gb_vpp_why_so_serious.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "name": "gb-vpp-why-so-serious.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.10"
    },
    "papermill": {
      "default_parameters": {},
      "duration": 14331.868776,
      "end_time": "2021-10-03T20:34:59.527828",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2021-10-03T16:36:07.659052",
      "version": "2.3.3"
    }
  },
  "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/gb_vpp_why_so_serious.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bvW2rA2PY43p"
      },
      "source": [
        "#!pip install pandas==1.3.2"
      ],
      "id": "bvW2rA2PY43p",
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z56aXDYfZB4Z",
        "outputId": "4d02e6df-b3f1-4223-ba58-03aece5d596d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/gdrive\", force_remount=True)"
      ],
      "id": "Z56aXDYfZB4Z",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8HueH8dZEtq",
        "outputId": "07dfa4f2-b895-49b7-a470-9ee9c11f9c47"
      },
      "source": [
        "!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 /kaggle/input/ventilator-pressure-prediction\n",
        "!unzip '*.zip' -d /kaggle/input/ventilator-pressure-prediction\n",
        "!ls /kaggle/input/ventilator-pressure-prediction"
      ],
      "id": "N8HueH8dZEtq",
      "execution_count": 3,
      "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: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.62.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (5.0.2)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2021.5.30)\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",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "Downloading test.csv.zip to /content\n",
            " 95% 72.0M/75.4M [00:00<00:00, 105MB/s] \n",
            "100% 75.4M/75.4M [00:00<00:00, 119MB/s]\n",
            "Downloading sample_submission.csv.zip to /content\n",
            "  0% 0.00/8.50M [00:00<?, ?B/s]\n",
            "100% 8.50M/8.50M [00:00<00:00, 140MB/s]\n",
            "Downloading train.csv.zip to /content\n",
            " 90% 125M/139M [00:00<00:00, 150MB/s]\n",
            "100% 139M/139M [00:00<00:00, 179MB/s]\n",
            "Archive:  sample_submission.csv.zip\n",
            "  inflating: /kaggle/input/ventilator-pressure-prediction/sample_submission.csv  \n",
            "\n",
            "Archive:  train.csv.zip\n",
            "  inflating: /kaggle/input/ventilator-pressure-prediction/train.csv  \n",
            "\n",
            "Archive:  test.csv.zip\n",
            "  inflating: /kaggle/input/ventilator-pressure-prediction/test.csv  \n",
            "\n",
            "3 archives were successfully processed.\n",
            "sample_submission.csv  test.csv  train.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7aed157a"
      },
      "source": [
        "## Import libraries"
      ],
      "id": "7aed157a"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "43065692"
      },
      "source": [
        "import gc\n",
        "import numpy as np\n",
        "import pandas as pd\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 Bidirectional, LSTM\n",
        "from tensorflow.keras.layers import Dense, Dropout, Input\n",
        "from tensorflow.keras.layers import Concatenate, Add, GRU\n",
        "\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)"
      ],
      "id": "43065692",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5f5be31b"
      },
      "source": [
        "## Load source datasets"
      ],
      "id": "5f5be31b"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "78258d25",
        "outputId": "b1624892-ad97-4fd6-b41c-82e81f25c25d"
      },
      "source": [
        "train_df = pd.read_csv('/kaggle/input/ventilator-pressure-prediction/train.csv')\n",
        "print(f\"train_df: {train_df.shape}\")\n",
        "train_df.head()"
      ],
      "id": "78258d25",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_df: (6036000, 8)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<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",
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              "      <td>0.067514</td>\n",
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              "    <tr>\n",
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              "      <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",
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            ],
            "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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "dc0ea23b",
        "outputId": "22a666e7-fe46-4930-b85b-081f24375c7c"
      },
      "source": [
        "test_df = pd.read_csv('/kaggle/input/ventilator-pressure-prediction/test.csv')\n",
        "print(f\"test_df: {test_df.shape}\")\n",
        "test_df.head()"
      ],
      "id": "dc0ea23b",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test_df: (4024000, 7)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "      <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",
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              "    <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",
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            ],
            "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": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ab29f89"
      },
      "source": [
        "## Feature Engineering"
      ],
      "id": "9ab29f89"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41230a55",
        "outputId": "21ec7f88-5fc8-4c95-a438-1dfe4510e011"
      },
      "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['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",
        "    \n",
        "    df['R'] = df['R'].astype(str)\n",
        "    df['C'] = 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-5...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)\n",
        "\n",
        "del train_df\n",
        "del test_df\n",
        "gc.collect()"
      ],
      "id": "41230a55",
      "execution_count": 7,
      "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",
            "\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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7c8c7dd4"
      },
      "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)"
      ],
      "id": "7c8c7dd4",
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9f7a72db",
        "outputId": "f173c1fd-427c-4121-cf09-6461a4430f40"
      },
      "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}\")"
      ],
      "id": "9f7a72db",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train: (75450, 80, 53) \n",
            "test: (50300, 80, 53) \n",
            "targets: (75450, 80)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3a71b457"
      },
      "source": [
        "## Hardware config"
      ],
      "id": "3a71b457"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8cc9f2a",
        "outputId": "4cca1d86-98e1-4685-978b-8ef64a2295df"
      },
      "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 {tpu_strategy.num_replicas_in_sync} replicas\")\n",
        "    print(f\"Batch Size: {BATCH_SIZE}\")"
      ],
      "id": "c8cc9f2a",
      "execution_count": 10,
      "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://10.77.189.178:8470\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.77.189.178:8470\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://10.77.189.178:8470\n",
            "Batch Size: 512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4c784edf"
      },
      "source": [
        "## Keras DNN Model"
      ],
      "id": "4c784edf"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6f78a191"
      },
      "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=256, return_sequences=True))(x2)\n",
        "    x4 = Bidirectional(LSTM(units=128, return_sequences=True))(x3)\n",
        "    \n",
        "    z2 = Bidirectional(GRU(units=256, return_sequences=True))(x2)\n",
        "    z3 = Bidirectional(GRU(units=128, return_sequences=True))(Add()([x3, z2]))\n",
        "    z4 = Bidirectional(GRU(units=64, return_sequences=True))(Add()([x4, z3]))\n",
        "    \n",
        "    x = Concatenate(axis=2)([x4, z2, z3, z4])\n",
        "    \n",
        "    x = Dense(units=128, activation='selu')(x)\n",
        "    \n",
        "    x_output = Dense(units=1)(x)\n",
        "\n",
        "    model = Model(inputs=x_input, outputs=x_output, \n",
        "                  name='DNN_Model')\n",
        "    return model"
      ],
      "id": "6f78a191",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f84d4434",
        "outputId": "e8e74bb0-4504-47ae-e34f-afb94cede51a"
      },
      "source": [
        "model = dnn_model()\n",
        "model.summary()"
      ],
      "id": "f84d4434",
      "execution_count": 12,
      "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, 53)]     0                                            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional (Bidirectional)   (None, 80, 1536)     5050368     input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_1 (Bidirectional) (None, 80, 1024)     8392704     bidirectional[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_2 (Bidirectional) (None, 80, 512)      2623488     bidirectional_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_4 (Bidirectional) (None, 80, 512)      1969152     bidirectional_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "add (Add)                       (None, 80, 512)      0           bidirectional_2[0][0]            \n",
            "                                                                 bidirectional_4[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_3 (Bidirectional) (None, 80, 256)      656384      bidirectional_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_5 (Bidirectional) (None, 80, 256)      493056      add[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 80, 256)      0           bidirectional_3[0][0]            \n",
            "                                                                 bidirectional_5[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "bidirectional_6 (Bidirectional) (None, 80, 128)      123648      add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 80, 1152)     0           bidirectional_3[0][0]            \n",
            "                                                                 bidirectional_4[0][0]            \n",
            "                                                                 bidirectional_5[0][0]            \n",
            "                                                                 bidirectional_6[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 80, 128)      147584      concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 80, 1)        129         dense[0][0]                      \n",
            "==================================================================================================\n",
            "Total params: 19,456,513\n",
            "Trainable params: 19,456,513\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "79877848",
        "outputId": "7cf613f4-e426-4e2e-b5d3-6cc240ae3be5"
      },
      "source": [
        "plot_model(\n",
        "    model, \n",
        "    to_file='Google_Brain_Keras_Model.png', \n",
        "    show_shapes=True,\n",
        "    show_layer_names=True\n",
        ")"
      ],
      "id": "79877848",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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ubm6jPZBQKJR+Ncft27cjIyMDAwPnzp07qlG9pGHdFAAAAExoyIAAAACAVI4cOdLU1CRl4zlz5uTn569du5Y5RXV8GtZNAQAAwISGDAgAAMAEduXKFWNjY4qi9u3bRwjJyMjg8XhcLregoGDp0qXq6uqGhobZ2dl04/T0dDabPXny5ICAAH19fTab7ejoeP36dbo2ODiYxWIxOwts2bKFx+NRFEW/+R8SEhIeHl5RUUFRlLm5+UuGfe7cOXV19Z07d0rTeLzd1OXLl62trTU0NNhstq2t7fnz5wkhmzZtojcQMTMzu3XrFiFkw4YNXC5XQ0Pj9OnThJCenp6PP/7Y2NiYw+HMnj07NzeXELJ7924ul6umptbU1BQeHm5gYFBaWir9NAIAAMCwIAMCAAAwgS1cuPDHH39kPgYFBYWGhgqFQjU1tdzc3IqKClNTU39/f5FIRAgJDg728/Pr6OjYunVrVVXVzZs3u7u733777ZqaGkJIenq6t7c309X+/fvj4+OZj2lpaW5ubmZmZmKx+OUPxaQPW+3t7ZWm8Xi7qcbGRh8fn6qqqvr6elVV1bVr1xJCDh8+7OnpqaioePny5Xnz5hFCMjMzV65cmZWV5e7uTgiJjIzcvXt3ampqQ0ODm5vbmjVrfvrpp48++igsLKytrS0pKcnExMTe3n7c7uoCAAAgB5ABAQAAkEOOjo7q6uq6urq+vr7t7e3V1dVMlZKS0syZM1VUVKytrTMyMlpbWzMzM8c4vOXLlwsEgtjY2GFdNU5uavXq1du3b9fS0tLW1nZ3d3/8+PHDhw8JIYGBgT09Pcy4AoHgxo0by5YtI4R0dnZmZGSsXLnS09NTU1MzJiZGWVlZMsJdu3a9//77+fn5VlZWoxQ2AAAA4CwYAAAAecZisQgh9HKJ/ubPn8/lcu/duze2Qb2s8XNTysrK5PclLW+++aaFhcXnn38eHR1NUVROTo6vr6+ioiIhpLS0tKOjw8bGhr6Kw+Ho6em9cIQ+Pj4+Pj4jdAfjGkVRsg4BXsTq1atlHQIAwMCQAQEAAHilqaio0EsY5Mmo3lRhYeGePXv4fL5AIJDMwlAUFRAQEBYW9v3337/11lv//Oc/v/rqK7qqvb2dEBITExMTE8O019fXf7EAQkJCHBwcXuIOJoDU1FRCSGhoqKwDgWGjnx0AwPiEDAgAAMCrSyQSPX361NDQUNaBjKTRuKni4uKff/45NDS0urp65cqVq1at+vzzz6dOnbp3796PPvqIaebn5xcdHX348GEjIyN1dfVp06bR5bq6uoSQ1NTUkJCQlw/GwcFBcnMTuZSXl0cIkfvblEv0swMAGJ+QAQEAAHh1FRUVicVie3t7+qOSktLzXi2ZQEbjpn7++Wcej0cIuXv3rkgkCgoKMjU1Jf1e09DS0vLx8cnJyVFTU/P392fKjYyM2Gx2SUnJS4YBAAAALwM7oQIAALxaent7nzx50t3dfefOnZCQEGNjYz8/P7rK3Ny8ubn51KlTIpHo4cOH9+/fl7xQW1u7vr6+qqqqtbX1JXMKZ8+elf40XGmM3k2JRKLGxsaioiI6A2JsbEwIuXjxYmdnZ1lZGXPsLiMwMPDZs2fffvutm5sbU8hmszds2JCdnZ2RkSEQCHp6empraxsaGkbq9gEAAEAayIAAAABMYPv27bOzsyOERERErFixIiMjg34Jf/bs2ZWVlYcOHQoPDyeELFmypKysjL6ks7PT1taWw+E4OTlZWFhcunRJRUWFrgoKClq0aNE777xjaWmZkJDA4XAIIQ4ODvTJsoGBgZMnT7a2tl62bFlzc/PggV27dm3hwoVTp069fv367du39fX1FyxYUFxcPOQdXb9+3cbG5rvvviOEzJw5Mykpacxu6siRI+bm5hUVFS0tLdTvWCyWnp7e6dOnuVwuIcTW1jYiImL//v36+vrbtm174403CCELFy6keyOE/OEPf5g3b96GDRuUlP5rpW1aWlpoaGhycrKOjo6+vn5ISMiTJ092796dkpJCCLGwsMjKyhpycgAAAOBlUDh2HgAAYHw6fvy4j4/PyP5NHRAQkJeX9/jx4xHsU+bG200tX7583759JiYmo9E5RVG5ublyv0GGl5cXwY4SExOeHQCMZ1gDAgAA8Gqhj26VMzK/KeYNmjt37rDZ7FFKfwAAAMDLQAYEAAAAhufevXvU8/n6+so6QBmIiIgoKyv79ddfN2zYkJCQIOtwXhUBAQHMF2/dunWSVRcvXoyKisrPzzc1NaUbvPvuu5INXF1d1dTUFBUVZ82adfPmzbEN/P87duyYnZ2dmpratGnTNmzY8ODBA8naK1euLFiwgMvl6uvrR0REPHv2TMpuExMT+/yHaWNjw9QmJydbWVlxOBwej2dlZRUbGysQCOiq06dPJycnS6YUT506xXQyadKkl75jAABZQgYEAADgVREdHZ2ZmdnS0mJiYnLixIkX7sfKykr8fDk5OSMY85BG6qZeEpfLtbKyeuutt+Li4qytrWUVxitIW1v77NmzpaWlR44cYQq3b9+enp4eHR3t6elZWVlpZmamo6OTlZVVWFjItLlw4UJeXp6bmxufz3/ttddkETvJzc1du3atl5dXbW1tQUFBcXHx0qVLu7u76Vo+n+/q6uri4vLw4cOTJ09+/vnngYGBIzLu5cuX/f39q6urGxsbExISkpOTV69eTVe5u7uz2WwXF5enT5/SJStWrKitrS0uLl62bNmIjA4AIEPIgAAAALwqkpKSnj17JhaLf/vtN+YHz0Q3Tm4qMTGxp6enurpa8giYiUgoFDo6Oo63rgbB4XCWLFliYWHB7H27a9eunJyc48ePq6mpMc3S09MVFBQ2b97c0tIy2iFJ77PPPps6deqHH36ooaExd+7csLCwkpIS5oChhIQEPT29+Ph4Ho/n4OAQERFx9OjRe/fuSdn5l19+KZma/OWXX5gqFou1ZcsWXV1dVVVVLy8vDw+P7777jjmcaOvWrXPmzFm2bBmdi6EoysDAwMnJacaMGSN69wAAMoAMCAAAAAD8P0eOHGlqahpvXUmvvLw8NjY2Pj6ezWZLljs6OoaEhNTV1X3wwQdjHNIgampq9PX1KYqiPxoZGRFC6AObu7u7CwsLnZ2dmdqlS5eKxeKCgoKXH/fkyZOS82NgYEAIaWtrY0ri4uJKSkrS0tJefiwAgHEFGRAAAAAAuSIWi1NSUmbOnKmioqKlpeXh4cEsHAgODqbP96U/btmyhcfjURT16NEjQkhISEh4eHhFRQVFUebm5unp6Ww2e/LkyQEBAfr6+mw229HRkVmhMKyuCCHnzp1TV1ffuXPnqN57enq6WCx2d3fvX5WYmGhhYXH48OGLFy8OeO0g85aRkcHj8bhcbkFBwdKlS9XV1Q0NDbOzs5lre3p6Pv74Y2NjYw6HM3v27NzcXGmiNTU1lUwS0ZuAmJqaEkIqKyvb2tqMjY2ZWjMzM0LInTt3pOl5WMrKyjQ1NadNm8aUaGlpOTs7p6Wl4dRIAJAzyIAAAAAAyJW4uLioqKht27Y1NTUVFxfX1NQ4OTk1NjYSQtLT0yVP0t2/f398fDzzMS0tzc3NzczMTCwWl5eXBwcH+/n5dXR0bN26taqq6ubNm93d3W+//XZNTc1wuyK/n9fT29s7qvdeWFhoaWnJ5XL7V3E4nKNHjyooKPj7+7e3t/dvMMi8BQUFhYaGCoVCNTW13NzciooKU1NTf39/5gygyMjI3bt3p6amNjQ0uLm5rVmz5qeffhoy2ujo6AcPHuzdu7e1tZXP56elpS1evNje3p78ng2RfJGHzWZzOBw6HmlERUVpaWmxWCwTExMPD48bN270aSASierq6vbt23fx4sW9e/eyWCzJ2nnz5tXV1d2+fVvK4QAAJgRkQAAAAADkh1AoTElJWbVq1bp16zQ0NGxtbQ8cOPDo0aODBw++WIdKSkr0sghra+uMjIzW1tbMzMwX6Gf58uUCgSA2NvbFwpBGe3v7b7/9Rq+VGJCDg0NoaGhVVVVkZGSfKinnzdHRUV1dXVdX19fXt729vbq6mhDS2dmZkZGxcuVKT09PTU3NmJgYZWVlaWbJ2dk5IiIiODhYXV3dxsamtbX18OHDdBV97IuioqJke2VlZaFQKM1UrF+//vTp0zU1NW1tbdnZ2dXV1c7Oznw+X7KNkZGRoaFhXFzc7t27fXx8+vRA7/px9+5daYYDAJgokAEBAAAAkB98Pr+trW3+/PlMiZ2dHYvFYt5eeRnz58/ncrnSb8Y5xpqamsRi8YALQBiJiYmWlpb79++/cuWKZPlw541eMUGvASktLe3o6GCOm+VwOHp6etLM0rZt2w4ePPj999+3tbVVVlY6Ojo6ODjQS2zofTqYc2FoXV1dHA5nyG4JIUZGRvPmzVNVVWWxWPb29pmZmUKhcP/+/ZJtampqmpqajh079sUXX8ybN6/Ppi30NEq/5AQAYEJABgQAAABAftCHmKqqqkoWampqtra2jkj/KioqDx8+HJGuRlxnZychhDkUZkBsNjszM5OiqI0bN0qup3iZeaPfqYmJiaF+d//+/Y6OjsGvamhoSE5O/stf/vLmm2/yeDwTE5NDhw7V19fv2bOHEEJvsCIQCJj2HR0dnZ2d+vr6Q8bTn62traKi4q+//ipZqKysrKur6+rqmpOTw+fzk5KSJGvpVAs9pQAAcgMZEAAAAAD5oampSQjp87v96dOnhoaGL9+5SCQaqa5GA/2jnd5wZBAODg5hYWFlZWUJCQlM4cvMm66uLiEkNTVV8vTZq1evDn5VWVlZT0/P1KlTmRJ1dXVtbW36XRUTExM1NTX6XBgavZ3K7Nmzh4ynv97e3t7e3uflhszNzRUVFfu8I9PV1UV+n1IAALmBDAgAAACA/LCxsVFVVZXchvP69etdXV2vv/46/VFJSYnZv3O4ioqKxGIxvVXnS3Y1GiZPnkxRVEtLy5AtExISrKysbt26xZQMOW+DMDIyYrPZJSUlw4q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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9esQVeej3un"
      },
      "source": [
        "skip_folds = [0, 1, 2, 3, 4, 5]"
      ],
      "id": "E9esQVeej3un",
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "33e58e5e",
        "outputId": "4d35aeeb-93ee-4670-f493-6e19ad6362da"
      },
      "source": [
        "with tpu_strategy.scope():\n",
        "    \n",
        "    VERBOSE = 1\n",
        "    test_preds = []\n",
        "    train_preds = []\n",
        "    kf = KFold(n_splits=7, shuffle=True, random_state=2021)\n",
        "    \n",
        "    for fold, (train_idx, test_idx) in enumerate(kf.split(train, targets)):\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",
        "        save_locally = tf.saved_model.SaveOptions(experimental_io_device='/job:localhost')\n",
        "        checkpoint_name = f'./Bidirect_LSTM_model_{fold+1}C.h5'\n",
        "        chk_point = ModelCheckpoint(checkpoint_name, options=save_locally, \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",
        "        if fold not in skip_folds:\n",
        "          model.fit(X_train, y_train, \n",
        "                    validation_data=(X_valid, y_valid),\n",
        "                    epochs=300,\n",
        "                    verbose=VERBOSE,\n",
        "                    batch_size=BATCH_SIZE, \n",
        "                    callbacks=[lr, chk_point, es])\n",
        "          with open('/gdrive/MyDrive/ventilator-pressure-prediction/'+checkpoint_name, 'wb') as f:\n",
        "            f.write(open(checkpoint_name, 'rb').read())\n",
        "        \n",
        "        print('Load pretrain weight from ', checkpoint_name)\n",
        "        load_locally = tf.saved_model.LoadOptions(experimental_io_device='/job:localhost')\n",
        "        model = load_model('/gdrive/MyDrive/ventilator-pressure-prediction/'+checkpoint_name, options=load_locally)\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",
        "        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",
        "        train_preds.append(model.predict(train, batch_size=BATCH_SIZE).squeeze().reshape(-1, 1).squeeze())"
      ],
      "id": "33e58e5e",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Load pretrain weight from  ./Bidirect_LSTM_model_1C.h5\n",
            "Fold-1 | OOF Score: 0.16627551694267306\n",
            "Load pretrain weight from  ./Bidirect_LSTM_model_2C.h5\n",
            "Fold-2 | OOF Score: 0.16356995324524526\n",
            "Load pretrain weight from  ./Bidirect_LSTM_model_3C.h5\n",
            "Fold-3 | OOF Score: 0.16470234984402665\n",
            "Load pretrain weight from  ./Bidirect_LSTM_model_4C.h5\n",
            "Fold-4 | OOF Score: 0.16750722863264772\n",
            "Load pretrain weight from  ./Bidirect_LSTM_model_5C.h5\n",
            "Fold-5 | OOF Score: 0.1682801366910449\n",
            "Epoch 1/300\n",
            "127/127 [==============================] - 145s 789ms/step - loss: 1.5878 - val_loss: 0.7962\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.79617, saving model to ./Bidirect_LSTM_model_6C.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 [==============================] - 61s 483ms/step - loss: 0.6830 - val_loss: 0.6540\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.79617 to 0.65396, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 3/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.5790 - val_loss: 0.6119\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.65396 to 0.61194, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 4/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.5085 - val_loss: 0.4780\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.61194 to 0.47797, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 5/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.4574 - val_loss: 0.4249\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.47797 to 0.42486, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 6/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.4226 - val_loss: 0.3906\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.42486 to 0.39060, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 7/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.4026 - val_loss: 0.3821\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.39060 to 0.38209, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 8/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.3736 - val_loss: 0.4067\n",
            "\n",
            "Epoch 00008: val_loss did not improve from 0.38209\n",
            "Epoch 9/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.3593 - val_loss: 0.3353\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.38209 to 0.33531, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 10/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.3493 - val_loss: 0.3261\n",
            "\n",
            "Epoch 00010: val_loss improved from 0.33531 to 0.32609, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 11/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.3288 - val_loss: 0.3120\n",
            "\n",
            "Epoch 00011: val_loss improved from 0.32609 to 0.31204, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 12/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.3160 - val_loss: 0.3229\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 0.31204\n",
            "Epoch 13/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.3161 - val_loss: 0.3121\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 0.31204\n",
            "Epoch 14/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.3144 - val_loss: 0.4004\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.31204\n",
            "Epoch 15/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.3281 - val_loss: 0.2989\n",
            "\n",
            "Epoch 00015: val_loss improved from 0.31204 to 0.29893, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 16/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2940 - val_loss: 0.3292\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.29893\n",
            "Epoch 17/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2914 - val_loss: 0.2949\n",
            "\n",
            "Epoch 00017: val_loss improved from 0.29893 to 0.29494, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 18/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2795 - val_loss: 0.2717\n",
            "\n",
            "Epoch 00018: val_loss improved from 0.29494 to 0.27167, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 19/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2805 - val_loss: 0.2811\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.27167\n",
            "Epoch 20/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2696 - val_loss: 0.2760\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.27167\n",
            "Epoch 21/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2582 - val_loss: 0.2637\n",
            "\n",
            "Epoch 00021: val_loss improved from 0.27167 to 0.26373, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 22/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2591 - val_loss: 0.2570\n",
            "\n",
            "Epoch 00022: val_loss improved from 0.26373 to 0.25698, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 23/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2677 - val_loss: 0.2908\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 0.25698\n",
            "Epoch 24/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2598 - val_loss: 0.2547\n",
            "\n",
            "Epoch 00024: val_loss improved from 0.25698 to 0.25466, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 25/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2574 - val_loss: 0.2645\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.25466\n",
            "Epoch 26/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2524 - val_loss: 0.2461\n",
            "\n",
            "Epoch 00026: val_loss improved from 0.25466 to 0.24615, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 27/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2420 - val_loss: 0.2427\n",
            "\n",
            "Epoch 00027: val_loss improved from 0.24615 to 0.24272, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 28/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2389 - val_loss: 0.2371\n",
            "\n",
            "Epoch 00028: val_loss improved from 0.24272 to 0.23714, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 29/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2617 - val_loss: 0.2658\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.23714\n",
            "Epoch 30/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2491 - val_loss: 0.2520\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.23714\n",
            "Epoch 31/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2347 - val_loss: 0.2495\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.23714\n",
            "Epoch 32/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2296 - val_loss: 0.2446\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.23714\n",
            "Epoch 33/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2316 - val_loss: 0.2291\n",
            "\n",
            "Epoch 00033: val_loss improved from 0.23714 to 0.22912, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 34/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2174 - val_loss: 0.2345\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.22912\n",
            "Epoch 35/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2242 - val_loss: 0.2525\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.22912\n",
            "Epoch 36/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2236 - val_loss: 0.2258\n",
            "\n",
            "Epoch 00036: val_loss improved from 0.22912 to 0.22578, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 37/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2167 - val_loss: 0.2261\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.22578\n",
            "Epoch 38/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2192 - val_loss: 0.2260\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.22578\n",
            "Epoch 39/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2217 - val_loss: 0.2503\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.22578\n",
            "Epoch 40/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2405 - val_loss: 0.2266\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.22578\n",
            "Epoch 41/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2190 - val_loss: 0.2305\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.22578\n",
            "Epoch 42/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2194 - val_loss: 0.2263\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.22578\n",
            "Epoch 43/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2143 - val_loss: 0.2344\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.22578\n",
            "Epoch 44/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2046 - val_loss: 0.2245\n",
            "\n",
            "Epoch 00044: val_loss improved from 0.22578 to 0.22451, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 45/300\n",
            "127/127 [==============================] - 61s 482ms/step - loss: 0.2008 - val_loss: 0.2158\n",
            "\n",
            "Epoch 00045: val_loss improved from 0.22451 to 0.21579, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 46/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2368 - val_loss: 0.2659\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.21579\n",
            "Epoch 47/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2329 - val_loss: 0.2356\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.21579\n",
            "Epoch 48/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2179 - val_loss: 0.2197\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.21579\n",
            "Epoch 49/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2142 - val_loss: 0.2236\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.21579\n",
            "Epoch 50/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2036 - val_loss: 0.2334\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 0.21579\n",
            "Epoch 51/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1950 - val_loss: 0.2098\n",
            "\n",
            "Epoch 00051: val_loss improved from 0.21579 to 0.20981, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 52/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1916 - val_loss: 0.2090\n",
            "\n",
            "Epoch 00052: val_loss improved from 0.20981 to 0.20905, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 53/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1986 - val_loss: 0.2240\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.20905\n",
            "Epoch 54/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1929 - val_loss: 0.2171\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.20905\n",
            "Epoch 55/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2087 - val_loss: 0.2223\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.20905\n",
            "Epoch 56/300\n",
            "127/127 [==============================] - 62s 485ms/step - loss: 0.1951 - val_loss: 0.2059\n",
            "\n",
            "Epoch 00056: val_loss improved from 0.20905 to 0.20587, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 57/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1921 - val_loss: 0.2223\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.20587\n",
            "Epoch 58/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1897 - val_loss: 0.2138\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.20587\n",
            "Epoch 59/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1893 - val_loss: 0.2009\n",
            "\n",
            "Epoch 00059: val_loss improved from 0.20587 to 0.20086, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 60/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1807 - val_loss: 0.1971\n",
            "\n",
            "Epoch 00060: val_loss improved from 0.20086 to 0.19710, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 61/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1755 - val_loss: 0.1996\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.19710\n",
            "Epoch 62/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1819 - val_loss: 0.2181\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 0.19710\n",
            "Epoch 63/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1803 - val_loss: 0.1935\n",
            "\n",
            "Epoch 00063: val_loss improved from 0.19710 to 0.19346, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 64/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1706 - val_loss: 0.2011\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.19346\n",
            "Epoch 65/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1722 - val_loss: 0.2010\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.19346\n",
            "Epoch 66/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1701 - val_loss: 0.2054\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.19346\n",
            "Epoch 67/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1808 - val_loss: 0.1955\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 0.19346\n",
            "Epoch 68/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1800 - val_loss: 0.2191\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.19346\n",
            "Epoch 69/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1734 - val_loss: 0.2006\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 0.19346\n",
            "Epoch 70/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1736 - val_loss: 0.2044\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 0.19346\n",
            "Epoch 71/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1789 - val_loss: 0.2197\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.19346\n",
            "Epoch 72/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1706 - val_loss: 0.1953\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.19346\n",
            "Epoch 73/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1668 - val_loss: 0.1892\n",
            "\n",
            "Epoch 00073: val_loss improved from 0.19346 to 0.18918, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 74/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1707 - val_loss: 0.2040\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.18918\n",
            "Epoch 75/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1662 - val_loss: 0.2023\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.18918\n",
            "Epoch 76/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1675 - val_loss: 0.1979\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.18918\n",
            "Epoch 77/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1621 - val_loss: 0.1858\n",
            "\n",
            "Epoch 00077: val_loss improved from 0.18918 to 0.18582, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 78/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1647 - val_loss: 0.2031\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.18582\n",
            "Epoch 79/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1590 - val_loss: 0.1921\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.18582\n",
            "Epoch 80/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1609 - val_loss: 0.1890\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.18582\n",
            "Epoch 81/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1591 - val_loss: 0.1867\n",
            "\n",
            "Epoch 00081: val_loss did not improve from 0.18582\n",
            "Epoch 82/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1549 - val_loss: 0.1840\n",
            "\n",
            "Epoch 00082: val_loss improved from 0.18582 to 0.18397, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 83/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1512 - val_loss: 0.1837\n",
            "\n",
            "Epoch 00083: val_loss improved from 0.18397 to 0.18367, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 84/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1587 - val_loss: 0.2001\n",
            "\n",
            "Epoch 00084: val_loss did not improve from 0.18367\n",
            "Epoch 85/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1594 - val_loss: 0.1888\n",
            "\n",
            "Epoch 00085: val_loss did not improve from 0.18367\n",
            "Epoch 86/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1673 - val_loss: 0.1888\n",
            "\n",
            "Epoch 00086: val_loss did not improve from 0.18367\n",
            "Epoch 87/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1519 - val_loss: 0.1997\n",
            "\n",
            "Epoch 00087: val_loss did not improve from 0.18367\n",
            "Epoch 88/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1614 - val_loss: 0.1933\n",
            "\n",
            "Epoch 00088: val_loss did not improve from 0.18367\n",
            "Epoch 89/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1641 - val_loss: 0.1971\n",
            "\n",
            "Epoch 00089: val_loss did not improve from 0.18367\n",
            "Epoch 90/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1478 - val_loss: 0.1825\n",
            "\n",
            "Epoch 00090: val_loss improved from 0.18367 to 0.18246, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 91/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1553 - val_loss: 0.1869\n",
            "\n",
            "Epoch 00091: val_loss did not improve from 0.18246\n",
            "Epoch 92/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1480 - val_loss: 0.1848\n",
            "\n",
            "Epoch 00092: val_loss did not improve from 0.18246\n",
            "Epoch 93/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1441 - val_loss: 0.1801\n",
            "\n",
            "Epoch 00093: val_loss improved from 0.18246 to 0.18010, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 94/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1402 - val_loss: 0.1767\n",
            "\n",
            "Epoch 00094: val_loss improved from 0.18010 to 0.17667, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 95/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1370 - val_loss: 0.1768\n",
            "\n",
            "Epoch 00095: val_loss did not improve from 0.17667\n",
            "Epoch 96/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1399 - val_loss: 0.1848\n",
            "\n",
            "Epoch 00096: val_loss did not improve from 0.17667\n",
            "Epoch 97/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1371 - val_loss: 0.1856\n",
            "\n",
            "Epoch 00097: val_loss did not improve from 0.17667\n",
            "Epoch 98/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1681 - val_loss: 0.1935\n",
            "\n",
            "Epoch 00098: val_loss did not improve from 0.17667\n",
            "Epoch 99/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1536 - val_loss: 0.1834\n",
            "\n",
            "Epoch 00099: val_loss did not improve from 0.17667\n",
            "Epoch 100/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1410 - val_loss: 0.1838\n",
            "\n",
            "Epoch 00100: val_loss did not improve from 0.17667\n",
            "Epoch 101/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1358 - val_loss: 0.1768\n",
            "\n",
            "Epoch 00101: val_loss did not improve from 0.17667\n",
            "Epoch 102/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1318 - val_loss: 0.1804\n",
            "\n",
            "Epoch 00102: val_loss did not improve from 0.17667\n",
            "Epoch 103/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1300 - val_loss: 0.1733\n",
            "\n",
            "Epoch 00103: val_loss improved from 0.17667 to 0.17331, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 104/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1315 - val_loss: 0.1793\n",
            "\n",
            "Epoch 00104: val_loss did not improve from 0.17331\n",
            "Epoch 105/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1285 - val_loss: 0.1786\n",
            "\n",
            "Epoch 00105: val_loss did not improve from 0.17331\n",
            "Epoch 106/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1327 - val_loss: 0.1806\n",
            "\n",
            "Epoch 00106: val_loss did not improve from 0.17331\n",
            "Epoch 107/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1342 - val_loss: 0.1794\n",
            "\n",
            "Epoch 00107: val_loss did not improve from 0.17331\n",
            "Epoch 108/300\n",
            "127/127 [==============================] - 62s 485ms/step - loss: 0.1373 - val_loss: 0.1871\n",
            "\n",
            "Epoch 00108: val_loss did not improve from 0.17331\n",
            "Epoch 109/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1360 - val_loss: 0.1875\n",
            "\n",
            "Epoch 00109: val_loss did not improve from 0.17331\n",
            "Epoch 110/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1525 - val_loss: 0.1859\n",
            "\n",
            "Epoch 00110: val_loss did not improve from 0.17331\n",
            "Epoch 111/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1359 - val_loss: 0.1806\n",
            "\n",
            "Epoch 00111: val_loss did not improve from 0.17331\n",
            "Epoch 112/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1278 - val_loss: 0.1750\n",
            "\n",
            "Epoch 00112: val_loss did not improve from 0.17331\n",
            "Epoch 113/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1242 - val_loss: 0.1768\n",
            "\n",
            "Epoch 00113: ReduceLROnPlateau reducing learning rate to 0.0007500000356230885.\n",
            "\n",
            "Epoch 00113: val_loss did not improve from 0.17331\n",
            "Epoch 114/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1139 - val_loss: 0.1698\n",
            "\n",
            "Epoch 00114: val_loss improved from 0.17331 to 0.16985, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 115/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1100 - val_loss: 0.1698\n",
            "\n",
            "Epoch 00115: val_loss improved from 0.16985 to 0.16975, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 116/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1085 - val_loss: 0.1768\n",
            "\n",
            "Epoch 00116: val_loss did not improve from 0.16975\n",
            "Epoch 117/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1082 - val_loss: 0.1682\n",
            "\n",
            "Epoch 00117: val_loss improved from 0.16975 to 0.16823, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 118/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1067 - val_loss: 0.1714\n",
            "\n",
            "Epoch 00118: val_loss did not improve from 0.16823\n",
            "Epoch 119/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.1064 - val_loss: 0.1712\n",
            "\n",
            "Epoch 00119: val_loss did not improve from 0.16823\n",
            "Epoch 120/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1066 - val_loss: 0.1696\n",
            "\n",
            "Epoch 00120: val_loss did not improve from 0.16823\n",
            "Epoch 121/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1045 - val_loss: 0.1721\n",
            "\n",
            "Epoch 00121: val_loss did not improve from 0.16823\n",
            "Epoch 122/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1035 - val_loss: 0.1696\n",
            "\n",
            "Epoch 00122: val_loss did not improve from 0.16823\n",
            "Epoch 123/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1027 - val_loss: 0.1698\n",
            "\n",
            "Epoch 00123: val_loss did not improve from 0.16823\n",
            "Epoch 124/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1020 - val_loss: 0.1686\n",
            "\n",
            "Epoch 00124: val_loss did not improve from 0.16823\n",
            "Epoch 125/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1021 - val_loss: 0.1732\n",
            "\n",
            "Epoch 00125: val_loss did not improve from 0.16823\n",
            "Epoch 126/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1016 - val_loss: 0.1722\n",
            "\n",
            "Epoch 00126: val_loss did not improve from 0.16823\n",
            "Epoch 127/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.1014 - val_loss: 0.1686\n",
            "\n",
            "Epoch 00127: ReduceLROnPlateau reducing learning rate to 0.0005625000048894435.\n",
            "\n",
            "Epoch 00127: val_loss did not improve from 0.16823\n",
            "Epoch 128/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0938 - val_loss: 0.1675\n",
            "\n",
            "Epoch 00128: val_loss improved from 0.16823 to 0.16751, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 129/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0913 - val_loss: 0.1667\n",
            "\n",
            "Epoch 00129: val_loss improved from 0.16751 to 0.16669, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 130/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0898 - val_loss: 0.1699\n",
            "\n",
            "Epoch 00130: val_loss did not improve from 0.16669\n",
            "Epoch 131/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0893 - val_loss: 0.1675\n",
            "\n",
            "Epoch 00131: val_loss did not improve from 0.16669\n",
            "Epoch 132/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0882 - val_loss: 0.1663\n",
            "\n",
            "Epoch 00132: val_loss improved from 0.16669 to 0.16631, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 133/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0884 - val_loss: 0.1692\n",
            "\n",
            "Epoch 00133: val_loss did not improve from 0.16631\n",
            "Epoch 134/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0878 - val_loss: 0.1678\n",
            "\n",
            "Epoch 00134: val_loss did not improve from 0.16631\n",
            "Epoch 135/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0877 - val_loss: 0.1676\n",
            "\n",
            "Epoch 00135: val_loss did not improve from 0.16631\n",
            "Epoch 136/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0865 - val_loss: 0.1680\n",
            "\n",
            "Epoch 00136: val_loss did not improve from 0.16631\n",
            "Epoch 137/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0863 - val_loss: 0.1674\n",
            "\n",
            "Epoch 00137: val_loss did not improve from 0.16631\n",
            "Epoch 138/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0853 - val_loss: 0.1684\n",
            "\n",
            "Epoch 00138: val_loss did not improve from 0.16631\n",
            "Epoch 139/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0848 - val_loss: 0.1683\n",
            "\n",
            "Epoch 00139: val_loss did not improve from 0.16631\n",
            "Epoch 140/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0836 - val_loss: 0.1670\n",
            "\n",
            "Epoch 00140: val_loss did not improve from 0.16631\n",
            "Epoch 141/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0830 - val_loss: 0.1689\n",
            "\n",
            "Epoch 00141: val_loss did not improve from 0.16631\n",
            "Epoch 142/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0839 - val_loss: 0.1687\n",
            "\n",
            "Epoch 00142: ReduceLROnPlateau reducing learning rate to 0.0004218749818392098.\n",
            "\n",
            "Epoch 00142: val_loss did not improve from 0.16631\n",
            "Epoch 143/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0781 - val_loss: 0.1662\n",
            "\n",
            "Epoch 00143: val_loss improved from 0.16631 to 0.16616, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 144/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0752 - val_loss: 0.1655\n",
            "\n",
            "Epoch 00144: val_loss improved from 0.16616 to 0.16550, saving model to ./Bidirect_LSTM_model_6C.h5\n",
            "Epoch 145/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0740 - val_loss: 0.1666\n",
            "\n",
            "Epoch 00145: val_loss did not improve from 0.16550\n",
            "Epoch 146/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0741 - val_loss: 0.1663\n",
            "\n",
            "Epoch 00146: val_loss did not improve from 0.16550\n",
            "Epoch 147/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0729 - val_loss: 0.1664\n",
            "\n",
            "Epoch 00147: val_loss did not improve from 0.16550\n",
            "Epoch 148/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0724 - val_loss: 0.1665\n",
            "\n",
            "Epoch 00148: val_loss did not improve from 0.16550\n",
            "Epoch 149/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0735 - val_loss: 0.1667\n",
            "\n",
            "Epoch 00149: val_loss did not improve from 0.16550\n",
            "Epoch 150/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0739 - val_loss: 0.1668\n",
            "\n",
            "Epoch 00150: val_loss did not improve from 0.16550\n",
            "Epoch 151/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0733 - val_loss: 0.1683\n",
            "\n",
            "Epoch 00151: val_loss did not improve from 0.16550\n",
            "Epoch 152/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0712 - val_loss: 0.1670\n",
            "\n",
            "Epoch 00152: val_loss did not improve from 0.16550\n",
            "Epoch 153/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0702 - val_loss: 0.1672\n",
            "\n",
            "Epoch 00153: val_loss did not improve from 0.16550\n",
            "Epoch 154/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0703 - val_loss: 0.1676\n",
            "\n",
            "Epoch 00154: ReduceLROnPlateau reducing learning rate to 0.00031640623637940735.\n",
            "\n",
            "Epoch 00154: val_loss did not improve from 0.16550\n",
            "Epoch 155/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0665 - val_loss: 0.1662\n",
            "\n",
            "Epoch 00155: val_loss did not improve from 0.16550\n",
            "Epoch 156/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0647 - val_loss: 0.1667\n",
            "\n",
            "Epoch 00156: val_loss did not improve from 0.16550\n",
            "Epoch 157/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0640 - val_loss: 0.1666\n",
            "\n",
            "Epoch 00157: val_loss did not improve from 0.16550\n",
            "Epoch 158/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0631 - val_loss: 0.1671\n",
            "\n",
            "Epoch 00158: val_loss did not improve from 0.16550\n",
            "Epoch 159/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0629 - val_loss: 0.1666\n",
            "\n",
            "Epoch 00159: val_loss did not improve from 0.16550\n",
            "Epoch 160/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0624 - val_loss: 0.1663\n",
            "\n",
            "Epoch 00160: val_loss did not improve from 0.16550\n",
            "Epoch 161/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0626 - val_loss: 0.1674\n",
            "\n",
            "Epoch 00161: val_loss did not improve from 0.16550\n",
            "Epoch 162/300\n",
            "127/127 [==============================] - 61s 484ms/step - loss: 0.0623 - val_loss: 0.1671\n",
            "\n",
            "Epoch 00162: val_loss did not improve from 0.16550\n",
            "Epoch 163/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0615 - val_loss: 0.1678\n",
            "\n",
            "Epoch 00163: val_loss did not improve from 0.16550\n",
            "Epoch 164/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0622 - val_loss: 0.1671\n",
            "\n",
            "Epoch 00164: ReduceLROnPlateau reducing learning rate to 0.00023730468819849193.\n",
            "\n",
            "Epoch 00164: val_loss did not improve from 0.16550\n",
            "Epoch 165/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0586 - val_loss: 0.1669\n",
            "\n",
            "Epoch 00165: val_loss did not improve from 0.16550\n",
            "Epoch 166/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0573 - val_loss: 0.1671\n",
            "\n",
            "Epoch 00166: val_loss did not improve from 0.16550\n",
            "Epoch 167/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0565 - val_loss: 0.1670\n",
            "\n",
            "Epoch 00167: val_loss did not improve from 0.16550\n",
            "Epoch 168/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0565 - val_loss: 0.1669\n",
            "\n",
            "Epoch 00168: val_loss did not improve from 0.16550\n",
            "Epoch 169/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0559 - val_loss: 0.1667\n",
            "\n",
            "Epoch 00169: val_loss did not improve from 0.16550\n",
            "Epoch 170/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0556 - val_loss: 0.1671\n",
            "\n",
            "Epoch 00170: val_loss did not improve from 0.16550\n",
            "Epoch 171/300\n",
            "127/127 [==============================] - 61s 484ms/step - loss: 0.0555 - val_loss: 0.1672\n",
            "\n",
            "Epoch 00171: val_loss did not improve from 0.16550\n",
            "Epoch 172/300\n",
            "127/127 [==============================] - 62s 487ms/step - loss: 0.0552 - val_loss: 0.1674\n",
            "\n",
            "Epoch 00172: val_loss did not improve from 0.16550\n",
            "Epoch 173/300\n",
            "127/127 [==============================] - 62s 486ms/step - loss: 0.0555 - val_loss: 0.1679\n",
            "\n",
            "Epoch 00173: val_loss did not improve from 0.16550\n",
            "Epoch 174/300\n",
            "127/127 [==============================] - 62s 486ms/step - loss: 0.0550 - val_loss: 0.1674\n",
            "\n",
            "Epoch 00174: ReduceLROnPlateau reducing learning rate to 0.00017797851614886895.\n",
            "\n",
            "Epoch 00174: val_loss did not improve from 0.16550\n",
            "Epoch 175/300\n",
            "127/127 [==============================] - 62s 485ms/step - loss: 0.0526 - val_loss: 0.1669\n",
            "\n",
            "Epoch 00175: val_loss did not improve from 0.16550\n",
            "Epoch 176/300\n",
            "127/127 [==============================] - 62s 485ms/step - loss: 0.0513 - val_loss: 0.1670\n",
            "\n",
            "Epoch 00176: val_loss did not improve from 0.16550\n",
            "Epoch 177/300\n",
            "127/127 [==============================] - 62s 485ms/step - loss: 0.0508 - val_loss: 0.1671\n",
            "\n",
            "Epoch 00177: val_loss did not improve from 0.16550\n",
            "Epoch 178/300\n",
            "127/127 [==============================] - 62s 485ms/step - loss: 0.0505 - val_loss: 0.1671\n",
            "\n",
            "Epoch 00178: val_loss did not improve from 0.16550\n",
            "Epoch 179/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0504 - val_loss: 0.1672\n",
            "\n",
            "Epoch 00179: val_loss did not improve from 0.16550\n",
            "Epoch 180/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0503 - val_loss: 0.1674\n",
            "\n",
            "Epoch 00180: val_loss did not improve from 0.16550\n",
            "Epoch 181/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0500 - val_loss: 0.1674\n",
            "\n",
            "Epoch 00181: val_loss did not improve from 0.16550\n",
            "Epoch 182/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0498 - val_loss: 0.1675\n",
            "\n",
            "Epoch 00182: val_loss did not improve from 0.16550\n",
            "Epoch 183/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0495 - val_loss: 0.1674\n",
            "\n",
            "Epoch 00183: val_loss did not improve from 0.16550\n",
            "Epoch 184/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0493 - val_loss: 0.1677\n",
            "\n",
            "Epoch 00184: ReduceLROnPlateau reducing learning rate to 0.0001334838816546835.\n",
            "\n",
            "Epoch 00184: val_loss did not improve from 0.16550\n",
            "Epoch 185/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0479 - val_loss: 0.1675\n",
            "\n",
            "Epoch 00185: val_loss did not improve from 0.16550\n",
            "Epoch 186/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0470 - val_loss: 0.1674\n",
            "\n",
            "Epoch 00186: val_loss did not improve from 0.16550\n",
            "Epoch 187/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0467 - val_loss: 0.1677\n",
            "\n",
            "Epoch 00187: val_loss did not improve from 0.16550\n",
            "Epoch 188/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0465 - val_loss: 0.1677\n",
            "\n",
            "Epoch 00188: val_loss did not improve from 0.16550\n",
            "Epoch 189/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0464 - val_loss: 0.1678\n",
            "\n",
            "Epoch 00189: val_loss did not improve from 0.16550\n",
            "Epoch 190/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0464 - val_loss: 0.1680\n",
            "\n",
            "Epoch 00190: val_loss did not improve from 0.16550\n",
            "Epoch 191/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0460 - val_loss: 0.1680\n",
            "\n",
            "Epoch 00191: val_loss did not improve from 0.16550\n",
            "Epoch 192/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.0461 - val_loss: 0.1681\n",
            "\n",
            "Epoch 00192: val_loss did not improve from 0.16550\n",
            "Epoch 193/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0460 - val_loss: 0.1681\n",
            "\n",
            "Epoch 00193: val_loss did not improve from 0.16550\n",
            "Epoch 194/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.0454 - val_loss: 0.1680\n",
            "\n",
            "Epoch 00194: ReduceLROnPlateau reducing learning rate to 0.00010011290578404441.\n",
            "\n",
            "Epoch 00194: val_loss did not improve from 0.16550\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00194: early stopping\n",
            "Load pretrain weight from  ./Bidirect_LSTM_model_6C.h5\n",
            "Fold-6 | OOF Score: 0.16549560054702672\n",
            "Epoch 1/300\n",
            "127/127 [==============================] - 146s 780ms/step - loss: 1.5687 - val_loss: 0.7455\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.74549, saving model to ./Bidirect_LSTM_model_7C.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 [==============================] - 61s 481ms/step - loss: 0.6646 - val_loss: 0.5948\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.74549 to 0.59479, saving model to ./Bidirect_LSTM_model_7C.h5\n",
            "Epoch 3/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.5555 - val_loss: 0.6304\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 0.59479\n",
            "Epoch 4/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.5043 - val_loss: 0.5085\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.59479 to 0.50846, saving model to ./Bidirect_LSTM_model_7C.h5\n",
            "Epoch 5/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.4538 - val_loss: 0.5616\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 0.50846\n",
            "Epoch 6/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.4238 - val_loss: 0.4008\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.50846 to 0.40083, saving model to ./Bidirect_LSTM_model_7C.h5\n",
            "Epoch 7/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.4083 - val_loss: 0.4220\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.40083\n",
            "Epoch 8/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.3807 - val_loss: 0.3689\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.40083 to 0.36887, saving model to ./Bidirect_LSTM_model_7C.h5\n",
            "Epoch 9/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.3555 - val_loss: 0.4055\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.36887\n",
            "Epoch 10/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.3614 - val_loss: 0.3795\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.36887\n",
            "Epoch 11/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.3435 - val_loss: 0.3401\n",
            "\n",
            "Epoch 00011: val_loss improved from 0.36887 to 0.34013, saving model to ./Bidirect_LSTM_model_7C.h5\n",
            "Epoch 12/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.3256 - val_loss: 0.3364\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.34013 to 0.33644, saving model to ./Bidirect_LSTM_model_7C.h5\n",
            "Epoch 13/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.3097 - val_loss: 0.3290\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.33644 to 0.32900, saving model to ./Bidirect_LSTM_model_7C.h5\n",
            "Epoch 14/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.3112 - val_loss: 0.3327\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.32900\n",
            "Epoch 15/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.3043 - val_loss: 0.3098\n",
            "\n",
            "Epoch 00015: val_loss improved from 0.32900 to 0.30982, saving model to ./Bidirect_LSTM_model_7C.h5\n",
            "Epoch 16/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2905 - val_loss: 0.3050\n",
            "\n",
            "Epoch 00016: val_loss improved from 0.30982 to 0.30497, saving model to ./Bidirect_LSTM_model_7C.h5\n",
            "Epoch 17/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2922 - val_loss: 0.2899\n",
            "\n",
            "Epoch 00017: val_loss improved from 0.30497 to 0.28993, saving model to ./Bidirect_LSTM_model_7C.h5\n",
            "Epoch 18/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2855 - val_loss: 0.3098\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 0.28993\n",
            "Epoch 19/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2801 - val_loss: 0.3058\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.28993\n",
            "Epoch 20/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2753 - val_loss: 0.3216\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.28993\n",
            "Epoch 21/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2781 - val_loss: 0.2748\n",
            "\n",
            "Epoch 00021: val_loss improved from 0.28993 to 0.27476, saving model to ./Bidirect_LSTM_model_7C.h5\n",
            "Epoch 22/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2659 - val_loss: 0.2760\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.27476\n",
            "Epoch 23/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2650 - val_loss: 0.3039\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 0.27476\n",
            "Epoch 24/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2787 - val_loss: 0.3063\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.27476\n",
            "Epoch 25/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2599 - val_loss: 0.2684\n",
            "\n",
            "Epoch 00025: val_loss improved from 0.27476 to 0.26844, saving model to ./Bidirect_LSTM_model_7C.h5\n",
            "Epoch 26/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2487 - val_loss: 0.3056\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.26844\n",
            "Epoch 27/300\n",
            "127/127 [==============================] - 61s 481ms/step - loss: 0.2566 - val_loss: 0.2509\n",
            "\n",
            "Epoch 00027: val_loss improved from 0.26844 to 0.25089, saving model to ./Bidirect_LSTM_model_7C.h5\n",
            "Epoch 28/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2404 - val_loss: 0.2850\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.25089\n",
            "Epoch 29/300\n",
            "127/127 [==============================] - 61s 480ms/step - loss: 0.2393 - val_loss: 0.2517\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.25089\n",
            "Epoch 30/300\n",
            " 50/127 [==========>...................] - ETA: 36s - loss: 0.2630"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-c0f71d346a3f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m                     \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mVERBOSE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\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---> 33\u001b[0;31m                     callbacks=[lr, chk_point, es])\n\u001b[0m\u001b[1;32m     34\u001b[0m           \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/gdrive/MyDrive/ventilator-pressure-prediction/'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcheckpoint_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m             \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\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/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   1187\u001b[0m               \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp_logs\u001b[0m  \u001b[0;31m# No error, now safe to assign to logs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1188\u001b[0m               \u001b[0mend_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1189\u001b[0;31m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\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   1190\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m                 \u001b[0;32mbreak\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/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    433\u001b[0m     \"\"\"\n\u001b[1;32m    434\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_call_train_batch_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 435\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogs\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    436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m    293\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_begin_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_end_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\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    296\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unrecognized hook: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhook\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/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_end_hook\u001b[0;34m(self, mode, batch, logs)\u001b[0m\n\u001b[1;32m    313\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhook_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\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    316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_batches_for_timing_check\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1098\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m       \u001b[0;31m# Only block async when verbose = 1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m       \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_to_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\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   1101\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m    514\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 516\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\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    517\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    518\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m    867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0;31m       \u001b[0mstructure\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[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\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    870\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0;31m       \u001b[0mstructure\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[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\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    870\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36m_to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    510\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\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--> 512\u001b[0;31m       \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\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    513\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    514\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m  \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\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/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1092\u001b[0m     \"\"\"\n\u001b[1;32m   1093\u001b[0m     \u001b[0;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1094\u001b[0;31m     \u001b[0mmaybe_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\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[0m\u001b[1;32m   1095\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1096\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_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1058\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\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[1;32m   1059\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-> 1060\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\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   1061\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\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[1;32m   1062\u001b[0m       \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\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;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cab26ba6"
      },
      "source": [
        "## Create submission file"
      ],
      "id": "cab26ba6"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f84c3a4d"
      },
      "source": [
        "submission = pd.read_csv('/kaggle/input/ventilator-pressure-prediction/sample_submission.csv')\n",
        "submission[\"pressure\"] = sum(test_preds)/5\n",
        "submission.to_csv('submission.csv', index=False)"
      ],
      "id": "f84c3a4d",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "108561ed"
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "train_df = pd.read_csv('/kaggle/input/ventilator-pressure-prediction/train.csv')\n",
        "mean_absolute_error(train_df['pressure'], sum(train_preds)/5)"
      ],
      "id": "108561ed",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-fvHJvyZ2ZL"
      },
      "source": [
        "!zip cnn_models.zip *.hdf5\n",
        "!zip submission.zip *.csv"
      ],
      "id": "5-fvHJvyZ2ZL",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUzQFzu_Z6N1"
      },
      "source": [
        "with open('/gdrive/MyDrive/ventilator-pressure-prediction/submission.zip', 'wb') as f:\n",
        "  f.write(open('submission.zip', 'rb').read())\n",
        "\n",
        "with open('/gdrive/MyDrive/ventilator-pressure-prediction/cnn_models.zip', 'wb') as f:\n",
        "  f.write(open('cnn_models.zip', 'rb').read())"
      ],
      "id": "GUzQFzu_Z6N1",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZlRIdVloQxVI"
      },
      "source": [
        "!ls"
      ],
      "id": "ZlRIdVloQxVI",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHPC875T5WbV"
      },
      "source": [
        ""
      ],
      "id": "IHPC875T5WbV",
      "execution_count": null,
      "outputs": []
    }
  ]
}