ventilator_pressure_transformer_V2.ipynb

{
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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "ventilator-pressure-transformer_V2.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"
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    "papermill": {
      "default_parameters": {},
      "duration": 5851.539198,
      "end_time": "2021-10-05T21:57:27.162714",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2021-10-05T20:19:55.623516",
      "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/ventilator_pressure_transformer_V2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TRRDmcGpsh4u"
      },
      "source": [
        "V1: Add PositionalEncoding\n",
        "\n",
        "V2: Adjust Transformer parameters"
      ],
      "id": "TRRDmcGpsh4u"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPJR-B_AhTBC"
      },
      "source": [
        "# Update pandas version for Colab TPU\n",
        "#!pip install pandas==1.3.2"
      ],
      "id": "EPJR-B_AhTBC",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJaAgiAOkUEt"
      },
      "source": [
        "try:\n",
        "  import google.colab\n",
        "  IN_COLAB = True\n",
        "except:\n",
        "  IN_COLAB = False"
      ],
      "id": "mJaAgiAOkUEt",
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oH5W_yWm-P8P",
        "outputId": "e148116f-c3a7-44e6-ad67-554798bf9e92"
      },
      "source": [
        "if IN_COLAB:\n",
        "  from google.colab import drive\n",
        "  drive.mount(\"/gdrive\", force_remount=True)"
      ],
      "id": "oH5W_yWm-P8P",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mhjzjzp5etAG",
        "outputId": "658c10e5-9f8b-4b23-e3ab-daf47bc92bd2"
      },
      "source": [
        "if IN_COLAB:\n",
        "  !pip install kaggle\n",
        "  !mkdir /root/.kaggle\n",
        "  !cp /gdrive/MyDrive/ventilator-pressure-prediction/kaggle.json /root/.kaggle\n",
        "  !kaggle competitions download -c ventilator-pressure-prediction\n",
        "  !mkdir -p /kaggle/input/ventilator-pressure-prediction\n",
        "  !unzip '*.zip' -d /kaggle/input/ventilator-pressure-prediction\n",
        "  !ls /kaggle/input/ventilator-pressure-prediction"
      ],
      "id": "mhjzjzp5etAG",
      "execution_count": 5,
      "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-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (5.0.2)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2021.5.30)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.62.3)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\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: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\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, 78.6MB/s]\n",
            "Downloading train.csv.zip to /content\n",
            " 98% 137M/139M [00:03<00:00, 69.2MB/s]\n",
            "100% 139M/139M [00:03<00:00, 43.9MB/s]\n",
            "Downloading test.csv.zip to /content\n",
            " 82% 62.0M/75.4M [00:02<00:00, 21.2MB/s]\n",
            "100% 75.4M/75.4M [00:02<00:00, 33.4MB/s]\n",
            "Archive:  sample_submission.csv.zip\n",
            "  inflating: /kaggle/input/ventilator-pressure-prediction/sample_submission.csv  \n",
            "\n",
            "Archive:  test.csv.zip\n",
            "  inflating: /kaggle/input/ventilator-pressure-prediction/test.csv  \n",
            "\n",
            "Archive:  train.csv.zip\n",
            "  inflating: /kaggle/input/ventilator-pressure-prediction/train.csv  \n",
            "\n",
            "3 archives were successfully processed.\n",
            "sample_submission.csv  test.csv  train.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a16844ae"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# import optuna\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from tensorflow.keras.callbacks import LearningRateScheduler, ReduceLROnPlateau\n",
        "from tensorflow.keras.optimizers.schedules import ExponentialDecay\n",
        "\n",
        "from sklearn.metrics import mean_absolute_error as mae\n",
        "from sklearn.preprocessing import RobustScaler, normalize\n",
        "from sklearn.model_selection import train_test_split, GroupKFold, KFold\n",
        "\n",
        "from IPython.display import display\n",
        "\n",
        "model_folder = '/gdrive/MyDrive/ventilator-pressure-prediction/transformer/'\n",
        "\n",
        "EPOCH = 300\n",
        "BATCH_SIZE = 512\n",
        "NUM_FOLDS = 10\n",
        "\n",
        "TRANSFORMER_HEAD_SIZES=[512, 256, 128, 64]\n",
        "TRANSFORMER_OUTPUT_DIMS=[None, None, None, None]\n",
        "TRANSFORMER_NUM_HEADS=[8, 8, 4, 4]\n",
        "TRANSFORMER_FF_DIMS=128,\n",
        "TRANSFORMER_NUM_TRANSFORMER_BLOCKS=4\n",
        "TRANSFORMER_MLP_UNITS=[32]\n",
        "TRANSFORMER_DROPOUT=0.01\n",
        "TRANSFORMER_MLP_DROPOUT=0.01"
      ],
      "id": "a16844ae",
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEHQxSneTcVX"
      },
      "source": [
        "import os\n",
        "import random\n",
        "# Deep Learning\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "# Metrics\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "# Random Seed Initialize\n",
        "RANDOM_SEED = 42\n",
        "\n",
        "def seed_everything(seed=RANDOM_SEED):\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    random.seed(seed)\n",
        "    tf.random.set_seed(seed)\n",
        "    \n",
        "seed_everything()"
      ],
      "id": "LEHQxSneTcVX",
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "564144b2"
      },
      "source": [
        "DEBUG = False\n",
        "\n",
        "train = pd.read_csv('/kaggle/input/ventilator-pressure-prediction/train.csv')\n",
        "test = pd.read_csv('/kaggle/input/ventilator-pressure-prediction/test.csv')\n",
        "submission = pd.read_csv('/kaggle/input/ventilator-pressure-prediction/sample_submission.csv')\n",
        "\n",
        "if DEBUG:\n",
        "    train = train[:80*1000]"
      ],
      "id": "564144b2",
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08fd8094",
        "outputId": "01825065-fca2-46c6-98e4-70a7215b49dd"
      },
      "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",
        "train = add_features(train)\n",
        "test = add_features(test)"
      ],
      "id": "08fd8094",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Step-1...Completed\n",
            "Step-2...Completed\n",
            "Step-3...Completed\n",
            "Step-4...Completed\n",
            "Step-5...Completed\n",
            "Step-1...Completed\n",
            "Step-2...Completed\n",
            "Step-3...Completed\n",
            "Step-4...Completed\n",
            "Step-5...Completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "431a7ee7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecc145dd-913b-4808-d6ba-f671fc551c73"
      },
      "source": [
        "targets = train[['pressure']].to_numpy().reshape(-1, 80)\n",
        "\n",
        "train.drop(['pressure','id', 'breath_id','one','count',\n",
        "            'breath_id_lag','breath_id_lag2','breath_id_lagsame',\n",
        "            'breath_id_lag2same'], axis=1, inplace=True)\n",
        "\n",
        "test = test.drop(['id', 'breath_id','one','count','breath_id_lag',\n",
        "                  'breath_id_lag2','breath_id_lagsame',\n",
        "                  'breath_id_lag2same'], axis=1)\n",
        "\n",
        "train.head(), train.shape"
      ],
      "id": "431a7ee7",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(   time_step       u_in  u_out  ...  R__C_5__10  R__C_5__20  R__C_5__50\n",
              " 0   0.000000   0.083334      0  ...           0           0           0\n",
              " 1   0.033652  18.383041      0  ...           0           0           0\n",
              " 2   0.067514  22.509278      0  ...           0           0           0\n",
              " 3   0.101542  22.808822      0  ...           0           0           0\n",
              " 4   0.135756  25.355850      0  ...           0           0           0\n",
              " \n",
              " [5 rows x 53 columns], (6036000, 53))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d24ef65c"
      },
      "source": [
        "RS = RobustScaler()\n",
        "train = RS.fit_transform(train)\n",
        "test = RS.transform(test)"
      ],
      "id": "d24ef65c",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6f1a3e19"
      },
      "source": [
        "train = train.reshape(-1, 80, train.shape[-1])\n",
        "test = test.reshape(-1, 80, train.shape[-1])"
      ],
      "id": "6f1a3e19",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4lOIi9HdExc"
      },
      "source": [
        "from tensorflow.keras import layers\n",
        "from tensorflow import keras"
      ],
      "id": "m4lOIi9HdExc",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqA2324UJKb_"
      },
      "source": [
        "# refer to https://www.tensorflow.org/text/tutorials/transformer#encoder\n",
        "def get_angles(pos, i, d_model):\n",
        "  angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))\n",
        "  return pos * angle_rates\n",
        "\n",
        "def get_positional_encoding(position, d_model):\n",
        "  angle_rads = get_angles(np.arange(position)[:, np.newaxis],\n",
        "                          np.arange(d_model)[np.newaxis, :],\n",
        "                          d_model)\n",
        "\n",
        "  # apply sin to even indices in the array; 2i\n",
        "  angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])\n",
        "\n",
        "  # apply cos to odd indices in the array; 2i+1\n",
        "  angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])\n",
        "\n",
        "  pos_encoding = angle_rads[np.newaxis, ...]\n",
        "\n",
        "  return tf.cast(pos_encoding, dtype=tf.float32)"
      ],
      "id": "dqA2324UJKb_",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5MeMc-0ddTj"
      },
      "source": [
        "#refer to https://keras.io/examples/timeseries/timeseries_classification_transformer/\n",
        "def transformer_encoder(inputs, head_size, num_heads, output_dim, ff_dim, dropout=0):\n",
        "    # Normalization and Attention\n",
        "    x = layers.LayerNormalization(epsilon=1e-6)(inputs)\n",
        "    if head_size == -1:\n",
        "      head_size = inputs.shape[-1]\n",
        "    print(head_size, num_heads, output_dim, dropout)\n",
        "    x = layers.MultiHeadAttention(\n",
        "        key_dim=head_size, num_heads=num_heads, output_shape=output_dim, dropout=dropout\n",
        "    )(x, x)\n",
        "    x = layers.Dropout(dropout)(x)\n",
        "    res = x + inputs\n",
        "\n",
        "    # Feed Forward Part\n",
        "    # Feed forward can be Den lay or RNN as long as it keep final dimension\n",
        "    x = layers.LayerNormalization(epsilon=1e-6)(res)\n",
        "    x = layers.Conv1D(filters=ff_dim, kernel_size=1, activation=\"relu\")(x)\n",
        "    x = layers.Dropout(dropout)(x)\n",
        "    x = layers.Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)\n",
        "\n",
        "    return x + res"
      ],
      "id": "i5MeMc-0ddTj",
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1uCDdywdhRH"
      },
      "source": [
        "# refer to https://www.tensorflow.org/text/tutorials/transformer#encoder\n",
        "# https://rubikscode.net/2019/08/19/transformer-with-python-and-tensorflow-2-0-encoder-decoder/\n",
        "# https://trungtran.io/2019/04/29/create-the-transformer-with-tensorflow-2-0/\n",
        "# https://github.com/ChunML/NLP/blob/master/chatbot/model.py\n",
        "def build_transfer_model(\n",
        "    input_shape,\n",
        "    head_sizes=TRANSFORMER_HEAD_SIZES,\n",
        "    output_dims=TRANSFORMER_OUTPUT_DIMS,\n",
        "    num_heads=TRANSFORMER_NUM_HEADS,\n",
        "    ff_dims=TRANSFORMER_FF_DIMS,\n",
        "    num_transformer_blocks=TRANSFORMER_NUM_TRANSFORMER_BLOCKS,\n",
        "    mlp_units=TRANSFORMER_MLP_UNITS,\n",
        "    dropout=TRANSFORMER_DROPOUT,\n",
        "    mlp_dropout=TRANSFORMER_MLP_DROPOUT,\n",
        "):\n",
        "\n",
        "    inputs = keras.Input(shape=input_shape)\n",
        "    time_lenght = input_shape[0]\n",
        "    positional_encoding = get_positional_encoding(time_lenght, input_shape[-1])\n",
        "    print('positional_encoding:', positional_encoding)\n",
        "\n",
        "    x = inputs\n",
        "\n",
        "    # increase x then add position encoding.\n",
        "    '''\n",
        "    The reason we increase the embedding values before the addition is to make \n",
        "    the positional encoding relatively smaller. This means the original meaning \n",
        "    in the embedding vector won’t be lost when we add them together.\n",
        "    '''\n",
        "    x *= tf.math.sqrt(tf.cast(input_shape[-1], tf.float32))\n",
        "    x += positional_encoding[:, :time_lenght, :]\n",
        "\n",
        "    for head_size, num_head, output_dim in zip(head_sizes, num_heads, output_dims):\n",
        "        x = transformer_encoder(x, head_size, num_head, output_dim, ff_dims, dropout)\n",
        "\n",
        "    x_pool = layers.GlobalAveragePooling1D(data_format=\"channels_first\", keepdims=True)(x)\n",
        "\n",
        "    x = layers.Concatenate()([x, x_pool])\n",
        "\n",
        "    for dim in mlp_units:\n",
        "        x = layers.Dense(dim, activation=\"selu\")(x)\n",
        "        x = layers.Dropout(mlp_dropout)(x)\n",
        "    outputs = layers.Dense(1)(x)\n",
        "\n",
        "\n",
        "    model = keras.Model(inputs, outputs)\n",
        "    model.summary()\n",
        "    return model"
      ],
      "id": "b1uCDdywdhRH",
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BvagsDTSiXYs",
        "outputId": "aca56231-0e4d-49db-e59b-c2dd7042a2a8"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "model = build_transfer_model(train.shape[1:])\n",
        "plot_model(\n",
        "    model, \n",
        "    to_file='Google_Brain_Keras_Model.png', \n",
        "    show_shapes=True,\n",
        "    show_layer_names=True\n",
        ")"
      ],
      "id": "BvagsDTSiXYs",
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "positional_encoding: tf.Tensor(\n",
            "[[[ 0.0000000e+00  1.0000000e+00  0.0000000e+00 ...  0.0000000e+00\n",
            "    1.0000000e+00  0.0000000e+00]\n",
            "  [ 8.4147096e-01  5.4030228e-01  6.4910674e-01 ...  1.6842831e-04\n",
            "    1.0000000e+00  1.1897938e-04]\n",
            "  [ 9.0929741e-01 -4.1614684e-01  9.8754758e-01 ...  3.3685661e-04\n",
            "    9.9999994e-01  2.3795875e-04]\n",
            "  ...\n",
            "  [ 9.9952018e-01 -3.0975033e-02 -8.3408141e-01 ...  1.2968617e-02\n",
            "    9.9991590e-01  9.1612842e-03]\n",
            "  [ 5.1397848e-01 -8.5780311e-01 -9.9255764e-01 ...  1.3137030e-02\n",
            "    9.9991369e-01  9.2802588e-03]\n",
            "  [-4.4411266e-01 -8.9597094e-01 -6.7599046e-01 ...  1.3305444e-02\n",
            "    9.9991149e-01  9.3992325e-03]]], shape=(1, 80, 53), dtype=float32)\n",
            "512 8 None 0.01\n",
            "256 8 None 0.01\n",
            "128 4 None 0.01\n",
            "64 4 None 0.01\n",
            "Model: \"model_14\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_20 (InputLayer)           [(None, 80, 53)]     0                                            \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.multiply_19 (TFOpLambda (None, 80, 53)       0           input_20[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_111 (TFOpL (None, 80, 53)       0           tf.math.multiply_19[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_118 (LayerN (None, 80, 53)       106         tf.__operators__.add_111[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "multi_head_attention_59 (MultiH (None, 80, 53)       880693      layer_normalization_118[0][0]    \n",
            "                                                                 layer_normalization_118[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_132 (Dropout)           (None, 80, 53)       0           multi_head_attention_59[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_112 (TFOpL (None, 80, 53)       0           dropout_132[0][0]                \n",
            "                                                                 tf.__operators__.add_111[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_119 (LayerN (None, 80, 53)       106         tf.__operators__.add_112[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_114 (Conv1D)             (None, 80, 128)      6912        layer_normalization_119[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_133 (Dropout)           (None, 80, 128)      0           conv1d_114[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_115 (Conv1D)             (None, 80, 53)       6837        dropout_133[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_113 (TFOpL (None, 80, 53)       0           conv1d_115[0][0]                 \n",
            "                                                                 tf.__operators__.add_112[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_120 (LayerN (None, 80, 53)       106         tf.__operators__.add_113[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "multi_head_attention_60 (MultiH (None, 80, 53)       440373      layer_normalization_120[0][0]    \n",
            "                                                                 layer_normalization_120[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_134 (Dropout)           (None, 80, 53)       0           multi_head_attention_60[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_114 (TFOpL (None, 80, 53)       0           dropout_134[0][0]                \n",
            "                                                                 tf.__operators__.add_113[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_121 (LayerN (None, 80, 53)       106         tf.__operators__.add_114[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_116 (Conv1D)             (None, 80, 128)      6912        layer_normalization_121[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_135 (Dropout)           (None, 80, 128)      0           conv1d_116[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_117 (Conv1D)             (None, 80, 53)       6837        dropout_135[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_115 (TFOpL (None, 80, 53)       0           conv1d_117[0][0]                 \n",
            "                                                                 tf.__operators__.add_114[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_122 (LayerN (None, 80, 53)       106         tf.__operators__.add_115[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "multi_head_attention_61 (MultiH (None, 80, 53)       110133      layer_normalization_122[0][0]    \n",
            "                                                                 layer_normalization_122[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_136 (Dropout)           (None, 80, 53)       0           multi_head_attention_61[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_116 (TFOpL (None, 80, 53)       0           dropout_136[0][0]                \n",
            "                                                                 tf.__operators__.add_115[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_123 (LayerN (None, 80, 53)       106         tf.__operators__.add_116[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_118 (Conv1D)             (None, 80, 128)      6912        layer_normalization_123[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_137 (Dropout)           (None, 80, 128)      0           conv1d_118[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_119 (Conv1D)             (None, 80, 53)       6837        dropout_137[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_117 (TFOpL (None, 80, 53)       0           conv1d_119[0][0]                 \n",
            "                                                                 tf.__operators__.add_116[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_124 (LayerN (None, 80, 53)       106         tf.__operators__.add_117[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "multi_head_attention_62 (MultiH (None, 80, 53)       55093       layer_normalization_124[0][0]    \n",
            "                                                                 layer_normalization_124[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_138 (Dropout)           (None, 80, 53)       0           multi_head_attention_62[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_118 (TFOpL (None, 80, 53)       0           dropout_138[0][0]                \n",
            "                                                                 tf.__operators__.add_117[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_125 (LayerN (None, 80, 53)       106         tf.__operators__.add_118[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_120 (Conv1D)             (None, 80, 128)      6912        layer_normalization_125[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_139 (Dropout)           (None, 80, 128)      0           conv1d_120[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_121 (Conv1D)             (None, 80, 53)       6837        dropout_139[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_119 (TFOpL (None, 80, 53)       0           conv1d_121[0][0]                 \n",
            "                                                                 tf.__operators__.add_118[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling1d_10 (Gl (None, 80, 1)        0           tf.__operators__.add_119[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_5 (Concatenate)     (None, 80, 54)       0           tf.__operators__.add_119[0][0]   \n",
            "                                                                 global_average_pooling1d_10[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "dense_28 (Dense)                (None, 80, 32)       1760        concatenate_5[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_140 (Dropout)           (None, 80, 32)       0           dense_28[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_29 (Dense)                (None, 80, 1)        33          dropout_140[0][0]                \n",
            "==================================================================================================\n",
            "Total params: 1,543,929\n",
            "Trainable params: 1,543,929\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WeSVrUJUWs-"
      },
      "source": [
        "# Function to get hardware strategy\n",
        "def get_hardware_strategy():\n",
        "    try:\n",
        "        # TPU detection. No parameters necessary if TPU_NAME environment variable is\n",
        "        # set: this is always the case on Kaggle.\n",
        "        tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n",
        "        print('Running on TPU ', tpu.master())\n",
        "    except ValueError:\n",
        "        tpu = None\n",
        "\n",
        "    if tpu:\n",
        "        tf.config.experimental_connect_to_cluster(tpu)\n",
        "        tf.tpu.experimental.initialize_tpu_system(tpu)\n",
        "        strategy = tf.distribute.experimental.TPUStrategy(tpu)\n",
        "        tf.config.optimizer.set_jit(True)\n",
        "    else:\n",
        "        # Default distribution strategy in Tensorflow. Works on CPU and single GPU.\n",
        "        strategy = tf.distribute.get_strategy()\n",
        "\n",
        "    return tpu, strategy\n",
        "\n",
        "tpu, strategy = get_hardware_strategy()"
      ],
      "id": "2WeSVrUJUWs-",
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12a089a5",
        "outputId": "67de7e29-7bf8-4883-9376-a93d8d841d17"
      },
      "source": [
        "from tensorflow.keras.optimizers.schedules import ExponentialDecay\n",
        "\n",
        "\n",
        "with strategy.scope():\n",
        "    kf = KFold(n_splits=NUM_FOLDS, shuffle=True, random_state=2021)\n",
        "    test_preds = []\n",
        "    train_preds = []\n",
        "    for fold, (train_idx, test_idx) in enumerate(kf.split(train, targets)):\n",
        "        print('-'*15, '>', f'Fold {fold+1}', '<', '-'*15)\n",
        "        X_train, X_valid = train[train_idx], train[test_idx]\n",
        "        y_train, y_valid = targets[train_idx], targets[test_idx]\n",
        "        #u_out_train, u_out_valid = u_outs[train_idx], u_outs[test_idx]\n",
        "        \n",
        "        #model = get_cnn_model(train)\n",
        "        model = build_transfer_model(train.shape[1:])\n",
        "        model.compile(optimizer='adam', \n",
        "                      # loss=GBVPP_loss,\n",
        "                      loss=\"mae\"\n",
        "                      )\n",
        "\n",
        "        #scheduler = ExponentialDecay(1e-3, 40*((len(train)*0.8)/BATCH_SIZE), 1e-5)\n",
        "        #lr = LearningRateScheduler(scheduler, verbose=1)\n",
        "        lr = ReduceLROnPlateau(monitor=\"val_loss\", factor=0.75, patience=10, verbose=1)\n",
        "        es = EarlyStopping(monitor=\"val_loss\", patience=50, verbose=1, mode=\"min\", restore_best_weights=True)\n",
        "        checkpoint_filepath = f\"folds{fold}.hdf5\"\n",
        "        sv = keras.callbacks.ModelCheckpoint(\n",
        "            checkpoint_filepath, monitor='val_loss', verbose=1, save_best_only=True,\n",
        "            save_weights_only=False, mode='auto', save_freq='epoch',\n",
        "            options=None)\n",
        "\n",
        "        history = model.fit(X_train,\n",
        "                            y_train,\n",
        "                            # validation_data=(X_valid, np.append(y_valid, u_out_valid, axis =1)), \n",
        "                            validation_data=(X_valid, y_valid), \n",
        "                            epochs=EPOCH, \n",
        "                            batch_size=1024, \n",
        "                            callbacks=[lr,\n",
        "                                       es,\n",
        "                                       sv])\n",
        "        \n",
        "        if IN_COLAB:\n",
        "          with open(model_folder+checkpoint_filepath, 'wb') as f:\n",
        "            f.write(open(checkpoint_filepath, 'rb').read())\n",
        "    \n",
        "        test_preds.append(model.predict(test).squeeze().reshape(-1, 1).squeeze())\n",
        "        train_preds.append(model.predict(train).squeeze().reshape(-1, 1).squeeze())\n"
      ],
      "id": "12a089a5",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------- > Fold 1 < ---------------\n",
            "positional_encoding: tf.Tensor(\n",
            "[[[ 0.0000000e+00  1.0000000e+00  0.0000000e+00 ...  0.0000000e+00\n",
            "    1.0000000e+00  0.0000000e+00]\n",
            "  [ 8.4147096e-01  5.4030228e-01  6.4910674e-01 ...  1.6842831e-04\n",
            "    1.0000000e+00  1.1897938e-04]\n",
            "  [ 9.0929741e-01 -4.1614684e-01  9.8754758e-01 ...  3.3685661e-04\n",
            "    9.9999994e-01  2.3795875e-04]\n",
            "  ...\n",
            "  [ 9.9952018e-01 -3.0975033e-02 -8.3408141e-01 ...  1.2968617e-02\n",
            "    9.9991590e-01  9.1612842e-03]\n",
            "  [ 5.1397848e-01 -8.5780311e-01 -9.9255764e-01 ...  1.3137030e-02\n",
            "    9.9991369e-01  9.2802588e-03]\n",
            "  [-4.4411266e-01 -8.9597094e-01 -6.7599046e-01 ...  1.3305444e-02\n",
            "    9.9991149e-01  9.3992325e-03]]], shape=(1, 80, 53), dtype=float32)\n",
            "512 8 None 0.01\n",
            "256 8 None 0.01\n",
            "128 4 None 0.01\n",
            "64 4 None 0.01\n",
            "Model: \"model_15\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_21 (InputLayer)           [(None, 80, 53)]     0                                            \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.multiply_20 (TFOpLambda (None, 80, 53)       0           input_21[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_120 (TFOpL (None, 80, 53)       0           tf.math.multiply_20[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_126 (LayerN (None, 80, 53)       106         tf.__operators__.add_120[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "multi_head_attention_63 (MultiH (None, 80, 53)       880693      layer_normalization_126[0][0]    \n",
            "                                                                 layer_normalization_126[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_141 (Dropout)           (None, 80, 53)       0           multi_head_attention_63[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_121 (TFOpL (None, 80, 53)       0           dropout_141[0][0]                \n",
            "                                                                 tf.__operators__.add_120[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_127 (LayerN (None, 80, 53)       106         tf.__operators__.add_121[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_122 (Conv1D)             (None, 80, 128)      6912        layer_normalization_127[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_142 (Dropout)           (None, 80, 128)      0           conv1d_122[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_123 (Conv1D)             (None, 80, 53)       6837        dropout_142[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_122 (TFOpL (None, 80, 53)       0           conv1d_123[0][0]                 \n",
            "                                                                 tf.__operators__.add_121[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_128 (LayerN (None, 80, 53)       106         tf.__operators__.add_122[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "multi_head_attention_64 (MultiH (None, 80, 53)       440373      layer_normalization_128[0][0]    \n",
            "                                                                 layer_normalization_128[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_143 (Dropout)           (None, 80, 53)       0           multi_head_attention_64[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_123 (TFOpL (None, 80, 53)       0           dropout_143[0][0]                \n",
            "                                                                 tf.__operators__.add_122[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_129 (LayerN (None, 80, 53)       106         tf.__operators__.add_123[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_124 (Conv1D)             (None, 80, 128)      6912        layer_normalization_129[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_144 (Dropout)           (None, 80, 128)      0           conv1d_124[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_125 (Conv1D)             (None, 80, 53)       6837        dropout_144[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_124 (TFOpL (None, 80, 53)       0           conv1d_125[0][0]                 \n",
            "                                                                 tf.__operators__.add_123[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_130 (LayerN (None, 80, 53)       106         tf.__operators__.add_124[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "multi_head_attention_65 (MultiH (None, 80, 53)       110133      layer_normalization_130[0][0]    \n",
            "                                                                 layer_normalization_130[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_145 (Dropout)           (None, 80, 53)       0           multi_head_attention_65[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_125 (TFOpL (None, 80, 53)       0           dropout_145[0][0]                \n",
            "                                                                 tf.__operators__.add_124[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_131 (LayerN (None, 80, 53)       106         tf.__operators__.add_125[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_126 (Conv1D)             (None, 80, 128)      6912        layer_normalization_131[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_146 (Dropout)           (None, 80, 128)      0           conv1d_126[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_127 (Conv1D)             (None, 80, 53)       6837        dropout_146[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_126 (TFOpL (None, 80, 53)       0           conv1d_127[0][0]                 \n",
            "                                                                 tf.__operators__.add_125[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_132 (LayerN (None, 80, 53)       106         tf.__operators__.add_126[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "multi_head_attention_66 (MultiH (None, 80, 53)       55093       layer_normalization_132[0][0]    \n",
            "                                                                 layer_normalization_132[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_147 (Dropout)           (None, 80, 53)       0           multi_head_attention_66[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_127 (TFOpL (None, 80, 53)       0           dropout_147[0][0]                \n",
            "                                                                 tf.__operators__.add_126[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "layer_normalization_133 (LayerN (None, 80, 53)       106         tf.__operators__.add_127[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_128 (Conv1D)             (None, 80, 128)      6912        layer_normalization_133[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "dropout_148 (Dropout)           (None, 80, 128)      0           conv1d_128[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1d_129 (Conv1D)             (None, 80, 53)       6837        dropout_148[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.add_128 (TFOpL (None, 80, 53)       0           conv1d_129[0][0]                 \n",
            "                                                                 tf.__operators__.add_127[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling1d_11 (Gl (None, 80, 1)        0           tf.__operators__.add_128[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_6 (Concatenate)     (None, 80, 54)       0           tf.__operators__.add_128[0][0]   \n",
            "                                                                 global_average_pooling1d_11[0][0]\n",
            "__________________________________________________________________________________________________\n",
            "dense_30 (Dense)                (None, 80, 32)       1760        concatenate_6[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_149 (Dropout)           (None, 80, 32)       0           dense_30[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_31 (Dense)                (None, 80, 1)        33          dropout_149[0][0]                \n",
            "==================================================================================================\n",
            "Total params: 1,543,929\n",
            "Trainable params: 1,543,929\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "Epoch 1/300\n",
            "67/67 [==============================] - 55s 772ms/step - loss: 5.2624 - val_loss: 1.9770\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 1.97704, saving model to folds0.hdf5\n",
            "Epoch 2/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 1.6274 - val_loss: 1.3341\n",
            "\n",
            "Epoch 00002: val_loss improved from 1.97704 to 1.33414, saving model to folds0.hdf5\n",
            "Epoch 3/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 1.2532 - val_loss: 1.1018\n",
            "\n",
            "Epoch 00003: val_loss improved from 1.33414 to 1.10180, saving model to folds0.hdf5\n",
            "Epoch 4/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 1.0957 - val_loss: 1.0059\n",
            "\n",
            "Epoch 00004: val_loss improved from 1.10180 to 1.00594, saving model to folds0.hdf5\n",
            "Epoch 5/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.9987 - val_loss: 0.9140\n",
            "\n",
            "Epoch 00005: val_loss improved from 1.00594 to 0.91403, saving model to folds0.hdf5\n",
            "Epoch 6/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.9392 - val_loss: 0.8647\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.91403 to 0.86470, saving model to folds0.hdf5\n",
            "Epoch 7/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.8916 - val_loss: 0.8234\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.86470 to 0.82339, saving model to folds0.hdf5\n",
            "Epoch 8/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.8406 - val_loss: 0.8122\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.82339 to 0.81222, saving model to folds0.hdf5\n",
            "Epoch 9/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.8091 - val_loss: 0.7619\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.81222 to 0.76187, saving model to folds0.hdf5\n",
            "Epoch 10/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.7714 - val_loss: 0.7431\n",
            "\n",
            "Epoch 00010: val_loss improved from 0.76187 to 0.74310, saving model to folds0.hdf5\n",
            "Epoch 11/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.7450 - val_loss: 0.7066\n",
            "\n",
            "Epoch 00011: val_loss improved from 0.74310 to 0.70663, saving model to folds0.hdf5\n",
            "Epoch 12/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.7228 - val_loss: 0.7051\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.70663 to 0.70506, saving model to folds0.hdf5\n",
            "Epoch 13/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.7047 - val_loss: 0.6557\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.70506 to 0.65567, saving model to folds0.hdf5\n",
            "Epoch 14/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.6995 - val_loss: 0.6433\n",
            "\n",
            "Epoch 00014: val_loss improved from 0.65567 to 0.64330, saving model to folds0.hdf5\n",
            "Epoch 15/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.6746 - val_loss: 0.6488\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.64330\n",
            "Epoch 16/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.6586 - val_loss: 0.6260\n",
            "\n",
            "Epoch 00016: val_loss improved from 0.64330 to 0.62595, saving model to folds0.hdf5\n",
            "Epoch 17/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.6584 - val_loss: 0.6393\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.62595\n",
            "Epoch 18/300\n",
            "67/67 [==============================] - 51s 763ms/step - loss: 0.6332 - val_loss: 0.6165\n",
            "\n",
            "Epoch 00018: val_loss improved from 0.62595 to 0.61648, saving model to folds0.hdf5\n",
            "Epoch 19/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.6319 - val_loss: 0.6005\n",
            "\n",
            "Epoch 00019: val_loss improved from 0.61648 to 0.60049, saving model to folds0.hdf5\n",
            "Epoch 20/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.6169 - val_loss: 0.6190\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.60049\n",
            "Epoch 21/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.6130 - val_loss: 0.5727\n",
            "\n",
            "Epoch 00021: val_loss improved from 0.60049 to 0.57268, saving model to folds0.hdf5\n",
            "Epoch 22/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.6011 - val_loss: 0.5758\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.57268\n",
            "Epoch 23/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5950 - val_loss: 0.6165\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 0.57268\n",
            "Epoch 24/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5891 - val_loss: 0.6025\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.57268\n",
            "Epoch 25/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5872 - val_loss: 0.5675\n",
            "\n",
            "Epoch 00025: val_loss improved from 0.57268 to 0.56747, saving model to folds0.hdf5\n",
            "Epoch 26/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5744 - val_loss: 0.6308\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.56747\n",
            "Epoch 27/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5868 - val_loss: 0.5543\n",
            "\n",
            "Epoch 00027: val_loss improved from 0.56747 to 0.55429, saving model to folds0.hdf5\n",
            "Epoch 28/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5643 - val_loss: 0.5398\n",
            "\n",
            "Epoch 00028: val_loss improved from 0.55429 to 0.53975, saving model to folds0.hdf5\n",
            "Epoch 29/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5611 - val_loss: 0.5333\n",
            "\n",
            "Epoch 00029: val_loss improved from 0.53975 to 0.53328, saving model to folds0.hdf5\n",
            "Epoch 30/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5628 - val_loss: 0.5362\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.53328\n",
            "Epoch 31/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5487 - val_loss: 0.5202\n",
            "\n",
            "Epoch 00031: val_loss improved from 0.53328 to 0.52017, saving model to folds0.hdf5\n",
            "Epoch 32/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5500 - val_loss: 0.5495\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.52017\n",
            "Epoch 33/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5586 - val_loss: 0.5285\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.52017\n",
            "Epoch 34/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5427 - val_loss: 0.5187\n",
            "\n",
            "Epoch 00034: val_loss improved from 0.52017 to 0.51871, saving model to folds0.hdf5\n",
            "Epoch 35/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5397 - val_loss: 0.5124\n",
            "\n",
            "Epoch 00035: val_loss improved from 0.51871 to 0.51236, saving model to folds0.hdf5\n",
            "Epoch 36/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5253 - val_loss: 0.5096\n",
            "\n",
            "Epoch 00036: val_loss improved from 0.51236 to 0.50963, saving model to folds0.hdf5\n",
            "Epoch 37/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5264 - val_loss: 0.5182\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.50963\n",
            "Epoch 38/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5275 - val_loss: 0.5104\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.50963\n",
            "Epoch 39/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5258 - val_loss: 0.5094\n",
            "\n",
            "Epoch 00039: val_loss improved from 0.50963 to 0.50937, saving model to folds0.hdf5\n",
            "Epoch 40/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5231 - val_loss: 0.5020\n",
            "\n",
            "Epoch 00040: val_loss improved from 0.50937 to 0.50195, saving model to folds0.hdf5\n",
            "Epoch 41/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5214 - val_loss: 0.5037\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.50195\n",
            "Epoch 42/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5133 - val_loss: 0.5094\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.50195\n",
            "Epoch 43/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5066 - val_loss: 0.5024\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.50195\n",
            "Epoch 44/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5041 - val_loss: 0.4976\n",
            "\n",
            "Epoch 00044: val_loss improved from 0.50195 to 0.49763, saving model to folds0.hdf5\n",
            "Epoch 45/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5069 - val_loss: 0.4807\n",
            "\n",
            "Epoch 00045: val_loss improved from 0.49763 to 0.48066, saving model to folds0.hdf5\n",
            "Epoch 46/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5050 - val_loss: 0.5304\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 0.48066\n",
            "Epoch 47/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.5030 - val_loss: 0.4813\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 0.48066\n",
            "Epoch 48/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4959 - val_loss: 0.4848\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 0.48066\n",
            "Epoch 49/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4935 - val_loss: 0.4810\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 0.48066\n",
            "Epoch 50/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4941 - val_loss: 0.4724\n",
            "\n",
            "Epoch 00050: val_loss improved from 0.48066 to 0.47244, saving model to folds0.hdf5\n",
            "Epoch 51/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4926 - val_loss: 0.4677\n",
            "\n",
            "Epoch 00051: val_loss improved from 0.47244 to 0.46769, saving model to folds0.hdf5\n",
            "Epoch 52/300\n",
            "67/67 [==============================] - 51s 765ms/step - loss: 0.4929 - val_loss: 0.4765\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 0.46769\n",
            "Epoch 53/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4836 - val_loss: 0.4777\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 0.46769\n",
            "Epoch 54/300\n",
            "67/67 [==============================] - 51s 765ms/step - loss: 0.4876 - val_loss: 0.4797\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 0.46769\n",
            "Epoch 55/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4827 - val_loss: 0.4744\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 0.46769\n",
            "Epoch 56/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4818 - val_loss: 0.4605\n",
            "\n",
            "Epoch 00056: val_loss improved from 0.46769 to 0.46051, saving model to folds0.hdf5\n",
            "Epoch 57/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4815 - val_loss: 0.4680\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 0.46051\n",
            "Epoch 58/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4936 - val_loss: 0.4799\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 0.46051\n",
            "Epoch 59/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4716 - val_loss: 0.4543\n",
            "\n",
            "Epoch 00059: val_loss improved from 0.46051 to 0.45429, saving model to folds0.hdf5\n",
            "Epoch 60/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4687 - val_loss: 0.4627\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 0.45429\n",
            "Epoch 61/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4730 - val_loss: 0.4782\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 0.45429\n",
            "Epoch 62/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4721 - val_loss: 0.4453\n",
            "\n",
            "Epoch 00062: val_loss improved from 0.45429 to 0.44525, saving model to folds0.hdf5\n",
            "Epoch 63/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4682 - val_loss: 0.4643\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 0.44525\n",
            "Epoch 64/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4640 - val_loss: 0.4467\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 0.44525\n",
            "Epoch 65/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4646 - val_loss: 0.4468\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 0.44525\n",
            "Epoch 66/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4642 - val_loss: 0.4703\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 0.44525\n",
            "Epoch 67/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4722 - val_loss: 0.4397\n",
            "\n",
            "Epoch 00067: val_loss improved from 0.44525 to 0.43970, saving model to folds0.hdf5\n",
            "Epoch 68/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4602 - val_loss: 0.4440\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 0.43970\n",
            "Epoch 69/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4598 - val_loss: 0.4342\n",
            "\n",
            "Epoch 00069: val_loss improved from 0.43970 to 0.43416, saving model to folds0.hdf5\n",
            "Epoch 70/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4560 - val_loss: 0.4306\n",
            "\n",
            "Epoch 00070: val_loss improved from 0.43416 to 0.43058, saving model to folds0.hdf5\n",
            "Epoch 71/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4529 - val_loss: 0.4385\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 0.43058\n",
            "Epoch 72/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4666 - val_loss: 0.4709\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 0.43058\n",
            "Epoch 73/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4577 - val_loss: 0.4544\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 0.43058\n",
            "Epoch 74/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4512 - val_loss: 0.4359\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 0.43058\n",
            "Epoch 75/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4540 - val_loss: 0.4430\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 0.43058\n",
            "Epoch 76/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4444 - val_loss: 0.4447\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 0.43058\n",
            "Epoch 77/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4560 - val_loss: 0.4225\n",
            "\n",
            "Epoch 00077: val_loss improved from 0.43058 to 0.42253, saving model to folds0.hdf5\n",
            "Epoch 78/300\n",
            "67/67 [==============================] - 51s 765ms/step - loss: 0.4478 - val_loss: 0.4381\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 0.42253\n",
            "Epoch 79/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4475 - val_loss: 0.4338\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 0.42253\n",
            "Epoch 80/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4413 - val_loss: 0.4240\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 0.42253\n",
            "Epoch 81/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4417 - val_loss: 0.4296\n",
            "\n",
            "Epoch 00081: val_loss did not improve from 0.42253\n",
            "Epoch 82/300\n",
            "67/67 [==============================] - 51s 765ms/step - loss: 0.4374 - val_loss: 0.4507\n",
            "\n",
            "Epoch 00082: val_loss did not improve from 0.42253\n",
            "Epoch 83/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4390 - val_loss: 0.4364\n",
            "\n",
            "Epoch 00083: val_loss did not improve from 0.42253\n",
            "Epoch 84/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4534 - val_loss: 0.4222\n",
            "\n",
            "Epoch 00084: val_loss improved from 0.42253 to 0.42219, saving model to folds0.hdf5\n",
            "Epoch 85/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4433 - val_loss: 0.4227\n",
            "\n",
            "Epoch 00085: val_loss did not improve from 0.42219\n",
            "Epoch 86/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4353 - val_loss: 0.4166\n",
            "\n",
            "Epoch 00086: val_loss improved from 0.42219 to 0.41656, saving model to folds0.hdf5\n",
            "Epoch 87/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4341 - val_loss: 0.4356\n",
            "\n",
            "Epoch 00087: val_loss did not improve from 0.41656\n",
            "Epoch 88/300\n",
            "67/67 [==============================] - 51s 765ms/step - loss: 0.4380 - val_loss: 0.4249\n",
            "\n",
            "Epoch 00088: val_loss did not improve from 0.41656\n",
            "Epoch 89/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4390 - val_loss: 0.4381\n",
            "\n",
            "Epoch 00089: val_loss did not improve from 0.41656\n",
            "Epoch 90/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4379 - val_loss: 0.4209\n",
            "\n",
            "Epoch 00090: val_loss did not improve from 0.41656\n",
            "Epoch 91/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4251 - val_loss: 0.4380\n",
            "\n",
            "Epoch 00091: val_loss did not improve from 0.41656\n",
            "Epoch 92/300\n",
            "67/67 [==============================] - 51s 765ms/step - loss: 0.4421 - val_loss: 0.4346\n",
            "\n",
            "Epoch 00092: val_loss did not improve from 0.41656\n",
            "Epoch 93/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4308 - val_loss: 0.4134\n",
            "\n",
            "Epoch 00093: val_loss improved from 0.41656 to 0.41335, saving model to folds0.hdf5\n",
            "Epoch 94/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4227 - val_loss: 0.4153\n",
            "\n",
            "Epoch 00094: val_loss did not improve from 0.41335\n",
            "Epoch 95/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4289 - val_loss: 0.4181\n",
            "\n",
            "Epoch 00095: val_loss did not improve from 0.41335\n",
            "Epoch 96/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4311 - val_loss: 0.4104\n",
            "\n",
            "Epoch 00096: val_loss improved from 0.41335 to 0.41035, saving model to folds0.hdf5\n",
            "Epoch 97/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4211 - val_loss: 0.4111\n",
            "\n",
            "Epoch 00097: val_loss did not improve from 0.41035\n",
            "Epoch 98/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4222 - val_loss: 0.4186\n",
            "\n",
            "Epoch 00098: val_loss did not improve from 0.41035\n",
            "Epoch 99/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4276 - val_loss: 0.4179\n",
            "\n",
            "Epoch 00099: val_loss did not improve from 0.41035\n",
            "Epoch 100/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4488 - val_loss: 0.4066\n",
            "\n",
            "Epoch 00100: val_loss improved from 0.41035 to 0.40662, saving model to folds0.hdf5\n",
            "Epoch 101/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4218 - val_loss: 0.4196\n",
            "\n",
            "Epoch 00101: val_loss did not improve from 0.40662\n",
            "Epoch 102/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4206 - val_loss: 0.4145\n",
            "\n",
            "Epoch 00102: val_loss did not improve from 0.40662\n",
            "Epoch 103/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4221 - val_loss: 0.4569\n",
            "\n",
            "Epoch 00103: val_loss did not improve from 0.40662\n",
            "Epoch 104/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4284 - val_loss: 0.4009\n",
            "\n",
            "Epoch 00104: val_loss improved from 0.40662 to 0.40095, saving model to folds0.hdf5\n",
            "Epoch 105/300\n",
            "67/67 [==============================] - 51s 765ms/step - loss: 0.4142 - val_loss: 0.4012\n",
            "\n",
            "Epoch 00105: val_loss did not improve from 0.40095\n",
            "Epoch 106/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4128 - val_loss: 0.4219\n",
            "\n",
            "Epoch 00106: val_loss did not improve from 0.40095\n",
            "Epoch 107/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4139 - val_loss: 0.4196\n",
            "\n",
            "Epoch 00107: val_loss did not improve from 0.40095\n",
            "Epoch 108/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4223 - val_loss: 0.4043\n",
            "\n",
            "Epoch 00108: val_loss did not improve from 0.40095\n",
            "Epoch 109/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4182 - val_loss: 0.4039\n",
            "\n",
            "Epoch 00109: val_loss did not improve from 0.40095\n",
            "Epoch 110/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4205 - val_loss: 0.4250\n",
            "\n",
            "Epoch 00110: val_loss did not improve from 0.40095\n",
            "Epoch 111/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4199 - val_loss: 0.4008\n",
            "\n",
            "Epoch 00111: val_loss improved from 0.40095 to 0.40076, saving model to folds0.hdf5\n",
            "Epoch 112/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4179 - val_loss: 0.4180\n",
            "\n",
            "Epoch 00112: val_loss did not improve from 0.40076\n",
            "Epoch 113/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4138 - val_loss: 0.3975\n",
            "\n",
            "Epoch 00113: val_loss improved from 0.40076 to 0.39754, saving model to folds0.hdf5\n",
            "Epoch 114/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4094 - val_loss: 0.4025\n",
            "\n",
            "Epoch 00114: val_loss did not improve from 0.39754\n",
            "Epoch 115/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4155 - val_loss: 0.4090\n",
            "\n",
            "Epoch 00115: val_loss did not improve from 0.39754\n",
            "Epoch 116/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4152 - val_loss: 0.4078\n",
            "\n",
            "Epoch 00116: val_loss did not improve from 0.39754\n",
            "Epoch 117/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4159 - val_loss: 0.4257\n",
            "\n",
            "Epoch 00117: val_loss did not improve from 0.39754\n",
            "Epoch 118/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4064 - val_loss: 0.4042\n",
            "\n",
            "Epoch 00118: val_loss did not improve from 0.39754\n",
            "Epoch 119/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4074 - val_loss: 0.4217\n",
            "\n",
            "Epoch 00119: val_loss did not improve from 0.39754\n",
            "Epoch 120/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4145 - val_loss: 0.3859\n",
            "\n",
            "Epoch 00120: val_loss improved from 0.39754 to 0.38592, saving model to folds0.hdf5\n",
            "Epoch 121/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4107 - val_loss: 0.4105\n",
            "\n",
            "Epoch 00121: val_loss did not improve from 0.38592\n",
            "Epoch 122/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4016 - val_loss: 0.4118\n",
            "\n",
            "Epoch 00122: val_loss did not improve from 0.38592\n",
            "Epoch 123/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4155 - val_loss: 0.4523\n",
            "\n",
            "Epoch 00123: val_loss did not improve from 0.38592\n",
            "Epoch 124/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4106 - val_loss: 0.3897\n",
            "\n",
            "Epoch 00124: val_loss did not improve from 0.38592\n",
            "Epoch 125/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4038 - val_loss: 0.3924\n",
            "\n",
            "Epoch 00125: val_loss did not improve from 0.38592\n",
            "Epoch 126/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4037 - val_loss: 0.4068\n",
            "\n",
            "Epoch 00126: val_loss did not improve from 0.38592\n",
            "Epoch 127/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4093 - val_loss: 0.3927\n",
            "\n",
            "Epoch 00127: val_loss did not improve from 0.38592\n",
            "Epoch 128/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4012 - val_loss: 0.3897\n",
            "\n",
            "Epoch 00128: val_loss did not improve from 0.38592\n",
            "Epoch 129/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3996 - val_loss: 0.3860\n",
            "\n",
            "Epoch 00129: val_loss did not improve from 0.38592\n",
            "Epoch 130/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.4030 - val_loss: 0.3906\n",
            "\n",
            "Epoch 00130: ReduceLROnPlateau reducing learning rate to 0.0007500000356230885.\n",
            "\n",
            "Epoch 00130: val_loss did not improve from 0.38592\n",
            "Epoch 131/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3871 - val_loss: 0.3754\n",
            "\n",
            "Epoch 00131: val_loss improved from 0.38592 to 0.37542, saving model to folds0.hdf5\n",
            "Epoch 132/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3842 - val_loss: 0.3757\n",
            "\n",
            "Epoch 00132: val_loss did not improve from 0.37542\n",
            "Epoch 133/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3865 - val_loss: 0.3780\n",
            "\n",
            "Epoch 00133: val_loss did not improve from 0.37542\n",
            "Epoch 134/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3888 - val_loss: 0.3787\n",
            "\n",
            "Epoch 00134: val_loss did not improve from 0.37542\n",
            "Epoch 135/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3853 - val_loss: 0.3809\n",
            "\n",
            "Epoch 00135: val_loss did not improve from 0.37542\n",
            "Epoch 136/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3856 - val_loss: 0.3843\n",
            "\n",
            "Epoch 00136: val_loss did not improve from 0.37542\n",
            "Epoch 137/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3878 - val_loss: 0.3741\n",
            "\n",
            "Epoch 00137: val_loss improved from 0.37542 to 0.37410, saving model to folds0.hdf5\n",
            "Epoch 138/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3919 - val_loss: 0.3754\n",
            "\n",
            "Epoch 00138: val_loss did not improve from 0.37410\n",
            "Epoch 139/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3831 - val_loss: 0.3768\n",
            "\n",
            "Epoch 00139: val_loss did not improve from 0.37410\n",
            "Epoch 140/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3872 - val_loss: 0.3871\n",
            "\n",
            "Epoch 00140: val_loss did not improve from 0.37410\n",
            "Epoch 141/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3846 - val_loss: 0.3804\n",
            "\n",
            "Epoch 00141: val_loss did not improve from 0.37410\n",
            "Epoch 142/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3843 - val_loss: 0.3842\n",
            "\n",
            "Epoch 00142: val_loss did not improve from 0.37410\n",
            "Epoch 143/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3864 - val_loss: 0.3775\n",
            "\n",
            "Epoch 00143: val_loss did not improve from 0.37410\n",
            "Epoch 144/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3834 - val_loss: 0.3896\n",
            "\n",
            "Epoch 00144: val_loss did not improve from 0.37410\n",
            "Epoch 145/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3838 - val_loss: 0.3827\n",
            "\n",
            "Epoch 00145: val_loss did not improve from 0.37410\n",
            "Epoch 146/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3837 - val_loss: 0.3768\n",
            "\n",
            "Epoch 00146: val_loss did not improve from 0.37410\n",
            "Epoch 147/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3834 - val_loss: 0.3742\n",
            "\n",
            "Epoch 00147: ReduceLROnPlateau reducing learning rate to 0.0005625000048894435.\n",
            "\n",
            "Epoch 00147: val_loss did not improve from 0.37410\n",
            "Epoch 148/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3764 - val_loss: 0.3666\n",
            "\n",
            "Epoch 00148: val_loss improved from 0.37410 to 0.36657, saving model to folds0.hdf5\n",
            "Epoch 149/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3770 - val_loss: 0.3655\n",
            "\n",
            "Epoch 00149: val_loss improved from 0.36657 to 0.36551, saving model to folds0.hdf5\n",
            "Epoch 150/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3717 - val_loss: 0.3664\n",
            "\n",
            "Epoch 00150: val_loss did not improve from 0.36551\n",
            "Epoch 151/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3720 - val_loss: 0.3706\n",
            "\n",
            "Epoch 00151: val_loss did not improve from 0.36551\n",
            "Epoch 152/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3740 - val_loss: 0.3684\n",
            "\n",
            "Epoch 00152: val_loss did not improve from 0.36551\n",
            "Epoch 153/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3710 - val_loss: 0.3778\n",
            "\n",
            "Epoch 00153: val_loss did not improve from 0.36551\n",
            "Epoch 154/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3743 - val_loss: 0.3680\n",
            "\n",
            "Epoch 00154: val_loss did not improve from 0.36551\n",
            "Epoch 155/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3763 - val_loss: 0.3807\n",
            "\n",
            "Epoch 00155: val_loss did not improve from 0.36551\n",
            "Epoch 156/300\n",
            "67/67 [==============================] - 51s 764ms/step - loss: 0.3737 - val_loss: 0.3661\n",
            "\n",
            "Epoch 00156: val_loss did not improve from 0.36551\n",
            "Epoch 157/300\n",
            " 5/67 [=>............................] - ETA: 45s - loss: 0.3700"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZR_KtDCXk-c"
      },
      "source": [
        "mae(targets, np.median(np.vstack(test_preds),axis=0))"
      ],
      "id": "0ZR_KtDCXk-c",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7025c5f6"
      },
      "source": [
        "PRESSURE_STEP = 0.07030214545120961\n",
        "PRESSURE_MIN = -1.8957442945646408\n",
        "PRESSURE_MAX = 64.82099173863948"
      ],
      "id": "7025c5f6",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41dffad5"
      },
      "source": [
        "# ENSEMBLE FOLDS WITH MEAN\n",
        "submission[\"pressure\"] = sum(test_preds)/NUM_FOLDS\n",
        "submission.to_csv('submission_mean.csv', index=False)"
      ],
      "id": "41dffad5",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bb56003"
      },
      "source": [
        "# ENSEMBLE FOLDS WITH MEDIAN\n",
        "submission[\"pressure\"] = np.median(np.vstack(test_preds),axis=0)\n",
        "submission.to_csv('submission_median.csv', index=False)"
      ],
      "id": "6bb56003",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9236b88e"
      },
      "source": [
        "# ENSEMBLE FOLDS WITH MEDIAN AND ROUND PREDICTIONS\n",
        "submission[\"pressure\"] =\\\n",
        "    np.round( (submission.pressure - PRESSURE_MIN)/PRESSURE_STEP ) * PRESSURE_STEP + PRESSURE_MIN\n",
        "submission.pressure = np.clip(submission.pressure, PRESSURE_MIN, PRESSURE_MAX)\n",
        "submission.to_csv('submission_median_round.csv', index=False)"
      ],
      "id": "9236b88e",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5z9iaJ--P8P"
      },
      "source": [
        "if IN_COLAB:\n",
        "  !zip cnn_models.zip *.hdf5\n",
        "  !zip submission.zip *.csv\n",
        "  with open(model_folder + 'submission.zip', 'wb') as f:\n",
        "    f.write(open('submission.zip', 'rb').read())\n",
        "  with open(model_folder + 'cnn_models.zip', 'wb') as f:\n",
        "    f.write(open('cnn_models.zip', 'rb').read())"
      ],
      "id": "K5z9iaJ--P8P",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xt5WI--rgkcM"
      },
      "source": [
        ""
      ],
      "id": "xt5WI--rgkcM",
      "execution_count": null,
      "outputs": []
    }
  ]
}