ventilator_gb_rescaling_eda_V3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "language": "python",
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.7.10",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "colab": {
      "name": "ventilator-gb-rescaling-eda-V3.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bobbercheng/ventilator-pressure-prediction/blob/master/ventilator_gb_rescaling_eda_V3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yh6Zg50KJQBQ"
      },
      "source": [
        "V1: Keep R/C, improve\n",
        "\n",
        "V2: Add R**2, no improve\n",
        "\n",
        "V3: Update model from https://www.kaggle.com/dlaststark/gb-vpp-pulp-fiction. Not much improve compare V1\n",
        "\n",
        "V4: Use whole db for test\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uslq2jL49nGk"
      },
      "source": [
        "# Update pandas version for Colab TPU\n",
        "#!pip install pandas==1.3.2"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RBJlpUNI9com"
      },
      "source": [
        "## Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXzLriPA9coq"
      },
      "source": [
        "try:\n",
        "  import google.colab\n",
        "  IN_COLAB = True\n",
        "except:\n",
        "  IN_COLAB = False"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9esF__aG9cor",
        "outputId": "a285e7cf-d85b-48fd-a1fe-89add1b1b779"
      },
      "source": [
        "if IN_COLAB:\n",
        "  from google.colab import drive\n",
        "  drive.mount(\"/gdrive\", force_remount=True)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZ_f3Z2M9cos",
        "outputId": "5e8ebb95-b4e6-497f-d1e4-ea642d68c9ab"
      },
      "source": [
        "if IN_COLAB:\n",
        "  !pip install kaggle\n",
        "  !mkdir /root/.kaggle\n",
        "  !cp /gdrive/MyDrive/ventilator-pressure-prediction/kaggle.json /root/.kaggle\n",
        "  !kaggle competitions download -c ventilator-pressure-prediction\n",
        "  !mkdir -p ../input/ventilator-pressure-prediction\n",
        "  !unzip '*.zip' -d ../input/ventilator-pressure-prediction\n",
        "  !ls ../input/ventilator-pressure-prediction\n",
        "  !mkdir /gdrive/MyDrive/ventilator-pressure-prediction/gb-rescaling-eda-v3\n",
        "  #Map Google driver folder to local folder model to save model\n",
        "  !ln -s /gdrive/MyDrive/ventilator-pressure-prediction/gb-rescaling-eda-v3 model\n",
        "else:\n",
        "  !mkdir ./model"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.62.3)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2021.5.30)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (5.0.2)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "train.csv.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "test.csv.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "sample_submission.csv.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "Archive:  train.csv.zip\n",
            "replace ../input/ventilator-pressure-prediction/train.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n",
            "\n",
            "Archive:  sample_submission.csv.zip\n",
            "\n",
            "Archive:  test.csv.zip\n",
            "\n",
            "3 archives were successfully processed.\n",
            "sample_submission.csv  test.csv  train.csv\n",
            "mkdir: cannot create directory ‘/gdrive/MyDrive/ventilator-pressure-prediction/gb-rescaling-eda-v3’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-21T17:30:55.782617Z",
          "iopub.execute_input": "2021-10-21T17:30:55.783998Z",
          "iopub.status.idle": "2021-10-21T17:30:55.793669Z",
          "shell.execute_reply.started": "2021-10-21T17:30:55.783937Z",
          "shell.execute_reply": "2021-10-21T17:30:55.792667Z"
        },
        "trusted": true,
        "id": "1TfjZidC9cos"
      },
      "source": [
        "import gc\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "import os\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.layers import Dense, Dropout, Input\n",
        "from tensorflow.keras.layers import Concatenate, LSTM, GRU\n",
        "from tensorflow.keras.layers import Bidirectional, Multiply\n",
        "\n",
        "\n",
        "\n",
        "# Random Seed Initialize\n",
        "RANDOM_SEED = 42\n",
        "\n",
        "def seed_everything(seed=RANDOM_SEED):\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    random.seed(seed)\n",
        "    tf.random.set_seed(seed)\n",
        "    \n",
        "seed_everything()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XycyrO7g9cot"
      },
      "source": [
        "## Load datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-21T17:24:55.352925Z",
          "iopub.execute_input": "2021-10-21T17:24:55.353455Z",
          "iopub.status.idle": "2021-10-21T17:25:06.101910Z",
          "shell.execute_reply.started": "2021-10-21T17:24:55.353418Z",
          "shell.execute_reply": "2021-10-21T17:25:06.100969Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "p_vUMe_39cot",
        "outputId": "6ab00e16-2031-41e2-c580-3f9ea82a1daa"
      },
      "source": [
        "train_df = pd.read_csv('../input/ventilator-pressure-prediction/train.csv')\n",
        "print(f\"train_df: {train_df.shape}\")\n",
        "train_df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_df: (6036000, 8)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>breath_id</th>\n",
              "      <th>R</th>\n",
              "      <th>C</th>\n",
              "      <th>time_step</th>\n",
              "      <th>u_in</th>\n",
              "      <th>u_out</th>\n",
              "      <th>pressure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0</td>\n",
              "      <td>5.837492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.033652</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>0</td>\n",
              "      <td>5.907794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.067514</td>\n",
              "      <td>22.509278</td>\n",
              "      <td>0</td>\n",
              "      <td>7.876254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.101542</td>\n",
              "      <td>22.808822</td>\n",
              "      <td>0</td>\n",
              "      <td>11.742872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.135756</td>\n",
              "      <td>25.355850</td>\n",
              "      <td>0</td>\n",
              "      <td>12.234987</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  breath_id   R   C  time_step       u_in  u_out   pressure\n",
              "0   1          1  20  50   0.000000   0.083334      0   5.837492\n",
              "1   2          1  20  50   0.033652  18.383041      0   5.907794\n",
              "2   3          1  20  50   0.067514  22.509278      0   7.876254\n",
              "3   4          1  20  50   0.101542  22.808822      0  11.742872\n",
              "4   5          1  20  50   0.135756  25.355850      0  12.234987"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-21T17:25:11.213742Z",
          "iopub.execute_input": "2021-10-21T17:25:11.214675Z",
          "iopub.status.idle": "2021-10-21T17:25:11.297152Z",
          "shell.execute_reply.started": "2021-10-21T17:25:11.214617Z",
          "shell.execute_reply": "2021-10-21T17:25:11.295985Z"
        },
        "trusted": true,
        "id": "R5oBMjYJ9cov"
      },
      "source": [
        "all_pressure = np.sort(train_df['pressure'].unique())\n",
        "pressure_min =  all_pressure[0].item()\n",
        "pressure_max = all_pressure[-1].item()\n",
        "pressure_step = (all_pressure[1] - all_pressure[0]).item()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80hIcGg09cou"
      },
      "source": [
        "DEBUG = False\n",
        "\n",
        "if DEBUG:\n",
        "    train_df = train_df[:80*10000]"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-21T17:25:06.103441Z",
          "iopub.execute_input": "2021-10-21T17:25:06.104426Z",
          "iopub.status.idle": "2021-10-21T17:25:11.211412Z",
          "shell.execute_reply.started": "2021-10-21T17:25:06.104376Z",
          "shell.execute_reply": "2021-10-21T17:25:11.209506Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "y-0XhQ3a9cov",
        "outputId": "8ccce4b5-df7c-453b-f0d7-3635df854713"
      },
      "source": [
        "test_df = pd.read_csv('../input/ventilator-pressure-prediction/test.csv')\n",
        "print(f\"test_df: {test_df.shape}\")\n",
        "test_df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test_df: (4024000, 7)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>breath_id</th>\n",
              "      <th>R</th>\n",
              "      <th>C</th>\n",
              "      <th>time_step</th>\n",
              "      <th>u_in</th>\n",
              "      <th>u_out</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>0.031904</td>\n",
              "      <td>7.515046</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>0.063827</td>\n",
              "      <td>14.651675</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>0.095751</td>\n",
              "      <td>21.230610</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>0.127644</td>\n",
              "      <td>26.320956</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  breath_id  R   C  time_step       u_in  u_out\n",
              "0   1          0  5  20   0.000000   0.000000      0\n",
              "1   2          0  5  20   0.031904   7.515046      0\n",
              "2   3          0  5  20   0.063827  14.651675      0\n",
              "3   4          0  5  20   0.095751  21.230610      0\n",
              "4   5          0  5  20   0.127644  26.320956      0"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVPWgQDL9cow"
      },
      "source": [
        "## Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-21T17:25:11.298767Z",
          "iopub.execute_input": "2021-10-21T17:25:11.299164Z",
          "iopub.status.idle": "2021-10-21T17:27:48.006762Z",
          "shell.execute_reply.started": "2021-10-21T17:25:11.299125Z",
          "shell.execute_reply": "2021-10-21T17:27:48.005658Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1NHNQSZp9cow",
        "outputId": "c9a848c2-43b2-4905-cf10-19addbb5dc0a"
      },
      "source": [
        "def add_features(df):\n",
        "    df['cross']= df['u_in'] * df['u_out']\n",
        "    df['cross2']= df['time_step'] * df['u_out']\n",
        "    df['area'] = df['time_step'] * df['u_in']\n",
        "    df['area'] = df.groupby('breath_id')['area'].cumsum()\n",
        "    df['time_step_cumsum'] = df.groupby(['breath_id'])['time_step'].cumsum()\n",
        "    df['u_in_cumsum'] = (df['u_in']).groupby(df['breath_id']).cumsum()\n",
        "    print(\"Step-1...Completed\")\n",
        "    \n",
        "    df['u_in_lag1'] = df.groupby('breath_id')['u_in'].shift(1)\n",
        "    df['u_out_lag1'] = df.groupby('breath_id')['u_out'].shift(1)\n",
        "    df['u_in_lag_back1'] = df.groupby('breath_id')['u_in'].shift(-1)\n",
        "    df['u_out_lag_back1'] = df.groupby('breath_id')['u_out'].shift(-1)\n",
        "    df['u_in_lag2'] = df.groupby('breath_id')['u_in'].shift(2)\n",
        "    df['u_out_lag2'] = df.groupby('breath_id')['u_out'].shift(2)\n",
        "    df['u_in_lag_back2'] = df.groupby('breath_id')['u_in'].shift(-2)\n",
        "    df['u_out_lag_back2'] = df.groupby('breath_id')['u_out'].shift(-2)\n",
        "    df['u_in_lag3'] = df.groupby('breath_id')['u_in'].shift(3)\n",
        "    df['u_out_lag3'] = df.groupby('breath_id')['u_out'].shift(3)\n",
        "    df['u_in_lag_back3'] = df.groupby('breath_id')['u_in'].shift(-3)\n",
        "    df['u_out_lag_back3'] = df.groupby('breath_id')['u_out'].shift(-3)\n",
        "    df['u_in_lag4'] = df.groupby('breath_id')['u_in'].shift(4)\n",
        "    df['u_out_lag4'] = df.groupby('breath_id')['u_out'].shift(4)\n",
        "    df['u_in_lag_back4'] = df.groupby('breath_id')['u_in'].shift(-4)\n",
        "    df['u_out_lag_back4'] = df.groupby('breath_id')['u_out'].shift(-4)\n",
        "    df = df.fillna(0)\n",
        "    print(\"Step-2...Completed\")\n",
        "    \n",
        "    df['breath_id__u_in__max'] = df.groupby(['breath_id'])['u_in'].transform('max')\n",
        "    df['breath_id__u_out__max'] = df.groupby(['breath_id'])['u_out'].transform('max')\n",
        "    df['breath_id__u_in__diffmax'] = df.groupby(['breath_id'])['u_in'].transform('max') - df['u_in']\n",
        "    df['breath_id__u_in__diffmean'] = df.groupby(['breath_id'])['u_in'].transform('mean') - df['u_in']\n",
        "    df['breath_id__u_in__diffmax'] = df.groupby(['breath_id'])['u_in'].transform('max') - df['u_in']\n",
        "    df['breath_id__u_in__diffmean'] = df.groupby(['breath_id'])['u_in'].transform('mean') - df['u_in']\n",
        "    print(\"Step-3...Completed\")\n",
        "    \n",
        "    df['u_in_diff1'] = df['u_in'] - df['u_in_lag1']\n",
        "    df['u_out_diff1'] = df['u_out'] - df['u_out_lag1']\n",
        "    df['u_in_diff2'] = df['u_in'] - df['u_in_lag2']\n",
        "    df['u_out_diff2'] = df['u_out'] - df['u_out_lag2']\n",
        "    df['u_in_diff3'] = df['u_in'] - df['u_in_lag3']\n",
        "    df['u_out_diff3'] = df['u_out'] - df['u_out_lag3']\n",
        "    df['u_in_diff4'] = df['u_in'] - df['u_in_lag4']\n",
        "    df['u_out_diff4'] = df['u_out'] - df['u_out_lag4']\n",
        "    print(\"Step-4...Completed\")\n",
        "    \n",
        "    df['one'] = 1\n",
        "    df['count'] = (df['one']).groupby(df['breath_id']).cumsum()\n",
        "    df['u_in_cummean'] =df['u_in_cumsum'] /df['count']\n",
        "    \n",
        "    df['breath_id_lag']=df['breath_id'].shift(1).fillna(0)\n",
        "    df['breath_id_lag2']=df['breath_id'].shift(2).fillna(0)\n",
        "    df['breath_id_lagsame']=np.select([df['breath_id_lag']==df['breath_id']],[1],0)\n",
        "    df['breath_id_lag2same']=np.select([df['breath_id_lag2']==df['breath_id']],[1],0)\n",
        "    df['breath_id__u_in_lag'] = df['u_in'].shift(1).fillna(0)\n",
        "    df['breath_id__u_in_lag'] = df['breath_id__u_in_lag'] * df['breath_id_lagsame']\n",
        "    df['breath_id__u_in_lag2'] = df['u_in'].shift(2).fillna(0)\n",
        "    df['breath_id__u_in_lag2'] = df['breath_id__u_in_lag2'] * df['breath_id_lag2same']\n",
        "    print(\"Step-5...Completed\")\n",
        "    \n",
        "    df['time_step_diff'] = df.groupby('breath_id')['time_step'].diff().fillna(0)\n",
        "    df['ewm_u_in_mean'] = (df\\\n",
        "                           .groupby('breath_id')['u_in']\\\n",
        "                           .ewm(halflife=9)\\\n",
        "                           .mean()\\\n",
        "                           .reset_index(level=0,drop=True))\n",
        "    df[[\"15_in_sum\",\"15_in_min\",\"15_in_max\",\"15_in_mean\"]] = (df\\\n",
        "                                                              .groupby('breath_id')['u_in']\\\n",
        "                                                              .rolling(window=15,min_periods=1)\\\n",
        "                                                              .agg({\"15_in_sum\":\"sum\",\n",
        "                                                                    \"15_in_min\":\"min\",\n",
        "                                                                    \"15_in_max\":\"max\",\n",
        "                                                                    \"15_in_mean\":\"mean\"\n",
        "                                                                    #\"15_in_std\":\"std\"\n",
        "                                                               })\\\n",
        "                                                               .reset_index(level=0,drop=True))\n",
        "    print(\"Step-6...Completed\")\n",
        "    \n",
        "    #df['u_in_diff_1_2'] = df['u_in_lag1'] - df['u_in_lag2']\n",
        "    #df['u_out_diff_1_2'] = df['u_out_lag1'] - df['u_out_lag2']\n",
        "    #df['u_in_lagback_diff_1_2'] = df['u_in_lag_back1'] - df['u_in_lag_back2']\n",
        "    #df['u_out_lagback_diff_1_2'] = df['u_out_lag_back1'] - df['u_out_lag_back2']\n",
        "    \n",
        "    df['u_in_lagback_diff1'] = df['u_in'] - df['u_in_lag_back1']\n",
        "    df['u_out_lagback_diff1'] = df['u_out'] - df['u_out_lag_back1']\n",
        "    df['u_in_lagback_diff2'] = df['u_in'] - df['u_in_lag_back2']\n",
        "    df['u_out_lagback_diff2'] = df['u_out'] - df['u_out_lag_back2']\n",
        "    print(\"Step-7...Completed\")\n",
        "    \n",
        "    df['R_cat'] = df['R'].astype(str)\n",
        "    df['C_cat'] = df['C'].astype(str)\n",
        "    df['R__C'] = df[\"R\"].astype(str) + '__' + df[\"C\"].astype(str)\n",
        "    df = pd.get_dummies(df)\n",
        "    print(\"Step-8...Completed\")\n",
        "    \n",
        "    return df\n",
        "\n",
        "\n",
        "print(\"Train data...\\n\")\n",
        "train = add_features(train_df)\n",
        "\n",
        "print(\"\\nTest data...\\n\")\n",
        "test = add_features(test_df)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train data...\n",
            "\n",
            "Step-1...Completed\n",
            "Step-2...Completed\n",
            "Step-3...Completed\n",
            "Step-4...Completed\n",
            "Step-5...Completed\n",
            "Step-6...Completed\n",
            "Step-7...Completed\n",
            "Step-8...Completed\n",
            "\n",
            "Test data...\n",
            "\n",
            "Step-1...Completed\n",
            "Step-2...Completed\n",
            "Step-3...Completed\n",
            "Step-4...Completed\n",
            "Step-5...Completed\n",
            "Step-6...Completed\n",
            "Step-7...Completed\n",
            "Step-8...Completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-21T17:28:07.762733Z",
          "iopub.execute_input": "2021-10-21T17:28:07.763099Z",
          "iopub.status.idle": "2021-10-21T17:28:07.795291Z",
          "shell.execute_reply.started": "2021-10-21T17:28:07.763065Z",
          "shell.execute_reply": "2021-10-21T17:28:07.794126Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "PgRvxlmB9co0",
        "outputId": "d6d58683-125f-4994-b977-68eda7a75fdd"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>breath_id</th>\n",
              "      <th>R</th>\n",
              "      <th>C</th>\n",
              "      <th>time_step</th>\n",
              "      <th>u_in</th>\n",
              "      <th>u_out</th>\n",
              "      <th>pressure</th>\n",
              "      <th>cross</th>\n",
              "      <th>cross2</th>\n",
              "      <th>area</th>\n",
              "      <th>time_step_cumsum</th>\n",
              "      <th>u_in_cumsum</th>\n",
              "      <th>u_in_lag1</th>\n",
              "      <th>u_out_lag1</th>\n",
              "      <th>u_in_lag_back1</th>\n",
              "      <th>u_out_lag_back1</th>\n",
              "      <th>u_in_lag2</th>\n",
              "      <th>u_out_lag2</th>\n",
              "      <th>u_in_lag_back2</th>\n",
              "      <th>u_out_lag_back2</th>\n",
              "      <th>u_in_lag3</th>\n",
              "      <th>u_out_lag3</th>\n",
              "      <th>u_in_lag_back3</th>\n",
              "      <th>u_out_lag_back3</th>\n",
              "      <th>u_in_lag4</th>\n",
              "      <th>u_out_lag4</th>\n",
              "      <th>u_in_lag_back4</th>\n",
              "      <th>u_out_lag_back4</th>\n",
              "      <th>breath_id__u_in__max</th>\n",
              "      <th>breath_id__u_out__max</th>\n",
              "      <th>breath_id__u_in__diffmax</th>\n",
              "      <th>breath_id__u_in__diffmean</th>\n",
              "      <th>u_in_diff1</th>\n",
              "      <th>u_out_diff1</th>\n",
              "      <th>u_in_diff2</th>\n",
              "      <th>u_out_diff2</th>\n",
              "      <th>u_in_diff3</th>\n",
              "      <th>u_out_diff3</th>\n",
              "      <th>u_in_diff4</th>\n",
              "      <th>u_out_diff4</th>\n",
              "      <th>one</th>\n",
              "      <th>count</th>\n",
              "      <th>u_in_cummean</th>\n",
              "      <th>breath_id_lag</th>\n",
              "      <th>breath_id_lag2</th>\n",
              "      <th>breath_id_lagsame</th>\n",
              "      <th>breath_id_lag2same</th>\n",
              "      <th>breath_id__u_in_lag</th>\n",
              "      <th>breath_id__u_in_lag2</th>\n",
              "      <th>time_step_diff</th>\n",
              "      <th>ewm_u_in_mean</th>\n",
              "      <th>15_in_sum</th>\n",
              "      <th>15_in_min</th>\n",
              "      <th>15_in_max</th>\n",
              "      <th>15_in_mean</th>\n",
              "      <th>u_in_lagback_diff1</th>\n",
              "      <th>u_out_lagback_diff1</th>\n",
              "      <th>u_in_lagback_diff2</th>\n",
              "      <th>u_out_lagback_diff2</th>\n",
              "      <th>R_cat_20</th>\n",
              "      <th>R_cat_5</th>\n",
              "      <th>R_cat_50</th>\n",
              "      <th>C_cat_10</th>\n",
              "      <th>C_cat_20</th>\n",
              "      <th>C_cat_50</th>\n",
              "      <th>R__C_20__10</th>\n",
              "      <th>R__C_20__20</th>\n",
              "      <th>R__C_20__50</th>\n",
              "      <th>R__C_50__10</th>\n",
              "      <th>R__C_50__20</th>\n",
              "      <th>R__C_50__50</th>\n",
              "      <th>R__C_5__10</th>\n",
              "      <th>R__C_5__20</th>\n",
              "      <th>R__C_5__50</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0</td>\n",
              "      <td>5.837492</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.509278</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.808822</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.355850</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.313036</td>\n",
              "      <td>1</td>\n",
              "      <td>28.229702</td>\n",
              "      <td>10.062673</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>-18.299707</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-22.425944</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.033652</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>0</td>\n",
              "      <td>5.907794</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.618632</td>\n",
              "      <td>0.033652</td>\n",
              "      <td>18.466375</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.509278</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.808822</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.355850</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.259866</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.313036</td>\n",
              "      <td>1</td>\n",
              "      <td>9.929994</td>\n",
              "      <td>-8.237035</td>\n",
              "      <td>18.299707</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>9.233188</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.033652</td>\n",
              "      <td>9.585358</td>\n",
              "      <td>18.466375</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>9.233188</td>\n",
              "      <td>-4.126236</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-4.425781</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.067514</td>\n",
              "      <td>22.509278</td>\n",
              "      <td>0</td>\n",
              "      <td>7.876254</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.138333</td>\n",
              "      <td>0.101167</td>\n",
              "      <td>40.975653</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.808822</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.355850</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.259866</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.127486</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.313036</td>\n",
              "      <td>1</td>\n",
              "      <td>5.803758</td>\n",
              "      <td>-12.363271</td>\n",
              "      <td>4.126236</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.425944</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.509278</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.509278</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>13.658551</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.033862</td>\n",
              "      <td>14.229040</td>\n",
              "      <td>40.975653</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>22.509278</td>\n",
              "      <td>13.658551</td>\n",
              "      <td>-0.299544</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-2.846573</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.101542</td>\n",
              "      <td>22.808822</td>\n",
              "      <td>0</td>\n",
              "      <td>11.742872</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.454391</td>\n",
              "      <td>0.202709</td>\n",
              "      <td>63.784476</td>\n",
              "      <td>22.509278</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.355850</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.259866</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.127486</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.807732</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.313036</td>\n",
              "      <td>1</td>\n",
              "      <td>5.504214</td>\n",
              "      <td>-12.662816</td>\n",
              "      <td>0.299544</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.425781</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.725488</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.808822</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>15.946119</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>22.509278</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>0.034028</td>\n",
              "      <td>16.627759</td>\n",
              "      <td>63.784476</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>22.808822</td>\n",
              "      <td>15.946119</td>\n",
              "      <td>-2.547028</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-4.451044</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>50</td>\n",
              "      <td>0.135756</td>\n",
              "      <td>25.355850</td>\n",
              "      <td>0</td>\n",
              "      <td>12.234987</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.896588</td>\n",
              "      <td>0.338464</td>\n",
              "      <td>89.140326</td>\n",
              "      <td>22.808822</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.259866</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.509278</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.127486</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.383041</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.807732</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.864715</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.313036</td>\n",
              "      <td>1</td>\n",
              "      <td>2.957185</td>\n",
              "      <td>-15.209844</td>\n",
              "      <td>2.547028</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.846573</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.972809</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.272516</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>17.828065</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>22.808822</td>\n",
              "      <td>22.509278</td>\n",
              "      <td>0.034213</td>\n",
              "      <td>18.652046</td>\n",
              "      <td>89.140326</td>\n",
              "      <td>0.083334</td>\n",
              "      <td>25.355850</td>\n",
              "      <td>17.828065</td>\n",
              "      <td>-1.904016</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.771635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  breath_id   R   C  ...  R__C_50__50  R__C_5__10  R__C_5__20  R__C_5__50\n",
              "0   1          1  20  50  ...            0           0           0           0\n",
              "1   2          1  20  50  ...            0           0           0           0\n",
              "2   3          1  20  50  ...            0           0           0           0\n",
              "3   4          1  20  50  ...            0           0           0           0\n",
              "4   5          1  20  50  ...            0           0           0           0\n",
              "\n",
              "[5 rows x 75 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zcyv-iig9co0",
        "outputId": "b5649508-9cb8-4c89-f1e3-e86a5018f1fe"
      },
      "source": [
        "del train_df\n",
        "del test_df\n",
        "gc.collect()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qip05b0r9co0",
        "outputId": "21ffcfb1-c070-46ea-e58d-1fbf37f81bc3"
      },
      "source": [
        "targets = train[['pressure']].to_numpy().reshape(-1, 80)\n",
        "\n",
        "train.drop(['pressure','id', 'breath_id','one','count',\n",
        "            'breath_id_lag','breath_id_lag2','breath_id_lagsame',\n",
        "            'breath_id_lag2same'], axis=1, inplace=True)\n",
        "\n",
        "test = test.drop(['id', 'breath_id','one','count','breath_id_lag',\n",
        "                  'breath_id_lag2','breath_id_lagsame',\n",
        "                  'breath_id_lag2same'], axis=1)\n",
        "\n",
        "print(f\"train: {train.shape} \\ntest: {test.shape}\")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train: (6036000, 66) \n",
            "test: (4024000, 66)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYWzQtL49co1",
        "outputId": "6075626d-b21a-42b2-8e65-e7013313a1ea"
      },
      "source": [
        "scaler = RobustScaler()\n",
        "train = scaler.fit_transform(train)\n",
        "test = scaler.transform(test)\n",
        "\n",
        "train = train.reshape(-1, 80, train.shape[-1])\n",
        "test = test.reshape(-1, 80, train.shape[-1])\n",
        "\n",
        "print(f\"train: {train.shape} \\ntest: {test.shape} \\ntargets: {targets.shape}\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train: (75450, 80, 66) \n",
            "test: (50300, 80, 66) \n",
            "targets: (75450, 80)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Osu14DeV9co1"
      },
      "source": [
        "## Hardware config"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CisxazED9co1",
        "outputId": "88d6b9a2-2e9f-42ee-b540-d941bfce7c95"
      },
      "source": [
        "try:\n",
        "    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n",
        "    tf.config.experimental_connect_to_cluster(tpu)\n",
        "    tf.tpu.experimental.initialize_tpu_system(tpu)\n",
        "    tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)\n",
        "    BATCH_SIZE = tpu_strategy.num_replicas_in_sync * 64\n",
        "    print(\"Running on TPU:\", tpu.master())\n",
        "    print(f\"Batch Size: {BATCH_SIZE}\")\n",
        "    \n",
        "except ValueError:\n",
        "    tpu_strategy = tf.distribute.get_strategy()\n",
        "    BATCH_SIZE = 512\n",
        "    print(f\"Running on {strategy.num_replicas_in_sync} replicas\")\n",
        "    print(f\"Batch Size: {BATCH_SIZE}\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.21.194.242:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.21.194.242:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n",
            "WARNING:absl:`tf.distribute.experimental.TPUStrategy` is deprecated, please use  the non experimental symbol `tf.distribute.TPUStrategy` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running on TPU: grpc://10.21.194.242:8470\n",
            "Batch Size: 512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GAZMw10A9co1"
      },
      "source": [
        "## Rescaling layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLou5BDt9co2"
      },
      "source": [
        "@tf.custom_gradient\n",
        "def round_with_gradients(x):\n",
        "    def grad(dy):\n",
        "        return dy\n",
        "    return tf.round(x), grad\n",
        "\n",
        "class ScaleLayer(tf.keras.layers.Layer):\n",
        "    def __init__(self):\n",
        "        super(ScaleLayer, self).__init__()\n",
        "        self.min = tf.constant(pressure_min, dtype=np.float32)\n",
        "        self.max = tf.constant(pressure_max, dtype=np.float32)\n",
        "        self.step = tf.constant(pressure_step, dtype=np.float32)\n",
        "\n",
        "    def call(self, inputs):\n",
        "        steps = tf.math.divide(tf.math.add(inputs, -self.min), self.step)\n",
        "        int_steps = round_with_gradients(steps)\n",
        "        rescaled_steps = tf.math.add(tf.math.multiply(int_steps, self.step), self.min)\n",
        "        clipped = tf.clip_by_value(rescaled_steps, self.min, self.max)\n",
        "        return clipped"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Ft0JBPC9co2"
      },
      "source": [
        "## Keras DNN Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_HuMFGY39co2"
      },
      "source": [
        "def dnn_model():\n",
        "    \n",
        "    x_input = Input(shape=(train.shape[-2:]))\n",
        "    \n",
        "    x1 = Bidirectional(LSTM(units=768, return_sequences=True))(x_input)\n",
        "    x2 = Bidirectional(LSTM(units=512, return_sequences=True))(x1)\n",
        "    x3 = Bidirectional(LSTM(units=384, return_sequences=True))(x2)\n",
        "    x4 = Bidirectional(LSTM(units=256, return_sequences=True))(x3)\n",
        "    x5 = Bidirectional(LSTM(units=128, return_sequences=True))(x4)\n",
        "    \n",
        "    z2 = Bidirectional(GRU(units=384, return_sequences=True))(x2)\n",
        "    \n",
        "    z31 = Multiply()([x3, z2])\n",
        "    z31 = BatchNormalization()(z31)\n",
        "    z3 = Bidirectional(GRU(units=256, return_sequences=True))(z31)\n",
        "    \n",
        "    z41 = Multiply()([x4, z3])\n",
        "    z41 = BatchNormalization()(z41)\n",
        "    z4 = Bidirectional(GRU(units=128, return_sequences=True))(z41)\n",
        "    \n",
        "    z51 = Multiply()([x5, z4])\n",
        "    z51 = BatchNormalization()(z51)\n",
        "    z5 = Bidirectional(GRU(units=64, return_sequences=True))(z51)\n",
        "    \n",
        "    x = Concatenate(axis=2)([x5, z2, z3, z4, z5])\n",
        "    \n",
        "    x = Dense(units=128, activation='selu')(x)\n",
        "    \n",
        "    x_output = Dense(units=1)(x)\n",
        "    \n",
        "    x_output = ScaleLayer()(x_output)\n",
        "\n",
        "    model = Model(inputs=x_input, outputs=x_output, \n",
        "                  name='DNN_Model')\n",
        "    return model"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_d8ybRJc9co2",
        "outputId": "d31cd627-2d12-45ef-f924-5ff6d16c6e80"
      },
      "source": [
        "model = dnn_model()\n",
        "model.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"DNN_Model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 80, 66)]     0                                            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional (Bidirectional)   (None, 80, 1536)     5130240     input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_1 (Bidirectional) (None, 80, 1024)     8392704     bidirectional[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_2 (Bidirectional) (None, 80, 768)      4328448     bidirectional_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_5 (Bidirectional) (None, 80, 768)      3248640     bidirectional_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "multiply (Multiply)             (None, 80, 768)      0           bidirectional_2[0][0]            \n",
            "                                                                 bidirectional_5[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 80, 768)      3072        multiply[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_3 (Bidirectional) (None, 80, 512)      2099200     bidirectional_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_6 (Bidirectional) (None, 80, 512)      1575936     batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "multiply_1 (Multiply)           (None, 80, 512)      0           bidirectional_3[0][0]            \n",
            "                                                                 bidirectional_6[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 80, 512)      2048        multiply_1[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_4 (Bidirectional) (None, 80, 256)      656384      bidirectional_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_7 (Bidirectional) (None, 80, 256)      493056      batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "multiply_2 (Multiply)           (None, 80, 256)      0           bidirectional_4[0][0]            \n",
            "                                                                 bidirectional_7[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 80, 256)      1024        multiply_2[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_8 (Bidirectional) (None, 80, 128)      123648      batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 80, 1920)     0           bidirectional_4[0][0]            \n",
            "                                                                 bidirectional_5[0][0]            \n",
            "                                                                 bidirectional_6[0][0]            \n",
            "                                                                 bidirectional_7[0][0]            \n",
            "                                                                 bidirectional_8[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 80, 128)      245888      concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 80, 1)        129         dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "scale_layer (ScaleLayer)        (None, 80, 1)        0           dense_1[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 26,300,417\n",
            "Trainable params: 26,297,345\n",
            "Non-trainable params: 3,072\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "e-M0ZMvC9co3",
        "outputId": "2b9f57e5-ba9e-4d23-dfe7-668bc85f4259"
      },
      "source": [
        "plot_model(\n",
        "    model, \n",
        "    to_file='Google_Brain_Keras_Model.png', \n",
        "    show_shapes=True,\n",
        "    show_layer_names=True\n",
        ")\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxWQVBYm-0VK",
        "outputId": "53246b1b-694c-4f70-8a03-a5b47fc0d322"
      },
      "source": [
        "del model\n",
        "gc.collect()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25244"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1v9SMji9co3"
      },
      "source": [
        "#reset Keras Session\n",
        "def reset_keras():\n",
        "    sess = tf.compat.v1.keras.backend.get_session()\n",
        "    tf.compat.v1.keras.backend.clear_session()\n",
        "    sess.close()\n",
        "    sess = tf.compat.v1.keras.backend.get_session()\n",
        "\n",
        "    # use the same config as you used to create the session\n",
        "    config = tf.compat.v1.ConfigProto()\n",
        "    config.gpu_options.per_process_gpu_memory_fraction = 1\n",
        "    config.gpu_options.visible_device_list = \"0\"\n",
        "    tf.compat.v1.keras.backend.set_session(tf.compat.v1.Session(config=config))\n",
        "    gc.collect()\n",
        "\n",
        "reset_keras()"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDsDMBnE9co3"
      },
      "source": [
        "train_pred = np.zeros(targets.shape)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SYJkCi1-9co3",
        "outputId": "c4f4adda-b665-4bb0-f4d1-c5af0c562a43"
      },
      "source": [
        "with tpu_strategy.scope():\n",
        "    \n",
        "    VERBOSE = 1\n",
        "    EPOCHS = 150\n",
        "    FOLDS = 7\n",
        "    if DEBUG:\n",
        "      EPOCHS = 100\n",
        "    test_preds = []\n",
        "    kf = KFold(n_splits=FOLDS, shuffle=True, random_state=2021)\n",
        "    \n",
        "    for fold, (train_idx, test_idx) in enumerate(kf.split(train, targets)):\n",
        "        if DEBUG and fold > 0:\n",
        "          continue\n",
        "        X_train, X_valid = train[train_idx], train[test_idx]\n",
        "        y_train, y_valid = targets[train_idx], targets[test_idx]\n",
        "        \n",
        "        model = dnn_model()\n",
        "        model.compile(optimizer=\"adam\", loss=\"mae\")\n",
        "\n",
        "        lr = ReduceLROnPlateau(monitor=\"val_loss\", factor=0.75, \n",
        "                               patience=10, verbose=VERBOSE)\n",
        "        \n",
        "        chk_point = ModelCheckpoint(f'./model/Bidirect_LSTM_model_{fold+1}C.h5',\n",
        "                                    monitor='val_loss', verbose=VERBOSE, \n",
        "                                    save_best_only=True, mode='min')\n",
        "\n",
        "        es = EarlyStopping(monitor=\"val_loss\", patience=50, \n",
        "                           verbose=VERBOSE, mode=\"min\", \n",
        "                           restore_best_weights=True)\n",
        "        \n",
        "        model.fit(X_train, y_train, \n",
        "                  validation_data=(X_valid, y_valid), \n",
        "                  epochs=EPOCHS,\n",
        "                  verbose=VERBOSE,\n",
        "                  batch_size=BATCH_SIZE, \n",
        "                  callbacks=[lr, chk_point, es])\n",
        "        \n",
        "        y_true = y_valid.squeeze().reshape(-1, 1)\n",
        "        y_pred = model.predict(X_valid, batch_size=BATCH_SIZE).squeeze().reshape(-1, 1)\n",
        "        train_pred[test_idx] = y_pred.reshape(-1, 80)\n",
        "        score = mean_absolute_error(y_true, y_pred)\n",
        "        print(f\"Fold-{fold+1} | OOF Score: {score}\")\n",
        "        \n",
        "        test_preds.append(model.predict(test, batch_size=BATCH_SIZE).squeeze().reshape(-1, 1).squeeze())\n",
        "        \n",
        "        del X_train, X_valid, y_train, y_valid, model\n",
        "        reset_keras()\n",
        "        gc.collect()\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "127/127 [==============================] - 195s 1s/step - loss: 1.5095 - val_loss: 1.2250\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 1.22496, saving model to ./Bidirect_LSTM_model_1C.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.6155 - val_loss: 1.2253\n",
            "\n",
            "Epoch 00002: val_loss did not improve from 1.22496\n",
            "Epoch 3/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.5402 - val_loss: 0.5515\n",
            "\n",
            "Epoch 00003: val_loss improved from 1.22496 to 0.55152, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 4/300\n",
            "127/127 [==============================] - 80s 632ms/step - loss: 0.4820 - val_loss: 0.4510\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.55152 to 0.45101, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 5/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.4247 - val_loss: 0.4190\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.45101 to 0.41902, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 6/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.4124 - val_loss: 0.3874\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.41902 to 0.38742, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 7/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.3917 - val_loss: 0.4389\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.38742\n",
            "Epoch 8/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.3980 - val_loss: 0.3533\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.38742 to 0.35330, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 9/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.3444 - val_loss: 0.3409\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.35330 to 0.34090, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 10/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.3355 - val_loss: 0.3448\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.34090\n",
            "Epoch 11/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.3260 - val_loss: 0.3600\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.34090\n",
            "Epoch 12/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.3186 - val_loss: 0.3500\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 0.34090\n",
            "Epoch 13/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.3096 - val_loss: 0.2921\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.34090 to 0.29215, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 14/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.3018 - val_loss: 0.2908\n",
            "\n",
            "Epoch 00014: val_loss improved from 0.29215 to 0.29083, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 15/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2951 - val_loss: 0.3078\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.29083\n",
            "Epoch 16/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2834 - val_loss: 0.3018\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.29083\n",
            "Epoch 17/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2836 - val_loss: 0.2751\n",
            "\n",
            "Epoch 00017: val_loss improved from 0.29083 to 0.27509, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 18/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2710 - val_loss: 0.2750\n",
            "\n",
            "Epoch 00018: val_loss improved from 0.27509 to 0.27504, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 19/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2688 - val_loss: 0.2829\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.27504\n",
            "Epoch 20/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2668 - val_loss: 0.2965\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.27504\n",
            "Epoch 21/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2566 - val_loss: 0.2712\n",
            "\n",
            "Epoch 00021: val_loss improved from 0.27504 to 0.27118, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 22/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.2617 - val_loss: 0.2586\n",
            "\n",
            "Epoch 00022: val_loss improved from 0.27118 to 0.25860, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 23/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.2548 - val_loss: 0.2677\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 0.25860\n",
            "Epoch 24/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2424 - val_loss: 0.2489\n",
            "\n",
            "Epoch 00024: val_loss improved from 0.25860 to 0.24892, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 25/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2442 - val_loss: 0.2578\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.24892\n",
            "Epoch 26/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2340 - val_loss: 0.2366\n",
            "\n",
            "Epoch 00026: val_loss improved from 0.24892 to 0.23665, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 27/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2272 - val_loss: 0.2402\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.23665\n",
            "Epoch 28/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.2337 - val_loss: 0.2542\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.23665\n",
            "Epoch 29/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2292 - val_loss: 0.2327\n",
            "\n",
            "Epoch 00029: val_loss improved from 0.23665 to 0.23269, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 30/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2187 - val_loss: 0.2513\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.23269\n",
            "Epoch 31/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2265 - val_loss: 0.2450\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.23269\n",
            "Epoch 32/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2338 - val_loss: 0.2591\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.23269\n",
            "Epoch 33/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2312 - val_loss: 0.2454\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.23269\n",
            "Epoch 34/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2185 - val_loss: 0.2226\n",
            "\n",
            "Epoch 00034: val_loss improved from 0.23269 to 0.22257, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 35/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2164 - val_loss: 0.2447\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.22257\n",
            "Epoch 36/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2188 - val_loss: 0.2352\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.22257\n",
            "Epoch 37/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2160 - val_loss: 0.2255\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.22257\n",
            "Epoch 38/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2149 - val_loss: 0.2333\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.22257\n",
            "Epoch 39/300\n",
            "127/127 [==============================] - 81s 634ms/step - loss: 0.2148 - val_loss: 0.2253\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.22257\n",
            "Epoch 40/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.2094 - val_loss: 0.2363\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.22257\n",
            "Epoch 41/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.2008 - val_loss: 0.2122\n",
            "\n",
            "Epoch 00041: val_loss improved from 0.22257 to 0.21219, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 42/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2069 - val_loss: 0.2258\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.21219\n",
            "Epoch 43/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1999 - val_loss: 0.2158\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.21219\n",
            "Epoch 44/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.2006 - val_loss: 0.2272\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.21219\n",
            "Epoch 45/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1973 - val_loss: 0.2117\n",
            "\n",
            "Epoch 00045: val_loss improved from 0.21219 to 0.21170, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 46/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1971 - val_loss: 0.2148\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.21170\n",
            "Epoch 47/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1975 - val_loss: 0.2217\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.21170\n",
            "Epoch 48/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.2002 - val_loss: 0.2159\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.21170\n",
            "Epoch 49/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1922 - val_loss: 0.2130\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.21170\n",
            "Epoch 50/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1883 - val_loss: 0.2039\n",
            "\n",
            "Epoch 00050: val_loss improved from 0.21170 to 0.20386, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 51/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1886 - val_loss: 0.2309\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 0.20386\n",
            "Epoch 52/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1905 - val_loss: 0.2242\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 0.20386\n",
            "Epoch 53/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1879 - val_loss: 0.2041\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.20386\n",
            "Epoch 54/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1772 - val_loss: 0.1965\n",
            "\n",
            "Epoch 00054: val_loss improved from 0.20386 to 0.19651, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 55/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1792 - val_loss: 0.1978\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.19651\n",
            "Epoch 56/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1779 - val_loss: 0.1983\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 0.19651\n",
            "Epoch 57/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1757 - val_loss: 0.2091\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.19651\n",
            "Epoch 58/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1844 - val_loss: 0.2076\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.19651\n",
            "Epoch 59/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1795 - val_loss: 0.1948\n",
            "\n",
            "Epoch 00059: val_loss improved from 0.19651 to 0.19483, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 60/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1745 - val_loss: 0.1884\n",
            "\n",
            "Epoch 00060: val_loss improved from 0.19483 to 0.18840, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 61/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1673 - val_loss: 0.1967\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.18840\n",
            "Epoch 62/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1703 - val_loss: 0.2044\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.18840\n",
            "Epoch 63/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1735 - val_loss: 0.2021\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.18840\n",
            "Epoch 64/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1705 - val_loss: 0.2102\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.18840\n",
            "Epoch 65/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1681 - val_loss: 0.1893\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.18840\n",
            "Epoch 66/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1718 - val_loss: 0.2036\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.18840\n",
            "Epoch 67/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1749 - val_loss: 0.1964\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.18840\n",
            "Epoch 68/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1722 - val_loss: 0.2045\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.18840\n",
            "Epoch 69/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1717 - val_loss: 0.2021\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.18840\n",
            "Epoch 70/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1748 - val_loss: 0.2074\n",
            "\n",
            "Epoch 00070: ReduceLROnPlateau reducing learning rate to 0.0007500000356230885.\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.18840\n",
            "Epoch 71/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1544 - val_loss: 0.1815\n",
            "\n",
            "Epoch 00071: val_loss improved from 0.18840 to 0.18154, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 72/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1480 - val_loss: 0.1840\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.18154\n",
            "Epoch 73/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1432 - val_loss: 0.1728\n",
            "\n",
            "Epoch 00073: val_loss improved from 0.18154 to 0.17281, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 74/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1417 - val_loss: 0.1744\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.17281\n",
            "Epoch 75/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1419 - val_loss: 0.1805\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.17281\n",
            "Epoch 76/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1402 - val_loss: 0.1823\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.17281\n",
            "Epoch 77/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1479 - val_loss: 0.1812\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 0.17281\n",
            "Epoch 78/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1425 - val_loss: 0.1778\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.17281\n",
            "Epoch 79/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1382 - val_loss: 0.1737\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.17281\n",
            "Epoch 80/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1371 - val_loss: 0.1764\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.17281\n",
            "Epoch 81/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1432 - val_loss: 0.1795\n",
            "\n",
            "Epoch 00081: val_loss did not improve from 0.17281\n",
            "Epoch 82/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1345 - val_loss: 0.1675\n",
            "\n",
            "Epoch 00082: val_loss improved from 0.17281 to 0.16751, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 83/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1341 - val_loss: 0.1702\n",
            "\n",
            "Epoch 00083: val_loss did not improve from 0.16751\n",
            "Epoch 84/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1400 - val_loss: 0.1740\n",
            "\n",
            "Epoch 00084: val_loss did not improve from 0.16751\n",
            "Epoch 85/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1360 - val_loss: 0.1733\n",
            "\n",
            "Epoch 00085: val_loss did not improve from 0.16751\n",
            "Epoch 86/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1317 - val_loss: 0.1681\n",
            "\n",
            "Epoch 00086: val_loss did not improve from 0.16751\n",
            "Epoch 87/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1370 - val_loss: 0.1969\n",
            "\n",
            "Epoch 00087: val_loss did not improve from 0.16751\n",
            "Epoch 88/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1431 - val_loss: 0.1879\n",
            "\n",
            "Epoch 00088: val_loss did not improve from 0.16751\n",
            "Epoch 89/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1307 - val_loss: 0.1684\n",
            "\n",
            "Epoch 00089: val_loss did not improve from 0.16751\n",
            "Epoch 90/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1310 - val_loss: 0.1708\n",
            "\n",
            "Epoch 00090: val_loss did not improve from 0.16751\n",
            "Epoch 91/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1294 - val_loss: 0.1748\n",
            "\n",
            "Epoch 00091: val_loss did not improve from 0.16751\n",
            "Epoch 92/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1288 - val_loss: 0.1722\n",
            "\n",
            "Epoch 00092: ReduceLROnPlateau reducing learning rate to 0.0005625000048894435.\n",
            "\n",
            "Epoch 00092: val_loss did not improve from 0.16751\n",
            "Epoch 93/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1177 - val_loss: 0.1631\n",
            "\n",
            "Epoch 00093: val_loss improved from 0.16751 to 0.16308, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 94/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1134 - val_loss: 0.1632\n",
            "\n",
            "Epoch 00094: val_loss did not improve from 0.16308\n",
            "Epoch 95/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1109 - val_loss: 0.1614\n",
            "\n",
            "Epoch 00095: val_loss improved from 0.16308 to 0.16140, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 96/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1111 - val_loss: 0.1689\n",
            "\n",
            "Epoch 00096: val_loss did not improve from 0.16140\n",
            "Epoch 97/300\n",
            "127/127 [==============================] - 81s 635ms/step - loss: 0.1108 - val_loss: 0.1679\n",
            "\n",
            "Epoch 00097: val_loss did not improve from 0.16140\n",
            "Epoch 98/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1090 - val_loss: 0.1649\n",
            "\n",
            "Epoch 00098: val_loss did not improve from 0.16140\n",
            "Epoch 99/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1099 - val_loss: 0.1668\n",
            "\n",
            "Epoch 00099: val_loss did not improve from 0.16140\n",
            "Epoch 100/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1087 - val_loss: 0.1626\n",
            "\n",
            "Epoch 00100: val_loss did not improve from 0.16140\n",
            "Epoch 101/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1063 - val_loss: 0.1633\n",
            "\n",
            "Epoch 00101: val_loss did not improve from 0.16140\n",
            "Epoch 102/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.1065 - val_loss: 0.1621\n",
            "\n",
            "Epoch 00102: val_loss did not improve from 0.16140\n",
            "Epoch 103/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1039 - val_loss: 0.1629\n",
            "\n",
            "Epoch 00103: val_loss did not improve from 0.16140\n",
            "Epoch 104/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1037 - val_loss: 0.1647\n",
            "\n",
            "Epoch 00104: val_loss did not improve from 0.16140\n",
            "Epoch 105/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.1034 - val_loss: 0.1623\n",
            "\n",
            "Epoch 00105: ReduceLROnPlateau reducing learning rate to 0.0004218749818392098.\n",
            "\n",
            "Epoch 00105: val_loss did not improve from 0.16140\n",
            "Epoch 106/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.0981 - val_loss: 0.1643\n",
            "\n",
            "Epoch 00106: val_loss did not improve from 0.16140\n",
            "Epoch 107/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.0972 - val_loss: 0.1606\n",
            "\n",
            "Epoch 00107: val_loss improved from 0.16140 to 0.16059, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 108/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0939 - val_loss: 0.1600\n",
            "\n",
            "Epoch 00108: val_loss improved from 0.16059 to 0.15998, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 109/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0931 - val_loss: 0.1603\n",
            "\n",
            "Epoch 00109: val_loss did not improve from 0.15998\n",
            "Epoch 110/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0959 - val_loss: 0.1612\n",
            "\n",
            "Epoch 00110: val_loss did not improve from 0.15998\n",
            "Epoch 111/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0931 - val_loss: 0.1665\n",
            "\n",
            "Epoch 00111: val_loss did not improve from 0.15998\n",
            "Epoch 112/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0926 - val_loss: 0.1612\n",
            "\n",
            "Epoch 00112: val_loss did not improve from 0.15998\n",
            "Epoch 113/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0913 - val_loss: 0.1615\n",
            "\n",
            "Epoch 00113: val_loss did not improve from 0.15998\n",
            "Epoch 114/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0903 - val_loss: 0.1612\n",
            "\n",
            "Epoch 00114: val_loss did not improve from 0.15998\n",
            "Epoch 115/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0899 - val_loss: 0.1608\n",
            "\n",
            "Epoch 00115: val_loss did not improve from 0.15998\n",
            "Epoch 116/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0893 - val_loss: 0.1620\n",
            "\n",
            "Epoch 00116: val_loss did not improve from 0.15998\n",
            "Epoch 117/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0889 - val_loss: 0.1609\n",
            "\n",
            "Epoch 00117: val_loss did not improve from 0.15998\n",
            "Epoch 118/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0882 - val_loss: 0.1607\n",
            "\n",
            "Epoch 00118: ReduceLROnPlateau reducing learning rate to 0.00031640623637940735.\n",
            "\n",
            "Epoch 00118: val_loss did not improve from 0.15998\n",
            "Epoch 119/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0831 - val_loss: 0.1591\n",
            "\n",
            "Epoch 00119: val_loss improved from 0.15998 to 0.15912, saving model to ./Bidirect_LSTM_model_1C.h5\n",
            "Epoch 120/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0804 - val_loss: 0.1611\n",
            "\n",
            "Epoch 00120: val_loss did not improve from 0.15912\n",
            "Epoch 121/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0792 - val_loss: 0.1599\n",
            "\n",
            "Epoch 00121: val_loss did not improve from 0.15912\n",
            "Epoch 122/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0781 - val_loss: 0.1649\n",
            "\n",
            "Epoch 00122: val_loss did not improve from 0.15912\n",
            "Epoch 123/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0779 - val_loss: 0.1603\n",
            "\n",
            "Epoch 00123: val_loss did not improve from 0.15912\n",
            "Epoch 124/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0769 - val_loss: 0.1601\n",
            "\n",
            "Epoch 00124: val_loss did not improve from 0.15912\n",
            "Epoch 125/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0775 - val_loss: 0.1620\n",
            "\n",
            "Epoch 00125: val_loss did not improve from 0.15912\n",
            "Epoch 126/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0777 - val_loss: 0.1604\n",
            "\n",
            "Epoch 00126: val_loss did not improve from 0.15912\n",
            "Epoch 127/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0768 - val_loss: 0.1608\n",
            "\n",
            "Epoch 00127: val_loss did not improve from 0.15912\n",
            "Epoch 128/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0766 - val_loss: 0.1615\n",
            "\n",
            "Epoch 00128: val_loss did not improve from 0.15912\n",
            "Epoch 129/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0749 - val_loss: 0.1618\n",
            "\n",
            "Epoch 00129: ReduceLROnPlateau reducing learning rate to 0.00023730468819849193.\n",
            "\n",
            "Epoch 00129: val_loss did not improve from 0.15912\n",
            "Epoch 130/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0713 - val_loss: 0.1615\n",
            "\n",
            "Epoch 00130: val_loss did not improve from 0.15912\n",
            "Epoch 131/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0695 - val_loss: 0.1605\n",
            "\n",
            "Epoch 00131: val_loss did not improve from 0.15912\n",
            "Epoch 132/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0691 - val_loss: 0.1594\n",
            "\n",
            "Epoch 00132: val_loss did not improve from 0.15912\n",
            "Epoch 133/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0684 - val_loss: 0.1614\n",
            "\n",
            "Epoch 00133: val_loss did not improve from 0.15912\n",
            "Epoch 134/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0683 - val_loss: 0.1626\n",
            "\n",
            "Epoch 00134: val_loss did not improve from 0.15912\n",
            "Epoch 135/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0678 - val_loss: 0.1606\n",
            "\n",
            "Epoch 00135: val_loss did not improve from 0.15912\n",
            "Epoch 136/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0679 - val_loss: 0.1620\n",
            "\n",
            "Epoch 00136: val_loss did not improve from 0.15912\n",
            "Epoch 137/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0674 - val_loss: 0.1603\n",
            "\n",
            "Epoch 00137: val_loss did not improve from 0.15912\n",
            "Epoch 138/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0663 - val_loss: 0.1608\n",
            "\n",
            "Epoch 00138: val_loss did not improve from 0.15912\n",
            "Epoch 139/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0666 - val_loss: 0.1605\n",
            "\n",
            "Epoch 00139: ReduceLROnPlateau reducing learning rate to 0.00017797851614886895.\n",
            "\n",
            "Epoch 00139: val_loss did not improve from 0.15912\n",
            "Epoch 140/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0639 - val_loss: 0.1608\n",
            "\n",
            "Epoch 00140: val_loss did not improve from 0.15912\n",
            "Epoch 141/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0623 - val_loss: 0.1596\n",
            "\n",
            "Epoch 00141: val_loss did not improve from 0.15912\n",
            "Epoch 142/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0618 - val_loss: 0.1611\n",
            "\n",
            "Epoch 00142: val_loss did not improve from 0.15912\n",
            "Epoch 143/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0614 - val_loss: 0.1602\n",
            "\n",
            "Epoch 00143: val_loss did not improve from 0.15912\n",
            "Epoch 144/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0612 - val_loss: 0.1622\n",
            "\n",
            "Epoch 00144: val_loss did not improve from 0.15912\n",
            "Epoch 145/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0613 - val_loss: 0.1613\n",
            "\n",
            "Epoch 00145: val_loss did not improve from 0.15912\n",
            "Epoch 146/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0609 - val_loss: 0.1608\n",
            "\n",
            "Epoch 00146: val_loss did not improve from 0.15912\n",
            "Epoch 147/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0606 - val_loss: 0.1618\n",
            "\n",
            "Epoch 00147: val_loss did not improve from 0.15912\n",
            "Epoch 148/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.0603 - val_loss: 0.1607\n",
            "\n",
            "Epoch 00148: val_loss did not improve from 0.15912\n",
            "Epoch 149/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0597 - val_loss: 0.1611\n",
            "\n",
            "Epoch 00149: ReduceLROnPlateau reducing learning rate to 0.0001334838816546835.\n",
            "\n",
            "Epoch 00149: val_loss did not improve from 0.15912\n",
            "Epoch 150/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0582 - val_loss: 0.1605\n",
            "\n",
            "Epoch 00150: val_loss did not improve from 0.15912\n",
            "Epoch 151/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.0571 - val_loss: 0.1612\n",
            "\n",
            "Epoch 00151: val_loss did not improve from 0.15912\n",
            "Epoch 152/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0569 - val_loss: 0.1607\n",
            "\n",
            "Epoch 00152: val_loss did not improve from 0.15912\n",
            "Epoch 153/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0568 - val_loss: 0.1608\n",
            "\n",
            "Epoch 00153: val_loss did not improve from 0.15912\n",
            "Epoch 154/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0566 - val_loss: 0.1612\n",
            "\n",
            "Epoch 00154: val_loss did not improve from 0.15912\n",
            "Epoch 155/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0564 - val_loss: 0.1605\n",
            "\n",
            "Epoch 00155: val_loss did not improve from 0.15912\n",
            "Epoch 156/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0560 - val_loss: 0.1613\n",
            "\n",
            "Epoch 00156: val_loss did not improve from 0.15912\n",
            "Epoch 157/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0558 - val_loss: 0.1616\n",
            "\n",
            "Epoch 00157: val_loss did not improve from 0.15912\n",
            "Epoch 158/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0556 - val_loss: 0.1622\n",
            "\n",
            "Epoch 00158: val_loss did not improve from 0.15912\n",
            "Epoch 159/300\n",
            "127/127 [==============================] - 81s 635ms/step - loss: 0.0552 - val_loss: 0.1607\n",
            "\n",
            "Epoch 00159: ReduceLROnPlateau reducing learning rate to 0.00010011290578404441.\n",
            "\n",
            "Epoch 00159: val_loss did not improve from 0.15912\n",
            "Epoch 160/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0540 - val_loss: 0.1610\n",
            "\n",
            "Epoch 00160: val_loss did not improve from 0.15912\n",
            "Epoch 161/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0534 - val_loss: 0.1611\n",
            "\n",
            "Epoch 00161: val_loss did not improve from 0.15912\n",
            "Epoch 162/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0532 - val_loss: 0.1615\n",
            "\n",
            "Epoch 00162: val_loss did not improve from 0.15912\n",
            "Epoch 163/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0530 - val_loss: 0.1613\n",
            "\n",
            "Epoch 00163: val_loss did not improve from 0.15912\n",
            "Epoch 164/300\n",
            "127/127 [==============================] - 80s 630ms/step - loss: 0.0530 - val_loss: 0.1623\n",
            "\n",
            "Epoch 00164: val_loss did not improve from 0.15912\n",
            "Epoch 165/300\n",
            "127/127 [==============================] - 80s 633ms/step - loss: 0.0528 - val_loss: 0.1618\n",
            "\n",
            "Epoch 00165: val_loss did not improve from 0.15912\n",
            "Epoch 166/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0527 - val_loss: 0.1609\n",
            "\n",
            "Epoch 00166: val_loss did not improve from 0.15912\n",
            "Epoch 167/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0525 - val_loss: 0.1612\n",
            "\n",
            "Epoch 00167: val_loss did not improve from 0.15912\n",
            "Epoch 168/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0523 - val_loss: 0.1621\n",
            "\n",
            "Epoch 00168: val_loss did not improve from 0.15912\n",
            "Epoch 169/300\n",
            "127/127 [==============================] - 80s 629ms/step - loss: 0.0522 - val_loss: 0.1627\n",
            "\n",
            "Epoch 00169: ReduceLROnPlateau reducing learning rate to 7.508467933803331e-05.\n",
            "\n",
            "Epoch 00169: val_loss did not improve from 0.15912\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00169: early stopping\n",
            "Fold-1 | OOF Score: 0.15912771348961205\n",
            "Epoch 1/300\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/lib/python3.7/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m    129\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mvariable_creator_scope\u001b[0;34m(variable_creator)\u001b[0m\n\u001b[1;32m   2832\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_creator_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariable_creator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2833\u001b[0;31m     \u001b[0;32myield\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-23-16e1c76b9e54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     35\u001b[0m                   \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m                   callbacks=[lr, chk_point, es])\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1183\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1184\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1185\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    884\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    932\u001b[0m       \u001b[0minitializers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 933\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    934\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[0;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[1;32m    759\u001b[0m         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\n\u001b[0;32m--> 760\u001b[0;31m             *args, **kwds))\n\u001b[0m\u001b[1;32m    761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3065\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3066\u001b[0;31m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3067\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   3462\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3463\u001b[0;31m           \u001b[0mgraph_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3464\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m   3307\u001b[0m             \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3308\u001b[0;31m             capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[1;32m   3309\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes, acd_record_initial_resource_uses)\u001b[0m\n\u001b[1;32m   1006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1007\u001b[0;31m       \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1008\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    667\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompile_with_xla\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 668\u001b[0;31m           \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    669\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    989\u001b[0m                     \u001b[0moptional_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mautograph_options\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 990\u001b[0;31m                     \u001b[0muser_requested\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    991\u001b[0m                 ))\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py\u001b[0m in \u001b[0;36mconverted_call\u001b[0;34m(f, args, kwargs, caller_fn_scope, options)\u001b[0m\n\u001b[1;32m    443\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconverted_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0meffective_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    445\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                     \u001b[0mretval_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mld\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfscope\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m                 \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py\u001b[0m in \u001b[0;36mconverted_call\u001b[0;34m(f, args, kwargs, caller_fn_scope, options)\u001b[0m\n\u001b[1;32m    381\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_requested\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_allowlisted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_call_unconverted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py\u001b[0m in \u001b[0;36m_call_unconverted\u001b[0;34m(f, args, kwargs, options, update_cache)\u001b[0m\n\u001b[1;32m    463\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 464\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mstep_function\u001b[0;34m(model, iterator)\u001b[0m\n\u001b[1;32m    841\u001b[0m       \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 842\u001b[0;31m       \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistribute_strategy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    843\u001b[0m       outputs = reduce_per_replica(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   1285\u001b[0m           fn, autograph_ctx.control_status_ctx(), convert_by_default=False)\n\u001b[0;32m-> 1286\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extended\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall_for_each_replica\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py\u001b[0m in \u001b[0;36mcall_for_each_replica\u001b[0;34m(self, fn, args, kwargs)\u001b[0m\n\u001b[1;32m   2848\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_container_strategy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2849\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_for_each_replica\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2850\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py\u001b[0m in \u001b[0;36m_call_for_each_replica\u001b[0;34m(self, fn, args, kwargs)\u001b[0m\n\u001b[1;32m   3631\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mReplicaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_container_strategy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplica_id_in_sync_group\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3632\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    691\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconversion_ctx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 692\u001b[0;31m           \u001b[0;32mreturn\u001b[0m \u001b[0mconverted_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    693\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py\u001b[0m in \u001b[0;36mconverted_call\u001b[0;34m(f, args, kwargs, caller_fn_scope, options)\u001b[0m\n\u001b[1;32m    381\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_requested\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_allowlisted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_call_unconverted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py\u001b[0m in \u001b[0;36m_call_unconverted\u001b[0;34m(f, args, kwargs, options, update_cache)\u001b[0m\n\u001b[1;32m    462\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    464\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mrun_step\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    834\u001b[0m       \u001b[0;32mdef\u001b[0m \u001b[0mrun_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 835\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    836\u001b[0m         \u001b[0;31m# Ensure counter is updated only if `train_step` succeeds.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_step\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    790\u001b[0m     \u001b[0;31m# Run backwards pass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 791\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainable_variables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    792\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompiled_metrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py\u001b[0m in \u001b[0;36mminimize\u001b[0;34m(self, loss, var_list, grad_loss, name, tape)\u001b[0m\n\u001b[1;32m    520\u001b[0m     grads_and_vars = self._compute_gradients(\n\u001b[0;32m--> 521\u001b[0;31m         loss, var_list=var_list, grad_loss=grad_loss, tape=tape)\n\u001b[0m\u001b[1;32m    522\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_gradients\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrads_and_vars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py\u001b[0m in \u001b[0;36m_compute_gradients\u001b[0;34m(self, loss, var_list, grad_loss, tape)\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"/gradients\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m       \u001b[0mgrads_and_vars\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_gradients\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py\u001b[0m in \u001b[0;36m_get_gradients\u001b[0;34m(self, tape, loss, var_list, grad_loss)\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;34m\"\"\"Called in `minimize` to compute gradients from loss.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m     \u001b[0mgrads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/backprop.py\u001b[0m in \u001b[0;36mgradient\u001b[0;34m(self, target, sources, output_gradients, unconnected_gradients)\u001b[0m\n\u001b[1;32m   1089\u001b[0m         \u001b[0msources_raw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mflat_sources_raw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1090\u001b[0;31m         unconnected_gradients=unconnected_gradients)\n\u001b[0m\u001b[1;32m   1091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/imperative_grad.py\u001b[0m in \u001b[0;36mimperative_grad\u001b[0;34m(tape, target, sources, output_gradients, sources_raw, unconnected_gradients)\u001b[0m\n\u001b[1;32m     76\u001b[0m       \u001b[0msources_raw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m       compat.as_str(unconnected_gradients.value))\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_backward_function\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m    844\u001b[0m       \u001b[0mcall_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 845\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rewrite_forward_and_call_backward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    846\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_backward_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_rewrite_forward_and_call_backward\u001b[0;34m(self, op, *doutputs)\u001b[0m\n\u001b[1;32m    759\u001b[0m     \u001b[0;34m\"\"\"Add outputs to the forward call and feed them to the grad function.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 760\u001b[0;31m     \u001b[0mforward_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackwards_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_backward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    761\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mbackwards_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mforward_backward\u001b[0;34m(self, num_doutputs)\u001b[0m\n\u001b[1;32m    692\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mforward_backward\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 693\u001b[0;31m     \u001b[0mforward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackward\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_forward_backward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_doutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    694\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cached_function_pairs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_doutputs\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackward\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_construct_forward_backward\u001b[0;34m(self, num_doutputs)\u001b[0m\n\u001b[1;32m    740\u001b[0m           \u001b[0msignature\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 741\u001b[0;31m           func_graph=backwards_graph)\n\u001b[0m\u001b[1;32m    742\u001b[0m       \u001b[0mbackwards_graph_captures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackwards_graph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexternal_captures\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes, acd_record_initial_resource_uses)\u001b[0m\n\u001b[1;32m   1006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1007\u001b[0;31m       \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1008\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_backprop_function\u001b[0;34m(*grad_ys)\u001b[0m\n\u001b[1;32m    730\u001b[0m             \u001b[0mgrad_ys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgrad_ys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 731\u001b[0;31m             src_graph=self._func_graph)\n\u001b[0m\u001b[1;32m    732\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gradients_util.py\u001b[0m in \u001b[0;36m_GradientsHelper\u001b[0;34m(ys, xs, grad_ys, name, colocate_gradients_with_ops, gate_gradients, aggregation_method, stop_gradients, unconnected_gradients, src_graph)\u001b[0m\n\u001b[1;32m    681\u001b[0m                 in_grads = _MaybeCompile(grad_scope, op, func_call,\n\u001b[0;32m--> 682\u001b[0;31m                                          lambda: grad_fn(op, *out_grads))\n\u001b[0m\u001b[1;32m    683\u001b[0m               \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gradients_util.py\u001b[0m in \u001b[0;36m_MaybeCompile\u001b[0;34m(scope, op, func, grad_fn)\u001b[0m\n\u001b[1;32m    337\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mxla_compile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 338\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgrad_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Exit early\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    339\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gradients_util.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    681\u001b[0m                 in_grads = _MaybeCompile(grad_scope, op, func_call,\n\u001b[0;32m--> 682\u001b[0;31m                                          lambda: grad_fn(op, *out_grads))\n\u001b[0m\u001b[1;32m    683\u001b[0m               \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/while_v2.py\u001b[0m in \u001b[0;36m_WhileGrad\u001b[0;34m(op, *grads)\u001b[0m\n\u001b[1;32m    448\u001b[0m   \u001b[0;31m# See comment in while_loop.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m   \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0marray_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    450\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0m_get_structured_grad_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody_grad_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/while_v2.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    448\u001b[0m   \u001b[0;31m# See comment in while_loop.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m   \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0marray_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    450\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0m_get_structured_grad_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrads\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody_grad_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    205\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    207\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36midentity\u001b[0;34m(input, name)\u001b[0m\n\u001b[1;32m    288\u001b[0m     \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 289\u001b[0;31m   \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0midentity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    290\u001b[0m   \u001b[0;31m# Propagate handle data for happier shape inference for resource variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_array_ops.py\u001b[0m in \u001b[0;36midentity\u001b[0;34m(input, name)\u001b[0m\n\u001b[1;32m   3945\u001b[0m   _, _, _op, _outputs = _op_def_library._apply_op_helper(\n\u001b[0;32m-> 3946\u001b[0;31m         \"Identity\", input=input, name=name)\n\u001b[0m\u001b[1;32m   3947\u001b[0m   \u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[0;34m(op_type_name, name, **keywords)\u001b[0m\n\u001b[1;32m    749\u001b[0m                                  \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 750\u001b[0;31m                                  attrs=attr_protos, op_def=op_def)\n\u001b[0m\u001b[1;32m    751\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36m_create_op_internal\u001b[0;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)\u001b[0m\n\u001b[1;32m    600\u001b[0m         \u001b[0mop_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_def\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m         compute_device)\n\u001b[0m\u001b[1;32m    602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_create_op_internal\u001b[0;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)\u001b[0m\n\u001b[1;32m   3559\u001b[0m     \u001b[0;31m# Session.run call cannot occur between creating and mutating the op.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3560\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mutation_lock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3561\u001b[0m       ret = Operation(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/lock_util.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    127\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_group_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/lock_util.py\u001b[0m in \u001b[0;36macquire\u001b[0;34m(self, group_id)\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ready\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m     \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_another_group_active\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     94\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ready\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/lock_util.py\u001b[0m in \u001b[0;36m_another_group_active\u001b[0;34m(self, group_id)\u001b[0m\n\u001b[1;32m    109\u001b[0m     return any(\n\u001b[0;32m--> 110\u001b[0;31m         c > 0 for g, c in enumerate(self._group_member_counts) if g != group_id)\n\u001b[0m\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-16e1c76b9e54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_valid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_valid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0mreset_keras\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, exception_type, exception_value, traceback)\u001b[0m\n\u001b[1;32m    431\u001b[0m                          \"tf.distribute.set_strategy() out of `with` scope.\"),\n\u001b[1;32m    432\u001b[0m             e)\n\u001b[0;32m--> 433\u001b[0;31m     \u001b[0m_pop_per_thread_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribution_strategy_context.py\u001b[0m in \u001b[0;36m_pop_per_thread_mode\u001b[0;34m()\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_pop_per_thread_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m   \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_distribution_strategy_stack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: pop from empty list"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUanGPDq9co3"
      },
      "source": [
        "if not DEBUG:\n",
        "  train_pred = train_pred.flatten()\n",
        "  targets = targets.flatten()\n",
        "  score = mean_absolute_error(targets, train_pred)\n",
        "  print(f\"Train Score: {score}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHx2oF849co4"
      },
      "source": [
        "## Create submission file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tS9VjV7K9co4"
      },
      "source": [
        "if not DEBUG:\n",
        "  submission = pd.read_csv('../input/ventilator-pressure-prediction/sample_submission.csv')\n",
        "  submission[\"pressure\"] = sum(test_preds)/5\n",
        "  submission.to_csv('submission.csv', index=False)\n",
        "\n",
        "  # ENSEMBLE FOLDS WITH MEDIAN\n",
        "  #取中位数\n",
        "  submission[\"pressure\"] = np.median(np.vstack(test_preds),axis=0)\n",
        "  submission.to_csv('submission_median.csv', index=False)\n",
        "\n",
        "\n",
        "  # ENSEMBLE FOLDS WITH MEDIAN AND ROUND PREDICTIONS\n",
        "  submission[\"pressure\"] =\\\n",
        "      np.round( (submission.pressure - pressure_min)/pressure_step ) * pressure_step + pressure_min\n",
        "  submission.pressure = np.clip(submission.pressure, pressure_min, pressure_max)\n",
        "  submission.to_csv('submission_median_round.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}