albumentations.Normalize(\n",
        "                mean, \n",
        "                std, \n",
        "                max_pixel_value=255.0, \n",
        "                always_apply=True\n",
        "            ),\n",
        "            albumentations.ShiftScaleRotate(\n",
        "                shift_limit=0.0625, \n",
        "                scale_limit=0.1, \n",
        "                rotate_limit=15\n",
        "            ),\n",
        "            albumentations.Flip(p=0.5)\n",
        "        ]\n",
        "    )\n",
        " \n",
        "    valid_aug = albumentations.Compose(\n",
        "        [\n",
        "            albumentations.CenterCrop(\n",
        "                height=300,\n",
        "                width=300,\n",
        "            ),\n",
        "            albumentations.Normalize(\n",
        "                mean, \n",
        "                std, \n",
        "                max_pixel_value=255.0,\n",
        "                always_apply=True\n",
        "            )\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    # df_train = df_train.iloc[:256]\n",
        "    # df_valid = df_valid.iloc[:128]\n",
        " \n",
        "    train_images = df_train.image_name.values.tolist()\n",
        "    train_images = [\n",
        "        os.path.join(training_data_path, i) for i in train_images\n",
        "    ]\n",
        "    train_targets = df_train.target.values\n",
        " \n",
        "    valid_images = df_valid.image_name.values.tolist()\n",
        "    valid_images = [\n",
        "        os.path.join(training_data_path, i) for i in valid_images\n",
        "    ]\n",
        "    valid_targets = df_valid.target.values\n",
        " \n",
        "    train_loader = ClassificationDataLoader(\n",
        "        image_paths=train_images,\n",
        "        targets=train_targets,\n",
        "        resize=None,\n",
        "        augmentations=train_aug,\n",
        "    ).fetch(\n",
        "        batch_size=train_bs, \n",
        "        drop_last=True, \n",
        "        num_workers=4, \n",
        "        shuffle=True, \n",
        "        tpu=True\n",
        "    )\n",
        " \n",
        "    valid_loader = ClassificationDataLoader(\n",
        "        image_paths=valid_images,\n",
        "        targets=valid_targets,\n",
        "        resize=None,\n",
        "        augmentations=valid_aug,\n",
        "    ).fetch(\n",
        "        batch_size=valid_bs, \n",
        "        drop_last=True,\n",
        "        num_workers=2, \n",
        "        shuffle=False, \n",
        "        tpu=True\n",
        "    )\n",
        " \n",
        "        \n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
        "\n",
        "    # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
        "    #     optimizer,\n",
        "    #     patience=3,\n",
        "    #     threshold=0.001,\n",
        "    #     mode=\"min\"\n",
        "    # )\n",
        "\n",
        "    scheduler = {'scheduler': torch.optim.lr_scheduler.OneCycleLR(\n",
        "                                        optimizer,\n",
        "                                        max_lr=1e-3,\n",
        "                                        steps_per_epoch=int(len(train_loader)),\n",
        "                                        epochs=NUM_EPOCHS,\n",
        "                                        anneal_strategy=\"linear\",\n",
        "                                        final_div_factor = 30,\n",
        "                                    ),\n",
        "                        'name': 'learning_rate',\n",
        "                        'interval':'step',\n",
        "                        'frequency': 1}\n",
        " \n",
        "    es = EarlyStopping(patience=15, mode=\"min\", tpu=True)\n",
        "    eng = Engine(model, optimizer, device=device, use_tpu=True, tpu_print=25)\n",
        " \n",
        " \n",
        " \n",
        "    for epoch in range(epochs):\n",
        "        train_loss = eng.train(train_loader)\n",
        "        valid_loss = eng.evaluate(valid_loader)[0]\n",
        "        xm.master_print(f\"Epoch = {epoch}, LOSS = {valid_loss}\")\n",
        "\n",
        "        #xm.master_print(valid_loss)\n",
        " \n",
        "        \n",
        "        es(valid_loss, model, model_path=f\"drive/MyDrive/highly-probable/results/tp1/model_fold_{fold}.bin\")\n",
        "        if es.early_stop:\n",
        "            xm.master_print(\"Early stopping\")\n",
        "            break\n",
        "        gc.collect()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcTpJ6OCrCQ0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNu8LjazZn18"
      },
      "source": [
        "def _mp_fn(rank, flags):\n",
        "    torch.set_default_tensor_type('torch.FloatTensor')\n",
        "    a = train()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYp9af9fQWUO"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RdCg6Z2BZnz7",
        "outputId": "579b80bb-7abd-4d38-b25f-6b85fdc9992d"
      },
      "source": [
        "FLAGS={}\n",
        "xmp.spawn(_mp_fn, args=(FLAGS,), nprocs=8, start_method='fork')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(277620, 3)\n",
            "(277620, 3)\n",
            "(277620, 3)\n",
            "(277620, 3)\n",
            "(277620, 3)\n",
            "(277620, 3)\n",
            "(277620, 3)\n",
            "(277620, 3)\n",
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            "Epoch = 0, LOSS = 0.9832415878084461\n",
            "Validation score improved (inf --> 0.9832415878084461). Saving model!\n",
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            "Epoch = 1, LOSS = 0.8615819464241935\n",
            "Validation score improved (0.9832415878084461 --> 0.8615819464241935). Saving model!\n",
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            "Epoch = 2, LOSS = 0.8067833852823006\n",
            "Validation score improved (0.8615819464241935 --> 0.8067833852823006). Saving model!\n",
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            "Epoch = 3, LOSS = 0.77784079932726\n",
            "Validation score improved (0.8067833852823006 --> 0.77784079932726). Saving model!\n",
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            "Epoch = 4, LOSS = 0.752372635085368\n",
            "Validation score improved (0.77784079932726 --> 0.752372635085368). Saving model!\n",
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            "Epoch = 5, LOSS = 0.739524664628313\n",
            "Validation score improved (0.752372635085368 --> 0.739524664628313). Saving model!\n",
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            "Epoch = 6, LOSS = 0.7289407044717952\n",
            "Validation score improved (0.739524664628313 --> 0.7289407044717952). Saving model!\n",
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            "Epoch = 7, LOSS = 0.7218483540532771\n",
            "Validation score improved (0.7289407044717952 --> 0.7218483540532771). Saving model!\n",
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            "Epoch = 8, LOSS = 0.7123822318626607\n",
            "Validation score improved (0.7218483540532771 --> 0.7123822318626607). Saving model!\n",
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            "Epoch = 9, LOSS = 0.7082243285746277\n",
            "Validation score improved (0.7123822318626607 --> 0.7082243285746277). Saving model!\n",
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            "Epoch = 10, LOSS = 0.7055777090388687\n",
            "Validation score improved (0.7082243285746277 --> 0.7055777090388687). Saving model!\n",
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            "Epoch = 11, LOSS = 0.7032612659226389\n",
            "Validation score improved (0.7055777090388687 --> 0.7032612659226389). Saving model!\n",
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            "Epoch = 13, LOSS = 0.7077643251859563\n",
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            "2021-02-26 04:59:52.181890: Batch 380 / 433, loss=0.7069012234842996\n",
            "2021-02-26 05:00:01.707830: Batch 390 / 433, loss=0.7095743156302615\n",
            "2021-02-26 05:00:15.398007: Batch 400 / 433, loss=0.7099943997110809\n",
            "2021-02-26 05:00:24.590768: Batch 410 / 433, loss=0.7105472164722545\n",
            "2021-02-26 05:00:39.221252: Batch 420 / 433, loss=0.7095453666961108\n",
            "2021-02-26 05:00:46.243141: Batch 430 / 433, loss=0.7087661448321597\n",
            "Epoch = 14, LOSS = 0.7099203500406296\n",
            "EarlyStopping counter: 3 out of 15\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RrTVIYqqZnv5"
      },
      "source": [
        "df[df.target=='']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBgx2gzwZntz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}