intro_to_modeling.ipynb

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      "name": "intro_to_modeling.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/javaidiqbal11/Python-Basic-Tutorial/blob/master/intro_to_modeling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LY0SJUCSY305"
      },
      "source": [
        "#### Copyright 2018 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gz_W_3Y5nVTb",
        "outputId": "479a9036-8dab-4dde-e271-c0e5ff3e9687"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y5tftaRtUcm7"
      },
      "source": [
        "#Intro to Modeling\n",
        "\n",
        "\n",
        "**Learning Objectives:**\n",
        "* Become familiar with pandas for handling small datasets\n",
        "* Use the tf.Estimator and Feature Column API to experiment with feature transformations\n",
        "* Use visualizations and run experiments to understand the value of feature transformations\n",
        "\n",
        "Please **make a copy** of this Colab notebook before starting this lab. To do so, choose **File**->**Save a copy in Drive**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TT_bZ9E0ZWaN"
      },
      "source": [
        "## Setup\n",
        "\n",
        "Let's start by importing our dependencies."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "both",
        "id": "wZ_T2SgDVKUH"
      },
      "source": [
        "%reset -f\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import math\n",
        "\n",
        "#import tensorflow as tf\n",
        "import tensorflow.compat.v1 as tf\n",
        "tf.disable_v2_behavior()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sx4-PWE-VaD_"
      },
      "source": [
        "## Pandas, a helpful data analysis library for in-memory dataset\n",
        "\n",
        "We use a package called [Pandas](http://pandas.pydata.org/) for reading in our data, exploring our data and doing some basic processing. It is really helpful for datasets that fit in memory! And it has some nice integrations, as you will see.\n",
        "\n",
        "First we set up some options to control how items are displayed and the maximum number of rows to show when displaying a table.  Feel free to change this setup to whatever you'd like."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKUIMcPCVRqv"
      },
      "source": [
        "# Set pandas output display to have one digit for decimal places and limit it to\n",
        "# printing 15 rows.\n",
        "pd.options.display.float_format = '{:.2f}'.format\n",
        "pd.options.display.max_rows = 15"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H_fTMztUVelY"
      },
      "source": [
        "### Load the dataset with pandas\n",
        "The car data set we will be using in this lab is provided as a comma separated file without a header row.  In order for each column to have a meaningful header name we must provide it.  We get the information about the columns from the [Automobile Data Set](https://archive.ics.uci.edu/ml/datasets/automobile).\n",
        "\n",
        "We will use the features of the car, to try to predict its price.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "code",
        "id": "Y38V73EgVYwt",
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          "base_uri": "https://localhost:8080/"
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      "source": [
        "# Provide the names for the columns since the CSV file with the data does\n",
        "# not have a header row.\n",
        "\n",
        "feature_names = ['symboling', 'normalized-losses', 'make', 'fuel-type',\n",
        "        'aspiration', 'num-doors', 'body-style', 'drive-wheels',\n",
        "        'engine-location', 'wheel-base', 'length', 'width', 'height', 'weight',\n",
        "        'engine-type', 'num-cylinders', 'engine-size', 'fuel-system', 'bore',\n",
        "        'stroke', 'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg',\n",
        "        'highway-mpg', 'price']\n",
        "\n",
        "\n",
        "# Load in the data from a CSV file that is comma separated.\n",
        "car_data = pd.read_csv('https://storage.googleapis.com/mledu-datasets/cars_data.csv',\n",
        "                        sep=',', names=feature_names, header=None, encoding='latin-1')\n",
        "\n",
        "\n",
        "# We'll then randomize the data, just to be sure not to get any pathological\n",
        "# ordering effects that might harm the performance of Stochastic Gradient\n",
        "# Descent.\n",
        "car_data = car_data.reindex(np.random.permutation(car_data.index))\n",
        "\n",
        "print(\"Data set loaded. Num examples: \", len(car_data))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data set loaded. Num examples:  205\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show the top five rows from dataset \n",
        "\n",
        "car_data.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "ip099Kx3vDWB",
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            "text/plain": [
              "     symboling normalized-losses    make fuel-type aspiration num-doors  \\\n",
              "30           2               137   honda       gas        std       two   \n",
              "101          0               128  nissan       gas        std      four   \n",
              "169          2               134  toyota       gas        std       two   \n",
              "104          3               194  nissan       gas        std       two   \n",
              "175         -1                65  toyota       gas        std      four   \n",
              "\n",
              "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
              "30   hatchback          fwd           front       86.60  ...           92   \n",
              "101      sedan          fwd           front      100.40  ...          181   \n",
              "169  hatchback          rwd           front       98.40  ...          146   \n",
              "104  hatchback          rwd           front       91.30  ...          181   \n",
              "175  hatchback          fwd           front      102.40  ...          122   \n",
              "\n",
              "     fuel-system  bore  stroke compression-ratio horsepower  peak-rpm  \\\n",
              "30          1bbl  2.91    3.41              9.60         58      4800   \n",
              "101         mpfi  3.43    3.27              9.00        152      5200   \n",
              "169         mpfi  3.62    3.50              9.30        116      4800   \n",
              "104         mpfi  3.43    3.27              9.00        160      5200   \n",
              "175         mpfi  3.31    3.54              8.70         92      4200   \n",
              "\n",
              "    city-mpg highway-mpg  price  \n",
              "30        49          54   6479  \n",
              "101       17          22  13499  \n",
              "169       24          30   9989  \n",
              "104       19          25  17199  \n",
              "175       27          32   9988  \n",
              "\n",
              "[5 rows x 26 columns]"
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              "      <th></th>\n",
              "      <th>symboling</th>\n",
              "      <th>normalized-losses</th>\n",
              "      <th>make</th>\n",
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              "      <th>compression-ratio</th>\n",
              "      <th>horsepower</th>\n",
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              "      <th>highway-mpg</th>\n",
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              "      <th>30</th>\n",
              "      <td>2</td>\n",
              "      <td>137</td>\n",
              "      <td>honda</td>\n",
              "      <td>gas</td>\n",
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              "      <td>4800</td>\n",
              "      <td>49</td>\n",
              "      <td>54</td>\n",
              "      <td>6479</td>\n",
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              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>0</td>\n",
              "      <td>128</td>\n",
              "      <td>nissan</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>100.40</td>\n",
              "      <td>...</td>\n",
              "      <td>181</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.43</td>\n",
              "      <td>3.27</td>\n",
              "      <td>9.00</td>\n",
              "      <td>152</td>\n",
              "      <td>5200</td>\n",
              "      <td>17</td>\n",
              "      <td>22</td>\n",
              "      <td>13499</td>\n",
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              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>2</td>\n",
              "      <td>134</td>\n",
              "      <td>toyota</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>98.40</td>\n",
              "      <td>...</td>\n",
              "      <td>146</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.62</td>\n",
              "      <td>3.50</td>\n",
              "      <td>9.30</td>\n",
              "      <td>116</td>\n",
              "      <td>4800</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>9989</td>\n",
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              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>3</td>\n",
              "      <td>194</td>\n",
              "      <td>nissan</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>91.30</td>\n",
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              "      <td>181</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.43</td>\n",
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              "      <td>160</td>\n",
              "      <td>5200</td>\n",
              "      <td>19</td>\n",
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              "      <td>17199</td>\n",
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              "      <td>toyota</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>102.40</td>\n",
              "      <td>...</td>\n",
              "      <td>122</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.31</td>\n",
              "      <td>3.54</td>\n",
              "      <td>8.70</td>\n",
              "      <td>92</td>\n",
              "      <td>4200</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "      <td>9988</td>\n",
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          },
          "metadata": {},
          "execution_count": 82
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    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "nAHZBtDlkmGa"
      },
      "source": [
        "This is a really small dataset! Only 205 examples.\n",
        "\n",
        "For simplicity in this codelab, we do not split the data further into training and validation. But you MUST do this on real datasets, or else you will overfit to your single dataset."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# for our convanous we split the data into train and test \n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train, test = train_test_split(car_data, test_size=0.2)"
      ],
      "metadata": {
        "id": "LfWGEfXavt-b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# number of examples for training data\n",
        "len(train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wFiGU78vv3Hz",
        "outputId": "38f5dfc4-a562-4826-a675-bd2c3ac44755"
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              "164"
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          "metadata": {},
          "execution_count": 85
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    {
      "cell_type": "code",
      "source": [
        "# number of examples for testing data \n",
        "\n",
        "len(test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_2NHNcGv5ji",
        "outputId": "5177fde5-179d-405b-8895-d180d7ef2661"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "41"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQ1HxLrOVqZk"
      },
      "source": [
        "## Task 0: Use pandas to explore and prepare the data\n",
        "\n",
        "- Use Pandas to inspect the data and manually curate a list of numeric_feature_names and categorical_feature_names.\n",
        "\n",
        "\n",
        "Useful functions:\n",
        "- `type()` called on any Python object describes the type of the object\n",
        "- `dataframe[4:7]` pulls out rows 4, 5, 6 in a Pandas dataframe\n",
        "- `dataframe[['mycol1', 'mycol2']]` pulls out the two requested columns into a new Pandas dataframe\n",
        "- `dataframe['mycol1']` returns a Pandas series -- not a dataframe!\n",
        "- `dataframe.describe()` prints out statistics for each dataframe column"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type('make')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zKFWHKLHyZqy",
        "outputId": "70ab909b-2924-424a-9ddb-0ec6c330eae5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(car_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y485SIk2wZTJ",
        "outputId": "2d08a210-6f48-4414-f54a-4d4d28502086"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfeHYeMf7PwQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "d119a43e-ebde-49d1-d8cb-fa31d9dd1d8b"
      },
      "source": [
        "# show the rows 4, 5 and 6 from dataframe \n",
        "\n",
        "car_data[4:7]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     symboling normalized-losses        make fuel-type aspiration num-doors  \\\n",
              "175         -1                65      toyota       gas        std      four   \n",
              "37           0               106       honda       gas        std       two   \n",
              "85           1               125  mitsubishi       gas        std      four   \n",
              "\n",
              "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
              "175  hatchback          fwd           front      102.40  ...          122   \n",
              "37   hatchback          fwd           front       96.50  ...          110   \n",
              "85       sedan          fwd           front       96.30  ...          122   \n",
              "\n",
              "     fuel-system  bore  stroke compression-ratio horsepower  peak-rpm  \\\n",
              "175         mpfi  3.31    3.54              8.70         92      4200   \n",
              "37          1bbl  3.15    3.58              9.00         86      5800   \n",
              "85          2bbl  3.35    3.46              8.50         88      5000   \n",
              "\n",
              "    city-mpg highway-mpg price  \n",
              "175       27          32  9988  \n",
              "37        27          33  7895  \n",
              "85        25          32  6989  \n",
              "\n",
              "[3 rows x 26 columns]"
            ],
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              "      <th></th>\n",
              "      <th>symboling</th>\n",
              "      <th>normalized-losses</th>\n",
              "      <th>make</th>\n",
              "      <th>fuel-type</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>num-doors</th>\n",
              "      <th>body-style</th>\n",
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              "      <th>engine-location</th>\n",
              "      <th>wheel-base</th>\n",
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              "      <th>engine-size</th>\n",
              "      <th>fuel-system</th>\n",
              "      <th>bore</th>\n",
              "      <th>stroke</th>\n",
              "      <th>compression-ratio</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>peak-rpm</th>\n",
              "      <th>city-mpg</th>\n",
              "      <th>highway-mpg</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>-1</td>\n",
              "      <td>65</td>\n",
              "      <td>toyota</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>102.40</td>\n",
              "      <td>...</td>\n",
              "      <td>122</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.31</td>\n",
              "      <td>3.54</td>\n",
              "      <td>8.70</td>\n",
              "      <td>92</td>\n",
              "      <td>4200</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "      <td>9988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0</td>\n",
              "      <td>106</td>\n",
              "      <td>honda</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>96.50</td>\n",
              "      <td>...</td>\n",
              "      <td>110</td>\n",
              "      <td>1bbl</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.58</td>\n",
              "      <td>9.00</td>\n",
              "      <td>86</td>\n",
              "      <td>5800</td>\n",
              "      <td>27</td>\n",
              "      <td>33</td>\n",
              "      <td>7895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>1</td>\n",
              "      <td>125</td>\n",
              "      <td>mitsubishi</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>96.30</td>\n",
              "      <td>...</td>\n",
              "      <td>122</td>\n",
              "      <td>2bbl</td>\n",
              "      <td>3.35</td>\n",
              "      <td>3.46</td>\n",
              "      <td>8.50</td>\n",
              "      <td>88</td>\n",
              "      <td>5000</td>\n",
              "      <td>25</td>\n",
              "      <td>32</td>\n",
              "      <td>6989</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 26 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
      "cell_type": "code",
      "source": [
        "# show the columns make and body-style \n",
        "\n",
        "car_data[['make','body-style']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "pmDFvPo4xYiQ",
        "outputId": "cbfb2945-43d9-4121-db57-15fb7ebf53b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           make body-style\n",
              "30        honda  hatchback\n",
              "101      nissan      sedan\n",
              "169      toyota  hatchback\n",
              "104      nissan  hatchback\n",
              "175      toyota  hatchback\n",
              "..          ...        ...\n",
              "15          bmw      sedan\n",
              "197       volvo      wagon\n",
              "27        dodge      sedan\n",
              "191  volkswagen      sedan\n",
              "199       volvo      wagon\n",
              "\n",
              "[205 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-972409a9-8b0b-47c6-a60a-ab92b22ddcc3\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>make</th>\n",
              "      <th>body-style</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>30</th>\n",
              "      <td>honda</td>\n",
              "      <td>hatchback</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>nissan</td>\n",
              "      <td>sedan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>toyota</td>\n",
              "      <td>hatchback</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>nissan</td>\n",
              "      <td>hatchback</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>toyota</td>\n",
              "      <td>hatchback</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>bmw</td>\n",
              "      <td>sedan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>volvo</td>\n",
              "      <td>wagon</td>\n",
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              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>dodge</td>\n",
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              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>volkswagen</td>\n",
              "      <td>sedan</td>\n",
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              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>volvo</td>\n",
              "      <td>wagon</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 2 columns</p>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-972409a9-8b0b-47c6-a60a-ab92b22ddcc3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show the single column \n",
        "\n",
        "car_data['horsepower']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oLRKgW4vxz47",
        "outputId": "e67d750d-4c2b-499f-a084-05714c1be3f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30      58\n",
              "101    152\n",
              "169    116\n",
              "104    160\n",
              "175     92\n",
              "      ... \n",
              "15     182\n",
              "197    114\n",
              "27     102\n",
              "191    110\n",
              "199    162\n",
              "Name: horsepower, Length: 205, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show statistics of each dataframe column \n",
        "\n",
        "car_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "gJGJoeP4yFIA",
        "outputId": "bc871b4b-0cea-48a6-a736-4bb4f7a380b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       symboling  wheel-base  length  width  height  weight  engine-size  \\\n",
              "count     205.00      205.00  205.00 205.00  205.00  205.00       205.00   \n",
              "mean        0.83       98.76  174.05  65.91   53.72 2555.57       126.91   \n",
              "std         1.25        6.02   12.34   2.15    2.44  520.68        41.64   \n",
              "min        -2.00       86.60  141.10  60.30   47.80 1488.00        61.00   \n",
              "25%         0.00       94.50  166.30  64.10   52.00 2145.00        97.00   \n",
              "50%         1.00       97.00  173.20  65.50   54.10 2414.00       120.00   \n",
              "75%         2.00      102.40  183.10  66.90   55.50 2935.00       141.00   \n",
              "max         3.00      120.90  208.10  72.30   59.80 4066.00       326.00   \n",
              "\n",
              "       compression-ratio  city-mpg  highway-mpg  \n",
              "count             205.00    205.00       205.00  \n",
              "mean               10.14     25.22        30.75  \n",
              "std                 3.97      6.54         6.89  \n",
              "min                 7.00     13.00        16.00  \n",
              "25%                 8.60     19.00        25.00  \n",
              "50%                 9.00     24.00        30.00  \n",
              "75%                 9.40     30.00        34.00  \n",
              "max                23.00     49.00        54.00  "
            ],
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              "      <th></th>\n",
              "      <th>symboling</th>\n",
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              "      <th>count</th>\n",
              "      <td>205.00</td>\n",
              "      <td>205.00</td>\n",
              "      <td>205.00</td>\n",
              "      <td>205.00</td>\n",
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              "      <th>mean</th>\n",
              "      <td>0.83</td>\n",
              "      <td>98.76</td>\n",
              "      <td>174.05</td>\n",
              "      <td>65.91</td>\n",
              "      <td>53.72</td>\n",
              "      <td>2555.57</td>\n",
              "      <td>126.91</td>\n",
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              "      <td>2.15</td>\n",
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              "      <td>41.64</td>\n",
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              "      <td>6.54</td>\n",
              "      <td>6.89</td>\n",
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              "      <th>min</th>\n",
              "      <td>-2.00</td>\n",
              "      <td>86.60</td>\n",
              "      <td>141.10</td>\n",
              "      <td>60.30</td>\n",
              "      <td>47.80</td>\n",
              "      <td>1488.00</td>\n",
              "      <td>61.00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>13.00</td>\n",
              "      <td>16.00</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.00</td>\n",
              "      <td>94.50</td>\n",
              "      <td>166.30</td>\n",
              "      <td>64.10</td>\n",
              "      <td>52.00</td>\n",
              "      <td>2145.00</td>\n",
              "      <td>97.00</td>\n",
              "      <td>8.60</td>\n",
              "      <td>19.00</td>\n",
              "      <td>25.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.00</td>\n",
              "      <td>97.00</td>\n",
              "      <td>173.20</td>\n",
              "      <td>65.50</td>\n",
              "      <td>54.10</td>\n",
              "      <td>2414.00</td>\n",
              "      <td>120.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>24.00</td>\n",
              "      <td>30.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.00</td>\n",
              "      <td>102.40</td>\n",
              "      <td>183.10</td>\n",
              "      <td>66.90</td>\n",
              "      <td>55.50</td>\n",
              "      <td>2935.00</td>\n",
              "      <td>141.00</td>\n",
              "      <td>9.40</td>\n",
              "      <td>30.00</td>\n",
              "      <td>34.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.00</td>\n",
              "      <td>120.90</td>\n",
              "      <td>208.10</td>\n",
              "      <td>72.30</td>\n",
              "      <td>59.80</td>\n",
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              "      <td>326.00</td>\n",
              "      <td>23.00</td>\n",
              "      <td>49.00</td>\n",
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          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "both",
        "id": "VsOUrVozoe9u"
      },
      "source": [
        "LABEL = 'price'\n",
        "\n",
        "numeric_feature_names = ['symboling','normalized-losses','wheel-base', 'length', 'width', 'height', 'weight', 'engine-size','bore','stroke','compression-ratio','horsepower','peak-rpm','city-mpg','highway-mpg']\n",
        "categorical_feature_names = list(set(feature_names) - set(numeric_feature_names) - set([LABEL]))\n",
        "\n",
        "# The correct solution will pass these assert statements.\n",
        "assert len(numeric_feature_names) == 15\n",
        "assert len(categorical_feature_names) == 10\n",
        "\n",
        "## here the error was to set the number of numeric features in the list where it was only 12 \n",
        "## remove the extra [] brackets as well"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lLFkHgBm8O1Y"
      },
      "source": [
        "#title Solution (to view code, from cell's menu, select Form -> Show Code)\n",
        "\n",
        "numeric_feature_names = ['symboling', 'normalized-losses', 'wheel-base',\n",
        "        'length', 'width', 'height', 'weight', 'engine-size', 'horsepower',\n",
        "        'peak-rpm', 'city-mpg', 'highway-mpg', 'bore', 'stroke',\n",
        "         'compression-ratio']\n",
        "\n",
        "categorical_feature_names = list(set(feature_names) - set(numeric_feature_names) - set([LABEL]))\n",
        "\n",
        "assert len(numeric_feature_names) == 15\n",
        "assert len(categorical_feature_names) == 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nabeQFGBpDEN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "outputId": "a62f67af-e9ed-4d21-9317-8b0b0df13ab5"
      },
      "source": [
        "# Run to inspect numeric features.\n",
        "\n",
        "car_data[numeric_feature_names]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     symboling normalized-losses  wheel-base  length  width  height  weight  \\\n",
              "30           2               137       86.60  144.60  63.90   50.80    1713   \n",
              "101          0               128      100.40  181.70  66.50   55.10    3095   \n",
              "169          2               134       98.40  176.20  65.60   52.00    2551   \n",
              "104          3               194       91.30  170.70  67.90   49.70    3071   \n",
              "175         -1                65      102.40  175.60  66.50   53.90    2414   \n",
              "..         ...               ...         ...     ...    ...     ...     ...   \n",
              "15           0                 ?      103.50  189.00  66.90   55.70    3230   \n",
              "197         -1                74      104.30  188.80  67.20   57.50    3042   \n",
              "27           1               148       93.70  157.30  63.80   50.60    2191   \n",
              "191          0                 ?      100.40  180.20  66.90   55.10    2661   \n",
              "199         -1                74      104.30  188.80  67.20   57.50    3157   \n",
              "\n",
              "     engine-size horsepower peak-rpm  city-mpg  highway-mpg  bore stroke  \\\n",
              "30            92         58     4800        49           54  2.91   3.41   \n",
              "101          181        152     5200        17           22  3.43   3.27   \n",
              "169          146        116     4800        24           30  3.62   3.50   \n",
              "104          181        160     5200        19           25  3.43   3.27   \n",
              "175          122         92     4200        27           32  3.31   3.54   \n",
              "..           ...        ...      ...       ...          ...   ...    ...   \n",
              "15           209        182     5400        16           22  3.62   3.39   \n",
              "197          141        114     5400        24           28  3.78   3.15   \n",
              "27            98        102     5500        24           30  3.03   3.39   \n",
              "191          136        110     5500        19           24  3.19   3.40   \n",
              "199          130        162     5100        17           22  3.62   3.15   \n",
              "\n",
              "     compression-ratio  \n",
              "30                9.60  \n",
              "101               9.00  \n",
              "169               9.30  \n",
              "104               9.00  \n",
              "175               8.70  \n",
              "..                 ...  \n",
              "15                8.00  \n",
              "197               9.50  \n",
              "27                7.60  \n",
              "191               8.50  \n",
              "199               7.50  \n",
              "\n",
              "[205 rows x 15 columns]"
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              "      <th>symboling</th>\n",
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              "      <th>101</th>\n",
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              "      <th>169</th>\n",
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              "      <td>176.20</td>\n",
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              "      <td>30</td>\n",
              "      <td>3.62</td>\n",
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              "      <td>9.30</td>\n",
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              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>3</td>\n",
              "      <td>194</td>\n",
              "      <td>91.30</td>\n",
              "      <td>170.70</td>\n",
              "      <td>67.90</td>\n",
              "      <td>49.70</td>\n",
              "      <td>3071</td>\n",
              "      <td>181</td>\n",
              "      <td>160</td>\n",
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              "      <td>19</td>\n",
              "      <td>25</td>\n",
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              "      <td>9.00</td>\n",
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              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>-1</td>\n",
              "      <td>65</td>\n",
              "      <td>102.40</td>\n",
              "      <td>175.60</td>\n",
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              "      <td>53.90</td>\n",
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              "      <td>122</td>\n",
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              "      <td>4200</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "      <td>3.31</td>\n",
              "      <td>3.54</td>\n",
              "      <td>8.70</td>\n",
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              "    <tr>\n",
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              "      <td>3.39</td>\n",
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              "    <tr>\n",
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              "      <td>74</td>\n",
              "      <td>104.30</td>\n",
              "      <td>188.80</td>\n",
              "      <td>67.20</td>\n",
              "      <td>57.50</td>\n",
              "      <td>3042</td>\n",
              "      <td>141</td>\n",
              "      <td>114</td>\n",
              "      <td>5400</td>\n",
              "      <td>24</td>\n",
              "      <td>28</td>\n",
              "      <td>3.78</td>\n",
              "      <td>3.15</td>\n",
              "      <td>9.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1</td>\n",
              "      <td>148</td>\n",
              "      <td>93.70</td>\n",
              "      <td>157.30</td>\n",
              "      <td>63.80</td>\n",
              "      <td>50.60</td>\n",
              "      <td>2191</td>\n",
              "      <td>98</td>\n",
              "      <td>102</td>\n",
              "      <td>5500</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>3.03</td>\n",
              "      <td>3.39</td>\n",
              "      <td>7.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>100.40</td>\n",
              "      <td>180.20</td>\n",
              "      <td>66.90</td>\n",
              "      <td>55.10</td>\n",
              "      <td>2661</td>\n",
              "      <td>136</td>\n",
              "      <td>110</td>\n",
              "      <td>5500</td>\n",
              "      <td>19</td>\n",
              "      <td>24</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>-1</td>\n",
              "      <td>74</td>\n",
              "      <td>104.30</td>\n",
              "      <td>188.80</td>\n",
              "      <td>67.20</td>\n",
              "      <td>57.50</td>\n",
              "      <td>3157</td>\n",
              "      <td>130</td>\n",
              "      <td>162</td>\n",
              "      <td>5100</td>\n",
              "      <td>17</td>\n",
              "      <td>22</td>\n",
              "      <td>3.62</td>\n",
              "      <td>3.15</td>\n",
              "      <td>7.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ecdda26b-8e4a-4022-971c-492ac0680b65')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ecdda26b-8e4a-4022-971c-492ac0680b65 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ecdda26b-8e4a-4022-971c-492ac0680b65');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1ss9Q7mpiBy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "a1d48100-f7ec-4da0-9629-794f6ecdfae3"
      },
      "source": [
        "# Run to inspect categorical features.\n",
        "\n",
        "car_data[categorical_feature_names]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    fuel-system drive-wheels aspiration engine-location engine-type  \\\n",
              "30         1bbl          fwd        std           front         ohc   \n",
              "101        mpfi          fwd        std           front        ohcv   \n",
              "169        mpfi          rwd        std           front         ohc   \n",
              "104        mpfi          rwd        std           front        ohcv   \n",
              "175        mpfi          fwd        std           front         ohc   \n",
              "..          ...          ...        ...             ...         ...   \n",
              "15         mpfi          rwd        std           front         ohc   \n",
              "197        mpfi          rwd        std           front         ohc   \n",
              "27         mpfi          fwd      turbo           front         ohc   \n",
              "191        mpfi          fwd        std           front         ohc   \n",
              "199        mpfi          rwd      turbo           front         ohc   \n",
              "\n",
              "           make num-doors fuel-type body-style num-cylinders  \n",
              "30        honda       two       gas  hatchback          four  \n",
              "101      nissan      four       gas      sedan           six  \n",
              "169      toyota       two       gas  hatchback          four  \n",
              "104      nissan       two       gas  hatchback           six  \n",
              "175      toyota      four       gas  hatchback          four  \n",
              "..          ...       ...       ...        ...           ...  \n",
              "15          bmw      four       gas      sedan           six  \n",
              "197       volvo      four       gas      wagon          four  \n",
              "27        dodge         ?       gas      sedan          four  \n",
              "191  volkswagen      four       gas      sedan          five  \n",
              "199       volvo      four       gas      wagon          four  \n",
              "\n",
              "[205 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0bc1374a-4124-47c3-8c33-d39bc959cf3e\">\n",
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fuel-system</th>\n",
              "      <th>drive-wheels</th>\n",
              "      <th>aspiration</th>\n",
              "      <th>engine-location</th>\n",
              "      <th>engine-type</th>\n",
              "      <th>make</th>\n",
              "      <th>num-doors</th>\n",
              "      <th>fuel-type</th>\n",
              "      <th>body-style</th>\n",
              "      <th>num-cylinders</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1bbl</td>\n",
              "      <td>fwd</td>\n",
              "      <td>std</td>\n",
              "      <td>front</td>\n",
              "      <td>ohc</td>\n",
              "      <td>honda</td>\n",
              "      <td>two</td>\n",
              "      <td>gas</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>four</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>mpfi</td>\n",
              "      <td>fwd</td>\n",
              "      <td>std</td>\n",
              "      <td>front</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>nissan</td>\n",
              "      <td>four</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>six</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>mpfi</td>\n",
              "      <td>rwd</td>\n",
              "      <td>std</td>\n",
              "      <td>front</td>\n",
              "      <td>ohc</td>\n",
              "      <td>toyota</td>\n",
              "      <td>two</td>\n",
              "      <td>gas</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>four</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>mpfi</td>\n",
              "      <td>rwd</td>\n",
              "      <td>std</td>\n",
              "      <td>front</td>\n",
              "      <td>ohcv</td>\n",
              "      <td>nissan</td>\n",
              "      <td>two</td>\n",
              "      <td>gas</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>six</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>mpfi</td>\n",
              "      <td>fwd</td>\n",
              "      <td>std</td>\n",
              "      <td>front</td>\n",
              "      <td>ohc</td>\n",
              "      <td>toyota</td>\n",
              "      <td>four</td>\n",
              "      <td>gas</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>four</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>mpfi</td>\n",
              "      <td>rwd</td>\n",
              "      <td>std</td>\n",
              "      <td>front</td>\n",
              "      <td>ohc</td>\n",
              "      <td>bmw</td>\n",
              "      <td>four</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>six</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>mpfi</td>\n",
              "      <td>rwd</td>\n",
              "      <td>std</td>\n",
              "      <td>front</td>\n",
              "      <td>ohc</td>\n",
              "      <td>volvo</td>\n",
              "      <td>four</td>\n",
              "      <td>gas</td>\n",
              "      <td>wagon</td>\n",
              "      <td>four</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>mpfi</td>\n",
              "      <td>fwd</td>\n",
              "      <td>turbo</td>\n",
              "      <td>front</td>\n",
              "      <td>ohc</td>\n",
              "      <td>dodge</td>\n",
              "      <td>?</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>four</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>mpfi</td>\n",
              "      <td>fwd</td>\n",
              "      <td>std</td>\n",
              "      <td>front</td>\n",
              "      <td>ohc</td>\n",
              "      <td>volkswagen</td>\n",
              "      <td>four</td>\n",
              "      <td>gas</td>\n",
              "      <td>sedan</td>\n",
              "      <td>five</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>mpfi</td>\n",
              "      <td>rwd</td>\n",
              "      <td>turbo</td>\n",
              "      <td>front</td>\n",
              "      <td>ohc</td>\n",
              "      <td>volvo</td>\n",
              "      <td>four</td>\n",
              "      <td>gas</td>\n",
              "      <td>wagon</td>\n",
              "      <td>four</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0bc1374a-4124-47c3-8c33-d39bc959cf3e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0bc1374a-4124-47c3-8c33-d39bc959cf3e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0bc1374a-4124-47c3-8c33-d39bc959cf3e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6OjDegBgqNnu"
      },
      "source": [
        "# Coerce the numeric features to numbers. This is necessary because the model\n",
        "# crashes because not all the values are numeric.\n",
        "\n",
        "for feature_name in numeric_feature_names + [LABEL]:\n",
        "  car_data[feature_name] = pd.to_numeric(car_data[feature_name], errors='coerce')\n",
        "\n",
        "# Fill missing values with 0.\n",
        "# Is this an OK thing to do? You may want to come back and revisit this decision later.\n",
        "\n",
        "car_data.fillna(0, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uq-t-8GPvnCW"
      },
      "source": [
        "## Task 1: Make your best model with numeric features. No normalization allowed.\n",
        "\n",
        "Modify the model provided below to achieve the lowest eval loss. You may want to change various hyperparameters:\n",
        "- learning rate\n",
        "- choice of optimizer\n",
        "- hidden layer dimensions -- make sure your choice here makes sense given the number of training examples\n",
        "- batch size\n",
        "- num training steps\n",
        "- (anything else you can think of changing)\n",
        "\n",
        "Do not use the `normalizer_fn` arg on `numeric_column`."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Code\n",
        "\n",
        "batch_size = 34\n",
        "\n",
        "print(numeric_feature_names)\n",
        "x_df = car_data[numeric_feature_names]\n",
        "y_series = car_data['price']\n",
        "\n",
        "train_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    num_epochs=None,\n",
        "    shuffle=True)\n",
        "\n",
        "eval_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "predict_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "\n",
        "model_feature_columns = [\n",
        "    tf.feature_column.numeric_column(feature_name) for feature_name in numeric_feature_names\n",
        "]\n",
        "print('model_feature_columns', model_feature_columns)\n",
        "\n",
        "est = tf.estimator.DNNRegressor(\n",
        "    feature_columns=model_feature_columns,\n",
        "    hidden_units=[84],\n",
        "    optimizer=tf.train.AdagradOptimizer(learning_rate=0.9),\n",
        "  )\n",
        "\n",
        "\n",
        "num_print_statements = 10\n",
        "num_training_steps = 100000\n",
        "for _ in range(num_print_statements):\n",
        "  est.train(train_input_fn, steps=num_training_steps // num_print_statements)\n",
        "  scores = est.evaluate(eval_input_fn)\n",
        "  \n",
        "  print('scores', scores)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U7xexZKYLG0h",
        "outputId": "a360dbd6-0029-4c63-8993-5fb941444b63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['symboling', 'normalized-losses', 'wheel-base', 'length', 'width', 'height', 'weight', 'engine-size', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'bore', 'stroke', 'compression-ratio']\n",
            "model_feature_columns [NumericColumn(key='symboling', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='normalized-losses', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='wheel-base', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='length', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='width', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='height', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='weight', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='engine-size', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='horsepower', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='peak-rpm', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='city-mpg', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='highway-mpg', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='bore', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='stroke', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='compression-ratio', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)]\n",
            "INFO:tensorflow:Using default config.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpb3dvddk8\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpb3dvddk8', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...\n",
            "INFO:tensorflow:loss = 8775748000.0, step = 1\n",
            "INFO:tensorflow:global_step/sec: 400.531\n",
            "INFO:tensorflow:loss = 2004069400.0, step = 101 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.26\n",
            "INFO:tensorflow:loss = 786818370.0, step = 201 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.055\n",
            "INFO:tensorflow:loss = 1033868900.0, step = 301 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.861\n",
            "INFO:tensorflow:loss = 1066959000.0, step = 401 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.951\n",
            "INFO:tensorflow:loss = 1293667300.0, step = 501 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.021\n",
            "INFO:tensorflow:loss = 197869170.0, step = 601 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.626\n",
            "INFO:tensorflow:loss = 712985100.0, step = 701 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.11\n",
            "INFO:tensorflow:loss = 982190400.0, step = 801 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.031\n",
            "INFO:tensorflow:loss = 789625660.0, step = 901 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.263\n",
            "INFO:tensorflow:loss = 406873500.0, step = 1001 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.513\n",
            "INFO:tensorflow:loss = 165678300.0, step = 1101 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.583\n",
            "INFO:tensorflow:loss = 714000830.0, step = 1201 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.14\n",
            "INFO:tensorflow:loss = 858433540.0, step = 1301 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.418\n",
            "INFO:tensorflow:loss = 1007812860.0, step = 1401 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.175\n",
            "INFO:tensorflow:loss = 535849300.0, step = 1501 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 452.971\n",
            "INFO:tensorflow:loss = 495218900.0, step = 1601 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.386\n",
            "INFO:tensorflow:loss = 941151700.0, step = 1701 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 445.935\n",
            "INFO:tensorflow:loss = 550894400.0, step = 1801 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.025\n",
            "INFO:tensorflow:loss = 254090030.0, step = 1901 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 432.033\n",
            "INFO:tensorflow:loss = 659390850.0, step = 2001 (0.221 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.281\n",
            "INFO:tensorflow:loss = 658234900.0, step = 2101 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.166\n",
            "INFO:tensorflow:loss = 934616000.0, step = 2201 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 343.825\n",
            "INFO:tensorflow:loss = 340346370.0, step = 2301 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.971\n",
            "INFO:tensorflow:loss = 278814940.0, step = 2401 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.889\n",
            "INFO:tensorflow:loss = 627677800.0, step = 2501 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.265\n",
            "INFO:tensorflow:loss = 360970620.0, step = 2601 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.815\n",
            "INFO:tensorflow:loss = 554392500.0, step = 2701 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 440.91\n",
            "INFO:tensorflow:loss = 213765820.0, step = 2801 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.782\n",
            "INFO:tensorflow:loss = 411642850.0, step = 2901 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.084\n",
            "INFO:tensorflow:loss = 1235015700.0, step = 3001 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.989\n",
            "INFO:tensorflow:loss = 496247420.0, step = 3101 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.972\n",
            "INFO:tensorflow:loss = 272296930.0, step = 3201 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.201\n",
            "INFO:tensorflow:loss = 680617150.0, step = 3301 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.226\n",
            "INFO:tensorflow:loss = 740555140.0, step = 3401 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.188\n",
            "INFO:tensorflow:loss = 553324860.0, step = 3501 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.642\n",
            "INFO:tensorflow:loss = 377727420.0, step = 3601 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.223\n",
            "INFO:tensorflow:loss = 325057920.0, step = 3701 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.422\n",
            "INFO:tensorflow:loss = 1053475400.0, step = 3801 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.611\n",
            "INFO:tensorflow:loss = 955069250.0, step = 3901 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.189\n",
            "INFO:tensorflow:loss = 637439940.0, step = 4001 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.512\n",
            "INFO:tensorflow:loss = 480546340.0, step = 4101 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.359\n",
            "INFO:tensorflow:loss = 405566560.0, step = 4201 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.375\n",
            "INFO:tensorflow:loss = 647664000.0, step = 4301 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.663\n",
            "INFO:tensorflow:loss = 519253120.0, step = 4401 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.847\n",
            "INFO:tensorflow:loss = 1183938400.0, step = 4501 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.655\n",
            "INFO:tensorflow:loss = 357777400.0, step = 4601 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.787\n",
            "INFO:tensorflow:loss = 252291090.0, step = 4701 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.887\n",
            "INFO:tensorflow:loss = 1060389000.0, step = 4801 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.148\n",
            "INFO:tensorflow:loss = 1020759040.0, step = 4901 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.183\n",
            "INFO:tensorflow:loss = 223831800.0, step = 5001 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.236\n",
            "INFO:tensorflow:loss = 643962300.0, step = 5101 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.203\n",
            "INFO:tensorflow:loss = 289364000.0, step = 5201 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.773\n",
            "INFO:tensorflow:loss = 592349060.0, step = 5301 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 421.92\n",
            "INFO:tensorflow:loss = 308683200.0, step = 5401 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.465\n",
            "INFO:tensorflow:loss = 1022240200.0, step = 5501 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.55\n",
            "INFO:tensorflow:loss = 799253760.0, step = 5601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.031\n",
            "INFO:tensorflow:loss = 667202500.0, step = 5701 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.198\n",
            "INFO:tensorflow:loss = 360388700.0, step = 5801 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.751\n",
            "INFO:tensorflow:loss = 660000060.0, step = 5901 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.359\n",
            "INFO:tensorflow:loss = 710230400.0, step = 6001 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.232\n",
            "INFO:tensorflow:loss = 310770880.0, step = 6101 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 428.247\n",
            "INFO:tensorflow:loss = 1456599000.0, step = 6201 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.755\n",
            "INFO:tensorflow:loss = 675982660.0, step = 6301 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.128\n",
            "INFO:tensorflow:loss = 314187230.0, step = 6401 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.098\n",
            "INFO:tensorflow:loss = 420648000.0, step = 6501 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.977\n",
            "INFO:tensorflow:loss = 692567400.0, step = 6601 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.897\n",
            "INFO:tensorflow:loss = 670816800.0, step = 6701 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.386\n",
            "INFO:tensorflow:loss = 329040500.0, step = 6801 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.961\n",
            "INFO:tensorflow:loss = 232161650.0, step = 6901 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.979\n",
            "INFO:tensorflow:loss = 299231330.0, step = 7001 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.463\n",
            "INFO:tensorflow:loss = 280909220.0, step = 7101 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.557\n",
            "INFO:tensorflow:loss = 781460540.0, step = 7201 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.535\n",
            "INFO:tensorflow:loss = 301796600.0, step = 7301 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.087\n",
            "INFO:tensorflow:loss = 170289500.0, step = 7401 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.848\n",
            "INFO:tensorflow:loss = 303806140.0, step = 7501 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.794\n",
            "INFO:tensorflow:loss = 1305301000.0, step = 7601 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.355\n",
            "INFO:tensorflow:loss = 340905250.0, step = 7701 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.132\n",
            "INFO:tensorflow:loss = 1439749800.0, step = 7801 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.227\n",
            "INFO:tensorflow:loss = 388319400.0, step = 7901 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.608\n",
            "INFO:tensorflow:loss = 302751300.0, step = 8001 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.64\n",
            "INFO:tensorflow:loss = 571939200.0, step = 8101 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.755\n",
            "INFO:tensorflow:loss = 587384600.0, step = 8201 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.976\n",
            "INFO:tensorflow:loss = 283238460.0, step = 8301 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.39\n",
            "INFO:tensorflow:loss = 899263740.0, step = 8401 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.374\n",
            "INFO:tensorflow:loss = 395138180.0, step = 8501 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.564\n",
            "INFO:tensorflow:loss = 706255000.0, step = 8601 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 427.599\n",
            "INFO:tensorflow:loss = 467770800.0, step = 8701 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.075\n",
            "INFO:tensorflow:loss = 335700580.0, step = 8801 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.113\n",
            "INFO:tensorflow:loss = 201391070.0, step = 8901 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.134\n",
            "INFO:tensorflow:loss = 519232130.0, step = 9001 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.751\n",
            "INFO:tensorflow:loss = 314281800.0, step = 9101 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.115\n",
            "INFO:tensorflow:loss = 1037449200.0, step = 9201 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.793\n",
            "INFO:tensorflow:loss = 587910000.0, step = 9301 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.953\n",
            "INFO:tensorflow:loss = 577300160.0, step = 9401 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.652\n",
            "INFO:tensorflow:loss = 408391680.0, step = 9501 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.825\n",
            "INFO:tensorflow:loss = 1023546800.0, step = 9601 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.035\n",
            "INFO:tensorflow:loss = 1743523000.0, step = 9701 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.277\n",
            "INFO:tensorflow:loss = 505683330.0, step = 9801 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 340.029\n",
            "INFO:tensorflow:loss = 170902660.0, step = 9901 (0.297 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...\n",
            "INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...\n",
            "INFO:tensorflow:Loss for final step: 809623200.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:01:13\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-10000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.25185s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:01:13\n",
            "INFO:tensorflow:Saving dict for global step 10000: average_loss = 16750581.0, global_step = 10000, label/mean = 12949.43, loss = 490552740.0, prediction/mean = 12843.3\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tmpb3dvddk8/model.ckpt-10000\n",
            "scores {'average_loss': 16750581.0, 'label/mean': 12949.43, 'loss': 490552740.0, 'prediction/mean': 12843.3, 'global_step': 10000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-10000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...\n",
            "INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...\n",
            "INFO:tensorflow:loss = 609421600.0, step = 10001\n",
            "INFO:tensorflow:global_step/sec: 340.609\n",
            "INFO:tensorflow:loss = 339831200.0, step = 10101 (0.307 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.474\n",
            "INFO:tensorflow:loss = 279723800.0, step = 10201 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.924\n",
            "INFO:tensorflow:loss = 867091000.0, step = 10301 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.609\n",
            "INFO:tensorflow:loss = 468135140.0, step = 10401 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.907\n",
            "INFO:tensorflow:loss = 165774720.0, step = 10501 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.386\n",
            "INFO:tensorflow:loss = 491191800.0, step = 10601 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.886\n",
            "INFO:tensorflow:loss = 971483700.0, step = 10701 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.848\n",
            "INFO:tensorflow:loss = 439133950.0, step = 10801 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.55\n",
            "INFO:tensorflow:loss = 771673900.0, step = 10901 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.614\n",
            "INFO:tensorflow:loss = 928706500.0, step = 11001 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.581\n",
            "INFO:tensorflow:loss = 661308540.0, step = 11101 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.023\n",
            "INFO:tensorflow:loss = 185473980.0, step = 11201 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 345.546\n",
            "INFO:tensorflow:loss = 1410546300.0, step = 11301 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.031\n",
            "INFO:tensorflow:loss = 793689150.0, step = 11401 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.612\n",
            "INFO:tensorflow:loss = 1072520700.0, step = 11501 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.185\n",
            "INFO:tensorflow:loss = 806124600.0, step = 11601 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.697\n",
            "INFO:tensorflow:loss = 962466050.0, step = 11701 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.159\n",
            "INFO:tensorflow:loss = 265765600.0, step = 11801 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.758\n",
            "INFO:tensorflow:loss = 281332260.0, step = 11901 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.642\n",
            "INFO:tensorflow:loss = 255204980.0, step = 12001 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.957\n",
            "INFO:tensorflow:loss = 1049624300.0, step = 12101 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.342\n",
            "INFO:tensorflow:loss = 292097180.0, step = 12201 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.171\n",
            "INFO:tensorflow:loss = 793411800.0, step = 12301 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.169\n",
            "INFO:tensorflow:loss = 748208600.0, step = 12401 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.801\n",
            "INFO:tensorflow:loss = 229810720.0, step = 12501 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.302\n",
            "INFO:tensorflow:loss = 1022885800.0, step = 12601 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.302\n",
            "INFO:tensorflow:loss = 626811300.0, step = 12701 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.448\n",
            "INFO:tensorflow:loss = 329348450.0, step = 12801 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.225\n",
            "INFO:tensorflow:loss = 341466100.0, step = 12901 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.794\n",
            "INFO:tensorflow:loss = 183754620.0, step = 13001 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.04\n",
            "INFO:tensorflow:loss = 422473300.0, step = 13101 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.745\n",
            "INFO:tensorflow:loss = 545736260.0, step = 13201 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 450.628\n",
            "INFO:tensorflow:loss = 378004350.0, step = 13301 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.453\n",
            "INFO:tensorflow:loss = 572499000.0, step = 13401 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.954\n",
            "INFO:tensorflow:loss = 554202940.0, step = 13501 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.736\n",
            "INFO:tensorflow:loss = 263711600.0, step = 13601 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.895\n",
            "INFO:tensorflow:loss = 1181436700.0, step = 13701 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.805\n",
            "INFO:tensorflow:loss = 326338100.0, step = 13801 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.878\n",
            "INFO:tensorflow:loss = 471264300.0, step = 13901 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.691\n",
            "INFO:tensorflow:loss = 480602880.0, step = 14001 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.87\n",
            "INFO:tensorflow:loss = 302476640.0, step = 14101 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.154\n",
            "INFO:tensorflow:loss = 188940930.0, step = 14201 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.46\n",
            "INFO:tensorflow:loss = 1831335200.0, step = 14301 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.372\n",
            "INFO:tensorflow:loss = 312072580.0, step = 14401 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.217\n",
            "INFO:tensorflow:loss = 331421020.0, step = 14501 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.488\n",
            "INFO:tensorflow:loss = 511624220.0, step = 14601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.118\n",
            "INFO:tensorflow:loss = 1315387900.0, step = 14701 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.833\n",
            "INFO:tensorflow:loss = 335985630.0, step = 14801 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.481\n",
            "INFO:tensorflow:loss = 490002940.0, step = 14901 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.766\n",
            "INFO:tensorflow:loss = 160221100.0, step = 15001 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.472\n",
            "INFO:tensorflow:loss = 165420620.0, step = 15101 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.458\n",
            "INFO:tensorflow:loss = 513821250.0, step = 15201 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.35\n",
            "INFO:tensorflow:loss = 510422660.0, step = 15301 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.656\n",
            "INFO:tensorflow:loss = 428186800.0, step = 15401 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.922\n",
            "INFO:tensorflow:loss = 532533380.0, step = 15501 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.667\n",
            "INFO:tensorflow:loss = 446058370.0, step = 15601 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.493\n",
            "INFO:tensorflow:loss = 567779700.0, step = 15701 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.154\n",
            "INFO:tensorflow:loss = 1092149800.0, step = 15801 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.811\n",
            "INFO:tensorflow:loss = 630040200.0, step = 15901 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.354\n",
            "INFO:tensorflow:loss = 526038100.0, step = 16001 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.192\n",
            "INFO:tensorflow:loss = 1066335200.0, step = 16101 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.464\n",
            "INFO:tensorflow:loss = 298370940.0, step = 16201 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.149\n",
            "INFO:tensorflow:loss = 315745150.0, step = 16301 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.978\n",
            "INFO:tensorflow:loss = 202877380.0, step = 16401 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.143\n",
            "INFO:tensorflow:loss = 645944450.0, step = 16501 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 447.751\n",
            "INFO:tensorflow:loss = 552374000.0, step = 16601 (0.222 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.563\n",
            "INFO:tensorflow:loss = 990573300.0, step = 16701 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.877\n",
            "INFO:tensorflow:loss = 560928260.0, step = 16801 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.856\n",
            "INFO:tensorflow:loss = 418443620.0, step = 16901 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.06\n",
            "INFO:tensorflow:loss = 1331810000.0, step = 17001 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.664\n",
            "INFO:tensorflow:loss = 554077200.0, step = 17101 (0.373 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.391\n",
            "INFO:tensorflow:loss = 698391200.0, step = 17201 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.268\n",
            "INFO:tensorflow:loss = 466979200.0, step = 17301 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.469\n",
            "INFO:tensorflow:loss = 371162180.0, step = 17401 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.035\n",
            "INFO:tensorflow:loss = 1162605200.0, step = 17501 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.002\n",
            "INFO:tensorflow:loss = 317364400.0, step = 17601 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.593\n",
            "INFO:tensorflow:loss = 811597300.0, step = 17701 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.164\n",
            "INFO:tensorflow:loss = 862342660.0, step = 17801 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.839\n",
            "INFO:tensorflow:loss = 492122300.0, step = 17901 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.335\n",
            "INFO:tensorflow:loss = 564071100.0, step = 18001 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.6\n",
            "INFO:tensorflow:loss = 452448500.0, step = 18101 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.246\n",
            "INFO:tensorflow:loss = 315580220.0, step = 18201 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.81\n",
            "INFO:tensorflow:loss = 849397500.0, step = 18301 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.04\n",
            "INFO:tensorflow:loss = 483971170.0, step = 18401 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 334.061\n",
            "INFO:tensorflow:loss = 1132963700.0, step = 18501 (0.297 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.362\n",
            "INFO:tensorflow:loss = 373745760.0, step = 18601 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.427\n",
            "INFO:tensorflow:loss = 243313470.0, step = 18701 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.837\n",
            "INFO:tensorflow:loss = 618517500.0, step = 18801 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 434.798\n",
            "INFO:tensorflow:loss = 917692500.0, step = 18901 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 430.242\n",
            "INFO:tensorflow:loss = 181449570.0, step = 19001 (0.223 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.039\n",
            "INFO:tensorflow:loss = 257149730.0, step = 19101 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.261\n",
            "INFO:tensorflow:loss = 685045500.0, step = 19201 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.872\n",
            "INFO:tensorflow:loss = 908792600.0, step = 19301 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.885\n",
            "INFO:tensorflow:loss = 801200060.0, step = 19401 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.107\n",
            "INFO:tensorflow:loss = 561600450.0, step = 19501 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.975\n",
            "INFO:tensorflow:loss = 186327040.0, step = 19601 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.277\n",
            "INFO:tensorflow:loss = 280810400.0, step = 19701 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.067\n",
            "INFO:tensorflow:loss = 513919580.0, step = 19801 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.966\n",
            "INFO:tensorflow:loss = 324093280.0, step = 19901 (0.282 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20000...\n",
            "INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20000...\n",
            "INFO:tensorflow:Loss for final step: 249979360.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:01:40\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-20000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.23348s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:01:40\n",
            "INFO:tensorflow:Saving dict for global step 20000: average_loss = 14880201.0, global_step = 20000, label/mean = 12949.43, loss = 435777300.0, prediction/mean = 13149.065\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20000: /tmp/tmpb3dvddk8/model.ckpt-20000\n",
            "scores {'average_loss': 14880201.0, 'label/mean': 12949.43, 'loss': 435777300.0, 'prediction/mean': 13149.065, 'global_step': 20000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-20000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20000...\n",
            "INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20000...\n",
            "INFO:tensorflow:loss = 221178800.0, step = 20001\n",
            "INFO:tensorflow:global_step/sec: 361.613\n",
            "INFO:tensorflow:loss = 691884300.0, step = 20101 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.699\n",
            "INFO:tensorflow:loss = 232375500.0, step = 20201 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 428.368\n",
            "INFO:tensorflow:loss = 540036200.0, step = 20301 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.247\n",
            "INFO:tensorflow:loss = 837060540.0, step = 20401 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.228\n",
            "INFO:tensorflow:loss = 470645200.0, step = 20501 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.075\n",
            "INFO:tensorflow:loss = 433850500.0, step = 20601 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.316\n",
            "INFO:tensorflow:loss = 411280960.0, step = 20701 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.043\n",
            "INFO:tensorflow:loss = 484626940.0, step = 20801 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 336.715\n",
            "INFO:tensorflow:loss = 809704200.0, step = 20901 (0.299 sec)\n",
            "INFO:tensorflow:global_step/sec: 344.49\n",
            "INFO:tensorflow:loss = 209797400.0, step = 21001 (0.290 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.886\n",
            "INFO:tensorflow:loss = 177766190.0, step = 21101 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.146\n",
            "INFO:tensorflow:loss = 857575700.0, step = 21201 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.955\n",
            "INFO:tensorflow:loss = 350027970.0, step = 21301 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.731\n",
            "INFO:tensorflow:loss = 408921800.0, step = 21401 (0.368 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.894\n",
            "INFO:tensorflow:loss = 245318030.0, step = 21501 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 315.566\n",
            "INFO:tensorflow:loss = 1006905860.0, step = 21601 (0.309 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.389\n",
            "INFO:tensorflow:loss = 494683840.0, step = 21701 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.575\n",
            "INFO:tensorflow:loss = 475091780.0, step = 21801 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.931\n",
            "INFO:tensorflow:loss = 354542720.0, step = 21901 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.997\n",
            "INFO:tensorflow:loss = 728093250.0, step = 22001 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.37\n",
            "INFO:tensorflow:loss = 207139470.0, step = 22101 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.222\n",
            "INFO:tensorflow:loss = 990129000.0, step = 22201 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.592\n",
            "INFO:tensorflow:loss = 680557500.0, step = 22301 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.388\n",
            "INFO:tensorflow:loss = 1249038300.0, step = 22401 (0.292 sec)\n",
            "INFO:tensorflow:global_step/sec: 334.175\n",
            "INFO:tensorflow:loss = 1160891100.0, step = 22501 (0.301 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.189\n",
            "INFO:tensorflow:loss = 202699140.0, step = 22601 (0.290 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.02\n",
            "INFO:tensorflow:loss = 407622140.0, step = 22701 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 441.191\n",
            "INFO:tensorflow:loss = 260400140.0, step = 22801 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.417\n",
            "INFO:tensorflow:loss = 484306700.0, step = 22901 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.633\n",
            "INFO:tensorflow:loss = 526439800.0, step = 23001 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.578\n",
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            "INFO:tensorflow:global_step/sec: 403.421\n",
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            "INFO:tensorflow:global_step/sec: 385.942\n",
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            "INFO:tensorflow:global_step/sec: 399.636\n",
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            "INFO:tensorflow:global_step/sec: 378.371\n",
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            "INFO:tensorflow:global_step/sec: 373.257\n",
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            "INFO:tensorflow:global_step/sec: 406.841\n",
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            "INFO:tensorflow:global_step/sec: 406.024\n",
            "INFO:tensorflow:loss = 292871040.0, step = 25101 (0.248 sec)\n",
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            "INFO:tensorflow:global_step/sec: 363.505\n",
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            "INFO:tensorflow:global_step/sec: 374.951\n",
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            "INFO:tensorflow:global_step/sec: 353.421\n",
            "INFO:tensorflow:loss = 674123700.0, step = 25601 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.76\n",
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            "INFO:tensorflow:global_step/sec: 436.999\n",
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            "INFO:tensorflow:global_step/sec: 391.884\n",
            "INFO:tensorflow:loss = 718505400.0, step = 25901 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.571\n",
            "INFO:tensorflow:loss = 783574900.0, step = 26001 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.204\n",
            "INFO:tensorflow:loss = 615910800.0, step = 26101 (0.289 sec)\n",
            "INFO:tensorflow:global_step/sec: 336.11\n",
            "INFO:tensorflow:loss = 605127740.0, step = 26201 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.345\n",
            "INFO:tensorflow:loss = 612488000.0, step = 26301 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.951\n",
            "INFO:tensorflow:loss = 239806960.0, step = 26401 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.428\n",
            "INFO:tensorflow:loss = 537366460.0, step = 26501 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.2\n",
            "INFO:tensorflow:loss = 513802530.0, step = 26601 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.472\n",
            "INFO:tensorflow:loss = 366561500.0, step = 26701 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 330.682\n",
            "INFO:tensorflow:loss = 177181780.0, step = 26801 (0.295 sec)\n",
            "INFO:tensorflow:global_step/sec: 315.178\n",
            "INFO:tensorflow:loss = 1010330600.0, step = 26901 (0.324 sec)\n",
            "INFO:tensorflow:global_step/sec: 319.152\n",
            "INFO:tensorflow:loss = 211297040.0, step = 27001 (0.307 sec)\n",
            "INFO:tensorflow:global_step/sec: 336.829\n",
            "INFO:tensorflow:loss = 126668504.0, step = 27101 (0.301 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.232\n",
            "INFO:tensorflow:loss = 372961800.0, step = 27201 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.078\n",
            "INFO:tensorflow:loss = 526117200.0, step = 27301 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 446.436\n",
            "INFO:tensorflow:loss = 484115680.0, step = 27401 (0.222 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.216\n",
            "INFO:tensorflow:loss = 376511870.0, step = 27501 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 444.433\n",
            "INFO:tensorflow:loss = 328581760.0, step = 27601 (0.223 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.793\n",
            "INFO:tensorflow:loss = 381799940.0, step = 27701 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.989\n",
            "INFO:tensorflow:loss = 1300065500.0, step = 27801 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.719\n",
            "INFO:tensorflow:loss = 299488830.0, step = 27901 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.739\n",
            "INFO:tensorflow:loss = 424110340.0, step = 28001 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.032\n",
            "INFO:tensorflow:loss = 422436960.0, step = 28101 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 441.717\n",
            "INFO:tensorflow:loss = 707635260.0, step = 28201 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.909\n",
            "INFO:tensorflow:loss = 603407300.0, step = 28301 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.157\n",
            "INFO:tensorflow:loss = 120900750.0, step = 28401 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.131\n",
            "INFO:tensorflow:loss = 263728100.0, step = 28501 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.878\n",
            "INFO:tensorflow:loss = 469532130.0, step = 28601 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.218\n",
            "INFO:tensorflow:loss = 300806720.0, step = 28701 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.203\n",
            "INFO:tensorflow:loss = 587910300.0, step = 28801 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.052\n",
            "INFO:tensorflow:loss = 226780420.0, step = 28901 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.495\n",
            "INFO:tensorflow:loss = 269325920.0, step = 29001 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.666\n",
            "INFO:tensorflow:loss = 504651330.0, step = 29101 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.573\n",
            "INFO:tensorflow:loss = 549866000.0, step = 29201 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.127\n",
            "INFO:tensorflow:loss = 284279260.0, step = 29301 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.572\n",
            "INFO:tensorflow:loss = 599010300.0, step = 29401 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.644\n",
            "INFO:tensorflow:loss = 116828390.0, step = 29501 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.98\n",
            "INFO:tensorflow:loss = 665318000.0, step = 29601 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.931\n",
            "INFO:tensorflow:loss = 186350820.0, step = 29701 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.572\n",
            "INFO:tensorflow:loss = 187064380.0, step = 29801 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.555\n",
            "INFO:tensorflow:loss = 484934270.0, step = 29901 (0.258 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 30000...\n",
            "INFO:tensorflow:Saving checkpoints for 30000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 30000...\n",
            "INFO:tensorflow:Loss for final step: 462276060.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:02:08\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-30000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.24441s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:02:08\n",
            "INFO:tensorflow:Saving dict for global step 30000: average_loss = 13289027.0, global_step = 30000, label/mean = 12949.43, loss = 389178660.0, prediction/mean = 13096.3\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 30000: /tmp/tmpb3dvddk8/model.ckpt-30000\n",
            "scores {'average_loss': 13289027.0, 'label/mean': 12949.43, 'loss': 389178660.0, 'prediction/mean': 13096.3, 'global_step': 30000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-30000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 30000...\n",
            "INFO:tensorflow:Saving checkpoints for 30000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 30000...\n",
            "INFO:tensorflow:loss = 176044580.0, step = 30001\n",
            "INFO:tensorflow:global_step/sec: 399.552\n",
            "INFO:tensorflow:loss = 668311900.0, step = 30101 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.466\n",
            "INFO:tensorflow:loss = 421357500.0, step = 30201 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.42\n",
            "INFO:tensorflow:loss = 287198900.0, step = 30301 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.744\n",
            "INFO:tensorflow:loss = 628921100.0, step = 30401 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.018\n",
            "INFO:tensorflow:loss = 218037840.0, step = 30501 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.007\n",
            "INFO:tensorflow:loss = 851282900.0, step = 30601 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.747\n",
            "INFO:tensorflow:loss = 262494820.0, step = 30701 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.496\n",
            "INFO:tensorflow:loss = 374659420.0, step = 30801 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.089\n",
            "INFO:tensorflow:loss = 469514560.0, step = 30901 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.798\n",
            "INFO:tensorflow:loss = 554998000.0, step = 31001 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 437.498\n",
            "INFO:tensorflow:loss = 252933140.0, step = 31101 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.447\n",
            "INFO:tensorflow:loss = 285236540.0, step = 31201 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.338\n",
            "INFO:tensorflow:loss = 572336900.0, step = 31301 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.207\n",
            "INFO:tensorflow:loss = 698909440.0, step = 31401 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.038\n",
            "INFO:tensorflow:loss = 475277660.0, step = 31501 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 351.863\n",
            "INFO:tensorflow:loss = 224666960.0, step = 31601 (0.290 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.444\n",
            "INFO:tensorflow:loss = 280846980.0, step = 31701 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.278\n",
            "INFO:tensorflow:loss = 750577540.0, step = 31801 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.597\n",
            "INFO:tensorflow:loss = 208614740.0, step = 31901 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.244\n",
            "INFO:tensorflow:loss = 237225570.0, step = 32001 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.015\n",
            "INFO:tensorflow:loss = 190783060.0, step = 32101 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.207\n",
            "INFO:tensorflow:loss = 376242370.0, step = 32201 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.126\n",
            "INFO:tensorflow:loss = 219148180.0, step = 32301 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.043\n",
            "INFO:tensorflow:loss = 255254770.0, step = 32401 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.324\n",
            "INFO:tensorflow:loss = 1083505400.0, step = 32501 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 346.308\n",
            "INFO:tensorflow:loss = 126753000.0, step = 32601 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.018\n",
            "INFO:tensorflow:loss = 736389250.0, step = 32701 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.57\n",
            "INFO:tensorflow:loss = 418051680.0, step = 32801 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.391\n",
            "INFO:tensorflow:loss = 301477060.0, step = 32901 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.552\n",
            "INFO:tensorflow:loss = 571850100.0, step = 33001 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.234\n",
            "INFO:tensorflow:loss = 941041200.0, step = 33101 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.028\n",
            "INFO:tensorflow:loss = 354221300.0, step = 33201 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.087\n",
            "INFO:tensorflow:loss = 367819500.0, step = 33301 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.904\n",
            "INFO:tensorflow:loss = 549716500.0, step = 33401 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.475\n",
            "INFO:tensorflow:loss = 646971140.0, step = 33501 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.731\n",
            "INFO:tensorflow:loss = 210766900.0, step = 33601 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.455\n",
            "INFO:tensorflow:loss = 396299400.0, step = 33701 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.21\n",
            "INFO:tensorflow:loss = 101458100.0, step = 33801 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.481\n",
            "INFO:tensorflow:loss = 673352500.0, step = 33901 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.586\n",
            "INFO:tensorflow:loss = 153981760.0, step = 34001 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.986\n",
            "INFO:tensorflow:loss = 556243200.0, step = 34101 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.53\n",
            "INFO:tensorflow:loss = 276235620.0, step = 34201 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.533\n",
            "INFO:tensorflow:loss = 920949950.0, step = 34301 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.238\n",
            "INFO:tensorflow:loss = 290688320.0, step = 34401 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 437.018\n",
            "INFO:tensorflow:loss = 436228450.0, step = 34501 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.478\n",
            "INFO:tensorflow:loss = 313483260.0, step = 34601 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.176\n",
            "INFO:tensorflow:loss = 794976800.0, step = 34701 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.718\n",
            "INFO:tensorflow:loss = 615278900.0, step = 34801 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.842\n",
            "INFO:tensorflow:loss = 1141176200.0, step = 34901 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.052\n",
            "INFO:tensorflow:loss = 393876600.0, step = 35001 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.572\n",
            "INFO:tensorflow:loss = 297273440.0, step = 35101 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.099\n",
            "INFO:tensorflow:loss = 313009760.0, step = 35201 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.345\n",
            "INFO:tensorflow:loss = 204496000.0, step = 35301 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.683\n",
            "INFO:tensorflow:loss = 541281150.0, step = 35401 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.942\n",
            "INFO:tensorflow:loss = 417792000.0, step = 35501 (0.219 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.6\n",
            "INFO:tensorflow:loss = 563433300.0, step = 35601 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.4\n",
            "INFO:tensorflow:loss = 389018340.0, step = 35701 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.518\n",
            "INFO:tensorflow:loss = 662074050.0, step = 35801 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 340.944\n",
            "INFO:tensorflow:loss = 524506880.0, step = 35901 (0.292 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.721\n",
            "INFO:tensorflow:loss = 286613340.0, step = 36001 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 427.989\n",
            "INFO:tensorflow:loss = 776670400.0, step = 36101 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.582\n",
            "INFO:tensorflow:loss = 466312830.0, step = 36201 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.406\n",
            "INFO:tensorflow:loss = 313472800.0, step = 36301 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.41\n",
            "INFO:tensorflow:loss = 342145540.0, step = 36401 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 430.558\n",
            "INFO:tensorflow:loss = 490582460.0, step = 36501 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.86\n",
            "INFO:tensorflow:loss = 372159740.0, step = 36601 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.104\n",
            "INFO:tensorflow:loss = 400302560.0, step = 36701 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.975\n",
            "INFO:tensorflow:loss = 350328060.0, step = 36801 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.68\n",
            "INFO:tensorflow:loss = 397415520.0, step = 36901 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.328\n",
            "INFO:tensorflow:loss = 513116380.0, step = 37001 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.948\n",
            "INFO:tensorflow:loss = 602993300.0, step = 37101 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 432.596\n",
            "INFO:tensorflow:loss = 319586240.0, step = 37201 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 337.107\n",
            "INFO:tensorflow:loss = 659019140.0, step = 37301 (0.302 sec)\n",
            "INFO:tensorflow:global_step/sec: 332.473\n",
            "INFO:tensorflow:loss = 331962340.0, step = 37401 (0.297 sec)\n",
            "INFO:tensorflow:global_step/sec: 346.991\n",
            "INFO:tensorflow:loss = 322709150.0, step = 37501 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.336\n",
            "INFO:tensorflow:loss = 1005981000.0, step = 37601 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.534\n",
            "INFO:tensorflow:loss = 574085100.0, step = 37701 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 440.782\n",
            "INFO:tensorflow:loss = 829714200.0, step = 37801 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.545\n",
            "INFO:tensorflow:loss = 474500670.0, step = 37901 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.901\n",
            "INFO:tensorflow:loss = 225174220.0, step = 38001 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.547\n",
            "INFO:tensorflow:loss = 217502770.0, step = 38101 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.745\n",
            "INFO:tensorflow:loss = 292423700.0, step = 38201 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 350.052\n",
            "INFO:tensorflow:loss = 170841570.0, step = 38301 (0.293 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.71\n",
            "INFO:tensorflow:loss = 958170940.0, step = 38401 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.173\n",
            "INFO:tensorflow:loss = 763211200.0, step = 38501 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.262\n",
            "INFO:tensorflow:loss = 427965470.0, step = 38601 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.451\n",
            "INFO:tensorflow:loss = 174690300.0, step = 38701 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.961\n",
            "INFO:tensorflow:loss = 241635680.0, step = 38801 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.796\n",
            "INFO:tensorflow:loss = 312950080.0, step = 38901 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.581\n",
            "INFO:tensorflow:loss = 681350600.0, step = 39001 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.276\n",
            "INFO:tensorflow:loss = 683483650.0, step = 39101 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.469\n",
            "INFO:tensorflow:loss = 485655700.0, step = 39201 (0.293 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.876\n",
            "INFO:tensorflow:loss = 224381630.0, step = 39301 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.196\n",
            "INFO:tensorflow:loss = 289104400.0, step = 39401 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.643\n",
            "INFO:tensorflow:loss = 206894240.0, step = 39501 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.522\n",
            "INFO:tensorflow:loss = 267667460.0, step = 39601 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 330.27\n",
            "INFO:tensorflow:loss = 829473800.0, step = 39701 (0.305 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.094\n",
            "INFO:tensorflow:loss = 229535790.0, step = 39801 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.78\n",
            "INFO:tensorflow:loss = 882717250.0, step = 39901 (0.238 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 40000...\n",
            "INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 40000...\n",
            "INFO:tensorflow:Loss for final step: 212700220.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:02:35\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-40000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.25455s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:02:35\n",
            "INFO:tensorflow:Saving dict for global step 40000: average_loss = 12261697.0, global_step = 40000, label/mean = 12949.43, loss = 359092540.0, prediction/mean = 12735.373\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 40000: /tmp/tmpb3dvddk8/model.ckpt-40000\n",
            "scores {'average_loss': 12261697.0, 'label/mean': 12949.43, 'loss': 359092540.0, 'prediction/mean': 12735.373, 'global_step': 40000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-40000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 40000...\n",
            "INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 40000...\n",
            "INFO:tensorflow:loss = 303237280.0, step = 40001\n",
            "INFO:tensorflow:global_step/sec: 391.447\n",
            "INFO:tensorflow:loss = 361042700.0, step = 40101 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.718\n",
            "INFO:tensorflow:loss = 363864830.0, step = 40201 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.641\n",
            "INFO:tensorflow:loss = 494889150.0, step = 40301 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.748\n",
            "INFO:tensorflow:loss = 443334750.0, step = 40401 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 321.561\n",
            "INFO:tensorflow:loss = 187868880.0, step = 40501 (0.308 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.152\n",
            "INFO:tensorflow:loss = 181450940.0, step = 40601 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.61\n",
            "INFO:tensorflow:loss = 402994000.0, step = 40701 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.464\n",
            "INFO:tensorflow:loss = 280862370.0, step = 40801 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 349.866\n",
            "INFO:tensorflow:loss = 564181600.0, step = 40901 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.543\n",
            "INFO:tensorflow:loss = 534532350.0, step = 41001 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.661\n",
            "INFO:tensorflow:loss = 526878900.0, step = 41101 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 345.652\n",
            "INFO:tensorflow:loss = 597002700.0, step = 41201 (0.304 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.463\n",
            "INFO:tensorflow:loss = 356709120.0, step = 41301 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.129\n",
            "INFO:tensorflow:loss = 614987200.0, step = 41401 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.104\n",
            "INFO:tensorflow:loss = 414496300.0, step = 41501 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.639\n",
            "INFO:tensorflow:loss = 296746300.0, step = 41601 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.609\n",
            "INFO:tensorflow:loss = 547318850.0, step = 41701 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.521\n",
            "INFO:tensorflow:loss = 386777600.0, step = 41801 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.11\n",
            "INFO:tensorflow:loss = 507674750.0, step = 41901 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.42\n",
            "INFO:tensorflow:loss = 736860350.0, step = 42001 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.604\n",
            "INFO:tensorflow:loss = 545206200.0, step = 42101 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.705\n",
            "INFO:tensorflow:loss = 278015000.0, step = 42201 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.724\n",
            "INFO:tensorflow:loss = 461965250.0, step = 42301 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.116\n",
            "INFO:tensorflow:loss = 567908200.0, step = 42401 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.385\n",
            "INFO:tensorflow:loss = 268471550.0, step = 42501 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.713\n",
            "INFO:tensorflow:loss = 323748400.0, step = 42601 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.866\n",
            "INFO:tensorflow:loss = 214577000.0, step = 42701 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.406\n",
            "INFO:tensorflow:loss = 338757280.0, step = 42801 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.211\n",
            "INFO:tensorflow:loss = 950698900.0, step = 42901 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.681\n",
            "INFO:tensorflow:loss = 464145100.0, step = 43001 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.486\n",
            "INFO:tensorflow:loss = 409523680.0, step = 43101 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.328\n",
            "INFO:tensorflow:loss = 334211000.0, step = 43201 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.203\n",
            "INFO:tensorflow:loss = 697294800.0, step = 43301 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.689\n",
            "INFO:tensorflow:loss = 411943940.0, step = 43401 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 339.931\n",
            "INFO:tensorflow:loss = 345457250.0, step = 43501 (0.298 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.859\n",
            "INFO:tensorflow:loss = 538910140.0, step = 43601 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.116\n",
            "INFO:tensorflow:loss = 554095800.0, step = 43701 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.295\n",
            "INFO:tensorflow:loss = 483696860.0, step = 43801 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.827\n",
            "INFO:tensorflow:loss = 240177820.0, step = 43901 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.611\n",
            "INFO:tensorflow:loss = 185089710.0, step = 44001 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.221\n",
            "INFO:tensorflow:loss = 697797600.0, step = 44101 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.885\n",
            "INFO:tensorflow:loss = 541122300.0, step = 44201 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 331.397\n",
            "INFO:tensorflow:loss = 701114000.0, step = 44301 (0.300 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.035\n",
            "INFO:tensorflow:loss = 450291200.0, step = 44401 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.628\n",
            "INFO:tensorflow:loss = 730081200.0, step = 44501 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 432.414\n",
            "INFO:tensorflow:loss = 528610000.0, step = 44601 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.296\n",
            "INFO:tensorflow:loss = 283594020.0, step = 44701 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 444.665\n",
            "INFO:tensorflow:loss = 132556610.0, step = 44801 (0.213 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.726\n",
            "INFO:tensorflow:loss = 335914000.0, step = 44901 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 449.301\n",
            "INFO:tensorflow:loss = 133520650.0, step = 45001 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.159\n",
            "INFO:tensorflow:loss = 272523300.0, step = 45101 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.456\n",
            "INFO:tensorflow:loss = 486681730.0, step = 45201 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.447\n",
            "INFO:tensorflow:loss = 435897600.0, step = 45301 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.726\n",
            "INFO:tensorflow:loss = 139251310.0, step = 45401 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.643\n",
            "INFO:tensorflow:loss = 338171260.0, step = 45501 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.721\n",
            "INFO:tensorflow:loss = 368869440.0, step = 45601 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.224\n",
            "INFO:tensorflow:loss = 273122270.0, step = 45701 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.621\n",
            "INFO:tensorflow:loss = 200472350.0, step = 45801 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.082\n",
            "INFO:tensorflow:loss = 139571790.0, step = 45901 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 448.051\n",
            "INFO:tensorflow:loss = 462123040.0, step = 46001 (0.220 sec)\n",
            "INFO:tensorflow:global_step/sec: 452.046\n",
            "INFO:tensorflow:loss = 523397950.0, step = 46101 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.699\n",
            "INFO:tensorflow:loss = 366031360.0, step = 46201 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.892\n",
            "INFO:tensorflow:loss = 695157600.0, step = 46301 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.337\n",
            "INFO:tensorflow:loss = 962020800.0, step = 46401 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.877\n",
            "INFO:tensorflow:loss = 219689460.0, step = 46501 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.431\n",
            "INFO:tensorflow:loss = 261676980.0, step = 46601 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.85\n",
            "INFO:tensorflow:loss = 244910270.0, step = 46701 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.376\n",
            "INFO:tensorflow:loss = 557722240.0, step = 46801 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.782\n",
            "INFO:tensorflow:loss = 337242900.0, step = 46901 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.677\n",
            "INFO:tensorflow:loss = 374050530.0, step = 47001 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.757\n",
            "INFO:tensorflow:loss = 387367900.0, step = 47101 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 437.185\n",
            "INFO:tensorflow:loss = 439648580.0, step = 47201 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.242\n",
            "INFO:tensorflow:loss = 379845900.0, step = 47301 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 349.074\n",
            "INFO:tensorflow:loss = 330446850.0, step = 47401 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.982\n",
            "INFO:tensorflow:loss = 330275520.0, step = 47501 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.759\n",
            "INFO:tensorflow:loss = 485365380.0, step = 47601 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.552\n",
            "INFO:tensorflow:loss = 307856800.0, step = 47701 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.98\n",
            "INFO:tensorflow:loss = 272516580.0, step = 47801 (0.298 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.644\n",
            "INFO:tensorflow:loss = 328975780.0, step = 47901 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.262\n",
            "INFO:tensorflow:loss = 687654000.0, step = 48001 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.806\n",
            "INFO:tensorflow:loss = 1053256500.0, step = 48101 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.573\n",
            "INFO:tensorflow:loss = 487557100.0, step = 48201 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.603\n",
            "INFO:tensorflow:loss = 117631750.0, step = 48301 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.17\n",
            "INFO:tensorflow:loss = 384131300.0, step = 48401 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.992\n",
            "INFO:tensorflow:loss = 246135920.0, step = 48501 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 339.102\n",
            "INFO:tensorflow:loss = 507509570.0, step = 48601 (0.297 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.141\n",
            "INFO:tensorflow:loss = 308351360.0, step = 48701 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.348\n",
            "INFO:tensorflow:loss = 415282720.0, step = 48801 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.709\n",
            "INFO:tensorflow:loss = 301632260.0, step = 48901 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.616\n",
            "INFO:tensorflow:loss = 676751740.0, step = 49001 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.538\n",
            "INFO:tensorflow:loss = 343910720.0, step = 49101 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 440.264\n",
            "INFO:tensorflow:loss = 825191360.0, step = 49201 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.388\n",
            "INFO:tensorflow:loss = 364956160.0, step = 49301 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.596\n",
            "INFO:tensorflow:loss = 351441540.0, step = 49401 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.225\n",
            "INFO:tensorflow:loss = 184358540.0, step = 49501 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.018\n",
            "INFO:tensorflow:loss = 660427800.0, step = 49601 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.866\n",
            "INFO:tensorflow:loss = 321200060.0, step = 49701 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.727\n",
            "INFO:tensorflow:loss = 527927400.0, step = 49801 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.807\n",
            "INFO:tensorflow:loss = 219912320.0, step = 49901 (0.256 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50000...\n",
            "INFO:tensorflow:Saving checkpoints for 50000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50000...\n",
            "INFO:tensorflow:Loss for final step: 245828180.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:03:03\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-50000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.25845s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:03:03\n",
            "INFO:tensorflow:Saving dict for global step 50000: average_loss = 11499034.0, global_step = 50000, label/mean = 12949.43, loss = 336757440.0, prediction/mean = 13265.643\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 50000: /tmp/tmpb3dvddk8/model.ckpt-50000\n",
            "scores {'average_loss': 11499034.0, 'label/mean': 12949.43, 'loss': 336757440.0, 'prediction/mean': 13265.643, 'global_step': 50000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-50000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50000...\n",
            "INFO:tensorflow:Saving checkpoints for 50000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50000...\n",
            "INFO:tensorflow:loss = 305280420.0, step = 50001\n",
            "INFO:tensorflow:global_step/sec: 365.346\n",
            "INFO:tensorflow:loss = 277270800.0, step = 50101 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.592\n",
            "INFO:tensorflow:loss = 441337570.0, step = 50201 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.527\n",
            "INFO:tensorflow:loss = 437745660.0, step = 50301 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.331\n",
            "INFO:tensorflow:loss = 599247100.0, step = 50401 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.056\n",
            "INFO:tensorflow:loss = 324807400.0, step = 50501 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.053\n",
            "INFO:tensorflow:loss = 203876670.0, step = 50601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.659\n",
            "INFO:tensorflow:loss = 216847170.0, step = 50701 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 344.813\n",
            "INFO:tensorflow:loss = 539162940.0, step = 50801 (0.290 sec)\n",
            "INFO:tensorflow:global_step/sec: 344.196\n",
            "INFO:tensorflow:loss = 596439400.0, step = 50901 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.839\n",
            "INFO:tensorflow:loss = 522411780.0, step = 51001 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.471\n",
            "INFO:tensorflow:loss = 583264300.0, step = 51101 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.992\n",
            "INFO:tensorflow:loss = 435757500.0, step = 51201 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.37\n",
            "INFO:tensorflow:loss = 569009000.0, step = 51301 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.451\n",
            "INFO:tensorflow:loss = 778593660.0, step = 51401 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.072\n",
            "INFO:tensorflow:loss = 493549500.0, step = 51501 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.443\n",
            "INFO:tensorflow:loss = 466723680.0, step = 51601 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.724\n",
            "INFO:tensorflow:loss = 193280530.0, step = 51701 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.691\n",
            "INFO:tensorflow:loss = 456049920.0, step = 51801 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.897\n",
            "INFO:tensorflow:loss = 455139650.0, step = 51901 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.916\n",
            "INFO:tensorflow:loss = 584998700.0, step = 52001 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.374\n",
            "INFO:tensorflow:loss = 148382370.0, step = 52101 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.891\n",
            "INFO:tensorflow:loss = 147617330.0, step = 52201 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.942\n",
            "INFO:tensorflow:loss = 563448000.0, step = 52301 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.791\n",
            "INFO:tensorflow:loss = 126517160.0, step = 52401 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.057\n",
            "INFO:tensorflow:loss = 276982430.0, step = 52501 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.196\n",
            "INFO:tensorflow:loss = 553495400.0, step = 52601 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.566\n",
            "INFO:tensorflow:loss = 624239800.0, step = 52701 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.929\n",
            "INFO:tensorflow:loss = 218404940.0, step = 52801 (0.382 sec)\n",
            "INFO:tensorflow:global_step/sec: 431.861\n",
            "INFO:tensorflow:loss = 403994020.0, step = 52901 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.005\n",
            "INFO:tensorflow:loss = 281994370.0, step = 53001 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.677\n",
            "INFO:tensorflow:loss = 206238340.0, step = 53101 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.789\n",
            "INFO:tensorflow:loss = 348683200.0, step = 53201 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.644\n",
            "INFO:tensorflow:loss = 148023180.0, step = 53301 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.583\n",
            "INFO:tensorflow:loss = 505262530.0, step = 53401 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.871\n",
            "INFO:tensorflow:loss = 240189660.0, step = 53501 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.703\n",
            "INFO:tensorflow:loss = 306284700.0, step = 53601 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.251\n",
            "INFO:tensorflow:loss = 332630820.0, step = 53701 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.994\n",
            "INFO:tensorflow:loss = 173877540.0, step = 53801 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.166\n",
            "INFO:tensorflow:loss = 149153260.0, step = 53901 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.459\n",
            "INFO:tensorflow:loss = 317946400.0, step = 54001 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.925\n",
            "INFO:tensorflow:loss = 158786260.0, step = 54101 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.845\n",
            "INFO:tensorflow:loss = 421503170.0, step = 54201 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.253\n",
            "INFO:tensorflow:loss = 251185020.0, step = 54301 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.953\n",
            "INFO:tensorflow:loss = 429067800.0, step = 54401 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.975\n",
            "INFO:tensorflow:loss = 416337440.0, step = 54501 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.553\n",
            "INFO:tensorflow:loss = 286056450.0, step = 54601 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.489\n",
            "INFO:tensorflow:loss = 418265540.0, step = 54701 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.918\n",
            "INFO:tensorflow:loss = 232395710.0, step = 54801 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.606\n",
            "INFO:tensorflow:loss = 206034300.0, step = 54901 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.724\n",
            "INFO:tensorflow:loss = 439629150.0, step = 55001 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.788\n",
            "INFO:tensorflow:loss = 639113200.0, step = 55101 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.409\n",
            "INFO:tensorflow:loss = 592210600.0, step = 55201 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.387\n",
            "INFO:tensorflow:loss = 154060800.0, step = 55301 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.604\n",
            "INFO:tensorflow:loss = 445251040.0, step = 55401 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.506\n",
            "INFO:tensorflow:loss = 572258000.0, step = 55501 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 350.254\n",
            "INFO:tensorflow:loss = 389248130.0, step = 55601 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.028\n",
            "INFO:tensorflow:loss = 476649000.0, step = 55701 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.394\n",
            "INFO:tensorflow:loss = 395295680.0, step = 55801 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.771\n",
            "INFO:tensorflow:loss = 320927500.0, step = 55901 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.37\n",
            "INFO:tensorflow:loss = 208068020.0, step = 56001 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.33\n",
            "INFO:tensorflow:loss = 522276800.0, step = 56101 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.471\n",
            "INFO:tensorflow:loss = 720226700.0, step = 56201 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.457\n",
            "INFO:tensorflow:loss = 231971950.0, step = 56301 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.156\n",
            "INFO:tensorflow:loss = 204595840.0, step = 56401 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.141\n",
            "INFO:tensorflow:loss = 290373060.0, step = 56501 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.014\n",
            "INFO:tensorflow:loss = 266995840.0, step = 56601 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.846\n",
            "INFO:tensorflow:loss = 225773600.0, step = 56701 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.352\n",
            "INFO:tensorflow:loss = 209621980.0, step = 56801 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.925\n",
            "INFO:tensorflow:loss = 228940380.0, step = 56901 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.324\n",
            "INFO:tensorflow:loss = 245277860.0, step = 57001 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.589\n",
            "INFO:tensorflow:loss = 158885340.0, step = 57101 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.239\n",
            "INFO:tensorflow:loss = 178077870.0, step = 57201 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.409\n",
            "INFO:tensorflow:loss = 880657100.0, step = 57301 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.889\n",
            "INFO:tensorflow:loss = 536294050.0, step = 57401 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.364\n",
            "INFO:tensorflow:loss = 381441380.0, step = 57501 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.93\n",
            "INFO:tensorflow:loss = 520250620.0, step = 57601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.527\n",
            "INFO:tensorflow:loss = 744281660.0, step = 57701 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 427.175\n",
            "INFO:tensorflow:loss = 242828300.0, step = 57801 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.316\n",
            "INFO:tensorflow:loss = 601658100.0, step = 57901 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 339.048\n",
            "INFO:tensorflow:loss = 595348540.0, step = 58001 (0.295 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.267\n",
            "INFO:tensorflow:loss = 161967620.0, step = 58101 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.697\n",
            "INFO:tensorflow:loss = 192871780.0, step = 58201 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.576\n",
            "INFO:tensorflow:loss = 606812300.0, step = 58301 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.379\n",
            "INFO:tensorflow:loss = 160253100.0, step = 58401 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.053\n",
            "INFO:tensorflow:loss = 231409890.0, step = 58501 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.19\n",
            "INFO:tensorflow:loss = 509798900.0, step = 58601 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.314\n",
            "INFO:tensorflow:loss = 151609340.0, step = 58701 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.781\n",
            "INFO:tensorflow:loss = 336533250.0, step = 58801 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.417\n",
            "INFO:tensorflow:loss = 431493400.0, step = 58901 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.955\n",
            "INFO:tensorflow:loss = 455551070.0, step = 59001 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.119\n",
            "INFO:tensorflow:loss = 337751000.0, step = 59101 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.76\n",
            "INFO:tensorflow:loss = 550603400.0, step = 59201 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.069\n",
            "INFO:tensorflow:loss = 437247870.0, step = 59301 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 447.024\n",
            "INFO:tensorflow:loss = 242570050.0, step = 59401 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.459\n",
            "INFO:tensorflow:loss = 532492220.0, step = 59501 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 448.742\n",
            "INFO:tensorflow:loss = 504187870.0, step = 59601 (0.216 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.858\n",
            "INFO:tensorflow:loss = 280776580.0, step = 59701 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 434.679\n",
            "INFO:tensorflow:loss = 283421220.0, step = 59801 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.819\n",
            "INFO:tensorflow:loss = 323711600.0, step = 59901 (0.254 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 60000...\n",
            "INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 60000...\n",
            "INFO:tensorflow:Loss for final step: 480619330.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:03:30\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-60000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.22884s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:03:30\n",
            "INFO:tensorflow:Saving dict for global step 60000: average_loss = 10793000.0, global_step = 60000, label/mean = 12949.43, loss = 316080700.0, prediction/mean = 12878.843\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 60000: /tmp/tmpb3dvddk8/model.ckpt-60000\n",
            "scores {'average_loss': 10793000.0, 'label/mean': 12949.43, 'loss': 316080700.0, 'prediction/mean': 12878.843, 'global_step': 60000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-60000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 60000...\n",
            "INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 60000...\n",
            "INFO:tensorflow:loss = 478700220.0, step = 60001\n",
            "INFO:tensorflow:global_step/sec: 265.217\n",
            "INFO:tensorflow:loss = 498850940.0, step = 60101 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.835\n",
            "INFO:tensorflow:loss = 288678400.0, step = 60201 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.094\n",
            "INFO:tensorflow:loss = 277765630.0, step = 60301 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.727\n",
            "INFO:tensorflow:loss = 359717250.0, step = 60401 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.587\n",
            "INFO:tensorflow:loss = 391861760.0, step = 60501 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.017\n",
            "INFO:tensorflow:loss = 140178620.0, step = 60601 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 350.959\n",
            "INFO:tensorflow:loss = 429640900.0, step = 60701 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 435.381\n",
            "INFO:tensorflow:loss = 210147400.0, step = 60801 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.625\n",
            "INFO:tensorflow:loss = 715843840.0, step = 60901 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.207\n",
            "INFO:tensorflow:loss = 492276160.0, step = 61001 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.805\n",
            "INFO:tensorflow:loss = 744453800.0, step = 61101 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.236\n",
            "INFO:tensorflow:loss = 605875000.0, step = 61201 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.4\n",
            "INFO:tensorflow:loss = 421416800.0, step = 61301 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.891\n",
            "INFO:tensorflow:loss = 491939550.0, step = 61401 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 441.179\n",
            "INFO:tensorflow:loss = 192060370.0, step = 61501 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 427.414\n",
            "INFO:tensorflow:loss = 642565060.0, step = 61601 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.649\n",
            "INFO:tensorflow:loss = 506241020.0, step = 61701 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 317.943\n",
            "INFO:tensorflow:loss = 371110400.0, step = 61801 (0.316 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.918\n",
            "INFO:tensorflow:loss = 342262300.0, step = 61901 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.894\n",
            "INFO:tensorflow:loss = 198274540.0, step = 62001 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.576\n",
            "INFO:tensorflow:loss = 524864060.0, step = 62101 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.623\n",
            "INFO:tensorflow:loss = 768998850.0, step = 62201 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.332\n",
            "INFO:tensorflow:loss = 249368740.0, step = 62301 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 351.23\n",
            "INFO:tensorflow:loss = 170852720.0, step = 62401 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.145\n",
            "INFO:tensorflow:loss = 298314460.0, step = 62501 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.012\n",
            "INFO:tensorflow:loss = 242770860.0, step = 62601 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.916\n",
            "INFO:tensorflow:loss = 434961380.0, step = 62701 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.06\n",
            "INFO:tensorflow:loss = 328303460.0, step = 62801 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.856\n",
            "INFO:tensorflow:loss = 522708670.0, step = 62901 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 308.664\n",
            "INFO:tensorflow:loss = 419324670.0, step = 63001 (0.327 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.501\n",
            "INFO:tensorflow:loss = 150998530.0, step = 63101 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.25\n",
            "INFO:tensorflow:loss = 290113540.0, step = 63201 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.481\n",
            "INFO:tensorflow:loss = 353923800.0, step = 63301 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.079\n",
            "INFO:tensorflow:loss = 244810990.0, step = 63401 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.732\n",
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            "INFO:tensorflow:global_step/sec: 375.627\n",
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            "INFO:tensorflow:global_step/sec: 362.519\n",
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            "INFO:tensorflow:global_step/sec: 385.753\n",
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            "INFO:tensorflow:loss = 489888580.0, step = 67501 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.907\n",
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            "INFO:tensorflow:global_step/sec: 376.928\n",
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            "INFO:tensorflow:global_step/sec: 384.697\n",
            "INFO:tensorflow:loss = 943146700.0, step = 68001 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.615\n",
            "INFO:tensorflow:loss = 441093000.0, step = 68101 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.576\n",
            "INFO:tensorflow:loss = 136806820.0, step = 68201 (0.266 sec)\n",
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            "INFO:tensorflow:loss = 431436030.0, step = 68401 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.824\n",
            "INFO:tensorflow:loss = 447631600.0, step = 68501 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.58\n",
            "INFO:tensorflow:loss = 333237730.0, step = 68601 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.373\n",
            "INFO:tensorflow:loss = 504787460.0, step = 68701 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.448\n",
            "INFO:tensorflow:loss = 103630740.0, step = 68801 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.24\n",
            "INFO:tensorflow:loss = 258930940.0, step = 68901 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.782\n",
            "INFO:tensorflow:loss = 296078140.0, step = 69001 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.755\n",
            "INFO:tensorflow:loss = 207560080.0, step = 69101 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.069\n",
            "INFO:tensorflow:loss = 741699500.0, step = 69201 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.304\n",
            "INFO:tensorflow:loss = 470204380.0, step = 69301 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.52\n",
            "INFO:tensorflow:loss = 414110200.0, step = 69401 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 434.081\n",
            "INFO:tensorflow:loss = 356473180.0, step = 69501 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.676\n",
            "INFO:tensorflow:loss = 212649840.0, step = 69601 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.439\n",
            "INFO:tensorflow:loss = 469705380.0, step = 69701 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.692\n",
            "INFO:tensorflow:loss = 310789020.0, step = 69801 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.107\n",
            "INFO:tensorflow:loss = 207022610.0, step = 69901 (0.267 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 70000...\n",
            "INFO:tensorflow:Saving checkpoints for 70000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 70000...\n",
            "INFO:tensorflow:Loss for final step: 296534720.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:03:57\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-70000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.24802s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:03:57\n",
            "INFO:tensorflow:Saving dict for global step 70000: average_loss = 10159960.0, global_step = 70000, label/mean = 12949.43, loss = 297541700.0, prediction/mean = 12948.97\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 70000: /tmp/tmpb3dvddk8/model.ckpt-70000\n",
            "scores {'average_loss': 10159960.0, 'label/mean': 12949.43, 'loss': 297541700.0, 'prediction/mean': 12948.97, 'global_step': 70000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-70000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 70000...\n",
            "INFO:tensorflow:Saving checkpoints for 70000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 70000...\n",
            "INFO:tensorflow:loss = 485422300.0, step = 70001\n",
            "INFO:tensorflow:global_step/sec: 349.311\n",
            "INFO:tensorflow:loss = 114135280.0, step = 70101 (0.302 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.039\n",
            "INFO:tensorflow:loss = 794089700.0, step = 70201 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.002\n",
            "INFO:tensorflow:loss = 405432400.0, step = 70301 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.661\n",
            "INFO:tensorflow:loss = 357354300.0, step = 70401 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.346\n",
            "INFO:tensorflow:loss = 628935700.0, step = 70501 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.23\n",
            "INFO:tensorflow:loss = 362963000.0, step = 70601 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.249\n",
            "INFO:tensorflow:loss = 318128500.0, step = 70701 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.899\n",
            "INFO:tensorflow:loss = 211883180.0, step = 70801 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.173\n",
            "INFO:tensorflow:loss = 383593540.0, step = 70901 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 340.791\n",
            "INFO:tensorflow:loss = 544571600.0, step = 71001 (0.291 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.334\n",
            "INFO:tensorflow:loss = 227949580.0, step = 71101 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.095\n",
            "INFO:tensorflow:loss = 476841660.0, step = 71201 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.166\n",
            "INFO:tensorflow:loss = 563140740.0, step = 71301 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.036\n",
            "INFO:tensorflow:loss = 290522500.0, step = 71401 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.058\n",
            "INFO:tensorflow:loss = 668356860.0, step = 71501 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.667\n",
            "INFO:tensorflow:loss = 431385180.0, step = 71601 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.261\n",
            "INFO:tensorflow:loss = 302984260.0, step = 71701 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.039\n",
            "INFO:tensorflow:loss = 348918700.0, step = 71801 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.142\n",
            "INFO:tensorflow:loss = 260170240.0, step = 71901 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 350.953\n",
            "INFO:tensorflow:loss = 250284600.0, step = 72001 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.896\n",
            "INFO:tensorflow:loss = 321339900.0, step = 72101 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.055\n",
            "INFO:tensorflow:loss = 422828800.0, step = 72201 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 351.78\n",
            "INFO:tensorflow:loss = 206164160.0, step = 72301 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.649\n",
            "INFO:tensorflow:loss = 388843040.0, step = 72401 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.465\n",
            "INFO:tensorflow:loss = 639511740.0, step = 72501 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 428.085\n",
            "INFO:tensorflow:loss = 543784800.0, step = 72601 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 431.39\n",
            "INFO:tensorflow:loss = 552169400.0, step = 72701 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.268\n",
            "INFO:tensorflow:loss = 454445570.0, step = 72801 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.984\n",
            "INFO:tensorflow:loss = 414537600.0, step = 72901 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.271\n",
            "INFO:tensorflow:loss = 155098420.0, step = 73001 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.414\n",
            "INFO:tensorflow:loss = 600155140.0, step = 73101 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.879\n",
            "INFO:tensorflow:loss = 223446660.0, step = 73201 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.701\n",
            "INFO:tensorflow:loss = 191752880.0, step = 73301 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.679\n",
            "INFO:tensorflow:loss = 586590900.0, step = 73401 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.47\n",
            "INFO:tensorflow:loss = 402195000.0, step = 73501 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 432.091\n",
            "INFO:tensorflow:loss = 477682400.0, step = 73601 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.516\n",
            "INFO:tensorflow:loss = 408019420.0, step = 73701 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.671\n",
            "INFO:tensorflow:loss = 306261950.0, step = 73801 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 319.252\n",
            "INFO:tensorflow:loss = 420987460.0, step = 73901 (0.318 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.86\n",
            "INFO:tensorflow:loss = 588495400.0, step = 74001 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.01\n",
            "INFO:tensorflow:loss = 267428320.0, step = 74101 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.442\n",
            "INFO:tensorflow:loss = 183987500.0, step = 74201 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.147\n",
            "INFO:tensorflow:loss = 477056030.0, step = 74301 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.057\n",
            "INFO:tensorflow:loss = 486446080.0, step = 74401 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.492\n",
            "INFO:tensorflow:loss = 133385520.0, step = 74501 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.454\n",
            "INFO:tensorflow:loss = 445746880.0, step = 74601 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.003\n",
            "INFO:tensorflow:loss = 233563200.0, step = 74701 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.752\n",
            "INFO:tensorflow:loss = 480637470.0, step = 74801 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.683\n",
            "INFO:tensorflow:loss = 345531650.0, step = 74901 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.868\n",
            "INFO:tensorflow:loss = 271604220.0, step = 75001 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.661\n",
            "INFO:tensorflow:loss = 473002850.0, step = 75101 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.975\n",
            "INFO:tensorflow:loss = 371977540.0, step = 75201 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.844\n",
            "INFO:tensorflow:loss = 515650100.0, step = 75301 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.744\n",
            "INFO:tensorflow:loss = 228705170.0, step = 75401 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.58\n",
            "INFO:tensorflow:loss = 107366600.0, step = 75501 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 344.978\n",
            "INFO:tensorflow:loss = 248277550.0, step = 75601 (0.297 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.127\n",
            "INFO:tensorflow:loss = 262145180.0, step = 75701 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.402\n",
            "INFO:tensorflow:loss = 140822750.0, step = 75801 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.493\n",
            "INFO:tensorflow:loss = 308538100.0, step = 75901 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.718\n",
            "INFO:tensorflow:loss = 583208400.0, step = 76001 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.381\n",
            "INFO:tensorflow:loss = 577239900.0, step = 76101 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.214\n",
            "INFO:tensorflow:loss = 216713520.0, step = 76201 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.537\n",
            "INFO:tensorflow:loss = 440906300.0, step = 76301 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.982\n",
            "INFO:tensorflow:loss = 408815200.0, step = 76401 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.17\n",
            "INFO:tensorflow:loss = 169301570.0, step = 76501 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.504\n",
            "INFO:tensorflow:loss = 399503260.0, step = 76601 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.719\n",
            "INFO:tensorflow:loss = 522583550.0, step = 76701 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.9\n",
            "INFO:tensorflow:loss = 279906700.0, step = 76801 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.263\n",
            "INFO:tensorflow:loss = 553849300.0, step = 76901 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.392\n",
            "INFO:tensorflow:loss = 357960740.0, step = 77001 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.784\n",
            "INFO:tensorflow:loss = 538027140.0, step = 77101 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.603\n",
            "INFO:tensorflow:loss = 418614340.0, step = 77201 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.192\n",
            "INFO:tensorflow:loss = 235212600.0, step = 77301 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.224\n",
            "INFO:tensorflow:loss = 207907860.0, step = 77401 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 436.518\n",
            "INFO:tensorflow:loss = 261785740.0, step = 77501 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.748\n",
            "INFO:tensorflow:loss = 404593700.0, step = 77601 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.098\n",
            "INFO:tensorflow:loss = 206526880.0, step = 77701 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.051\n",
            "INFO:tensorflow:loss = 408260740.0, step = 77801 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.714\n",
            "INFO:tensorflow:loss = 136364020.0, step = 77901 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.762\n",
            "INFO:tensorflow:loss = 311007330.0, step = 78001 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.443\n",
            "INFO:tensorflow:loss = 541959000.0, step = 78101 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.406\n",
            "INFO:tensorflow:loss = 399062560.0, step = 78201 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.696\n",
            "INFO:tensorflow:loss = 555425300.0, step = 78301 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.606\n",
            "INFO:tensorflow:loss = 176893140.0, step = 78401 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.001\n",
            "INFO:tensorflow:loss = 472255300.0, step = 78501 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 342.087\n",
            "INFO:tensorflow:loss = 469876640.0, step = 78601 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.849\n",
            "INFO:tensorflow:loss = 255608740.0, step = 78701 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.374\n",
            "INFO:tensorflow:loss = 329792160.0, step = 78801 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.061\n",
            "INFO:tensorflow:loss = 265869100.0, step = 78901 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.289\n",
            "INFO:tensorflow:loss = 295803260.0, step = 79001 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.376\n",
            "INFO:tensorflow:loss = 200245500.0, step = 79101 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.038\n",
            "INFO:tensorflow:loss = 206000510.0, step = 79201 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.825\n",
            "INFO:tensorflow:loss = 200585550.0, step = 79301 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.078\n",
            "INFO:tensorflow:loss = 522777440.0, step = 79401 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.728\n",
            "INFO:tensorflow:loss = 545294100.0, step = 79501 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.394\n",
            "INFO:tensorflow:loss = 192321490.0, step = 79601 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.093\n",
            "INFO:tensorflow:loss = 611268500.0, step = 79701 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.658\n",
            "INFO:tensorflow:loss = 407472160.0, step = 79801 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.608\n",
            "INFO:tensorflow:loss = 265872420.0, step = 79901 (0.261 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 80000...\n",
            "INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 80000...\n",
            "INFO:tensorflow:Loss for final step: 287854140.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:04:25\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-80000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.24442s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:04:25\n",
            "INFO:tensorflow:Saving dict for global step 80000: average_loss = 9938102.0, global_step = 80000, label/mean = 12949.43, loss = 291044400.0, prediction/mean = 12599.191\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 80000: /tmp/tmpb3dvddk8/model.ckpt-80000\n",
            "scores {'average_loss': 9938102.0, 'label/mean': 12949.43, 'loss': 291044400.0, 'prediction/mean': 12599.191, 'global_step': 80000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-80000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 80000...\n",
            "INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 80000...\n",
            "INFO:tensorflow:loss = 195949700.0, step = 80001\n",
            "INFO:tensorflow:global_step/sec: 375.986\n",
            "INFO:tensorflow:loss = 273609600.0, step = 80101 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.605\n",
            "INFO:tensorflow:loss = 498443100.0, step = 80201 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.772\n",
            "INFO:tensorflow:loss = 233503700.0, step = 80301 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.848\n",
            "INFO:tensorflow:loss = 262418020.0, step = 80401 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.963\n",
            "INFO:tensorflow:loss = 143423230.0, step = 80501 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.427\n",
            "INFO:tensorflow:loss = 121271320.0, step = 80601 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.182\n",
            "INFO:tensorflow:loss = 521821660.0, step = 80701 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.143\n",
            "INFO:tensorflow:loss = 415935040.0, step = 80801 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.685\n",
            "INFO:tensorflow:loss = 553937500.0, step = 80901 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.07\n",
            "INFO:tensorflow:loss = 447594780.0, step = 81001 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.503\n",
            "INFO:tensorflow:loss = 372401800.0, step = 81101 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.621\n",
            "INFO:tensorflow:loss = 209509700.0, step = 81201 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.253\n",
            "INFO:tensorflow:loss = 271419070.0, step = 81301 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.101\n",
            "INFO:tensorflow:loss = 231431870.0, step = 81401 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.837\n",
            "INFO:tensorflow:loss = 226770370.0, step = 81501 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.575\n",
            "INFO:tensorflow:loss = 109951656.0, step = 81601 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.538\n",
            "INFO:tensorflow:loss = 392541380.0, step = 81701 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.357\n",
            "INFO:tensorflow:loss = 371686620.0, step = 81801 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.24\n",
            "INFO:tensorflow:loss = 222524750.0, step = 81901 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.033\n",
            "INFO:tensorflow:loss = 198549250.0, step = 82001 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.597\n",
            "INFO:tensorflow:loss = 279413660.0, step = 82101 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.761\n",
            "INFO:tensorflow:loss = 165530640.0, step = 82201 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.856\n",
            "INFO:tensorflow:loss = 408112580.0, step = 82301 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.92\n",
            "INFO:tensorflow:loss = 171814960.0, step = 82401 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.077\n",
            "INFO:tensorflow:loss = 481402370.0, step = 82501 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.676\n",
            "INFO:tensorflow:loss = 192303630.0, step = 82601 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.961\n",
            "INFO:tensorflow:loss = 386028960.0, step = 82701 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.14\n",
            "INFO:tensorflow:loss = 340625920.0, step = 82801 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.272\n",
            "INFO:tensorflow:loss = 357257540.0, step = 82901 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.217\n",
            "INFO:tensorflow:loss = 434499620.0, step = 83001 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.439\n",
            "INFO:tensorflow:loss = 550442500.0, step = 83101 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.707\n",
            "INFO:tensorflow:loss = 482640260.0, step = 83201 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.276\n",
            "INFO:tensorflow:loss = 504614900.0, step = 83301 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.036\n",
            "INFO:tensorflow:loss = 487300350.0, step = 83401 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.134\n",
            "INFO:tensorflow:loss = 331394080.0, step = 83501 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.879\n",
            "INFO:tensorflow:loss = 373227840.0, step = 83601 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.49\n",
            "INFO:tensorflow:loss = 256143360.0, step = 83701 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.604\n",
            "INFO:tensorflow:loss = 389382300.0, step = 83801 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.414\n",
            "INFO:tensorflow:loss = 320779100.0, step = 83901 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.714\n",
            "INFO:tensorflow:loss = 609869300.0, step = 84001 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 354.777\n",
            "INFO:tensorflow:loss = 627383900.0, step = 84101 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 427.635\n",
            "INFO:tensorflow:loss = 138697170.0, step = 84201 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.153\n",
            "INFO:tensorflow:loss = 330561700.0, step = 84301 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.993\n",
            "INFO:tensorflow:loss = 246727140.0, step = 84401 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.156\n",
            "INFO:tensorflow:loss = 340852220.0, step = 84501 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.159\n",
            "INFO:tensorflow:loss = 180281800.0, step = 84601 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.791\n",
            "INFO:tensorflow:loss = 397179000.0, step = 84701 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.802\n",
            "INFO:tensorflow:loss = 383786430.0, step = 84801 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.84\n",
            "INFO:tensorflow:loss = 298816830.0, step = 84901 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.804\n",
            "INFO:tensorflow:loss = 392561280.0, step = 85001 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 276.853\n",
            "INFO:tensorflow:loss = 376840670.0, step = 85101 (0.352 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.263\n",
            "INFO:tensorflow:loss = 143811220.0, step = 85201 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.765\n",
            "INFO:tensorflow:loss = 315513440.0, step = 85301 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.655\n",
            "INFO:tensorflow:loss = 223218100.0, step = 85401 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.103\n",
            "INFO:tensorflow:loss = 157943440.0, step = 85501 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.14\n",
            "INFO:tensorflow:loss = 208922510.0, step = 85601 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.101\n",
            "INFO:tensorflow:loss = 125381780.0, step = 85701 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.865\n",
            "INFO:tensorflow:loss = 283119520.0, step = 85801 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 431.946\n",
            "INFO:tensorflow:loss = 254314690.0, step = 85901 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.618\n",
            "INFO:tensorflow:loss = 196648780.0, step = 86001 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.392\n",
            "INFO:tensorflow:loss = 348470050.0, step = 86101 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.242\n",
            "INFO:tensorflow:loss = 444406940.0, step = 86201 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.062\n",
            "INFO:tensorflow:loss = 269297730.0, step = 86301 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.639\n",
            "INFO:tensorflow:loss = 391975840.0, step = 86401 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.94\n",
            "INFO:tensorflow:loss = 499098370.0, step = 86501 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.653\n",
            "INFO:tensorflow:loss = 436639300.0, step = 86601 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.973\n",
            "INFO:tensorflow:loss = 396687100.0, step = 86701 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.553\n",
            "INFO:tensorflow:loss = 263207260.0, step = 86801 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.503\n",
            "INFO:tensorflow:loss = 418604130.0, step = 86901 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.003\n",
            "INFO:tensorflow:loss = 218739620.0, step = 87001 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.563\n",
            "INFO:tensorflow:loss = 379849570.0, step = 87101 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.192\n",
            "INFO:tensorflow:loss = 423933340.0, step = 87201 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.047\n",
            "INFO:tensorflow:loss = 153904850.0, step = 87301 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 354.102\n",
            "INFO:tensorflow:loss = 195074880.0, step = 87401 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.327\n",
            "INFO:tensorflow:loss = 273128800.0, step = 87501 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.858\n",
            "INFO:tensorflow:loss = 270184930.0, step = 87601 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 343.11\n",
            "INFO:tensorflow:loss = 110595300.0, step = 87701 (0.302 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.886\n",
            "INFO:tensorflow:loss = 215723300.0, step = 87801 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.416\n",
            "INFO:tensorflow:loss = 235761760.0, step = 87901 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.47\n",
            "INFO:tensorflow:loss = 328300030.0, step = 88001 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.534\n",
            "INFO:tensorflow:loss = 653519360.0, step = 88101 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.923\n",
            "INFO:tensorflow:loss = 280753500.0, step = 88201 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.107\n",
            "INFO:tensorflow:loss = 311426200.0, step = 88301 (0.290 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.457\n",
            "INFO:tensorflow:loss = 213793620.0, step = 88401 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.419\n",
            "INFO:tensorflow:loss = 805573800.0, step = 88501 (0.301 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.238\n",
            "INFO:tensorflow:loss = 330032770.0, step = 88601 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.715\n",
            "INFO:tensorflow:loss = 366941600.0, step = 88701 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 354.423\n",
            "INFO:tensorflow:loss = 225094420.0, step = 88801 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.636\n",
            "INFO:tensorflow:loss = 250756200.0, step = 88901 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.224\n",
            "INFO:tensorflow:loss = 420762500.0, step = 89001 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.026\n",
            "INFO:tensorflow:loss = 162100050.0, step = 89101 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.539\n",
            "INFO:tensorflow:loss = 294836060.0, step = 89201 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.73\n",
            "INFO:tensorflow:loss = 237889920.0, step = 89301 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.215\n",
            "INFO:tensorflow:loss = 633688200.0, step = 89401 (0.293 sec)\n",
            "INFO:tensorflow:global_step/sec: 333.608\n",
            "INFO:tensorflow:loss = 372968830.0, step = 89501 (0.295 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.479\n",
            "INFO:tensorflow:loss = 289573100.0, step = 89601 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.318\n",
            "INFO:tensorflow:loss = 486386020.0, step = 89701 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.761\n",
            "INFO:tensorflow:loss = 452413980.0, step = 89801 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.791\n",
            "INFO:tensorflow:loss = 246472020.0, step = 89901 (0.242 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 90000...\n",
            "INFO:tensorflow:Saving checkpoints for 90000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 90000...\n",
            "INFO:tensorflow:Loss for final step: 208390900.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:04:52\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-90000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.25209s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:04:53\n",
            "INFO:tensorflow:Saving dict for global step 90000: average_loss = 9340979.0, global_step = 90000, label/mean = 12949.43, loss = 273557220.0, prediction/mean = 12962.274\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 90000: /tmp/tmpb3dvddk8/model.ckpt-90000\n",
            "scores {'average_loss': 9340979.0, 'label/mean': 12949.43, 'loss': 273557220.0, 'prediction/mean': 12962.274, 'global_step': 90000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-90000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 90000...\n",
            "INFO:tensorflow:Saving checkpoints for 90000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 90000...\n",
            "INFO:tensorflow:loss = 209576240.0, step = 90001\n",
            "INFO:tensorflow:global_step/sec: 384.201\n",
            "INFO:tensorflow:loss = 258155840.0, step = 90101 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.837\n",
            "INFO:tensorflow:loss = 167906510.0, step = 90201 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.692\n",
            "INFO:tensorflow:loss = 335063550.0, step = 90301 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.117\n",
            "INFO:tensorflow:loss = 430523840.0, step = 90401 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.104\n",
            "INFO:tensorflow:loss = 323202700.0, step = 90501 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.375\n",
            "INFO:tensorflow:loss = 327900960.0, step = 90601 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.758\n",
            "INFO:tensorflow:loss = 332407140.0, step = 90701 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.79\n",
            "INFO:tensorflow:loss = 187311230.0, step = 90801 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.498\n",
            "INFO:tensorflow:loss = 219289520.0, step = 90901 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.184\n",
            "INFO:tensorflow:loss = 532897860.0, step = 91001 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.439\n",
            "INFO:tensorflow:loss = 247200580.0, step = 91101 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.988\n",
            "INFO:tensorflow:loss = 251968830.0, step = 91201 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.245\n",
            "INFO:tensorflow:loss = 307092160.0, step = 91301 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.873\n",
            "INFO:tensorflow:loss = 216146030.0, step = 91401 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.82\n",
            "INFO:tensorflow:loss = 114451340.0, step = 91501 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.61\n",
            "INFO:tensorflow:loss = 193117100.0, step = 91601 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.352\n",
            "INFO:tensorflow:loss = 305233100.0, step = 91701 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.381\n",
            "INFO:tensorflow:loss = 249606770.0, step = 91801 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.911\n",
            "INFO:tensorflow:loss = 515818050.0, step = 91901 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.169\n",
            "INFO:tensorflow:loss = 369898660.0, step = 92001 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.648\n",
            "INFO:tensorflow:loss = 371583000.0, step = 92101 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.867\n",
            "INFO:tensorflow:loss = 264748530.0, step = 92201 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.775\n",
            "INFO:tensorflow:loss = 574849800.0, step = 92301 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.182\n",
            "INFO:tensorflow:loss = 364526270.0, step = 92401 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 340.97\n",
            "INFO:tensorflow:loss = 130319370.0, step = 92501 (0.290 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.501\n",
            "INFO:tensorflow:loss = 598701440.0, step = 92601 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 454.016\n",
            "INFO:tensorflow:loss = 185805920.0, step = 92701 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.245\n",
            "INFO:tensorflow:loss = 257121740.0, step = 92801 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.053\n",
            "INFO:tensorflow:loss = 200717000.0, step = 92901 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.561\n",
            "INFO:tensorflow:loss = 237409700.0, step = 93001 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.793\n",
            "INFO:tensorflow:loss = 390003870.0, step = 93101 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.546\n",
            "INFO:tensorflow:loss = 202622140.0, step = 93201 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 343.229\n",
            "INFO:tensorflow:loss = 311342270.0, step = 93301 (0.291 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.031\n",
            "INFO:tensorflow:loss = 337317800.0, step = 93401 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.344\n",
            "INFO:tensorflow:loss = 547555400.0, step = 93501 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.339\n",
            "INFO:tensorflow:loss = 207294930.0, step = 93601 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.719\n",
            "INFO:tensorflow:loss = 327046370.0, step = 93701 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.913\n",
            "INFO:tensorflow:loss = 211568140.0, step = 93801 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.976\n",
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            "INFO:tensorflow:global_step/sec: 430.652\n",
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            "INFO:tensorflow:global_step/sec: 370.664\n",
            "INFO:tensorflow:loss = 143351170.0, step = 94101 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 432.357\n",
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            "INFO:tensorflow:global_step/sec: 390.645\n",
            "INFO:tensorflow:loss = 211949500.0, step = 94301 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.08\n",
            "INFO:tensorflow:loss = 414338050.0, step = 94401 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.732\n",
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            "INFO:tensorflow:loss = 441017470.0, step = 94601 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.943\n",
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            "INFO:tensorflow:global_step/sec: 391.7\n",
            "INFO:tensorflow:loss = 585177800.0, step = 94801 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.893\n",
            "INFO:tensorflow:loss = 443554000.0, step = 94901 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.973\n",
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            "INFO:tensorflow:global_step/sec: 367.484\n",
            "INFO:tensorflow:loss = 213007580.0, step = 95101 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.19\n",
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            "INFO:tensorflow:global_step/sec: 356.662\n",
            "INFO:tensorflow:loss = 184775710.0, step = 95301 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.661\n",
            "INFO:tensorflow:loss = 338154370.0, step = 95401 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.682\n",
            "INFO:tensorflow:loss = 307813900.0, step = 95501 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.628\n",
            "INFO:tensorflow:loss = 196533680.0, step = 95601 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.797\n",
            "INFO:tensorflow:loss = 376835460.0, step = 95701 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.926\n",
            "INFO:tensorflow:loss = 453545020.0, step = 95801 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.436\n",
            "INFO:tensorflow:loss = 433521020.0, step = 95901 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 430.187\n",
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            "INFO:tensorflow:global_step/sec: 372.739\n",
            "INFO:tensorflow:loss = 283533760.0, step = 96101 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.462\n",
            "INFO:tensorflow:loss = 182203310.0, step = 96201 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 350.398\n",
            "INFO:tensorflow:loss = 219275920.0, step = 96301 (0.289 sec)\n",
            "INFO:tensorflow:global_step/sec: 430.621\n",
            "INFO:tensorflow:loss = 520968450.0, step = 96401 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.15\n",
            "INFO:tensorflow:loss = 383253800.0, step = 96501 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.613\n",
            "INFO:tensorflow:loss = 191340050.0, step = 96601 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.74\n",
            "INFO:tensorflow:loss = 190056080.0, step = 96701 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 450.023\n",
            "INFO:tensorflow:loss = 243303150.0, step = 96801 (0.214 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.123\n",
            "INFO:tensorflow:loss = 204642400.0, step = 96901 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.253\n",
            "INFO:tensorflow:loss = 176402160.0, step = 97001 (0.298 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.501\n",
            "INFO:tensorflow:loss = 917714940.0, step = 97101 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.545\n",
            "INFO:tensorflow:loss = 103433850.0, step = 97201 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.289\n",
            "INFO:tensorflow:loss = 311368580.0, step = 97301 (0.292 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.819\n",
            "INFO:tensorflow:loss = 190228900.0, step = 97401 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.992\n",
            "INFO:tensorflow:loss = 252437170.0, step = 97501 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.415\n",
            "INFO:tensorflow:loss = 152870830.0, step = 97601 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.711\n",
            "INFO:tensorflow:loss = 375992300.0, step = 97701 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.459\n",
            "INFO:tensorflow:loss = 511824030.0, step = 97801 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.873\n",
            "INFO:tensorflow:loss = 239467140.0, step = 97901 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.338\n",
            "INFO:tensorflow:loss = 366503680.0, step = 98001 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.741\n",
            "INFO:tensorflow:loss = 238187420.0, step = 98101 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.273\n",
            "INFO:tensorflow:loss = 262799170.0, step = 98201 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.572\n",
            "INFO:tensorflow:loss = 280054560.0, step = 98301 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.62\n",
            "INFO:tensorflow:loss = 193852580.0, step = 98401 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.889\n",
            "INFO:tensorflow:loss = 450118600.0, step = 98501 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 349.246\n",
            "INFO:tensorflow:loss = 453455680.0, step = 98601 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.806\n",
            "INFO:tensorflow:loss = 330121180.0, step = 98701 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.477\n",
            "INFO:tensorflow:loss = 205375040.0, step = 98801 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.774\n",
            "INFO:tensorflow:loss = 250659620.0, step = 98901 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.502\n",
            "INFO:tensorflow:loss = 207613330.0, step = 99001 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.223\n",
            "INFO:tensorflow:loss = 173408880.0, step = 99101 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.601\n",
            "INFO:tensorflow:loss = 678535200.0, step = 99201 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.942\n",
            "INFO:tensorflow:loss = 425830200.0, step = 99301 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.506\n",
            "INFO:tensorflow:loss = 939356900.0, step = 99401 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.172\n",
            "INFO:tensorflow:loss = 324347500.0, step = 99501 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.9\n",
            "INFO:tensorflow:loss = 270058660.0, step = 99601 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 337.106\n",
            "INFO:tensorflow:loss = 227102600.0, step = 99701 (0.290 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.237\n",
            "INFO:tensorflow:loss = 392839680.0, step = 99801 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.103\n",
            "INFO:tensorflow:loss = 226634370.0, step = 99901 (0.273 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 100000...\n",
            "INFO:tensorflow:Saving checkpoints for 100000 into /tmp/tmpb3dvddk8/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 100000...\n",
            "INFO:tensorflow:Loss for final step: 259736720.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:05:20\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-100000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.24807s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:05:20\n",
            "INFO:tensorflow:Saving dict for global step 100000: average_loss = 9090241.0, global_step = 100000, label/mean = 12949.43, loss = 266214190.0, prediction/mean = 12957.06\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 100000: /tmp/tmpb3dvddk8/model.ckpt-100000\n",
            "scores {'average_loss': 9090241.0, 'label/mean': 12949.43, 'loss': 266214190.0, 'prediction/mean': 12957.06, 'global_step': 100000}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "code",
        "id": "3ptcfj_9Xi9M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c7489d8-472f-4420-d92a-0844b9d7d3e1"
      },
      "source": [
        "#@title Possible solution\n",
        "# Here is one possible solution:\n",
        "# The only necessary change to fix the NaN training loss was the choice of optimizer.\n",
        "\n",
        "# Changing other parameters could improve model quality, but take it with a\n",
        "# grain of salt. The dataset is very small.\n",
        "\n",
        "batch_size = 24\n",
        "\n",
        "print(numeric_feature_names)\n",
        "x_df = car_data[numeric_feature_names]\n",
        "y_series = car_data['price']\n",
        "\n",
        "train_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    num_epochs=None,\n",
        "    shuffle=True)\n",
        "\n",
        "eval_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "predict_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "# Feature columns allow the model to parse the data, perform common\n",
        "# preprocessing, and automatically generate an input layer for the tf.Estimator.\n",
        "model_feature_columns = [\n",
        "    tf.feature_column.numeric_column(feature_name) for feature_name in numeric_feature_names\n",
        "]\n",
        "print('model_feature_columns', model_feature_columns)\n",
        "\n",
        "est = tf.estimator.DNNRegressor(\n",
        "    feature_columns=model_feature_columns,\n",
        "    hidden_units=[64],\n",
        "    optimizer=tf.train.AdagradOptimizer(learning_rate=0.01),\n",
        "  )\n",
        "\n",
        "# TRAIN\n",
        "num_print_statements = 10\n",
        "num_training_steps = 10000\n",
        "for _ in range(num_print_statements):\n",
        "  est.train(train_input_fn, steps=num_training_steps // num_print_statements)\n",
        "  scores = est.evaluate(eval_input_fn)\n",
        "  \n",
        "  # The `scores` dictionary has several metrics automatically generated by the \n",
        "  # canned Estimator.\n",
        "  # `average_loss` is the average loss for an individual example.\n",
        "  # `loss` is the summed loss for the batch.\n",
        "  # In addition to these scalar losses, you may find the visualization functions\n",
        "  # in the next cell helpful for debugging model quality.\n",
        "  print('scores', scores)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['symboling', 'normalized-losses', 'wheel-base', 'length', 'width', 'height', 'weight', 'engine-size', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'bore', 'stroke', 'compression-ratio']\n",
            "model_feature_columns [NumericColumn(key='symboling', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='normalized-losses', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='wheel-base', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='length', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='width', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='height', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='weight', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='engine-size', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='horsepower', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='peak-rpm', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='city-mpg', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='highway-mpg', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='bore', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='stroke', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='compression-ratio', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)]\n",
            "INFO:tensorflow:Using default config.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpppvy1et0\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpppvy1et0', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...\n",
            "INFO:tensorflow:loss = 5338875400.0, step = 1\n",
            "INFO:tensorflow:global_step/sec: 364.024\n",
            "INFO:tensorflow:loss = 462299460.0, step = 101 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.26\n",
            "INFO:tensorflow:loss = 477066000.0, step = 201 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.188\n",
            "INFO:tensorflow:loss = 1069975230.0, step = 301 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.972\n",
            "INFO:tensorflow:loss = 807969540.0, step = 401 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.847\n",
            "INFO:tensorflow:loss = 391193760.0, step = 501 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.819\n",
            "INFO:tensorflow:loss = 905442400.0, step = 601 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.37\n",
            "INFO:tensorflow:loss = 937746370.0, step = 701 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.953\n",
            "INFO:tensorflow:loss = 564327300.0, step = 801 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.166\n",
            "INFO:tensorflow:loss = 915040640.0, step = 901 (0.251 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...\n",
            "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...\n",
            "INFO:tensorflow:Loss for final step: 769329540.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:06:02\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-1000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.24070s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:06:02\n",
            "INFO:tensorflow:Saving dict for global step 1000: average_loss = 39121884.0, global_step = 1000, label/mean = 12949.43, loss = 891109600.0, prediction/mean = 13193.621\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: /tmp/tmpppvy1et0/model.ckpt-1000\n",
            "scores {'average_loss': 39121884.0, 'label/mean': 12949.43, 'loss': 891109600.0, 'prediction/mean': 13193.621, 'global_step': 1000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-1000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...\n",
            "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...\n",
            "INFO:tensorflow:loss = 379872740.0, step = 1001\n",
            "INFO:tensorflow:global_step/sec: 404.25\n",
            "INFO:tensorflow:loss = 1171211000.0, step = 1101 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 421.813\n",
            "INFO:tensorflow:loss = 1583889700.0, step = 1201 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 439.51\n",
            "INFO:tensorflow:loss = 1641926400.0, step = 1301 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.257\n",
            "INFO:tensorflow:loss = 308656130.0, step = 1401 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.512\n",
            "INFO:tensorflow:loss = 357411170.0, step = 1501 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.435\n",
            "INFO:tensorflow:loss = 1264884500.0, step = 1601 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 430.25\n",
            "INFO:tensorflow:loss = 1044269950.0, step = 1701 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 455.808\n",
            "INFO:tensorflow:loss = 412963840.0, step = 1801 (0.221 sec)\n",
            "INFO:tensorflow:global_step/sec: 459.857\n",
            "INFO:tensorflow:loss = 575922050.0, step = 1901 (0.225 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2000...\n",
            "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2000...\n",
            "INFO:tensorflow:Loss for final step: 729407900.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:06:06\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-2000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.39180s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:06:06\n",
            "INFO:tensorflow:Saving dict for global step 2000: average_loss = 30793674.0, global_step = 2000, label/mean = 12949.43, loss = 701411460.0, prediction/mean = 13327.999\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: /tmp/tmpppvy1et0/model.ckpt-2000\n",
            "scores {'average_loss': 30793674.0, 'label/mean': 12949.43, 'loss': 701411460.0, 'prediction/mean': 13327.999, 'global_step': 2000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-2000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2000...\n",
            "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2000...\n",
            "INFO:tensorflow:loss = 453936640.0, step = 2001\n",
            "INFO:tensorflow:global_step/sec: 441.667\n",
            "INFO:tensorflow:loss = 1661208000.0, step = 2101 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.48\n",
            "INFO:tensorflow:loss = 1574229500.0, step = 2201 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.157\n",
            "INFO:tensorflow:loss = 413997500.0, step = 2301 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.094\n",
            "INFO:tensorflow:loss = 491097900.0, step = 2401 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 455.199\n",
            "INFO:tensorflow:loss = 181933570.0, step = 2501 (0.220 sec)\n",
            "INFO:tensorflow:global_step/sec: 435.668\n",
            "INFO:tensorflow:loss = 840368830.0, step = 2601 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.74\n",
            "INFO:tensorflow:loss = 1030487800.0, step = 2701 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 523.794\n",
            "INFO:tensorflow:loss = 782161540.0, step = 2801 (0.193 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.331\n",
            "INFO:tensorflow:loss = 523535800.0, step = 2901 (0.263 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3000...\n",
            "INFO:tensorflow:Saving checkpoints for 3000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3000...\n",
            "INFO:tensorflow:Loss for final step: 392572320.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:06:10\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-3000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.23561s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:06:10\n",
            "INFO:tensorflow:Saving dict for global step 3000: average_loss = 27188828.0, global_step = 3000, label/mean = 12949.43, loss = 619301100.0, prediction/mean = 13104.99\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3000: /tmp/tmpppvy1et0/model.ckpt-3000\n",
            "scores {'average_loss': 27188828.0, 'label/mean': 12949.43, 'loss': 619301100.0, 'prediction/mean': 13104.99, 'global_step': 3000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-3000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3000...\n",
            "INFO:tensorflow:Saving checkpoints for 3000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3000...\n",
            "INFO:tensorflow:loss = 188145000.0, step = 3001\n",
            "INFO:tensorflow:global_step/sec: 429.907\n",
            "INFO:tensorflow:loss = 1157314200.0, step = 3101 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.372\n",
            "INFO:tensorflow:loss = 1250826800.0, step = 3201 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 439.135\n",
            "INFO:tensorflow:loss = 487462100.0, step = 3301 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 474.493\n",
            "INFO:tensorflow:loss = 1044214660.0, step = 3401 (0.216 sec)\n",
            "INFO:tensorflow:global_step/sec: 467.376\n",
            "INFO:tensorflow:loss = 707027260.0, step = 3501 (0.214 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.604\n",
            "INFO:tensorflow:loss = 196872780.0, step = 3601 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.035\n",
            "INFO:tensorflow:loss = 335819620.0, step = 3701 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.516\n",
            "INFO:tensorflow:loss = 1255117800.0, step = 3801 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.538\n",
            "INFO:tensorflow:loss = 234392060.0, step = 3901 (0.252 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4000...\n",
            "INFO:tensorflow:Saving checkpoints for 4000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4000...\n",
            "INFO:tensorflow:Loss for final step: 615608260.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:06:14\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-4000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.22870s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:06:14\n",
            "INFO:tensorflow:Saving dict for global step 4000: average_loss = 25587260.0, global_step = 4000, label/mean = 12949.43, loss = 582820900.0, prediction/mean = 13155.971\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4000: /tmp/tmpppvy1et0/model.ckpt-4000\n",
            "scores {'average_loss': 25587260.0, 'label/mean': 12949.43, 'loss': 582820900.0, 'prediction/mean': 13155.971, 'global_step': 4000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-4000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4000...\n",
            "INFO:tensorflow:Saving checkpoints for 4000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4000...\n",
            "INFO:tensorflow:loss = 211330000.0, step = 4001\n",
            "INFO:tensorflow:global_step/sec: 372.02\n",
            "INFO:tensorflow:loss = 530108220.0, step = 4101 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.983\n",
            "INFO:tensorflow:loss = 278692540.0, step = 4201 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.994\n",
            "INFO:tensorflow:loss = 167900030.0, step = 4301 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.779\n",
            "INFO:tensorflow:loss = 520944700.0, step = 4401 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.254\n",
            "INFO:tensorflow:loss = 731428100.0, step = 4501 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.88\n",
            "INFO:tensorflow:loss = 564819100.0, step = 4601 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.261\n",
            "INFO:tensorflow:loss = 665367800.0, step = 4701 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.622\n",
            "INFO:tensorflow:loss = 618698400.0, step = 4801 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.857\n",
            "INFO:tensorflow:loss = 319710340.0, step = 4901 (0.251 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5000...\n",
            "INFO:tensorflow:Saving checkpoints for 5000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5000...\n",
            "INFO:tensorflow:Loss for final step: 627768400.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:06:17\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-5000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.24021s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:06:18\n",
            "INFO:tensorflow:Saving dict for global step 5000: average_loss = 24808068.0, global_step = 5000, label/mean = 12949.43, loss = 565072640.0, prediction/mean = 13040.1875\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 5000: /tmp/tmpppvy1et0/model.ckpt-5000\n",
            "scores {'average_loss': 24808068.0, 'label/mean': 12949.43, 'loss': 565072640.0, 'prediction/mean': 13040.1875, 'global_step': 5000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-5000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5000...\n",
            "INFO:tensorflow:Saving checkpoints for 5000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5000...\n",
            "INFO:tensorflow:loss = 349602000.0, step = 5001\n",
            "INFO:tensorflow:global_step/sec: 429.627\n",
            "INFO:tensorflow:loss = 137151220.0, step = 5101 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.1\n",
            "INFO:tensorflow:loss = 386618600.0, step = 5201 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.389\n",
            "INFO:tensorflow:loss = 592621440.0, step = 5301 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 431.486\n",
            "INFO:tensorflow:loss = 154408900.0, step = 5401 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.999\n",
            "INFO:tensorflow:loss = 667114800.0, step = 5501 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 453.809\n",
            "INFO:tensorflow:loss = 188308270.0, step = 5601 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.937\n",
            "INFO:tensorflow:loss = 317568260.0, step = 5701 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.864\n",
            "INFO:tensorflow:loss = 244607340.0, step = 5801 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.826\n",
            "INFO:tensorflow:loss = 240317380.0, step = 5901 (0.236 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6000...\n",
            "INFO:tensorflow:Saving checkpoints for 6000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6000...\n",
            "INFO:tensorflow:Loss for final step: 292470800.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:06:21\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-6000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.24329s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:06:21\n",
            "INFO:tensorflow:Saving dict for global step 6000: average_loss = 24326652.0, global_step = 6000, label/mean = 12949.43, loss = 554107100.0, prediction/mean = 13122.014\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 6000: /tmp/tmpppvy1et0/model.ckpt-6000\n",
            "scores {'average_loss': 24326652.0, 'label/mean': 12949.43, 'loss': 554107100.0, 'prediction/mean': 13122.014, 'global_step': 6000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-6000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6000...\n",
            "INFO:tensorflow:Saving checkpoints for 6000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6000...\n",
            "INFO:tensorflow:loss = 1594606100.0, step = 6001\n",
            "INFO:tensorflow:global_step/sec: 370.155\n",
            "INFO:tensorflow:loss = 620545540.0, step = 6101 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.801\n",
            "INFO:tensorflow:loss = 1047583600.0, step = 6201 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 439.647\n",
            "INFO:tensorflow:loss = 642879360.0, step = 6301 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.135\n",
            "INFO:tensorflow:loss = 643563650.0, step = 6401 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.763\n",
            "INFO:tensorflow:loss = 428491360.0, step = 6501 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.93\n",
            "INFO:tensorflow:loss = 579638900.0, step = 6601 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 440.399\n",
            "INFO:tensorflow:loss = 375964700.0, step = 6701 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 449.896\n",
            "INFO:tensorflow:loss = 373448900.0, step = 6801 (0.219 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.486\n",
            "INFO:tensorflow:loss = 1034883700.0, step = 6901 (0.241 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7000...\n",
            "INFO:tensorflow:Saving checkpoints for 7000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7000...\n",
            "INFO:tensorflow:Loss for final step: 781273900.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:06:25\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-7000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.23265s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:06:25\n",
            "INFO:tensorflow:Saving dict for global step 7000: average_loss = 24003978.0, global_step = 7000, label/mean = 12949.43, loss = 546757300.0, prediction/mean = 13087.653\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 7000: /tmp/tmpppvy1et0/model.ckpt-7000\n",
            "scores {'average_loss': 24003978.0, 'label/mean': 12949.43, 'loss': 546757300.0, 'prediction/mean': 13087.653, 'global_step': 7000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-7000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7000...\n",
            "INFO:tensorflow:Saving checkpoints for 7000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7000...\n",
            "INFO:tensorflow:loss = 580587700.0, step = 7001\n",
            "INFO:tensorflow:global_step/sec: 419.876\n",
            "INFO:tensorflow:loss = 755204000.0, step = 7101 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.691\n",
            "INFO:tensorflow:loss = 297680500.0, step = 7201 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 444.939\n",
            "INFO:tensorflow:loss = 540795900.0, step = 7301 (0.222 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.497\n",
            "INFO:tensorflow:loss = 774612860.0, step = 7401 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.688\n",
            "INFO:tensorflow:loss = 803864960.0, step = 7501 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 430.801\n",
            "INFO:tensorflow:loss = 150499490.0, step = 7601 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.568\n",
            "INFO:tensorflow:loss = 285229470.0, step = 7701 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.258\n",
            "INFO:tensorflow:loss = 619980000.0, step = 7801 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.91\n",
            "INFO:tensorflow:loss = 527055260.0, step = 7901 (0.248 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8000...\n",
            "INFO:tensorflow:Saving checkpoints for 8000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8000...\n",
            "INFO:tensorflow:Loss for final step: 260155200.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:06:29\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-8000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.24707s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:06:29\n",
            "INFO:tensorflow:Saving dict for global step 8000: average_loss = 23756468.0, global_step = 8000, label/mean = 12949.43, loss = 541119550.0, prediction/mean = 13055.7705\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 8000: /tmp/tmpppvy1et0/model.ckpt-8000\n",
            "scores {'average_loss': 23756468.0, 'label/mean': 12949.43, 'loss': 541119550.0, 'prediction/mean': 13055.7705, 'global_step': 8000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-8000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8000...\n",
            "INFO:tensorflow:Saving checkpoints for 8000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8000...\n",
            "INFO:tensorflow:loss = 223109220.0, step = 8001\n",
            "INFO:tensorflow:global_step/sec: 395.908\n",
            "INFO:tensorflow:loss = 130415730.0, step = 8101 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.003\n",
            "INFO:tensorflow:loss = 225265860.0, step = 8201 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.647\n",
            "INFO:tensorflow:loss = 630843900.0, step = 8301 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.04\n",
            "INFO:tensorflow:loss = 266152600.0, step = 8401 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.365\n",
            "INFO:tensorflow:loss = 1053253800.0, step = 8501 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.53\n",
            "INFO:tensorflow:loss = 201315260.0, step = 8601 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.228\n",
            "INFO:tensorflow:loss = 252927300.0, step = 8701 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.735\n",
            "INFO:tensorflow:loss = 664672900.0, step = 8801 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.059\n",
            "INFO:tensorflow:loss = 985891200.0, step = 8901 (0.260 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9000...\n",
            "INFO:tensorflow:Saving checkpoints for 9000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9000...\n",
            "INFO:tensorflow:Loss for final step: 240900600.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:06:33\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-9000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.23549s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:06:33\n",
            "INFO:tensorflow:Saving dict for global step 9000: average_loss = 23538604.0, global_step = 9000, label/mean = 12949.43, loss = 536157060.0, prediction/mean = 13082.401\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 9000: /tmp/tmpppvy1et0/model.ckpt-9000\n",
            "scores {'average_loss': 23538604.0, 'label/mean': 12949.43, 'loss': 536157060.0, 'prediction/mean': 13082.401, 'global_step': 9000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-9000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9000...\n",
            "INFO:tensorflow:Saving checkpoints for 9000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9000...\n",
            "INFO:tensorflow:loss = 442745800.0, step = 9001\n",
            "INFO:tensorflow:global_step/sec: 389.832\n",
            "INFO:tensorflow:loss = 793469440.0, step = 9101 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.568\n",
            "INFO:tensorflow:loss = 1506453000.0, step = 9201 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.137\n",
            "INFO:tensorflow:loss = 985112700.0, step = 9301 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 428.486\n",
            "INFO:tensorflow:loss = 953414850.0, step = 9401 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.991\n",
            "INFO:tensorflow:loss = 196296210.0, step = 9501 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.247\n",
            "INFO:tensorflow:loss = 259752900.0, step = 9601 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.118\n",
            "INFO:tensorflow:loss = 130550430.0, step = 9701 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.058\n",
            "INFO:tensorflow:loss = 497344580.0, step = 9801 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.759\n",
            "INFO:tensorflow:loss = 542789100.0, step = 9901 (0.258 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...\n",
            "INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tmpppvy1et0/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...\n",
            "INFO:tensorflow:Loss for final step: 123597020.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:06:37\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpppvy1et0/model.ckpt-10000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.23694s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:06:37\n",
            "INFO:tensorflow:Saving dict for global step 10000: average_loss = 23312594.0, global_step = 10000, label/mean = 12949.43, loss = 531009100.0, prediction/mean = 13106.869\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tmpppvy1et0/model.ckpt-10000\n",
            "scores {'average_loss': 23312594.0, 'label/mean': 12949.43, 'loss': 531009100.0, 'prediction/mean': 13106.869, 'global_step': 10000}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rxto3DwsjYw4"
      },
      "source": [
        "### Visualize your model's predictions\n",
        "\n",
        "After you have a trained model, it may be helpful to understand how your model's inference differs from the actual data.\n",
        "\n",
        "This helper function `scatter_plot_inference` does that for you. Real data is in grey. Your model's predictions are in orange.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uGSXwX2fju1N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "71a750ff-2a2b-41b3-b3d6-7185e589da34"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "\n",
        "\n",
        "def scatter_plot_inference_grid(est, x_df, feature_names):\n",
        "  \"\"\"Plots the predictions of the model against each feature.\n",
        "  \n",
        "  Args:\n",
        "    est: The trained tf.Estimator.\n",
        "    x_df: The pandas dataframe with the input data (used to create\n",
        "      predict_input_fn).\n",
        "    feature_names: An iterable of string feature names to plot.\n",
        "  \"\"\"\n",
        "  def scatter_plot_inference(axis,\n",
        "                             x_axis_feature_name,\n",
        "                             y_axis_feature_name,\n",
        "                             predictions):\n",
        "    \"\"\"Generate one subplot.\"\"\"\n",
        "    # Plot the real data in grey.\n",
        "    y_axis_feature_name = 'price'\n",
        "    axis.set_ylabel(y_axis_feature_name)\n",
        "    axis.set_xlabel(x_axis_feature_name)\n",
        "    axis.scatter(car_data[x_axis_feature_name],\n",
        "                 car_data[y_axis_feature_name],\n",
        "                 c='grey')\n",
        "\n",
        "    # Plot the predicted data in orange.\n",
        "    axis.scatter(car_data[x_axis_feature_name], predictions, c='orange')\n",
        "\n",
        "  predict_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "  predictions = [\n",
        "    x['predictions'][0]\n",
        "    for x in est.predict(predict_input_fn)\n",
        "  ]\n",
        "  \n",
        "  num_cols = 3\n",
        "  num_rows = int(math.ceil(len(feature_names)/float(num_cols)))\n",
        "  f, axarr = plt.subplots(num_rows, num_cols)\n",
        "  size = 4.5\n",
        "  f.set_size_inches(num_cols*size, num_rows*size)\n",
        "  \n",
        "  for i, feature_name in enumerate(numeric_feature_names):\n",
        "    axis = axarr[int(i/num_cols), i%num_cols]\n",
        "    scatter_plot_inference(axis, feature_name, 'price', predictions)\n",
        "  plt.show()\n",
        "\n",
        "scatter_plot_inference_grid(est, x_df, numeric_feature_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpb3dvddk8/model.ckpt-100000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 972x1620 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBZI8f_8Yfph"
      },
      "source": [
        "## Task 2: Take your best numeric model from earlier. Add normalization.\n",
        "\n",
        "### Add normalization to your best numeric model from earlier\n",
        "\n",
        "- You decide what type of normalization to add, and for which features\n",
        "- You will need to use the `normalizer_fn` arg on [`numeric_column`](https://g3doc.corp.google.com/learning/brain/public/g3doc/api_docs/python/tf/feature_column/numeric_column.md?cl=head)\n",
        "    - An example of a silly normalizer_fn that shifts inputs down by 1, and then negates the value:\n",
        "    \n",
        "         normalizer_fn = lambda x: tf.neg(tf.subtract(x, 1))\n",
        "\n",
        "- You may find these pandas functions helpful:\n",
        "    - dataframe.mean()['your_feature_name']\n",
        "    - dataframe.std()['your_feature_name']\n",
        "- You will need to retune the hyperparameters from earlier.\n",
        "\n",
        "\n",
        "**Does normalization improve model quality on this dataset? Why or why not?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jY_C_QgcZg1-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "83a9a2f5-b083-4f45-cfb1-28b2c2fca972"
      },
      "source": [
        "# This 1D visualization of each numeric feature might inform your normalization\n",
        "# decisions.\n",
        "for feature_name in numeric_feature_names:\n",
        "  car_data.hist(column=feature_name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEICAYAAABGaK+TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVf0lEQVR4nO3dYZAkd3nf8e8PhOCixSdAsDlOKp9cYByiM8JaYwhJvCvAJRAFuIqioAglBfCBEyhiX2wL8sLYDokwCBlXSJVlJHMvgIUIFLAAGxlYKCpBsAeCkxAOinwEHeIUgqSwWBF18OTF9MGyN3s7uzN7vf/V91M1tdP/6el+ntvp3/X0znSnqpAktedBfRcgSdoYA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuJqW5HCSZ27gebckmd3MdUibzQDXA1JV/eOqWhh3OUlmk9wxgZKkdTPAJalRBri2g/OTfCXJvUnel+RhAEmem+SmJPck+W9JfvH4E5YfFkmyI8mBJHcnuTXJ7w7Zqz5hHUnOAD4GPDbJUnd77CnrWg94Bri2gxcBFwHnAr8IXJrkycA1wKuARwF/Bnw4yUOHPP/3gT3AzwHPAv7FKOuoqu8Dzwa+VVVT3e1bk2xMOhkDXNvBn1bVt6rqu8BfAucD+4A/q6obq+qHVXUAuB946pDnvwj4D1V1d1XdAfzpiOuQemWAazv49rL7fw9MAT8L7O8On9yT5B7gHGDYIY7HAt9cNv3NIfMMW4fUKwNc29U3gTdV1ZnLbv+gqt47ZN47gbOXTZ+zjvV4Ok/1xgDXdvXnwKuT/EoGzkhycZKHD5n3/cDrkzwiyW7gNetYz1HgUUl2TqJoaT0McG1LVbUI/Abwn4C7gduAS1eZ/Q+BO4C/A/4GuJbB8fJR1vM14L3A7d2hGj+FolMmXtBB+mlJfhN4cVX9at+1SCfjHrge8JLsSvL0JA9K8gRgP3Bd33VJazmt7wKkLeB0Bp8TPxe4B5gH/nOvFUkj8BCKJDXKQyiS1KhTegjlrLPOqj179kxsed///vc544wzJra8PtnL1rNd+gB72apG7eXgwYPfqapHrxw/pQG+Z88eFhcXJ7a8hYUFZmdnJ7a8PtnL1rNd+gB72apG7SXJN4aNewhFkhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIa5dkIJWDPZR85YWz/3mNcOmR80g5ffvGmr0Pbk3vgktSokQM8yYOTfCnJ9d30uUluTHJbkvclOX3zypQkrbSePfDXAbcum34zcGVVPY7BNQdfMcnCJEknN1KAJzkbuBh4Zzcd4EIGF38FOAC8YDMKlCQNN9IVeZJcC/xH4OHAv2Vwde/PdXvfJDkH+FhVnTfkufuAfQDT09MXzM/PT6z4paUlpqamJra8PtlLvw4dufeEsekdcPS+zV/33t07N30dLf5OVvNA7GVubu5gVc2sHF/zUyhJngvcVVUHk8yut8Cqugq4CmBmZqYmeR7fB+J5gVvQYi/DPm2yf+8xrji0+R/UOvzS2U1fR4u/k9XYy0+M8up8OvC8JM8BHgb8DPB24Mwkp1XVMeBs4MiGq5Akrduax8Cr6vVVdXZV7QFeDHyyql4KfAp4YTfbJcCHNq1KSdIJxvkc+O8Bv53kNuBRwNWTKUmSNIp1HeCrqgVgobt/O/CUyZckSRqF38SUpEYZ4JLUKANckhplgEtSozydrNSzYaeynbRhp8b1NLbtcw9ckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElq1JoBnuRhST6f5MtJbknyB934u5L8XZKbutv5m1+uJOm4UU5mdT9wYVUtJXkI8NkkH+se+52qunbzypMkrWbNAK+qApa6yYd0t9rMoiRJa8sgn9eYKXkwcBB4HPCOqvq9JO8CnsZgD/0TwGVVdf+Q5+4D9gFMT09fMD8/P7Hil5aWmJqamtjy+mQv/Tp05N4TxqZ3wNH7eihmEwzrZe/unf0UM6YWX1+rGbWXubm5g1U1s3J8pAD/8czJmcB1wGuB/wN8GzgduAr4n1X1hyd7/szMTC0uLo68vrUsLCwwOzs7seX1yV76Neyc3Pv3HuOKQ9vjlPnDemn1fOAtvr5WM2ovSYYG+Lo+hVJV9wCfAi6qqjtr4H7gL/AK9ZJ0So3yKZRHd3veJNkBPAv4WpJd3ViAFwA3b2ahkqSfNsr7w13Age44+IOA91fV9Uk+meTRQICbgFdvYp2SpBVG+RTKV4AnDxm/cFMqkiSNxG9iSlKjDHBJapQBLkmNMsAlqVHb41sK2jaGfaFG0nDugUtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0a5ZJqD0vy+SRfTnJLkj/oxs9NcmOS25K8L8npm1+uJOm4UfbA7wcurKonAecDFyV5KvBm4MqqehxwN/CKzStTkrTSmgHeXXl+qZt8SHcr4ELg2m78AIMLG0uSTpFU1dozDS5ofBB4HPAO4C3A57q9b5KcA3ysqs4b8tx9wD6A6enpC+bn5ydW/NLSElNTUxNbXp/sZeDQkXsnXM3GTe+Ao/f1XcVkDOtl7+6d/RQzpgfitjI3N3ewqmZWjo90PvCq+iFwfpIzgeuAXxi1wKq6CrgKYGZmpmZnZ0d96poWFhaY5PL6ZC8Dl26h84Hv33uMKw5tj1PmD+vl8Etn+ylmTG4rP7GuT6FU1T3Ap4CnAWcmOf6KOBs4suEqJEnrNsqnUB7d7XmTZAfwLOBWBkH+wm62S4APbVaRkqQTjfL+cBdwoDsO/iDg/VV1fZKvAvNJ/j3wJeDqTaxTkrTCmgFeVV8Bnjxk/HbgKZtRlCRpbX4TU5IaZYBLUqMMcElqlAEuSY3aHt9S2Kb29PSllsOXX9zLeiWtj3vgktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSo0a5pNo5ST6V5KtJbknyum78jUmOJLmpuz1n88uVJB03ysmsjgH7q+qLSR4OHExyQ/fYlVX11s0rT5K0mlEuqXYncGd3/3tJbgV2b3ZhkqSTW9cx8CR7GFwf88Zu6DVJvpLkmiSPmHBtkqSTSFWNNmMyBXwaeFNVfTDJNPAdoIA/AnZV1cuHPG8fsA9genr6gvn5+UnVztLSElNTUxNbXp+G9XLoyL09VTOe6R1w9L6+qxjfdukDhveyd/fOfooZ03bf7oeZm5s7WFUzK8dHCvAkDwGuB/66qt425PE9wPVVdd7JljMzM1OLi4trrm9UCwsLzM7OTmx5fRrWS18XdBjX/r3HuOJQ+9cK2S59wPBeWr1wx3bf7odJMjTAR/kUSoCrgVuXh3eSXctm+3Xg5lEKliRNxii7F08HXgYcSnJTN/YG4CVJzmdwCOUw8KpNqVCSNNQon0L5LJAhD3108uVIkkblNzElqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUaNcE/OcJJ9K8tUktyR5XTf+yCQ3JPl69/MRm1+uJOm4UfbAjwH7q+qJwFOBf53kicBlwCeq6vHAJ7ppSdIpsmaAV9WdVfXF7v73gFuB3cDzgQPdbAeAF2xWkZKkE6WqRp852QN8BjgP+F9VdWY3HuDu49MrnrMP2AcwPT19wfz8/PhVd5aWlpiamprY8vo0rJdDR+7tqZrxTO+Ao/f1XcX4tksfMLyXvbt39lPMmLb7dj/M3NzcwaqaWTk+coAnmQI+Dbypqj6Y5J7lgZ3k7qo66XHwmZmZWlxcHGl9o1hYWGB2dnZiy+vTsF72XPaRfooZ0/69x7ji0Gl9lzG27dIHDO/l8OUX91TNeLb7dj9MkqEBPtKnUJI8BPgA8O6q+mA3fDTJru7xXcBdoxYtSRrfKJ9CCXA1cGtVvW3ZQx8GLunuXwJ8aPLlSZJWM8r7w6cDLwMOJbmpG3sDcDnw/iSvAL4BvGhzSpQkDbNmgFfVZ4Gs8vAzJluOJGlUfhNTkhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktSoUS6pdk2Su5LcvGzsjUmOJLmpuz1nc8uUJK00yh74u4CLhoxfWVXnd7ePTrYsSdJa1gzwqvoM8N1TUIskaR1SVWvPlOwBrq+q87rpNwKXAv8XWAT2V9Xdqzx3H7APYHp6+oL5+fkJlD2wtLTE1NTUxJbXp2G9HDpyb0/VjGd6Bxy9r+8qxrdd+oCt18ve3Ts3/Nztvt0PMzc3d7CqZlaObzTAp4HvAAX8EbCrql6+1nJmZmZqcXFxzfWNamFhgdnZ2Yktr0/Detlz2Uf6KWZM+/ce44pDa14ve8vbLn3A1uvl8OUXb/i52327HybJ0ADf0KdQqupoVf2wqn4E/DnwlI0sR5K0cRsK8CS7lk3+OnDzavNKkjbHmu+pkrwXmAXOSnIH8PvAbJLzGRxCOQy8ahNrlCQNsWaAV9VLhgxfvQm1SJLWwW9iSlKjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIatWaAJ7kmyV1Jbl429sgkNyT5evfzEZtbpiRppVH2wN8FXLRi7DLgE1X1eOAT3bQk6RRaM8Cr6jPAd1cMPx840N0/ALxgwnVJktaQqlp7pmQPcH1VnddN31NVZ3b3A9x9fHrIc/cB+wCmp6cvmJ+fn0zlwNLSElNTUxNbXp+G9XLoyL09VTOe6R1w9L6+qxjfdukDtl4ve3fv3PBzt/t2P8zc3NzBqppZOb7mRY3XUlWVZNX/BarqKuAqgJmZmZqdnR13lT+2sLDAJJfXp2G9XHrZR/opZkz79x7jikNjv7R6t136gK3Xy+GXzm74udt9u1+PjX4K5WiSXQDdz7s2XIEkaUM2GuAfBi7p7l8CfGgy5UiSRjXKxwjfC/x34AlJ7kjyCuBy4FlJvg48s5uWJJ1Cax4Uq6qXrPLQMyZcy5a15xQci96/91izx7ylVpyKbXk1hy+/eOLL9JuYktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGjXWV0ySHge8BPwSODbtqsiRpc0ziMtVzVfWdCSxHkrQOHkKRpEaNG+AFfDzJwST7JlGQJGk0qaqNPznZXVVHkjwGuAF4bVV9ZsU8+4B9ANPT0xfMz8+PU+9PWVpaYmpqamLLW82hI/du+jqmd8DR+zZ9NafEdullu/QBW6+Xvbt3bvi542z3p2JbXs2wnkftZW5u7uCwvzGOFeA/taDkjcBSVb11tXlmZmZqcXFxIusDWFhYYHZ2dmLLW82puir9FYcm8SeJ/m2XXrZLH7D1ehnnCu3jbPdb7ar0o/aSZGiAb/gQSpIzkjz8+H3g14CbN7o8SdL6jPNf8jRwXZLjy3lPVf3VRKqSJK1pwwFeVbcDT5pgLZKkddg6B8UkPWCMcyx6/95jXNrjseytxM+BS1KjDHBJapQBLkmNMsAlqVHN/BFz2B89/GOGpAcy98AlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGjRXgSS5K8rdJbkty2aSKkiStbZyLGj8YeAfwbOCJwEuSPHFShUmSTm6cPfCnALdV1e1V9QNgHnj+ZMqSJK0lVbWxJyYvBC6qqld20y8DfqWqXrNivn3Avm7yCcDfbrzcE5wFfGeCy+uTvWw926UPsJetatRefraqHr1ycNPPB15VVwFXbcaykyxW1cxmLPtUs5etZ7v0AfayVY3byziHUI4A5yybPrsbkySdAuME+BeAxyc5N8npwIuBD0+mLEnSWjZ8CKWqjiV5DfDXwIOBa6rqlolVNppNOTTTE3vZerZLH2AvW9VYvWz4j5iSpH75TUxJapQBLkmNairAkzw4yZeSXN9NPyPJF5PclOSzSR7Xd42jSHI4yaGu7sVu7JFJbkjy9e7nI/qucxSr9PKWJF9L8pUk1yU5s+86RzGsl2WP7U9SSc7qq771WK2XJK/tfje3JPnjPmscxSqvr/OTfO74WJKn9F3nKJKcmeTa7t//1iRPG3u7r6pmbsBvA+8Bru+m/wfwj7r7/wp4V981jtjHYeCsFWN/DFzW3b8MeHPfdY7Ry68Bp3X339xyL934OQz+WP+NYY9vxdsqv5c54G+Ah3bTj+m7zg328XHg2d395wALfdc5Yi8HgFd2908Hzhx3u29mDzzJ2cDFwDuXDRfwM939ncC3TnVdE/R8Br9gup8v6LGWsVTVx6vqWDf5OQbfEWjZlcDvMni9tew3gcur6n6Aqrqr53o2qrntPslO4J8DVwNU1Q+q6h7G3O6bCXDgTxhsRD9aNvZK4KNJ7gBeBlzeR2EbUMDHkxzsTjUAMF1Vd3b3vw1M91Paug3rZbmXAx87xTVt1Am9JHk+cKSqvtxvaes27Pfy88A/S3Jjkk8n+eUe6xvVsD7+DfCWJN8E3gq8vrfqRncu8L+Bv+gOA78zyRmMud1v+lfpJyHJc4G7qupgktllD/0W8JyqujHJ7wBvYxDqW90/raojSR4D3JDka8sfrKpK0sre3gm9VNVnAJL8O+AY8O5eKxzdsN/LGxgcEmrNsF5OAx4JPBX4ZeD9SX6uuvfvW9SwPl4I/FZVfSDJixjs1T6z1yrXdhrwS8Bru7x6O4NDJj+2ke2+lT3wpwPPS3KYwVkPL0zyEeBJVXVjN8/7gH/SU33rUlVHup93AdcxOLPj0SS7ALqfTby9XaUXklwKPBd46RYPiB8b0suvMthz+nL32jsb+GKSf9hbkSNa5fdyB/DBGvg8g3ezW/qPsqv0cQnwwW6W/9KNbXV3AHcsy6trGQT6WNt9EwFeVa+vqrOrag+Dr+x/ksGxo51Jfr6b7VnArT2VOLIkZyR5+PH7DPbubmZwGoJLutkuAT7UT4WjW62XJBcxONz1vKr6+z5rHNUqvXyhqh5TVXu6194dwC9V1bd7LHVNJ3mN/VcGf8ik225OZwuf1e8kfXyLwX+uABcCX++nwtF1r5lvJnlCN/QM4KuMud03cQhlmBp8lf83gA8k+RFwN4PjrVvdNHBdEhj8+7+nqv4qyRcYvKV9BYNPO7yoxxpHtVovtwEPZfCWF+BzVfXq/socydBe+i1pw1b7vZwOXJPkZuAHwCVb/N3Ran0sAW9Pchrw//jJ6aq3utcC7+5+D7cD/5LBTvSGt3u/Si9JjWriEIok6UQGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWrU/wca+rvEnbp3NQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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j4GuBB+Y8fhD/4OkkJBkDXgrsHu4ky0/n8sBdwCPA7VXlGj3V3wDvAn4x7EGgjYBLfZNkBPgc8Paq+tGw51luqurnVfUSZu+svjDJi4Y903KR5HLgkaraO+xZntRCwL1lX32R5BRm4/3Jqvr8sOdZzqrqMLALf7Yy18XAq5McZPZS7iVJPjHMgVoIuLfs66QlCXAjsL+qPjTseZajJM9Nsqbz8enA7wPfGe5Uy0dVvaeq1lXVGLMd+mpVvXGYMy37gFfVDPDkLfv7gc8M+Jb95iS5GfhX4PwkDya5btgzLUMXA1cze9Z0V+fXK4c91DJzNrArybeYPXG6vaqG/lY5HZu30ktSo5b9GbgkaWEGXJIaZcAlqVEGXJIaZcAlqVEGXJIaZcAlqVH/B/TP4jSDKdLTAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oiEpDZKSj8pN"
      },
      "source": [
        "###Train your model with numeric features + normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c30Y6IiR8iVn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3315ddeb-49c1-4d7f-f2af-a6505d0dc862"
      },
      "source": [
        "# Code\n",
        "\n",
        "batch_size = 34\n",
        "\n",
        "print(numeric_feature_names) #numeric features \n",
        "x_df = car_data[numeric_feature_names]\n",
        "y_series = car_data['price']\n",
        "\n",
        "train_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    num_epochs=None,\n",
        "    shuffle=True)\n",
        "\n",
        "eval_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "predict_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "\n",
        "epsilon = 0.000001\n",
        "model_feature_columns = [\n",
        "    tf.feature_column.numeric_column(feature_name,\n",
        "                                     normalizer_fn=lambda val: (val - x_df.mean()[feature_name]) / (epsilon + x_df.std()[feature_name]))\n",
        "    for feature_name in numeric_feature_names\n",
        "]\n",
        "print('model_feature_columns', model_feature_columns)\n",
        "\n",
        "est = tf.estimator.DNNRegressor(\n",
        "    feature_columns=model_feature_columns,\n",
        "    hidden_units=[84],\n",
        "    optimizer=tf.train.AdagradOptimizer(learning_rate=0.9),\n",
        "  )\n",
        "\n",
        "# Training \n",
        "num_print_statements = 10\n",
        "num_training_steps = 100000\n",
        "for _ in range(num_print_statements):\n",
        "  est.train(train_input_fn, steps=num_training_steps // num_print_statements)\n",
        "  scores = est.evaluate(eval_input_fn)\n",
        "  \n",
        "  print('scores', scores)\n",
        "\n",
        "scatter_plot_inference_grid(est, x_df, numeric_feature_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['symboling', 'normalized-losses', 'wheel-base', 'length', 'width', 'height', 'weight', 'engine-size', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'bore', 'stroke', 'compression-ratio']\n",
            "model_feature_columns [NumericColumn(key='symboling', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f85445da950>), NumericColumn(key='normalized-losses', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8546ba44d0>), NumericColumn(key='wheel-base', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8546ba4830>), NumericColumn(key='length', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8546ba47a0>), NumericColumn(key='width', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f854707a830>), NumericColumn(key='height', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f854707aa70>), NumericColumn(key='weight', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8548fa58c0>), NumericColumn(key='engine-size', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547ff2440>), NumericColumn(key='horsepower', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547ff23b0>), NumericColumn(key='peak-rpm', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547ff2170>), NumericColumn(key='city-mpg', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547ff24d0>), NumericColumn(key='highway-mpg', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547ff2560>), NumericColumn(key='bore', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547ff25f0>), NumericColumn(key='stroke', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547ff2050>), NumericColumn(key='compression-ratio', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547ff2320>)]\n",
            "INFO:tensorflow:Using default config.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp4160q49c\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp4160q49c', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...\n",
            "INFO:tensorflow:loss = 5645886500.0, step = 1\n",
            "INFO:tensorflow:global_step/sec: 185.296\n",
            "INFO:tensorflow:loss = 558035140.0, step = 101 (0.542 sec)\n",
            "INFO:tensorflow:global_step/sec: 219.526\n",
            "INFO:tensorflow:loss = 878447500.0, step = 201 (0.458 sec)\n",
            "INFO:tensorflow:global_step/sec: 199.897\n",
            "INFO:tensorflow:loss = 293061380.0, step = 301 (0.503 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.298\n",
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            "INFO:tensorflow:global_step/sec: 396.946\n",
            "INFO:tensorflow:loss = 184059500.0, step = 501 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.068\n",
            "INFO:tensorflow:loss = 490810140.0, step = 601 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 350.745\n",
            "INFO:tensorflow:loss = 576873200.0, step = 701 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.293\n",
            "INFO:tensorflow:loss = 395688420.0, step = 801 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 346.025\n",
            "INFO:tensorflow:loss = 419430270.0, step = 901 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.175\n",
            "INFO:tensorflow:loss = 727317800.0, step = 1001 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 435.006\n",
            "INFO:tensorflow:loss = 983703550.0, step = 1101 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.441\n",
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            "INFO:tensorflow:global_step/sec: 399.205\n",
            "INFO:tensorflow:loss = 232642720.0, step = 1301 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.87\n",
            "INFO:tensorflow:loss = 880352000.0, step = 1401 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 337.847\n",
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            "INFO:tensorflow:global_step/sec: 369.967\n",
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            "INFO:tensorflow:global_step/sec: 368.502\n",
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            "INFO:tensorflow:global_step/sec: 378.75\n",
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            "INFO:tensorflow:global_step/sec: 380.168\n",
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            "INFO:tensorflow:global_step/sec: 383.104\n",
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            "INFO:tensorflow:global_step/sec: 346.799\n",
            "INFO:tensorflow:loss = 1055776800.0, step = 2501 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.987\n",
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            "INFO:tensorflow:global_step/sec: 371.16\n",
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            "INFO:tensorflow:global_step/sec: 387.113\n",
            "INFO:tensorflow:loss = 1114801200.0, step = 2801 (0.264 sec)\n",
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            "INFO:tensorflow:global_step/sec: 423.54\n",
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            "INFO:tensorflow:global_step/sec: 377.088\n",
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            "INFO:tensorflow:global_step/sec: 418.141\n",
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            "INFO:tensorflow:global_step/sec: 372.75\n",
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            "INFO:tensorflow:global_step/sec: 397.335\n",
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            "INFO:tensorflow:global_step/sec: 399.534\n",
            "INFO:tensorflow:loss = 1070299840.0, step = 8801 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.209\n",
            "INFO:tensorflow:loss = 277852640.0, step = 8901 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.646\n",
            "INFO:tensorflow:loss = 549755000.0, step = 9001 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.565\n",
            "INFO:tensorflow:loss = 374597150.0, step = 9101 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.32\n",
            "INFO:tensorflow:loss = 341400860.0, step = 9201 (0.290 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.203\n",
            "INFO:tensorflow:loss = 396756600.0, step = 9301 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 434.253\n",
            "INFO:tensorflow:loss = 412428500.0, step = 9401 (0.221 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.088\n",
            "INFO:tensorflow:loss = 853658700.0, step = 9501 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.681\n",
            "INFO:tensorflow:loss = 200365740.0, step = 9601 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.594\n",
            "INFO:tensorflow:loss = 221041470.0, step = 9701 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.012\n",
            "INFO:tensorflow:loss = 317081500.0, step = 9801 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.642\n",
            "INFO:tensorflow:loss = 880172540.0, step = 9901 (0.271 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...\n",
            "INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...\n",
            "INFO:tensorflow:Loss for final step: 462104060.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:14:00\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-10000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.25704s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:14:01\n",
            "INFO:tensorflow:Saving dict for global step 10000: average_loss = 12997451.0, global_step = 10000, label/mean = 12949.43, loss = 380639650.0, prediction/mean = 12888.132\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tmp4160q49c/model.ckpt-10000\n",
            "scores {'average_loss': 12997451.0, 'label/mean': 12949.43, 'loss': 380639650.0, 'prediction/mean': 12888.132, 'global_step': 10000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-10000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...\n",
            "INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...\n",
            "INFO:tensorflow:loss = 311550820.0, step = 10001\n",
            "INFO:tensorflow:global_step/sec: 331.768\n",
            "INFO:tensorflow:loss = 542849500.0, step = 10101 (0.311 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.664\n",
            "INFO:tensorflow:loss = 157453120.0, step = 10201 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.779\n",
            "INFO:tensorflow:loss = 469791550.0, step = 10301 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 342.078\n",
            "INFO:tensorflow:loss = 686541060.0, step = 10401 (0.294 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.93\n",
            "INFO:tensorflow:loss = 541610600.0, step = 10501 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.934\n",
            "INFO:tensorflow:loss = 489675900.0, step = 10601 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.306\n",
            "INFO:tensorflow:loss = 295169380.0, step = 10701 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.364\n",
            "INFO:tensorflow:loss = 189780370.0, step = 10801 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.302\n",
            "INFO:tensorflow:loss = 700651000.0, step = 10901 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.107\n",
            "INFO:tensorflow:loss = 136910800.0, step = 11001 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.068\n",
            "INFO:tensorflow:loss = 137311310.0, step = 11101 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.82\n",
            "INFO:tensorflow:loss = 233982110.0, step = 11201 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.373\n",
            "INFO:tensorflow:loss = 313679870.0, step = 11301 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.045\n",
            "INFO:tensorflow:loss = 405697860.0, step = 11401 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.083\n",
            "INFO:tensorflow:loss = 169879870.0, step = 11501 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.112\n",
            "INFO:tensorflow:loss = 261293920.0, step = 11601 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.935\n",
            "INFO:tensorflow:loss = 131133680.0, step = 11701 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.401\n",
            "INFO:tensorflow:loss = 825323260.0, step = 11801 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.667\n",
            "INFO:tensorflow:loss = 325738940.0, step = 11901 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.681\n",
            "INFO:tensorflow:loss = 947195000.0, step = 12001 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 350.349\n",
            "INFO:tensorflow:loss = 162654640.0, step = 12101 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.696\n",
            "INFO:tensorflow:loss = 512514800.0, step = 12201 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 339.031\n",
            "INFO:tensorflow:loss = 557082750.0, step = 12301 (0.299 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.426\n",
            "INFO:tensorflow:loss = 292102700.0, step = 12401 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.363\n",
            "INFO:tensorflow:loss = 369812800.0, step = 12501 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.057\n",
            "INFO:tensorflow:loss = 239053570.0, step = 12601 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.326\n",
            "INFO:tensorflow:loss = 742714560.0, step = 12701 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.918\n",
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            "INFO:tensorflow:global_step/sec: 336.008\n",
            "INFO:tensorflow:loss = 407412400.0, step = 12901 (0.302 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.706\n",
            "INFO:tensorflow:loss = 224181500.0, step = 13001 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.599\n",
            "INFO:tensorflow:loss = 406100200.0, step = 13101 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.312\n",
            "INFO:tensorflow:loss = 220380540.0, step = 13201 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.476\n",
            "INFO:tensorflow:loss = 324585730.0, step = 13301 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.215\n",
            "INFO:tensorflow:loss = 206914110.0, step = 13401 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.294\n",
            "INFO:tensorflow:loss = 479118850.0, step = 13501 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.729\n",
            "INFO:tensorflow:loss = 270696670.0, step = 13601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.56\n",
            "INFO:tensorflow:loss = 194030620.0, step = 13701 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.7\n",
            "INFO:tensorflow:loss = 459243500.0, step = 13801 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.893\n",
            "INFO:tensorflow:loss = 623217200.0, step = 13901 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.24\n",
            "INFO:tensorflow:loss = 188181040.0, step = 14001 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.957\n",
            "INFO:tensorflow:loss = 267670260.0, step = 14101 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.898\n",
            "INFO:tensorflow:loss = 1214642700.0, step = 14201 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.36\n",
            "INFO:tensorflow:loss = 480547870.0, step = 14301 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 337.361\n",
            "INFO:tensorflow:loss = 531515460.0, step = 14401 (0.302 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.485\n",
            "INFO:tensorflow:loss = 167661840.0, step = 14501 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.754\n",
            "INFO:tensorflow:loss = 271551420.0, step = 14601 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.472\n",
            "INFO:tensorflow:loss = 194646460.0, step = 14701 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.469\n",
            "INFO:tensorflow:loss = 385375680.0, step = 14801 (0.296 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.692\n",
            "INFO:tensorflow:loss = 422689250.0, step = 14901 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.646\n",
            "INFO:tensorflow:loss = 432805470.0, step = 15001 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.932\n",
            "INFO:tensorflow:loss = 656688260.0, step = 15101 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 324.315\n",
            "INFO:tensorflow:loss = 327328060.0, step = 15201 (0.312 sec)\n",
            "INFO:tensorflow:global_step/sec: 327.937\n",
            "INFO:tensorflow:loss = 249414510.0, step = 15301 (0.303 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.585\n",
            "INFO:tensorflow:loss = 538157300.0, step = 15401 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.701\n",
            "INFO:tensorflow:loss = 330901950.0, step = 15501 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.232\n",
            "INFO:tensorflow:loss = 550099100.0, step = 15601 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.343\n",
            "INFO:tensorflow:loss = 325973630.0, step = 15701 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.228\n",
            "INFO:tensorflow:loss = 212204400.0, step = 15801 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.209\n",
            "INFO:tensorflow:loss = 258239820.0, step = 15901 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.513\n",
            "INFO:tensorflow:loss = 210597500.0, step = 16001 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.37\n",
            "INFO:tensorflow:loss = 714102340.0, step = 16101 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.473\n",
            "INFO:tensorflow:loss = 464967650.0, step = 16201 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.789\n",
            "INFO:tensorflow:loss = 326933250.0, step = 16301 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.832\n",
            "INFO:tensorflow:loss = 699476900.0, step = 16401 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.024\n",
            "INFO:tensorflow:loss = 251687490.0, step = 16501 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.106\n",
            "INFO:tensorflow:loss = 476137280.0, step = 16601 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.279\n",
            "INFO:tensorflow:loss = 450114270.0, step = 16701 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 328.899\n",
            "INFO:tensorflow:loss = 284451420.0, step = 16801 (0.308 sec)\n",
            "INFO:tensorflow:global_step/sec: 323.758\n",
            "INFO:tensorflow:loss = 618428350.0, step = 16901 (0.301 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.417\n",
            "INFO:tensorflow:loss = 551999360.0, step = 17001 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.791\n",
            "INFO:tensorflow:loss = 313116380.0, step = 17101 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.569\n",
            "INFO:tensorflow:loss = 234242200.0, step = 17201 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.207\n",
            "INFO:tensorflow:loss = 381167740.0, step = 17301 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.057\n",
            "INFO:tensorflow:loss = 308140960.0, step = 17401 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.271\n",
            "INFO:tensorflow:loss = 282245280.0, step = 17501 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.688\n",
            "INFO:tensorflow:loss = 229532510.0, step = 17601 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.275\n",
            "INFO:tensorflow:loss = 382824960.0, step = 17701 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.265\n",
            "INFO:tensorflow:loss = 214101150.0, step = 17801 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 427.646\n",
            "INFO:tensorflow:loss = 230665200.0, step = 17901 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.425\n",
            "INFO:tensorflow:loss = 147460460.0, step = 18001 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.298\n",
            "INFO:tensorflow:loss = 386435300.0, step = 18101 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.653\n",
            "INFO:tensorflow:loss = 431819700.0, step = 18201 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.513\n",
            "INFO:tensorflow:loss = 284896130.0, step = 18301 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 329.273\n",
            "INFO:tensorflow:loss = 815575550.0, step = 18401 (0.314 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.793\n",
            "INFO:tensorflow:loss = 211617220.0, step = 18501 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.472\n",
            "INFO:tensorflow:loss = 246854830.0, step = 18601 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 344.693\n",
            "INFO:tensorflow:loss = 196782480.0, step = 18701 (0.294 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.189\n",
            "INFO:tensorflow:loss = 810925400.0, step = 18801 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 343.693\n",
            "INFO:tensorflow:loss = 490495460.0, step = 18901 (0.289 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.927\n",
            "INFO:tensorflow:loss = 444137380.0, step = 19001 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.913\n",
            "INFO:tensorflow:loss = 408950100.0, step = 19101 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.155\n",
            "INFO:tensorflow:loss = 430174530.0, step = 19201 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 336.126\n",
            "INFO:tensorflow:loss = 433226620.0, step = 19301 (0.291 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.329\n",
            "INFO:tensorflow:loss = 627688960.0, step = 19401 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.626\n",
            "INFO:tensorflow:loss = 765363200.0, step = 19501 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.701\n",
            "INFO:tensorflow:loss = 500534700.0, step = 19601 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.173\n",
            "INFO:tensorflow:loss = 289083400.0, step = 19701 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.362\n",
            "INFO:tensorflow:loss = 405062820.0, step = 19801 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.989\n",
            "INFO:tensorflow:loss = 600592300.0, step = 19901 (0.260 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20000...\n",
            "INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20000...\n",
            "INFO:tensorflow:Loss for final step: 434507970.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:14:28\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-20000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.27054s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:14:29\n",
            "INFO:tensorflow:Saving dict for global step 20000: average_loss = 11036717.0, global_step = 20000, label/mean = 12949.43, loss = 323218140.0, prediction/mean = 13105.415\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20000: /tmp/tmp4160q49c/model.ckpt-20000\n",
            "scores {'average_loss': 11036717.0, 'label/mean': 12949.43, 'loss': 323218140.0, 'prediction/mean': 13105.415, 'global_step': 20000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-20000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20000...\n",
            "INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20000...\n",
            "INFO:tensorflow:loss = 529549760.0, step = 20001\n",
            "INFO:tensorflow:global_step/sec: 350.879\n",
            "INFO:tensorflow:loss = 180211790.0, step = 20101 (0.294 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.639\n",
            "INFO:tensorflow:loss = 258160640.0, step = 20201 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.623\n",
            "INFO:tensorflow:loss = 309436030.0, step = 20301 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 335.866\n",
            "INFO:tensorflow:loss = 506549280.0, step = 20401 (0.300 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.537\n",
            "INFO:tensorflow:loss = 110930390.0, step = 20501 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.273\n",
            "INFO:tensorflow:loss = 324990000.0, step = 20601 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.102\n",
            "INFO:tensorflow:loss = 530844580.0, step = 20701 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.279\n",
            "INFO:tensorflow:loss = 188060100.0, step = 20801 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.213\n",
            "INFO:tensorflow:loss = 608396800.0, step = 20901 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.333\n",
            "INFO:tensorflow:loss = 222315700.0, step = 21001 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.344\n",
            "INFO:tensorflow:loss = 349665570.0, step = 21101 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.046\n",
            "INFO:tensorflow:loss = 218689540.0, step = 21201 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.017\n",
            "INFO:tensorflow:loss = 820212300.0, step = 21301 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.908\n",
            "INFO:tensorflow:loss = 327388900.0, step = 21401 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.4\n",
            "INFO:tensorflow:loss = 244546180.0, step = 21501 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 338.19\n",
            "INFO:tensorflow:loss = 117356410.0, step = 21601 (0.295 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.919\n",
            "INFO:tensorflow:loss = 261812200.0, step = 21701 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.242\n",
            "INFO:tensorflow:loss = 557905400.0, step = 21801 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.484\n",
            "INFO:tensorflow:loss = 411112160.0, step = 21901 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.968\n",
            "INFO:tensorflow:loss = 296585600.0, step = 22001 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.159\n",
            "INFO:tensorflow:loss = 240740690.0, step = 22101 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.783\n",
            "INFO:tensorflow:loss = 225128590.0, step = 22201 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.069\n",
            "INFO:tensorflow:loss = 350641700.0, step = 22301 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 428.539\n",
            "INFO:tensorflow:loss = 994944060.0, step = 22401 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 327.075\n",
            "INFO:tensorflow:loss = 295402050.0, step = 22501 (0.299 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.951\n",
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            "INFO:tensorflow:global_step/sec: 439.088\n",
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            "INFO:tensorflow:global_step/sec: 398.785\n",
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            "INFO:tensorflow:global_step/sec: 396.167\n",
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            "INFO:tensorflow:global_step/sec: 427.084\n",
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            "INFO:tensorflow:global_step/sec: 423.484\n",
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            "INFO:tensorflow:global_step/sec: 439.022\n",
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            "INFO:tensorflow:global_step/sec: 435.34\n",
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            "INFO:tensorflow:global_step/sec: 433.324\n",
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            "INFO:tensorflow:global_step/sec: 340.768\n",
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            "INFO:tensorflow:global_step/sec: 376.536\n",
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            "INFO:tensorflow:loss = 359185800.0, step = 28601 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.845\n",
            "INFO:tensorflow:loss = 131860550.0, step = 28701 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.075\n",
            "INFO:tensorflow:loss = 276126900.0, step = 28801 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.6\n",
            "INFO:tensorflow:loss = 302183600.0, step = 28901 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.946\n",
            "INFO:tensorflow:loss = 199525500.0, step = 29001 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.831\n",
            "INFO:tensorflow:loss = 408064900.0, step = 29101 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.648\n",
            "INFO:tensorflow:loss = 790103300.0, step = 29201 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.966\n",
            "INFO:tensorflow:loss = 495910240.0, step = 29301 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.746\n",
            "INFO:tensorflow:loss = 167615920.0, step = 29401 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.622\n",
            "INFO:tensorflow:loss = 189927790.0, step = 29501 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.849\n",
            "INFO:tensorflow:loss = 124617420.0, step = 29601 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.047\n",
            "INFO:tensorflow:loss = 509044540.0, step = 29701 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.901\n",
            "INFO:tensorflow:loss = 559062600.0, step = 29801 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.353\n",
            "INFO:tensorflow:loss = 339012700.0, step = 29901 (0.270 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 30000...\n",
            "INFO:tensorflow:Saving checkpoints for 30000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 30000...\n",
            "INFO:tensorflow:Loss for final step: 363360400.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:14:56\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-30000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.26204s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:14:56\n",
            "INFO:tensorflow:Saving dict for global step 30000: average_loss = 10073705.0, global_step = 30000, label/mean = 12949.43, loss = 295015650.0, prediction/mean = 12721.901\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 30000: /tmp/tmp4160q49c/model.ckpt-30000\n",
            "scores {'average_loss': 10073705.0, 'label/mean': 12949.43, 'loss': 295015650.0, 'prediction/mean': 12721.901, 'global_step': 30000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-30000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 30000...\n",
            "INFO:tensorflow:Saving checkpoints for 30000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 30000...\n",
            "INFO:tensorflow:loss = 316565000.0, step = 30001\n",
            "INFO:tensorflow:global_step/sec: 368.698\n",
            "INFO:tensorflow:loss = 173632620.0, step = 30101 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.218\n",
            "INFO:tensorflow:loss = 386333440.0, step = 30201 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 349.078\n",
            "INFO:tensorflow:loss = 454791520.0, step = 30301 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.61\n",
            "INFO:tensorflow:loss = 337887300.0, step = 30401 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.475\n",
            "INFO:tensorflow:loss = 251989340.0, step = 30501 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.829\n",
            "INFO:tensorflow:loss = 102307896.0, step = 30601 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 346.33\n",
            "INFO:tensorflow:loss = 538387200.0, step = 30701 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.305\n",
            "INFO:tensorflow:loss = 368056800.0, step = 30801 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.898\n",
            "INFO:tensorflow:loss = 424212380.0, step = 30901 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.466\n",
            "INFO:tensorflow:loss = 258229950.0, step = 31001 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.866\n",
            "INFO:tensorflow:loss = 206088860.0, step = 31101 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.722\n",
            "INFO:tensorflow:loss = 286835330.0, step = 31201 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.607\n",
            "INFO:tensorflow:loss = 199325650.0, step = 31301 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.804\n",
            "INFO:tensorflow:loss = 219790660.0, step = 31401 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.183\n",
            "INFO:tensorflow:loss = 245363500.0, step = 31501 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.414\n",
            "INFO:tensorflow:loss = 183469730.0, step = 31601 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.081\n",
            "INFO:tensorflow:loss = 618741200.0, step = 31701 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 350.769\n",
            "INFO:tensorflow:loss = 286322050.0, step = 31801 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.364\n",
            "INFO:tensorflow:loss = 450001600.0, step = 31901 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.125\n",
            "INFO:tensorflow:loss = 319121150.0, step = 32001 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 431.052\n",
            "INFO:tensorflow:loss = 256745000.0, step = 32101 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 442.715\n",
            "INFO:tensorflow:loss = 412553950.0, step = 32201 (0.223 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.848\n",
            "INFO:tensorflow:loss = 123140780.0, step = 32301 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.282\n",
            "INFO:tensorflow:loss = 510815680.0, step = 32401 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 453.72\n",
            "INFO:tensorflow:loss = 355369900.0, step = 32501 (0.214 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.906\n",
            "INFO:tensorflow:loss = 498631800.0, step = 32601 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.979\n",
            "INFO:tensorflow:loss = 385735070.0, step = 32701 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.803\n",
            "INFO:tensorflow:loss = 597934660.0, step = 32801 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.738\n",
            "INFO:tensorflow:loss = 773607100.0, step = 32901 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.604\n",
            "INFO:tensorflow:loss = 438442370.0, step = 33001 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.872\n",
            "INFO:tensorflow:loss = 375612930.0, step = 33101 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.998\n",
            "INFO:tensorflow:loss = 615582300.0, step = 33201 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.602\n",
            "INFO:tensorflow:loss = 617087300.0, step = 33301 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 448.922\n",
            "INFO:tensorflow:loss = 239306380.0, step = 33401 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.694\n",
            "INFO:tensorflow:loss = 375606530.0, step = 33501 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.983\n",
            "INFO:tensorflow:loss = 298536480.0, step = 33601 (0.223 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.719\n",
            "INFO:tensorflow:loss = 540015900.0, step = 33701 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 431.235\n",
            "INFO:tensorflow:loss = 403700260.0, step = 33801 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.983\n",
            "INFO:tensorflow:loss = 490274750.0, step = 33901 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 427.858\n",
            "INFO:tensorflow:loss = 407110820.0, step = 34001 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.252\n",
            "INFO:tensorflow:loss = 584133800.0, step = 34101 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.141\n",
            "INFO:tensorflow:loss = 407539650.0, step = 34201 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 428.759\n",
            "INFO:tensorflow:loss = 430252260.0, step = 34301 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.487\n",
            "INFO:tensorflow:loss = 225729520.0, step = 34401 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.385\n",
            "INFO:tensorflow:loss = 235426540.0, step = 34501 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.323\n",
            "INFO:tensorflow:loss = 687379500.0, step = 34601 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.628\n",
            "INFO:tensorflow:loss = 217740130.0, step = 34701 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 430.731\n",
            "INFO:tensorflow:loss = 170921760.0, step = 34801 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.989\n",
            "INFO:tensorflow:loss = 217149420.0, step = 34901 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 340.112\n",
            "INFO:tensorflow:loss = 444342530.0, step = 35001 (0.294 sec)\n",
            "INFO:tensorflow:global_step/sec: 334.666\n",
            "INFO:tensorflow:loss = 667365900.0, step = 35101 (0.301 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.487\n",
            "INFO:tensorflow:loss = 235268480.0, step = 35201 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.329\n",
            "INFO:tensorflow:loss = 319408860.0, step = 35301 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.361\n",
            "INFO:tensorflow:loss = 278574620.0, step = 35401 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.647\n",
            "INFO:tensorflow:loss = 295622270.0, step = 35501 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.852\n",
            "INFO:tensorflow:loss = 148931950.0, step = 35601 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.498\n",
            "INFO:tensorflow:loss = 290979550.0, step = 35701 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.717\n",
            "INFO:tensorflow:loss = 364677570.0, step = 35801 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.312\n",
            "INFO:tensorflow:loss = 462093020.0, step = 35901 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.534\n",
            "INFO:tensorflow:loss = 186216530.0, step = 36001 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.392\n",
            "INFO:tensorflow:loss = 309218270.0, step = 36101 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.706\n",
            "INFO:tensorflow:loss = 158935970.0, step = 36201 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.671\n",
            "INFO:tensorflow:loss = 303435330.0, step = 36301 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.074\n",
            "INFO:tensorflow:loss = 291628740.0, step = 36401 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.428\n",
            "INFO:tensorflow:loss = 485348500.0, step = 36501 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.916\n",
            "INFO:tensorflow:loss = 243955090.0, step = 36601 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.307\n",
            "INFO:tensorflow:loss = 355846240.0, step = 36701 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 343.629\n",
            "INFO:tensorflow:loss = 425257000.0, step = 36801 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.891\n",
            "INFO:tensorflow:loss = 254496690.0, step = 36901 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.374\n",
            "INFO:tensorflow:loss = 275145300.0, step = 37001 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.58\n",
            "INFO:tensorflow:loss = 178580180.0, step = 37101 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.624\n",
            "INFO:tensorflow:loss = 346020450.0, step = 37201 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 436.024\n",
            "INFO:tensorflow:loss = 221262400.0, step = 37301 (0.218 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.161\n",
            "INFO:tensorflow:loss = 351939230.0, step = 37401 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 428.78\n",
            "INFO:tensorflow:loss = 155699660.0, step = 37501 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.524\n",
            "INFO:tensorflow:loss = 627596900.0, step = 37601 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.318\n",
            "INFO:tensorflow:loss = 479863140.0, step = 37701 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.185\n",
            "INFO:tensorflow:loss = 545838660.0, step = 37801 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.267\n",
            "INFO:tensorflow:loss = 185135220.0, step = 37901 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 337.849\n",
            "INFO:tensorflow:loss = 568721000.0, step = 38001 (0.295 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.388\n",
            "INFO:tensorflow:loss = 383671200.0, step = 38101 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.262\n",
            "INFO:tensorflow:loss = 139897490.0, step = 38201 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.586\n",
            "INFO:tensorflow:loss = 593533060.0, step = 38301 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.702\n",
            "INFO:tensorflow:loss = 222331490.0, step = 38401 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.678\n",
            "INFO:tensorflow:loss = 636389400.0, step = 38501 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.196\n",
            "INFO:tensorflow:loss = 312698500.0, step = 38601 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.483\n",
            "INFO:tensorflow:loss = 247071860.0, step = 38701 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.069\n",
            "INFO:tensorflow:loss = 420496670.0, step = 38801 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.895\n",
            "INFO:tensorflow:loss = 602511900.0, step = 38901 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.648\n",
            "INFO:tensorflow:loss = 186824940.0, step = 39001 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 336.219\n",
            "INFO:tensorflow:loss = 279030340.0, step = 39101 (0.300 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.272\n",
            "INFO:tensorflow:loss = 152841550.0, step = 39201 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.588\n",
            "INFO:tensorflow:loss = 612138200.0, step = 39301 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.866\n",
            "INFO:tensorflow:loss = 180319140.0, step = 39401 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.324\n",
            "INFO:tensorflow:loss = 348190100.0, step = 39501 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.343\n",
            "INFO:tensorflow:loss = 200390300.0, step = 39601 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.152\n",
            "INFO:tensorflow:loss = 148909920.0, step = 39701 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.698\n",
            "INFO:tensorflow:loss = 298350720.0, step = 39801 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.02\n",
            "INFO:tensorflow:loss = 489076400.0, step = 39901 (0.261 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 40000...\n",
            "INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 40000...\n",
            "INFO:tensorflow:Loss for final step: 466784770.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:15:24\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-40000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.28568s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:15:24\n",
            "INFO:tensorflow:Saving dict for global step 40000: average_loss = 9500731.0, global_step = 40000, label/mean = 12949.43, loss = 278235700.0, prediction/mean = 13249.383\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 40000: /tmp/tmp4160q49c/model.ckpt-40000\n",
            "scores {'average_loss': 9500731.0, 'label/mean': 12949.43, 'loss': 278235700.0, 'prediction/mean': 13249.383, 'global_step': 40000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-40000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 40000...\n",
            "INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 40000...\n",
            "INFO:tensorflow:loss = 372181820.0, step = 40001\n",
            "INFO:tensorflow:global_step/sec: 387.782\n",
            "INFO:tensorflow:loss = 164427100.0, step = 40101 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.199\n",
            "INFO:tensorflow:loss = 233421440.0, step = 40201 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.86\n",
            "INFO:tensorflow:loss = 147144780.0, step = 40301 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.593\n",
            "INFO:tensorflow:loss = 227770690.0, step = 40401 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 312.882\n",
            "INFO:tensorflow:loss = 230828400.0, step = 40501 (0.328 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.795\n",
            "INFO:tensorflow:loss = 222479300.0, step = 40601 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.132\n",
            "INFO:tensorflow:loss = 544079700.0, step = 40701 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.5\n",
            "INFO:tensorflow:loss = 302411460.0, step = 40801 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.122\n",
            "INFO:tensorflow:loss = 332679000.0, step = 40901 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.668\n",
            "INFO:tensorflow:loss = 356101700.0, step = 41001 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.466\n",
            "INFO:tensorflow:loss = 415018180.0, step = 41101 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.226\n",
            "INFO:tensorflow:loss = 213717200.0, step = 41201 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.184\n",
            "INFO:tensorflow:loss = 302245120.0, step = 41301 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.655\n",
            "INFO:tensorflow:loss = 220968130.0, step = 41401 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 452.826\n",
            "INFO:tensorflow:loss = 293099900.0, step = 41501 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.351\n",
            "INFO:tensorflow:loss = 411007870.0, step = 41601 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.798\n",
            "INFO:tensorflow:loss = 301980100.0, step = 41701 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.776\n",
            "INFO:tensorflow:loss = 136270270.0, step = 41801 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.887\n",
            "INFO:tensorflow:loss = 154823550.0, step = 41901 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.223\n",
            "INFO:tensorflow:loss = 189831200.0, step = 42001 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.224\n",
            "INFO:tensorflow:loss = 328071360.0, step = 42101 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.535\n",
            "INFO:tensorflow:loss = 167335000.0, step = 42201 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 333.645\n",
            "INFO:tensorflow:loss = 212000450.0, step = 42301 (0.289 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.962\n",
            "INFO:tensorflow:loss = 391143100.0, step = 42401 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.117\n",
            "INFO:tensorflow:loss = 391832450.0, step = 42501 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.947\n",
            "INFO:tensorflow:loss = 430365730.0, step = 42601 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.391\n",
            "INFO:tensorflow:loss = 256277310.0, step = 42701 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.672\n",
            "INFO:tensorflow:loss = 276208740.0, step = 42801 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.707\n",
            "INFO:tensorflow:loss = 434771550.0, step = 42901 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.212\n",
            "INFO:tensorflow:loss = 134558700.0, step = 43001 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.233\n",
            "INFO:tensorflow:loss = 220719660.0, step = 43101 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.742\n",
            "INFO:tensorflow:loss = 293274200.0, step = 43201 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.684\n",
            "INFO:tensorflow:loss = 206688030.0, step = 43301 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.114\n",
            "INFO:tensorflow:loss = 323994880.0, step = 43401 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.662\n",
            "INFO:tensorflow:loss = 324976450.0, step = 43501 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.577\n",
            "INFO:tensorflow:loss = 546014900.0, step = 43601 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.466\n",
            "INFO:tensorflow:loss = 473818560.0, step = 43701 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.05\n",
            "INFO:tensorflow:loss = 550239940.0, step = 43801 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.683\n",
            "INFO:tensorflow:loss = 288932740.0, step = 43901 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.096\n",
            "INFO:tensorflow:loss = 187384690.0, step = 44001 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.563\n",
            "INFO:tensorflow:loss = 126154230.0, step = 44101 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.635\n",
            "INFO:tensorflow:loss = 442119460.0, step = 44201 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.366\n",
            "INFO:tensorflow:loss = 687694600.0, step = 44301 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.885\n",
            "INFO:tensorflow:loss = 290498050.0, step = 44401 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.969\n",
            "INFO:tensorflow:loss = 192990980.0, step = 44501 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.284\n",
            "INFO:tensorflow:loss = 305434780.0, step = 44601 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.251\n",
            "INFO:tensorflow:loss = 211052110.0, step = 44701 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.856\n",
            "INFO:tensorflow:loss = 474282140.0, step = 44801 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.119\n",
            "INFO:tensorflow:loss = 274106750.0, step = 44901 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.651\n",
            "INFO:tensorflow:loss = 200014430.0, step = 45001 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.467\n",
            "INFO:tensorflow:loss = 210582460.0, step = 45101 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.51\n",
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            "INFO:tensorflow:global_step/sec: 438.897\n",
            "INFO:tensorflow:loss = 212014860.0, step = 45301 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.644\n",
            "INFO:tensorflow:loss = 388958270.0, step = 45401 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.297\n",
            "INFO:tensorflow:loss = 368404860.0, step = 45501 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.263\n",
            "INFO:tensorflow:loss = 229315280.0, step = 45601 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.828\n",
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            "INFO:tensorflow:loss = 515191870.0, step = 45801 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.818\n",
            "INFO:tensorflow:loss = 396750200.0, step = 45901 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 349.883\n",
            "INFO:tensorflow:loss = 278297400.0, step = 46001 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 337.784\n",
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            "INFO:tensorflow:global_step/sec: 362.051\n",
            "INFO:tensorflow:loss = 272206000.0, step = 46201 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 246.635\n",
            "INFO:tensorflow:loss = 320919970.0, step = 46301 (0.400 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.953\n",
            "INFO:tensorflow:loss = 278143840.0, step = 46401 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 349.608\n",
            "INFO:tensorflow:loss = 479091940.0, step = 46501 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.944\n",
            "INFO:tensorflow:loss = 257743870.0, step = 46601 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.303\n",
            "INFO:tensorflow:loss = 231706620.0, step = 46701 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.906\n",
            "INFO:tensorflow:loss = 377306720.0, step = 46801 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 336.769\n",
            "INFO:tensorflow:loss = 524936400.0, step = 46901 (0.301 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.12\n",
            "INFO:tensorflow:loss = 198025630.0, step = 47001 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 337.866\n",
            "INFO:tensorflow:loss = 314792960.0, step = 47101 (0.292 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.919\n",
            "INFO:tensorflow:loss = 226278050.0, step = 47201 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.983\n",
            "INFO:tensorflow:loss = 211634130.0, step = 47301 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 324.575\n",
            "INFO:tensorflow:loss = 237500180.0, step = 47401 (0.311 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.281\n",
            "INFO:tensorflow:loss = 297995420.0, step = 47501 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.291\n",
            "INFO:tensorflow:loss = 252160270.0, step = 47601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.761\n",
            "INFO:tensorflow:loss = 162551970.0, step = 47701 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.294\n",
            "INFO:tensorflow:loss = 495460100.0, step = 47801 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.684\n",
            "INFO:tensorflow:loss = 287617250.0, step = 47901 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 351.027\n",
            "INFO:tensorflow:loss = 396134340.0, step = 48001 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 339.224\n",
            "INFO:tensorflow:loss = 231895550.0, step = 48101 (0.296 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.009\n",
            "INFO:tensorflow:loss = 295171600.0, step = 48201 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.14\n",
            "INFO:tensorflow:loss = 274073150.0, step = 48301 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.182\n",
            "INFO:tensorflow:loss = 331553200.0, step = 48401 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.249\n",
            "INFO:tensorflow:loss = 245544530.0, step = 48501 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.254\n",
            "INFO:tensorflow:loss = 295314880.0, step = 48601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.874\n",
            "INFO:tensorflow:loss = 426664800.0, step = 48701 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.778\n",
            "INFO:tensorflow:loss = 232915520.0, step = 48801 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 340.62\n",
            "INFO:tensorflow:loss = 129215360.0, step = 48901 (0.291 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.17\n",
            "INFO:tensorflow:loss = 345481340.0, step = 49001 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.055\n",
            "INFO:tensorflow:loss = 273271300.0, step = 49101 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.799\n",
            "INFO:tensorflow:loss = 848708300.0, step = 49201 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.004\n",
            "INFO:tensorflow:loss = 239398420.0, step = 49301 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.05\n",
            "INFO:tensorflow:loss = 272252350.0, step = 49401 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.868\n",
            "INFO:tensorflow:loss = 130375096.0, step = 49501 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.27\n",
            "INFO:tensorflow:loss = 219830900.0, step = 49601 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.783\n",
            "INFO:tensorflow:loss = 199362240.0, step = 49701 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.599\n",
            "INFO:tensorflow:loss = 390439200.0, step = 49801 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.624\n",
            "INFO:tensorflow:loss = 432366340.0, step = 49901 (0.248 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50000...\n",
            "INFO:tensorflow:Saving checkpoints for 50000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50000...\n",
            "INFO:tensorflow:Loss for final step: 105871100.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:15:52\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-50000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.26670s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:15:52\n",
            "INFO:tensorflow:Saving dict for global step 50000: average_loss = 9498326.0, global_step = 50000, label/mean = 12949.43, loss = 278165250.0, prediction/mean = 12483.636\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 50000: /tmp/tmp4160q49c/model.ckpt-50000\n",
            "scores {'average_loss': 9498326.0, 'label/mean': 12949.43, 'loss': 278165250.0, 'prediction/mean': 12483.636, 'global_step': 50000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-50000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50000...\n",
            "INFO:tensorflow:Saving checkpoints for 50000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50000...\n",
            "INFO:tensorflow:loss = 215058430.0, step = 50001\n",
            "INFO:tensorflow:global_step/sec: 392.56\n",
            "INFO:tensorflow:loss = 421768400.0, step = 50101 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.787\n",
            "INFO:tensorflow:loss = 244558980.0, step = 50201 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.37\n",
            "INFO:tensorflow:loss = 153733630.0, step = 50301 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.443\n",
            "INFO:tensorflow:loss = 388792930.0, step = 50401 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 436.508\n",
            "INFO:tensorflow:loss = 142340220.0, step = 50501 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 337.201\n",
            "INFO:tensorflow:loss = 229809310.0, step = 50601 (0.290 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.682\n",
            "INFO:tensorflow:loss = 477220200.0, step = 50701 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.671\n",
            "INFO:tensorflow:loss = 318775460.0, step = 50801 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.654\n",
            "INFO:tensorflow:loss = 207536110.0, step = 50901 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.341\n",
            "INFO:tensorflow:loss = 366057540.0, step = 51001 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.926\n",
            "INFO:tensorflow:loss = 116554680.0, step = 51101 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 343.345\n",
            "INFO:tensorflow:loss = 203819340.0, step = 51201 (0.297 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.647\n",
            "INFO:tensorflow:loss = 101068220.0, step = 51301 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 350.257\n",
            "INFO:tensorflow:loss = 529584600.0, step = 51401 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.069\n",
            "INFO:tensorflow:loss = 556883800.0, step = 51501 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.341\n",
            "INFO:tensorflow:loss = 170486050.0, step = 51601 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.802\n",
            "INFO:tensorflow:loss = 175816830.0, step = 51701 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.314\n",
            "INFO:tensorflow:loss = 212204700.0, step = 51801 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 346.844\n",
            "INFO:tensorflow:loss = 275243230.0, step = 51901 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.408\n",
            "INFO:tensorflow:loss = 765239360.0, step = 52001 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.612\n",
            "INFO:tensorflow:loss = 123892980.0, step = 52101 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.521\n",
            "INFO:tensorflow:loss = 217745520.0, step = 52201 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.471\n",
            "INFO:tensorflow:loss = 653725100.0, step = 52301 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.583\n",
            "INFO:tensorflow:loss = 208787140.0, step = 52401 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.851\n",
            "INFO:tensorflow:loss = 246051580.0, step = 52501 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.021\n",
            "INFO:tensorflow:loss = 445866370.0, step = 52601 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.337\n",
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            "INFO:tensorflow:global_step/sec: 373.577\n",
            "INFO:tensorflow:loss = 575565950.0, step = 52801 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.226\n",
            "INFO:tensorflow:loss = 420198980.0, step = 52901 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.695\n",
            "INFO:tensorflow:loss = 589653760.0, step = 53001 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.847\n",
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            "INFO:tensorflow:global_step/sec: 419.147\n",
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            "INFO:tensorflow:global_step/sec: 365.953\n",
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            "INFO:tensorflow:global_step/sec: 411.776\n",
            "INFO:tensorflow:loss = 504762620.0, step = 56701 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 436.351\n",
            "INFO:tensorflow:loss = 404498370.0, step = 56801 (0.222 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.842\n",
            "INFO:tensorflow:loss = 224203800.0, step = 56901 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.333\n",
            "INFO:tensorflow:loss = 523257300.0, step = 57001 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 449.876\n",
            "INFO:tensorflow:loss = 352680860.0, step = 57101 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 345.234\n",
            "INFO:tensorflow:loss = 387035900.0, step = 57201 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.509\n",
            "INFO:tensorflow:loss = 518377630.0, step = 57301 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.235\n",
            "INFO:tensorflow:loss = 270860540.0, step = 57401 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 435.53\n",
            "INFO:tensorflow:loss = 395204260.0, step = 57501 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.428\n",
            "INFO:tensorflow:loss = 169127000.0, step = 57601 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.985\n",
            "INFO:tensorflow:loss = 423388640.0, step = 57701 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.166\n",
            "INFO:tensorflow:loss = 511683140.0, step = 57801 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 440.382\n",
            "INFO:tensorflow:loss = 369409000.0, step = 57901 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.225\n",
            "INFO:tensorflow:loss = 99751570.0, step = 58001 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.44\n",
            "INFO:tensorflow:loss = 355937000.0, step = 58101 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.322\n",
            "INFO:tensorflow:loss = 269259740.0, step = 58201 (0.292 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.499\n",
            "INFO:tensorflow:loss = 526673470.0, step = 58301 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.533\n",
            "INFO:tensorflow:loss = 559831230.0, step = 58401 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.719\n",
            "INFO:tensorflow:loss = 608469300.0, step = 58501 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.639\n",
            "INFO:tensorflow:loss = 198316860.0, step = 58601 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.121\n",
            "INFO:tensorflow:loss = 476052300.0, step = 58701 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.04\n",
            "INFO:tensorflow:loss = 216364820.0, step = 58801 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.2\n",
            "INFO:tensorflow:loss = 167683100.0, step = 58901 (0.401 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.304\n",
            "INFO:tensorflow:loss = 379453600.0, step = 59001 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.719\n",
            "INFO:tensorflow:loss = 320629060.0, step = 59101 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.972\n",
            "INFO:tensorflow:loss = 268227520.0, step = 59201 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.817\n",
            "INFO:tensorflow:loss = 251896350.0, step = 59301 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.746\n",
            "INFO:tensorflow:loss = 249136350.0, step = 59401 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.352\n",
            "INFO:tensorflow:loss = 162170110.0, step = 59501 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.128\n",
            "INFO:tensorflow:loss = 165426340.0, step = 59601 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.811\n",
            "INFO:tensorflow:loss = 172566510.0, step = 59701 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.928\n",
            "INFO:tensorflow:loss = 527189980.0, step = 59801 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.894\n",
            "INFO:tensorflow:loss = 311730600.0, step = 59901 (0.250 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 60000...\n",
            "INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 60000...\n",
            "INFO:tensorflow:Loss for final step: 209784620.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:16:20\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-60000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.27021s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:16:20\n",
            "INFO:tensorflow:Saving dict for global step 60000: average_loss = 9090951.0, global_step = 60000, label/mean = 12949.43, loss = 266234990.0, prediction/mean = 12793.32\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 60000: /tmp/tmp4160q49c/model.ckpt-60000\n",
            "scores {'average_loss': 9090951.0, 'label/mean': 12949.43, 'loss': 266234990.0, 'prediction/mean': 12793.32, 'global_step': 60000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-60000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 60000...\n",
            "INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 60000...\n",
            "INFO:tensorflow:loss = 370220580.0, step = 60001\n",
            "INFO:tensorflow:global_step/sec: 342.898\n",
            "INFO:tensorflow:loss = 330298500.0, step = 60101 (0.305 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.246\n",
            "INFO:tensorflow:loss = 347736350.0, step = 60201 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.201\n",
            "INFO:tensorflow:loss = 138767580.0, step = 60301 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.274\n",
            "INFO:tensorflow:loss = 394691170.0, step = 60401 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.75\n",
            "INFO:tensorflow:loss = 86055910.0, step = 60501 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.451\n",
            "INFO:tensorflow:loss = 295607200.0, step = 60601 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.495\n",
            "INFO:tensorflow:loss = 271291940.0, step = 60701 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.29\n",
            "INFO:tensorflow:loss = 168840820.0, step = 60801 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.847\n",
            "INFO:tensorflow:loss = 439630560.0, step = 60901 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.919\n",
            "INFO:tensorflow:loss = 344592060.0, step = 61001 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.131\n",
            "INFO:tensorflow:loss = 259533280.0, step = 61101 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.997\n",
            "INFO:tensorflow:loss = 580489100.0, step = 61201 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.676\n",
            "INFO:tensorflow:loss = 231745310.0, step = 61301 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.997\n",
            "INFO:tensorflow:loss = 551416800.0, step = 61401 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.821\n",
            "INFO:tensorflow:loss = 268622900.0, step = 61501 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.611\n",
            "INFO:tensorflow:loss = 183255520.0, step = 61601 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.996\n",
            "INFO:tensorflow:loss = 149241820.0, step = 61701 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.339\n",
            "INFO:tensorflow:loss = 845714750.0, step = 61801 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.949\n",
            "INFO:tensorflow:loss = 180541170.0, step = 61901 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.371\n",
            "INFO:tensorflow:loss = 203868980.0, step = 62001 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.871\n",
            "INFO:tensorflow:loss = 346471680.0, step = 62101 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.865\n",
            "INFO:tensorflow:loss = 104594040.0, step = 62201 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.437\n",
            "INFO:tensorflow:loss = 212496080.0, step = 62301 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.021\n",
            "INFO:tensorflow:loss = 425324060.0, step = 62401 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.432\n",
            "INFO:tensorflow:loss = 231919950.0, step = 62501 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.467\n",
            "INFO:tensorflow:loss = 427145760.0, step = 62601 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.906\n",
            "INFO:tensorflow:loss = 439723620.0, step = 62701 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 336.316\n",
            "INFO:tensorflow:loss = 211249100.0, step = 62801 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 328.75\n",
            "INFO:tensorflow:loss = 346095840.0, step = 62901 (0.312 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.368\n",
            "INFO:tensorflow:loss = 209542400.0, step = 63001 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.082\n",
            "INFO:tensorflow:loss = 415861630.0, step = 63101 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.391\n",
            "INFO:tensorflow:loss = 168434160.0, step = 63201 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 436.458\n",
            "INFO:tensorflow:loss = 137150830.0, step = 63301 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.702\n",
            "INFO:tensorflow:loss = 698590850.0, step = 63401 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.841\n",
            "INFO:tensorflow:loss = 224160500.0, step = 63501 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.305\n",
            "INFO:tensorflow:loss = 278634340.0, step = 63601 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.406\n",
            "INFO:tensorflow:loss = 276832640.0, step = 63701 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.197\n",
            "INFO:tensorflow:loss = 359939780.0, step = 63801 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.441\n",
            "INFO:tensorflow:loss = 556488960.0, step = 63901 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.832\n",
            "INFO:tensorflow:loss = 373690050.0, step = 64001 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.352\n",
            "INFO:tensorflow:loss = 318373900.0, step = 64101 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.167\n",
            "INFO:tensorflow:loss = 442395550.0, step = 64201 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.91\n",
            "INFO:tensorflow:loss = 273253730.0, step = 64301 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.577\n",
            "INFO:tensorflow:loss = 354038500.0, step = 64401 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.99\n",
            "INFO:tensorflow:loss = 406886820.0, step = 64501 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.696\n",
            "INFO:tensorflow:loss = 257018620.0, step = 64601 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.17\n",
            "INFO:tensorflow:loss = 157310610.0, step = 64701 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.713\n",
            "INFO:tensorflow:loss = 287249300.0, step = 64801 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.493\n",
            "INFO:tensorflow:loss = 334541630.0, step = 64901 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.554\n",
            "INFO:tensorflow:loss = 231714200.0, step = 65001 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.314\n",
            "INFO:tensorflow:loss = 339073200.0, step = 65101 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.334\n",
            "INFO:tensorflow:loss = 224584340.0, step = 65201 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.491\n",
            "INFO:tensorflow:loss = 178459820.0, step = 65301 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 350.139\n",
            "INFO:tensorflow:loss = 486319170.0, step = 65401 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.665\n",
            "INFO:tensorflow:loss = 120986370.0, step = 65501 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.47\n",
            "INFO:tensorflow:loss = 208594270.0, step = 65601 (0.293 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.645\n",
            "INFO:tensorflow:loss = 269952060.0, step = 65701 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 351.548\n",
            "INFO:tensorflow:loss = 235712160.0, step = 65801 (0.289 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.255\n",
            "INFO:tensorflow:loss = 208227380.0, step = 65901 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.437\n",
            "INFO:tensorflow:loss = 572251100.0, step = 66001 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.667\n",
            "INFO:tensorflow:loss = 311313700.0, step = 66101 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 334.636\n",
            "INFO:tensorflow:loss = 224967820.0, step = 66201 (0.291 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.039\n",
            "INFO:tensorflow:loss = 549896060.0, step = 66301 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.261\n",
            "INFO:tensorflow:loss = 117135800.0, step = 66401 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.168\n",
            "INFO:tensorflow:loss = 300571500.0, step = 66501 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.819\n",
            "INFO:tensorflow:loss = 276600420.0, step = 66601 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 322.419\n",
            "INFO:tensorflow:loss = 273273500.0, step = 66701 (0.313 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.678\n",
            "INFO:tensorflow:loss = 377443420.0, step = 66801 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.636\n",
            "INFO:tensorflow:loss = 550360450.0, step = 66901 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.102\n",
            "INFO:tensorflow:loss = 211940140.0, step = 67001 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.265\n",
            "INFO:tensorflow:loss = 575340900.0, step = 67101 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.684\n",
            "INFO:tensorflow:loss = 159085840.0, step = 67201 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.704\n",
            "INFO:tensorflow:loss = 377249700.0, step = 67301 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.256\n",
            "INFO:tensorflow:loss = 424013760.0, step = 67401 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.289\n",
            "INFO:tensorflow:loss = 466053700.0, step = 67501 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.754\n",
            "INFO:tensorflow:loss = 164312370.0, step = 67601 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.716\n",
            "INFO:tensorflow:loss = 247831070.0, step = 67701 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.983\n",
            "INFO:tensorflow:loss = 458102300.0, step = 67801 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.425\n",
            "INFO:tensorflow:loss = 311806720.0, step = 67901 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.054\n",
            "INFO:tensorflow:loss = 258195650.0, step = 68001 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.328\n",
            "INFO:tensorflow:loss = 276815600.0, step = 68101 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 343.601\n",
            "INFO:tensorflow:loss = 441581220.0, step = 68201 (0.292 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.66\n",
            "INFO:tensorflow:loss = 438279420.0, step = 68301 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 354.084\n",
            "INFO:tensorflow:loss = 549849800.0, step = 68401 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.086\n",
            "INFO:tensorflow:loss = 215526380.0, step = 68501 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.919\n",
            "INFO:tensorflow:loss = 466810600.0, step = 68601 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.945\n",
            "INFO:tensorflow:loss = 225760160.0, step = 68701 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.34\n",
            "INFO:tensorflow:loss = 215532670.0, step = 68801 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 351.581\n",
            "INFO:tensorflow:loss = 200012220.0, step = 68901 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.306\n",
            "INFO:tensorflow:loss = 361465660.0, step = 69001 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.432\n",
            "INFO:tensorflow:loss = 105383880.0, step = 69101 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.239\n",
            "INFO:tensorflow:loss = 791133300.0, step = 69201 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 332.994\n",
            "INFO:tensorflow:loss = 256767060.0, step = 69301 (0.298 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.392\n",
            "INFO:tensorflow:loss = 342639040.0, step = 69401 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.95\n",
            "INFO:tensorflow:loss = 193483620.0, step = 69501 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.834\n",
            "INFO:tensorflow:loss = 104191816.0, step = 69601 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.071\n",
            "INFO:tensorflow:loss = 278830900.0, step = 69701 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.71\n",
            "INFO:tensorflow:loss = 423080580.0, step = 69801 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.558\n",
            "INFO:tensorflow:loss = 577198100.0, step = 69901 (0.258 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 70000...\n",
            "INFO:tensorflow:Saving checkpoints for 70000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 70000...\n",
            "INFO:tensorflow:Loss for final step: 201961860.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:16:48\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-70000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.28571s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:16:48\n",
            "INFO:tensorflow:Saving dict for global step 70000: average_loss = 8841581.0, global_step = 70000, label/mean = 12949.43, loss = 258932020.0, prediction/mean = 12959.729\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 70000: /tmp/tmp4160q49c/model.ckpt-70000\n",
            "scores {'average_loss': 8841581.0, 'label/mean': 12949.43, 'loss': 258932020.0, 'prediction/mean': 12959.729, 'global_step': 70000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-70000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 70000...\n",
            "INFO:tensorflow:Saving checkpoints for 70000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 70000...\n",
            "INFO:tensorflow:loss = 428315170.0, step = 70001\n",
            "INFO:tensorflow:global_step/sec: 364.384\n",
            "INFO:tensorflow:loss = 183832590.0, step = 70101 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.524\n",
            "INFO:tensorflow:loss = 200241460.0, step = 70201 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.322\n",
            "INFO:tensorflow:loss = 123590470.0, step = 70301 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.645\n",
            "INFO:tensorflow:loss = 98790350.0, step = 70401 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.424\n",
            "INFO:tensorflow:loss = 438668160.0, step = 70501 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.779\n",
            "INFO:tensorflow:loss = 223350240.0, step = 70601 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.188\n",
            "INFO:tensorflow:loss = 128460670.0, step = 70701 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.178\n",
            "INFO:tensorflow:loss = 272638430.0, step = 70801 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.044\n",
            "INFO:tensorflow:loss = 592124700.0, step = 70901 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.098\n",
            "INFO:tensorflow:loss = 494252400.0, step = 71001 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.266\n",
            "INFO:tensorflow:loss = 506571230.0, step = 71101 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.749\n",
            "INFO:tensorflow:loss = 171294880.0, step = 71201 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.094\n",
            "INFO:tensorflow:loss = 131046070.0, step = 71301 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.189\n",
            "INFO:tensorflow:loss = 572948000.0, step = 71401 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.144\n",
            "INFO:tensorflow:loss = 378933000.0, step = 71501 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.069\n",
            "INFO:tensorflow:loss = 160108420.0, step = 71601 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.179\n",
            "INFO:tensorflow:loss = 121279700.0, step = 71701 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.506\n",
            "INFO:tensorflow:loss = 136057140.0, step = 71801 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.255\n",
            "INFO:tensorflow:loss = 330964200.0, step = 71901 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.701\n",
            "INFO:tensorflow:loss = 175537860.0, step = 72001 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.459\n",
            "INFO:tensorflow:loss = 128677064.0, step = 72101 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.673\n",
            "INFO:tensorflow:loss = 234035680.0, step = 72201 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.749\n",
            "INFO:tensorflow:loss = 457345570.0, step = 72301 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.865\n",
            "INFO:tensorflow:loss = 194854640.0, step = 72401 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.311\n",
            "INFO:tensorflow:loss = 268470700.0, step = 72501 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.807\n",
            "INFO:tensorflow:loss = 363656450.0, step = 72601 (0.292 sec)\n",
            "INFO:tensorflow:global_step/sec: 432.986\n",
            "INFO:tensorflow:loss = 104653310.0, step = 72701 (0.219 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.806\n",
            "INFO:tensorflow:loss = 543867650.0, step = 72801 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.245\n",
            "INFO:tensorflow:loss = 497033540.0, step = 72901 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.772\n",
            "INFO:tensorflow:loss = 318082560.0, step = 73001 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.781\n",
            "INFO:tensorflow:loss = 407388500.0, step = 73101 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.793\n",
            "INFO:tensorflow:loss = 212136740.0, step = 73201 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.291\n",
            "INFO:tensorflow:loss = 329566050.0, step = 73301 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.926\n",
            "INFO:tensorflow:loss = 436324960.0, step = 73401 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 343.01\n",
            "INFO:tensorflow:loss = 187836600.0, step = 73501 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.676\n",
            "INFO:tensorflow:loss = 277835230.0, step = 73601 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.711\n",
            "INFO:tensorflow:loss = 214464910.0, step = 73701 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.893\n",
            "INFO:tensorflow:loss = 268271950.0, step = 73801 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.568\n",
            "INFO:tensorflow:loss = 169185170.0, step = 73901 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.904\n",
            "INFO:tensorflow:loss = 149147980.0, step = 74001 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.642\n",
            "INFO:tensorflow:loss = 161601120.0, step = 74101 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.524\n",
            "INFO:tensorflow:loss = 163494130.0, step = 74201 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.524\n",
            "INFO:tensorflow:loss = 192932940.0, step = 74301 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.747\n",
            "INFO:tensorflow:loss = 630177600.0, step = 74401 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.793\n",
            "INFO:tensorflow:loss = 200680420.0, step = 74501 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.113\n",
            "INFO:tensorflow:loss = 200136480.0, step = 74601 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 350.536\n",
            "INFO:tensorflow:loss = 315949570.0, step = 74701 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.136\n",
            "INFO:tensorflow:loss = 167288140.0, step = 74801 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.435\n",
            "INFO:tensorflow:loss = 640546000.0, step = 74901 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.154\n",
            "INFO:tensorflow:loss = 151243420.0, step = 75001 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.284\n",
            "INFO:tensorflow:loss = 365612540.0, step = 75101 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.836\n",
            "INFO:tensorflow:loss = 195175120.0, step = 75201 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.533\n",
            "INFO:tensorflow:loss = 570805900.0, step = 75301 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.46\n",
            "INFO:tensorflow:loss = 166142260.0, step = 75401 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.72\n",
            "INFO:tensorflow:loss = 169536580.0, step = 75501 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.903\n",
            "INFO:tensorflow:loss = 171713760.0, step = 75601 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.072\n",
            "INFO:tensorflow:loss = 290365920.0, step = 75701 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 330.241\n",
            "INFO:tensorflow:loss = 333193180.0, step = 75801 (0.306 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.807\n",
            "INFO:tensorflow:loss = 177532240.0, step = 75901 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 333.876\n",
            "INFO:tensorflow:loss = 544213000.0, step = 76001 (0.298 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.417\n",
            "INFO:tensorflow:loss = 157561150.0, step = 76101 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.003\n",
            "INFO:tensorflow:loss = 462319420.0, step = 76201 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.4\n",
            "INFO:tensorflow:loss = 224796690.0, step = 76301 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.068\n",
            "INFO:tensorflow:loss = 161366780.0, step = 76401 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.071\n",
            "INFO:tensorflow:loss = 206867410.0, step = 76501 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 328.705\n",
            "INFO:tensorflow:loss = 327112830.0, step = 76601 (0.309 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.011\n",
            "INFO:tensorflow:loss = 256545650.0, step = 76701 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.965\n",
            "INFO:tensorflow:loss = 457522370.0, step = 76801 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.26\n",
            "INFO:tensorflow:loss = 133366690.0, step = 76901 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.677\n",
            "INFO:tensorflow:loss = 206092060.0, step = 77001 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.08\n",
            "INFO:tensorflow:loss = 265130020.0, step = 77101 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.826\n",
            "INFO:tensorflow:loss = 504311070.0, step = 77201 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.52\n",
            "INFO:tensorflow:loss = 329001060.0, step = 77301 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.232\n",
            "INFO:tensorflow:loss = 90054040.0, step = 77401 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.972\n",
            "INFO:tensorflow:loss = 368109730.0, step = 77501 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.587\n",
            "INFO:tensorflow:loss = 379342750.0, step = 77601 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.455\n",
            "INFO:tensorflow:loss = 416636450.0, step = 77701 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.477\n",
            "INFO:tensorflow:loss = 293397300.0, step = 77801 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.495\n",
            "INFO:tensorflow:loss = 109442530.0, step = 77901 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.483\n",
            "INFO:tensorflow:loss = 164678580.0, step = 78001 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.087\n",
            "INFO:tensorflow:loss = 415629950.0, step = 78101 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.461\n",
            "INFO:tensorflow:loss = 353957630.0, step = 78201 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.996\n",
            "INFO:tensorflow:loss = 296995550.0, step = 78301 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.491\n",
            "INFO:tensorflow:loss = 548419200.0, step = 78401 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.269\n",
            "INFO:tensorflow:loss = 312488500.0, step = 78501 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.036\n",
            "INFO:tensorflow:loss = 124129470.0, step = 78601 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.258\n",
            "INFO:tensorflow:loss = 316746270.0, step = 78701 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 428.897\n",
            "INFO:tensorflow:loss = 285281600.0, step = 78801 (0.219 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.398\n",
            "INFO:tensorflow:loss = 516498300.0, step = 78901 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 434.671\n",
            "INFO:tensorflow:loss = 183092780.0, step = 79001 (0.217 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.152\n",
            "INFO:tensorflow:loss = 322544860.0, step = 79101 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 318.95\n",
            "INFO:tensorflow:loss = 484597980.0, step = 79201 (0.308 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.923\n",
            "INFO:tensorflow:loss = 298520000.0, step = 79301 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.958\n",
            "INFO:tensorflow:loss = 308826180.0, step = 79401 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.95\n",
            "INFO:tensorflow:loss = 169347490.0, step = 79501 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.533\n",
            "INFO:tensorflow:loss = 632000200.0, step = 79601 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.642\n",
            "INFO:tensorflow:loss = 348314850.0, step = 79701 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.669\n",
            "INFO:tensorflow:loss = 194901000.0, step = 79801 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.471\n",
            "INFO:tensorflow:loss = 224454990.0, step = 79901 (0.270 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 80000...\n",
            "INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 80000...\n",
            "INFO:tensorflow:Loss for final step: 302888860.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:17:16\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-80000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.25973s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:17:16\n",
            "INFO:tensorflow:Saving dict for global step 80000: average_loss = 8737997.0, global_step = 80000, label/mean = 12949.43, loss = 255898480.0, prediction/mean = 12851.887\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 80000: /tmp/tmp4160q49c/model.ckpt-80000\n",
            "scores {'average_loss': 8737997.0, 'label/mean': 12949.43, 'loss': 255898480.0, 'prediction/mean': 12851.887, 'global_step': 80000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-80000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 80000...\n",
            "INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 80000...\n",
            "INFO:tensorflow:loss = 257176860.0, step = 80001\n",
            "INFO:tensorflow:global_step/sec: 361.808\n",
            "INFO:tensorflow:loss = 458956450.0, step = 80101 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 332.566\n",
            "INFO:tensorflow:loss = 219927100.0, step = 80201 (0.295 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.584\n",
            "INFO:tensorflow:loss = 275048130.0, step = 80301 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.145\n",
            "INFO:tensorflow:loss = 190774620.0, step = 80401 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.562\n",
            "INFO:tensorflow:loss = 516542140.0, step = 80501 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.306\n",
            "INFO:tensorflow:loss = 358447650.0, step = 80601 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 349.824\n",
            "INFO:tensorflow:loss = 547052540.0, step = 80701 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.129\n",
            "INFO:tensorflow:loss = 358072130.0, step = 80801 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.587\n",
            "INFO:tensorflow:loss = 251262600.0, step = 80901 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 336.724\n",
            "INFO:tensorflow:loss = 388918700.0, step = 81001 (0.299 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.439\n",
            "INFO:tensorflow:loss = 407431070.0, step = 81101 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.805\n",
            "INFO:tensorflow:loss = 355226620.0, step = 81201 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.689\n",
            "INFO:tensorflow:loss = 305584580.0, step = 81301 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.044\n",
            "INFO:tensorflow:loss = 303275940.0, step = 81401 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 310.997\n",
            "INFO:tensorflow:loss = 145461520.0, step = 81501 (0.318 sec)\n",
            "INFO:tensorflow:global_step/sec: 338.526\n",
            "INFO:tensorflow:loss = 486415260.0, step = 81601 (0.296 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.119\n",
            "INFO:tensorflow:loss = 318092540.0, step = 81701 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.89\n",
            "INFO:tensorflow:loss = 302175650.0, step = 81801 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.519\n",
            "INFO:tensorflow:loss = 143472130.0, step = 81901 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.05\n",
            "INFO:tensorflow:loss = 330079500.0, step = 82001 (0.297 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.922\n",
            "INFO:tensorflow:loss = 130142510.0, step = 82101 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 459.235\n",
            "INFO:tensorflow:loss = 158321570.0, step = 82201 (0.223 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.384\n",
            "INFO:tensorflow:loss = 349684740.0, step = 82301 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.541\n",
            "INFO:tensorflow:loss = 241094720.0, step = 82401 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.13\n",
            "INFO:tensorflow:loss = 284186270.0, step = 82501 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.862\n",
            "INFO:tensorflow:loss = 395166300.0, step = 82601 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.801\n",
            "INFO:tensorflow:loss = 200760800.0, step = 82701 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.814\n",
            "INFO:tensorflow:loss = 141782260.0, step = 82801 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 439.851\n",
            "INFO:tensorflow:loss = 296606900.0, step = 82901 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.765\n",
            "INFO:tensorflow:loss = 169873070.0, step = 83001 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 351.187\n",
            "INFO:tensorflow:loss = 293103040.0, step = 83101 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.881\n",
            "INFO:tensorflow:loss = 164821230.0, step = 83201 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.427\n",
            "INFO:tensorflow:loss = 389541000.0, step = 83301 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 421.814\n",
            "INFO:tensorflow:loss = 150127500.0, step = 83401 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.802\n",
            "INFO:tensorflow:loss = 282178940.0, step = 83501 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 346.72\n",
            "INFO:tensorflow:loss = 427320160.0, step = 83601 (0.293 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.052\n",
            "INFO:tensorflow:loss = 271085100.0, step = 83701 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 427.188\n",
            "INFO:tensorflow:loss = 365167330.0, step = 83801 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.777\n",
            "INFO:tensorflow:loss = 217863940.0, step = 83901 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.837\n",
            "INFO:tensorflow:loss = 125714456.0, step = 84001 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.548\n",
            "INFO:tensorflow:loss = 176344210.0, step = 84101 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.923\n",
            "INFO:tensorflow:loss = 327230560.0, step = 84201 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 323.654\n",
            "INFO:tensorflow:loss = 126777790.0, step = 84301 (0.308 sec)\n",
            "INFO:tensorflow:global_step/sec: 323.099\n",
            "INFO:tensorflow:loss = 200902580.0, step = 84401 (0.306 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.15\n",
            "INFO:tensorflow:loss = 221826560.0, step = 84501 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.07\n",
            "INFO:tensorflow:loss = 177148380.0, step = 84601 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 444.751\n",
            "INFO:tensorflow:loss = 398614720.0, step = 84701 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.969\n",
            "INFO:tensorflow:loss = 451714340.0, step = 84801 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.8\n",
            "INFO:tensorflow:loss = 353703500.0, step = 84901 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.019\n",
            "INFO:tensorflow:loss = 175878100.0, step = 85001 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.777\n",
            "INFO:tensorflow:loss = 250471940.0, step = 85101 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.383\n",
            "INFO:tensorflow:loss = 378837060.0, step = 85201 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.445\n",
            "INFO:tensorflow:loss = 286990180.0, step = 85301 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.858\n",
            "INFO:tensorflow:loss = 171248050.0, step = 85401 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.254\n",
            "INFO:tensorflow:loss = 399916200.0, step = 85501 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.338\n",
            "INFO:tensorflow:loss = 245644590.0, step = 85601 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.969\n",
            "INFO:tensorflow:loss = 193412620.0, step = 85701 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.311\n",
            "INFO:tensorflow:loss = 187301000.0, step = 85801 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.029\n",
            "INFO:tensorflow:loss = 429964160.0, step = 85901 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 317.332\n",
            "INFO:tensorflow:loss = 316154180.0, step = 86001 (0.320 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.34\n",
            "INFO:tensorflow:loss = 407272960.0, step = 86101 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.263\n",
            "INFO:tensorflow:loss = 168108620.0, step = 86201 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.624\n",
            "INFO:tensorflow:loss = 213103000.0, step = 86301 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.781\n",
            "INFO:tensorflow:loss = 366747300.0, step = 86401 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.871\n",
            "INFO:tensorflow:loss = 114345620.0, step = 86501 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.48\n",
            "INFO:tensorflow:loss = 189257840.0, step = 86601 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.852\n",
            "INFO:tensorflow:loss = 192248030.0, step = 86701 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.191\n",
            "INFO:tensorflow:loss = 264912200.0, step = 86801 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.086\n",
            "INFO:tensorflow:loss = 256843340.0, step = 86901 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.572\n",
            "INFO:tensorflow:loss = 156104340.0, step = 87001 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.966\n",
            "INFO:tensorflow:loss = 439923900.0, step = 87101 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.584\n",
            "INFO:tensorflow:loss = 264721700.0, step = 87201 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.604\n",
            "INFO:tensorflow:loss = 205124830.0, step = 87301 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.44\n",
            "INFO:tensorflow:loss = 309728500.0, step = 87401 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.286\n",
            "INFO:tensorflow:loss = 115538800.0, step = 87501 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.466\n",
            "INFO:tensorflow:loss = 213407300.0, step = 87601 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.351\n",
            "INFO:tensorflow:loss = 63670070.0, step = 87701 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.014\n",
            "INFO:tensorflow:loss = 406924480.0, step = 87801 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 342.357\n",
            "INFO:tensorflow:loss = 272359460.0, step = 87901 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.857\n",
            "INFO:tensorflow:loss = 209864600.0, step = 88001 (0.292 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.754\n",
            "INFO:tensorflow:loss = 283435780.0, step = 88101 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.709\n",
            "INFO:tensorflow:loss = 149594930.0, step = 88201 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 446.712\n",
            "INFO:tensorflow:loss = 231854060.0, step = 88301 (0.221 sec)\n",
            "INFO:tensorflow:global_step/sec: 346.039\n",
            "INFO:tensorflow:loss = 288412540.0, step = 88401 (0.290 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.571\n",
            "INFO:tensorflow:loss = 192249460.0, step = 88501 (0.290 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.067\n",
            "INFO:tensorflow:loss = 432563620.0, step = 88601 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.62\n",
            "INFO:tensorflow:loss = 227581340.0, step = 88701 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.521\n",
            "INFO:tensorflow:loss = 511936400.0, step = 88801 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.818\n",
            "INFO:tensorflow:loss = 290301820.0, step = 88901 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.58\n",
            "INFO:tensorflow:loss = 154146370.0, step = 89001 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.268\n",
            "INFO:tensorflow:loss = 349917280.0, step = 89101 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.955\n",
            "INFO:tensorflow:loss = 294303400.0, step = 89201 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.214\n",
            "INFO:tensorflow:loss = 215257900.0, step = 89301 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.325\n",
            "INFO:tensorflow:loss = 450582880.0, step = 89401 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.955\n",
            "INFO:tensorflow:loss = 224840450.0, step = 89501 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.471\n",
            "INFO:tensorflow:loss = 170489920.0, step = 89601 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.446\n",
            "INFO:tensorflow:loss = 250797150.0, step = 89701 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 349.841\n",
            "INFO:tensorflow:loss = 356510300.0, step = 89801 (0.289 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.014\n",
            "INFO:tensorflow:loss = 287780100.0, step = 89901 (0.260 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 90000...\n",
            "INFO:tensorflow:Saving checkpoints for 90000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 90000...\n",
            "INFO:tensorflow:Loss for final step: 265795860.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:17:44\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-90000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.27852s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:17:44\n",
            "INFO:tensorflow:Saving dict for global step 90000: average_loss = 8637896.0, global_step = 90000, label/mean = 12949.43, loss = 252966960.0, prediction/mean = 12881.092\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 90000: /tmp/tmp4160q49c/model.ckpt-90000\n",
            "scores {'average_loss': 8637896.0, 'label/mean': 12949.43, 'loss': 252966960.0, 'prediction/mean': 12881.092, 'global_step': 90000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-90000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 90000...\n",
            "INFO:tensorflow:Saving checkpoints for 90000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 90000...\n",
            "INFO:tensorflow:loss = 209859890.0, step = 90001\n",
            "INFO:tensorflow:global_step/sec: 357.433\n",
            "INFO:tensorflow:loss = 480622980.0, step = 90101 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 434.299\n",
            "INFO:tensorflow:loss = 289350600.0, step = 90201 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.901\n",
            "INFO:tensorflow:loss = 216236800.0, step = 90301 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.638\n",
            "INFO:tensorflow:loss = 295081200.0, step = 90401 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.104\n",
            "INFO:tensorflow:loss = 449331780.0, step = 90501 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.265\n",
            "INFO:tensorflow:loss = 301865600.0, step = 90601 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 322.709\n",
            "INFO:tensorflow:loss = 193028080.0, step = 90701 (0.308 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.818\n",
            "INFO:tensorflow:loss = 338512700.0, step = 90801 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.036\n",
            "INFO:tensorflow:loss = 302850200.0, step = 90901 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.497\n",
            "INFO:tensorflow:loss = 186188100.0, step = 91001 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.442\n",
            "INFO:tensorflow:loss = 449091500.0, step = 91101 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.797\n",
            "INFO:tensorflow:loss = 138403280.0, step = 91201 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.123\n",
            "INFO:tensorflow:loss = 303611520.0, step = 91301 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.697\n",
            "INFO:tensorflow:loss = 105958390.0, step = 91401 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 338.236\n",
            "INFO:tensorflow:loss = 223599020.0, step = 91501 (0.293 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.127\n",
            "INFO:tensorflow:loss = 242275740.0, step = 91601 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 432.571\n",
            "INFO:tensorflow:loss = 121049770.0, step = 91701 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 337.071\n",
            "INFO:tensorflow:loss = 299474560.0, step = 91801 (0.293 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.552\n",
            "INFO:tensorflow:loss = 328441800.0, step = 91901 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.091\n",
            "INFO:tensorflow:loss = 240570850.0, step = 92001 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.038\n",
            "INFO:tensorflow:loss = 261985630.0, step = 92101 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.419\n",
            "INFO:tensorflow:loss = 250755340.0, step = 92201 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.416\n",
            "INFO:tensorflow:loss = 324074180.0, step = 92301 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.297\n",
            "INFO:tensorflow:loss = 298197950.0, step = 92401 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.124\n",
            "INFO:tensorflow:loss = 269044670.0, step = 92501 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.584\n",
            "INFO:tensorflow:loss = 286881020.0, step = 92601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 444.214\n",
            "INFO:tensorflow:loss = 270364600.0, step = 92701 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.475\n",
            "INFO:tensorflow:loss = 457869950.0, step = 92801 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.564\n",
            "INFO:tensorflow:loss = 333895460.0, step = 92901 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.154\n",
            "INFO:tensorflow:loss = 464988320.0, step = 93001 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.237\n",
            "INFO:tensorflow:loss = 485433300.0, step = 93101 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.592\n",
            "INFO:tensorflow:loss = 316698460.0, step = 93201 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.859\n",
            "INFO:tensorflow:loss = 310657440.0, step = 93301 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.824\n",
            "INFO:tensorflow:loss = 439892200.0, step = 93401 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.67\n",
            "INFO:tensorflow:loss = 262503070.0, step = 93501 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.644\n",
            "INFO:tensorflow:loss = 233212940.0, step = 93601 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.247\n",
            "INFO:tensorflow:loss = 335513900.0, step = 93701 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.363\n",
            "INFO:tensorflow:loss = 284975870.0, step = 93801 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.682\n",
            "INFO:tensorflow:loss = 245633180.0, step = 93901 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.264\n",
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            "INFO:tensorflow:global_step/sec: 388.175\n",
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            "INFO:tensorflow:global_step/sec: 381.849\n",
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            "INFO:tensorflow:loss = 223844060.0, step = 94401 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.183\n",
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            "INFO:tensorflow:loss = 291148900.0, step = 94601 (0.280 sec)\n",
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            "INFO:tensorflow:global_step/sec: 368.294\n",
            "INFO:tensorflow:loss = 352029200.0, step = 94901 (0.265 sec)\n",
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            "INFO:tensorflow:global_step/sec: 345.855\n",
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            "INFO:tensorflow:global_step/sec: 349.084\n",
            "INFO:tensorflow:loss = 741724500.0, step = 95501 (0.285 sec)\n",
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            "INFO:tensorflow:global_step/sec: 359\n",
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            "INFO:tensorflow:global_step/sec: 387.984\n",
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            "INFO:tensorflow:loss = 377567170.0, step = 97501 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.388\n",
            "INFO:tensorflow:loss = 433880030.0, step = 97601 (0.277 sec)\n",
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            "INFO:tensorflow:global_step/sec: 371.359\n",
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            "INFO:tensorflow:global_step/sec: 390.563\n",
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            "INFO:tensorflow:global_step/sec: 390.526\n",
            "INFO:tensorflow:loss = 393206530.0, step = 98201 (0.258 sec)\n",
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            "INFO:tensorflow:loss = 235185260.0, step = 98401 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.443\n",
            "INFO:tensorflow:loss = 627984830.0, step = 98501 (0.279 sec)\n",
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            "INFO:tensorflow:global_step/sec: 362.875\n",
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            "INFO:tensorflow:global_step/sec: 403.48\n",
            "INFO:tensorflow:loss = 398367780.0, step = 98901 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.104\n",
            "INFO:tensorflow:loss = 144651800.0, step = 99001 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.348\n",
            "INFO:tensorflow:loss = 441667360.0, step = 99101 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.851\n",
            "INFO:tensorflow:loss = 194386880.0, step = 99201 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.623\n",
            "INFO:tensorflow:loss = 123733300.0, step = 99301 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.675\n",
            "INFO:tensorflow:loss = 174630290.0, step = 99401 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.918\n",
            "INFO:tensorflow:loss = 243440480.0, step = 99501 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.775\n",
            "INFO:tensorflow:loss = 478130140.0, step = 99601 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.318\n",
            "INFO:tensorflow:loss = 282703680.0, step = 99701 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.637\n",
            "INFO:tensorflow:loss = 277012600.0, step = 99801 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.132\n",
            "INFO:tensorflow:loss = 301032770.0, step = 99901 (0.273 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 100000...\n",
            "INFO:tensorflow:Saving checkpoints for 100000 into /tmp/tmp4160q49c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 100000...\n",
            "INFO:tensorflow:Loss for final step: 175439060.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:18:12\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-100000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.28084s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:18:12\n",
            "INFO:tensorflow:Saving dict for global step 100000: average_loss = 8589280.0, global_step = 100000, label/mean = 12949.43, loss = 251543200.0, prediction/mean = 12954.939\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 100000: /tmp/tmp4160q49c/model.ckpt-100000\n",
            "scores {'average_loss': 8589280.0, 'label/mean': 12949.43, 'loss': 251543200.0, 'prediction/mean': 12954.939, 'global_step': 100000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp4160q49c/model.ckpt-100000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 972x1620 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxYJy71zaZsy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6609c5e9-65c0-4e55-9a0b-07450937a4b5"
      },
      "source": [
        "#@title Possible solution\n",
        "# This does Z-score normalization since the distributions for most features looked\n",
        "# roughly normally distributed.\n",
        "\n",
        "# Z-score normalization subtracts the mean and divides by the standard deviation,\n",
        "# to give a roughly standard normal distribution (mean = 0, std = 1) under a\n",
        "# normal distribution assumption. Epsilon prevents divide by zero.\n",
        "\n",
        "# With normalization, are you able to get the model working with\n",
        "# GradientDescentOptimizer? Z-score normalization doesn't seem to be able to get\n",
        "# SGD working. Maybe a different type of normalization would?\n",
        "\n",
        "batch_size = 16\n",
        "\n",
        "print(numeric_feature_names)\n",
        "x_df = car_data[numeric_feature_names]\n",
        "y_series = car_data['price']\n",
        "\n",
        "train_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    num_epochs=None,\n",
        "    shuffle=True)\n",
        "\n",
        "eval_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "predict_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "# Epsilon prevents divide by zero.\n",
        "epsilon = 0.000001\n",
        "model_feature_columns = [\n",
        "    tf.feature_column.numeric_column(feature_name,\n",
        "                                     normalizer_fn=lambda val: (val - x_df.mean()[feature_name]) / (epsilon + x_df.std()[feature_name]))\n",
        "    for feature_name in numeric_feature_names\n",
        "]\n",
        "print('model_feature_columns', model_feature_columns)\n",
        "\n",
        "est = tf.estimator.DNNRegressor(\n",
        "    feature_columns=model_feature_columns,\n",
        "    hidden_units=[64],\n",
        "    optimizer=tf.train.AdagradOptimizer(learning_rate=0.01),\n",
        "  )\n",
        "\n",
        "# TRAIN\n",
        "num_print_statements = 10\n",
        "num_training_steps = 10000\n",
        "for _ in range(num_print_statements):\n",
        "  est.train(train_input_fn, steps=num_training_steps // num_print_statements)\n",
        "  scores = est.evaluate(eval_input_fn)\n",
        "  \n",
        "  # The `scores` dictionary has several metrics automatically generated by the \n",
        "  # canned Estimator.\n",
        "  # `average_loss` is the average loss for an individual example.\n",
        "  # `loss` is the summed loss for the batch.\n",
        "  # In addition to these scalar losses, you may find the visualization functions\n",
        "  # in the next cell helpful for debugging model quality.\n",
        "  print('scores', scores)\n",
        "\n",
        "scatter_plot_inference_grid(est, x_df, numeric_feature_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['symboling', 'normalized-losses', 'wheel-base', 'length', 'width', 'height', 'weight', 'engine-size', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'bore', 'stroke', 'compression-ratio']\n",
            "model_feature_columns [NumericColumn(key='symboling', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f85470b4440>), NumericColumn(key='normalized-losses', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8546c30290>), NumericColumn(key='wheel-base', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8541519f80>), NumericColumn(key='length', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8541696dd0>), NumericColumn(key='width', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8541696200>), NumericColumn(key='height', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8541696cb0>), NumericColumn(key='weight', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8548d50830>), NumericColumn(key='engine-size', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8548d50290>), NumericColumn(key='horsepower', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8548d507a0>), NumericColumn(key='peak-rpm', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8548d504d0>), NumericColumn(key='city-mpg', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f854740d680>), NumericColumn(key='highway-mpg', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f854740ddd0>), NumericColumn(key='bore', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f854740def0>), NumericColumn(key='stroke', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f854740d5f0>), NumericColumn(key='compression-ratio', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f854740d710>)]\n",
            "INFO:tensorflow:Using default config.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp_83455ux\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp_83455ux', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...\n",
            "INFO:tensorflow:loss = 4750334500.0, step = 1\n",
            "INFO:tensorflow:global_step/sec: 374.834\n",
            "INFO:tensorflow:loss = 1717842000.0, step = 101 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 436.677\n",
            "INFO:tensorflow:loss = 955299400.0, step = 201 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.777\n",
            "INFO:tensorflow:loss = 1340305900.0, step = 301 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.234\n",
            "INFO:tensorflow:loss = 178118820.0, step = 401 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.728\n",
            "INFO:tensorflow:loss = 211321220.0, step = 501 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.093\n",
            "INFO:tensorflow:loss = 190880740.0, step = 601 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.488\n",
            "INFO:tensorflow:loss = 151411620.0, step = 701 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.409\n",
            "INFO:tensorflow:loss = 215367250.0, step = 801 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 454.344\n",
            "INFO:tensorflow:loss = 316290340.0, step = 901 (0.222 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...\n",
            "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...\n",
            "INFO:tensorflow:Loss for final step: 898092400.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:35:04\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-1000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.45354s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:35:05\n",
            "INFO:tensorflow:Saving dict for global step 1000: average_loss = 49488600.0, global_step = 1000, label/mean = 12949.43, loss = 780397200.0, prediction/mean = 13173.524\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: /tmp/tmp_83455ux/model.ckpt-1000\n",
            "scores {'average_loss': 49488600.0, 'label/mean': 12949.43, 'loss': 780397200.0, 'prediction/mean': 13173.524, 'global_step': 1000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-1000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...\n",
            "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...\n",
            "INFO:tensorflow:loss = 456973600.0, step = 1001\n",
            "INFO:tensorflow:global_step/sec: 287.526\n",
            "INFO:tensorflow:loss = 680631230.0, step = 1101 (0.350 sec)\n",
            "INFO:tensorflow:global_step/sec: 237.865\n",
            "INFO:tensorflow:loss = 138670940.0, step = 1201 (0.426 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.89\n",
            "INFO:tensorflow:loss = 673611140.0, step = 1301 (0.418 sec)\n",
            "INFO:tensorflow:global_step/sec: 188.959\n",
            "INFO:tensorflow:loss = 400768320.0, step = 1401 (0.517 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.953\n",
            "INFO:tensorflow:loss = 224901440.0, step = 1501 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 465.275\n",
            "INFO:tensorflow:loss = 269934530.0, step = 1601 (0.210 sec)\n",
            "INFO:tensorflow:global_step/sec: 440.665\n",
            "INFO:tensorflow:loss = 494683700.0, step = 1701 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 470.454\n",
            "INFO:tensorflow:loss = 162096640.0, step = 1801 (0.218 sec)\n",
            "INFO:tensorflow:global_step/sec: 448.951\n",
            "INFO:tensorflow:loss = 790564500.0, step = 1901 (0.228 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2000...\n",
            "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2000...\n",
            "INFO:tensorflow:Loss for final step: 188805220.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:35:10\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-2000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.25893s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:35:10\n",
            "INFO:tensorflow:Saving dict for global step 2000: average_loss = 44958836.0, global_step = 2000, label/mean = 12949.43, loss = 708966300.0, prediction/mean = 13154.417\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: /tmp/tmp_83455ux/model.ckpt-2000\n",
            "scores {'average_loss': 44958836.0, 'label/mean': 12949.43, 'loss': 708966300.0, 'prediction/mean': 13154.417, 'global_step': 2000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-2000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2000...\n",
            "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2000...\n",
            "INFO:tensorflow:loss = 1190757600.0, step = 2001\n",
            "INFO:tensorflow:global_step/sec: 385.245\n",
            "INFO:tensorflow:loss = 917816960.0, step = 2101 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 443.455\n",
            "INFO:tensorflow:loss = 516981760.0, step = 2201 (0.223 sec)\n",
            "INFO:tensorflow:global_step/sec: 482.457\n",
            "INFO:tensorflow:loss = 368382140.0, step = 2301 (0.214 sec)\n",
            "INFO:tensorflow:global_step/sec: 459.853\n",
            "INFO:tensorflow:loss = 560542660.0, step = 2401 (0.217 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.092\n",
            "INFO:tensorflow:loss = 562548350.0, step = 2501 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 439.402\n",
            "INFO:tensorflow:loss = 223945060.0, step = 2601 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 452.148\n",
            "INFO:tensorflow:loss = 924614850.0, step = 2701 (0.222 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.584\n",
            "INFO:tensorflow:loss = 443685950.0, step = 2801 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.385\n",
            "INFO:tensorflow:loss = 326080960.0, step = 2901 (0.256 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3000...\n",
            "INFO:tensorflow:Saving checkpoints for 3000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3000...\n",
            "INFO:tensorflow:Loss for final step: 536295600.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:35:14\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-3000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.24395s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:35:14\n",
            "INFO:tensorflow:Saving dict for global step 3000: average_loss = 39779696.0, global_step = 3000, label/mean = 12949.43, loss = 627295200.0, prediction/mean = 13197.095\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3000: /tmp/tmp_83455ux/model.ckpt-3000\n",
            "scores {'average_loss': 39779696.0, 'label/mean': 12949.43, 'loss': 627295200.0, 'prediction/mean': 13197.095, 'global_step': 3000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-3000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3000...\n",
            "INFO:tensorflow:Saving checkpoints for 3000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3000...\n",
            "INFO:tensorflow:loss = 1032265500.0, step = 3001\n",
            "INFO:tensorflow:global_step/sec: 407.57\n",
            "INFO:tensorflow:loss = 1054706300.0, step = 3101 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 431.653\n",
            "INFO:tensorflow:loss = 680782400.0, step = 3201 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.472\n",
            "INFO:tensorflow:loss = 874674800.0, step = 3301 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.525\n",
            "INFO:tensorflow:loss = 685927550.0, step = 3401 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 439.112\n",
            "INFO:tensorflow:loss = 514599520.0, step = 3501 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 463.206\n",
            "INFO:tensorflow:loss = 200796000.0, step = 3601 (0.214 sec)\n",
            "INFO:tensorflow:global_step/sec: 448.375\n",
            "INFO:tensorflow:loss = 384511460.0, step = 3701 (0.222 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.274\n",
            "INFO:tensorflow:loss = 283186460.0, step = 3801 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 457.945\n",
            "INFO:tensorflow:loss = 981823100.0, step = 3901 (0.222 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4000...\n",
            "INFO:tensorflow:Saving checkpoints for 4000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4000...\n",
            "INFO:tensorflow:Loss for final step: 654540700.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:35:17\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-4000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.26106s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:35:18\n",
            "INFO:tensorflow:Saving dict for global step 4000: average_loss = 36126030.0, global_step = 4000, label/mean = 12949.43, loss = 569679740.0, prediction/mean = 13152.511\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4000: /tmp/tmp_83455ux/model.ckpt-4000\n",
            "scores {'average_loss': 36126030.0, 'label/mean': 12949.43, 'loss': 569679740.0, 'prediction/mean': 13152.511, 'global_step': 4000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-4000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4000...\n",
            "INFO:tensorflow:Saving checkpoints for 4000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4000...\n",
            "INFO:tensorflow:loss = 168674430.0, step = 4001\n",
            "INFO:tensorflow:global_step/sec: 388.886\n",
            "INFO:tensorflow:loss = 568252000.0, step = 4101 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 443.392\n",
            "INFO:tensorflow:loss = 285548700.0, step = 4201 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 432.506\n",
            "INFO:tensorflow:loss = 200727200.0, step = 4301 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.32\n",
            "INFO:tensorflow:loss = 395641700.0, step = 4401 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 421.078\n",
            "INFO:tensorflow:loss = 255528220.0, step = 4501 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.64\n",
            "INFO:tensorflow:loss = 475604580.0, step = 4601 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.411\n",
            "INFO:tensorflow:loss = 1103353700.0, step = 4701 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.684\n",
            "INFO:tensorflow:loss = 241097460.0, step = 4801 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 437.816\n",
            "INFO:tensorflow:loss = 201093980.0, step = 4901 (0.230 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5000...\n",
            "INFO:tensorflow:Saving checkpoints for 5000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5000...\n",
            "INFO:tensorflow:Loss for final step: 258561280.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:35:21\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-5000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.25855s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:35:22\n",
            "INFO:tensorflow:Saving dict for global step 5000: average_loss = 33283582.0, global_step = 5000, label/mean = 12949.43, loss = 524856480.0, prediction/mean = 13186.783\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 5000: /tmp/tmp_83455ux/model.ckpt-5000\n",
            "scores {'average_loss': 33283582.0, 'label/mean': 12949.43, 'loss': 524856480.0, 'prediction/mean': 13186.783, 'global_step': 5000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-5000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5000...\n",
            "INFO:tensorflow:Saving checkpoints for 5000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5000...\n",
            "INFO:tensorflow:loss = 272851870.0, step = 5001\n",
            "INFO:tensorflow:global_step/sec: 401.084\n",
            "INFO:tensorflow:loss = 1087119100.0, step = 5101 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 455.842\n",
            "INFO:tensorflow:loss = 295511300.0, step = 5201 (0.217 sec)\n",
            "INFO:tensorflow:global_step/sec: 471.642\n",
            "INFO:tensorflow:loss = 436796220.0, step = 5301 (0.211 sec)\n",
            "INFO:tensorflow:global_step/sec: 482.459\n",
            "INFO:tensorflow:loss = 507701000.0, step = 5401 (0.213 sec)\n",
            "INFO:tensorflow:global_step/sec: 500.801\n",
            "INFO:tensorflow:loss = 660553800.0, step = 5501 (0.193 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.567\n",
            "INFO:tensorflow:loss = 299406140.0, step = 5601 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 465.652\n",
            "INFO:tensorflow:loss = 368827800.0, step = 5701 (0.212 sec)\n",
            "INFO:tensorflow:global_step/sec: 447.472\n",
            "INFO:tensorflow:loss = 456496300.0, step = 5801 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 447.207\n",
            "INFO:tensorflow:loss = 199060540.0, step = 5901 (0.220 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6000...\n",
            "INFO:tensorflow:Saving checkpoints for 6000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6000...\n",
            "INFO:tensorflow:Loss for final step: 147036020.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:35:25\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-6000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.24628s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:35:25\n",
            "INFO:tensorflow:Saving dict for global step 6000: average_loss = 31095406.0, global_step = 6000, label/mean = 12949.43, loss = 490350620.0, prediction/mean = 13175.306\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 6000: /tmp/tmp_83455ux/model.ckpt-6000\n",
            "scores {'average_loss': 31095406.0, 'label/mean': 12949.43, 'loss': 490350620.0, 'prediction/mean': 13175.306, 'global_step': 6000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-6000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6000...\n",
            "INFO:tensorflow:Saving checkpoints for 6000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6000...\n",
            "INFO:tensorflow:loss = 210509220.0, step = 6001\n",
            "INFO:tensorflow:global_step/sec: 391.66\n",
            "INFO:tensorflow:loss = 508555300.0, step = 6101 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.522\n",
            "INFO:tensorflow:loss = 286140580.0, step = 6201 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 430.411\n",
            "INFO:tensorflow:loss = 163802200.0, step = 6301 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.312\n",
            "INFO:tensorflow:loss = 159010720.0, step = 6401 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.476\n",
            "INFO:tensorflow:loss = 734078800.0, step = 6501 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.759\n",
            "INFO:tensorflow:loss = 1039392600.0, step = 6601 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 443.821\n",
            "INFO:tensorflow:loss = 675529000.0, step = 6701 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.738\n",
            "INFO:tensorflow:loss = 921639230.0, step = 6801 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 457.655\n",
            "INFO:tensorflow:loss = 274198980.0, step = 6901 (0.220 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7000...\n",
            "INFO:tensorflow:Saving checkpoints for 7000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7000...\n",
            "INFO:tensorflow:Loss for final step: 721916100.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:35:29\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-7000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.24580s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:35:29\n",
            "INFO:tensorflow:Saving dict for global step 7000: average_loss = 29428764.0, global_step = 7000, label/mean = 12949.43, loss = 464068960.0, prediction/mean = 13189.795\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 7000: /tmp/tmp_83455ux/model.ckpt-7000\n",
            "scores {'average_loss': 29428764.0, 'label/mean': 12949.43, 'loss': 464068960.0, 'prediction/mean': 13189.795, 'global_step': 7000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-7000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7000...\n",
            "INFO:tensorflow:Saving checkpoints for 7000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7000...\n",
            "INFO:tensorflow:loss = 313034240.0, step = 7001\n",
            "INFO:tensorflow:global_step/sec: 417.8\n",
            "INFO:tensorflow:loss = 547002200.0, step = 7101 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 435.585\n",
            "INFO:tensorflow:loss = 398722500.0, step = 7201 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.945\n",
            "INFO:tensorflow:loss = 685422660.0, step = 7301 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.892\n",
            "INFO:tensorflow:loss = 365700400.0, step = 7401 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 464.387\n",
            "INFO:tensorflow:loss = 376749440.0, step = 7501 (0.223 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.383\n",
            "INFO:tensorflow:loss = 668364300.0, step = 7601 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.99\n",
            "INFO:tensorflow:loss = 650047900.0, step = 7701 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.617\n",
            "INFO:tensorflow:loss = 158333340.0, step = 7801 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 431.353\n",
            "INFO:tensorflow:loss = 631111300.0, step = 7901 (0.226 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8000...\n",
            "INFO:tensorflow:Saving checkpoints for 8000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8000...\n",
            "INFO:tensorflow:Loss for final step: 803946100.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:35:33\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-8000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.64973s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:35:33\n",
            "INFO:tensorflow:Saving dict for global step 8000: average_loss = 28165578.0, global_step = 8000, label/mean = 12949.43, loss = 444149470.0, prediction/mean = 13122.502\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 8000: /tmp/tmp_83455ux/model.ckpt-8000\n",
            "scores {'average_loss': 28165578.0, 'label/mean': 12949.43, 'loss': 444149470.0, 'prediction/mean': 13122.502, 'global_step': 8000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-8000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8000...\n",
            "INFO:tensorflow:Saving checkpoints for 8000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8000...\n",
            "INFO:tensorflow:loss = 161287980.0, step = 8001\n",
            "INFO:tensorflow:global_step/sec: 371.716\n",
            "INFO:tensorflow:loss = 749709800.0, step = 8101 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.652\n",
            "INFO:tensorflow:loss = 192163300.0, step = 8201 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.74\n",
            "INFO:tensorflow:loss = 631688600.0, step = 8301 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.77\n",
            "INFO:tensorflow:loss = 1045177200.0, step = 8401 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 442.839\n",
            "INFO:tensorflow:loss = 453819870.0, step = 8501 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.581\n",
            "INFO:tensorflow:loss = 680852000.0, step = 8601 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 430.97\n",
            "INFO:tensorflow:loss = 136498000.0, step = 8701 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 490.641\n",
            "INFO:tensorflow:loss = 805870800.0, step = 8801 (0.197 sec)\n",
            "INFO:tensorflow:global_step/sec: 447.263\n",
            "INFO:tensorflow:loss = 906094600.0, step = 8901 (0.224 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9000...\n",
            "INFO:tensorflow:Saving checkpoints for 9000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9000...\n",
            "INFO:tensorflow:Loss for final step: 347615500.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:35:37\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-9000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.25360s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:35:37\n",
            "INFO:tensorflow:Saving dict for global step 9000: average_loss = 27200852.0, global_step = 9000, label/mean = 12949.43, loss = 428936500.0, prediction/mean = 13128.199\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 9000: /tmp/tmp_83455ux/model.ckpt-9000\n",
            "scores {'average_loss': 27200852.0, 'label/mean': 12949.43, 'loss': 428936500.0, 'prediction/mean': 13128.199, 'global_step': 9000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-9000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9000...\n",
            "INFO:tensorflow:Saving checkpoints for 9000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9000...\n",
            "INFO:tensorflow:loss = 146892670.0, step = 9001\n",
            "INFO:tensorflow:global_step/sec: 412.68\n",
            "INFO:tensorflow:loss = 323585060.0, step = 9101 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 439.257\n",
            "INFO:tensorflow:loss = 122112960.0, step = 9201 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.427\n",
            "INFO:tensorflow:loss = 549676800.0, step = 9301 (0.220 sec)\n",
            "INFO:tensorflow:global_step/sec: 447.227\n",
            "INFO:tensorflow:loss = 308519330.0, step = 9401 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 443.563\n",
            "INFO:tensorflow:loss = 288648220.0, step = 9501 (0.222 sec)\n",
            "INFO:tensorflow:global_step/sec: 430.629\n",
            "INFO:tensorflow:loss = 401861250.0, step = 9601 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 431.17\n",
            "INFO:tensorflow:loss = 162287460.0, step = 9701 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 458.436\n",
            "INFO:tensorflow:loss = 151707000.0, step = 9801 (0.220 sec)\n",
            "INFO:tensorflow:global_step/sec: 464.341\n",
            "INFO:tensorflow:loss = 141762080.0, step = 9901 (0.215 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...\n",
            "INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tmp_83455ux/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...\n",
            "INFO:tensorflow:Loss for final step: 450523900.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T19:35:41\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-10000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.25508s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-19:35:41\n",
            "INFO:tensorflow:Saving dict for global step 10000: average_loss = 26462106.0, global_step = 10000, label/mean = 12949.43, loss = 417287040.0, prediction/mean = 13155.412\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tmp_83455ux/model.ckpt-10000\n",
            "scores {'average_loss': 26462106.0, 'label/mean': 12949.43, 'loss': 417287040.0, 'prediction/mean': 13155.412, 'global_step': 10000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp_83455ux/model.ckpt-10000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 972x1620 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fruh0Qj5_Bos"
      },
      "source": [
        "## Task 3: Make your best model using only categorical features\n",
        "\n",
        "- Look at the possible feature columns for categorical features. They begin with `categorical_column_with_` in go/tf-ops.\n",
        "- You may find `dataframe[categorical_feature_names].unique()` helpful.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "code",
        "id": "-udhHnNS2WvN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f17d503-1a4c-466b-cff3-9e9b040496d9"
      },
      "source": [
        "# Code\n",
        "\n",
        "batch_size = 34\n",
        "\n",
        "x_df = car_data[categorical_feature_names]  # categorical features \n",
        "y_series = car_data['price']\n",
        "\n",
        "train_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    num_epochs=None,\n",
        "    shuffle=True)\n",
        "\n",
        "eval_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "predict_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "model_feature_columns = [\n",
        "    tf.feature_column.indicator_column(\n",
        "        tf.feature_column.categorical_column_with_vocabulary_list(\n",
        "            feature_name, vocabulary_list=car_data[feature_name].unique()))\n",
        "    for feature_name in categorical_feature_names\n",
        "]\n",
        "print('model_feature_columns', model_feature_columns)\n",
        "\n",
        "est = tf.estimator.DNNRegressor(\n",
        "    feature_columns=model_feature_columns,\n",
        "    hidden_units=[84],\n",
        "    optimizer=tf.train.AdagradOptimizer(learning_rate=0.9),\n",
        "  )\n",
        "\n",
        "# Training \n",
        "num_print_statements = 10\n",
        "num_training_steps = 100000\n",
        "for _ in range(num_print_statements):\n",
        "  est.train(train_input_fn, steps=num_training_steps // num_print_statements)\n",
        "  scores = est.evaluate(eval_input_fn)\n",
        "  \n",
        "  \n",
        "  print('scores', scores)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model_feature_columns [IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='fuel-system', vocabulary_list=('1bbl', 'mpfi', '2bbl', '4bbl', 'idi', 'spdi', 'mfi', 'spfi'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='drive-wheels', vocabulary_list=('fwd', 'rwd', '4wd'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='aspiration', vocabulary_list=('std', 'turbo'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='engine-location', vocabulary_list=('front', 'rear'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='engine-type', vocabulary_list=('ohc', 'ohcv', 'ohcf', 'l', 'dohc', 'rotor', 'dohcv'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='make', vocabulary_list=('honda', 'nissan', 'toyota', 'mitsubishi', 'mazda', 'subaru', 'dodge', 'peugot', 'porsche', 'alfa-romero', 'plymouth', 'audi', 'volkswagen', 'isuzu', 'volvo', 'saab', 'chevrolet', 'bmw', 'mercedes-benz', 'jaguar', 'renault', 'mercury'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='num-doors', vocabulary_list=('two', 'four', '?'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='fuel-type', vocabulary_list=('gas', 'diesel'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='body-style', vocabulary_list=('hatchback', 'sedan', 'wagon', 'hardtop', 'convertible'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='num-cylinders', vocabulary_list=('four', 'six', 'five', 'two', 'eight', 'twelve', 'three'), dtype=tf.string, default_value=-1, num_oov_buckets=0))]\n",
            "INFO:tensorflow:Using default config.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp8o_hcd66\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp8o_hcd66', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...\n",
            "INFO:tensorflow:loss = 8711212000.0, step = 1\n",
            "INFO:tensorflow:global_step/sec: 332.686\n",
            "INFO:tensorflow:loss = 793107500.0, step = 101 (0.305 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.053\n",
            "INFO:tensorflow:loss = 407194940.0, step = 201 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.908\n",
            "INFO:tensorflow:loss = 289654820.0, step = 301 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.082\n",
            "INFO:tensorflow:loss = 110307860.0, step = 401 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.301\n",
            "INFO:tensorflow:loss = 246938020.0, step = 501 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.764\n",
            "INFO:tensorflow:loss = 436626180.0, step = 601 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.569\n",
            "INFO:tensorflow:loss = 376603800.0, step = 701 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.05\n",
            "INFO:tensorflow:loss = 384711420.0, step = 801 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.201\n",
            "INFO:tensorflow:loss = 248712850.0, step = 901 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 436.737\n",
            "INFO:tensorflow:loss = 393217180.0, step = 1001 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.474\n",
            "INFO:tensorflow:loss = 84343576.0, step = 1101 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.046\n",
            "INFO:tensorflow:loss = 403613380.0, step = 1201 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.938\n",
            "INFO:tensorflow:loss = 99637624.0, step = 1301 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.638\n",
            "INFO:tensorflow:loss = 86945940.0, step = 1401 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.564\n",
            "INFO:tensorflow:loss = 410711970.0, step = 1501 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 428.123\n",
            "INFO:tensorflow:loss = 296760670.0, step = 1601 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.328\n",
            "INFO:tensorflow:loss = 322270850.0, step = 1701 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 339.242\n",
            "INFO:tensorflow:loss = 160282060.0, step = 1801 (0.291 sec)\n",
            "INFO:tensorflow:global_step/sec: 421.55\n",
            "INFO:tensorflow:loss = 146474850.0, step = 1901 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.791\n",
            "INFO:tensorflow:loss = 230851200.0, step = 2001 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.54\n",
            "INFO:tensorflow:loss = 429959940.0, step = 2101 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 435.419\n",
            "INFO:tensorflow:loss = 110003460.0, step = 2201 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.161\n",
            "INFO:tensorflow:loss = 101353780.0, step = 2301 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.048\n",
            "INFO:tensorflow:loss = 190780940.0, step = 2401 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.127\n",
            "INFO:tensorflow:loss = 125085464.0, step = 2501 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.61\n",
            "INFO:tensorflow:loss = 395349630.0, step = 2601 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.452\n",
            "INFO:tensorflow:loss = 372179500.0, step = 2701 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.439\n",
            "INFO:tensorflow:loss = 335123170.0, step = 2801 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.326\n",
            "INFO:tensorflow:loss = 207566930.0, step = 2901 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.089\n",
            "INFO:tensorflow:loss = 316350880.0, step = 3001 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.72\n",
            "INFO:tensorflow:loss = 241317420.0, step = 3101 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.049\n",
            "INFO:tensorflow:loss = 335516800.0, step = 3201 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 345.099\n",
            "INFO:tensorflow:loss = 288646980.0, step = 3301 (0.291 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.127\n",
            "INFO:tensorflow:loss = 448530750.0, step = 3401 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.429\n",
            "INFO:tensorflow:loss = 94979000.0, step = 3501 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.396\n",
            "INFO:tensorflow:loss = 170077070.0, step = 3601 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.857\n",
            "INFO:tensorflow:loss = 132975416.0, step = 3701 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.415\n",
            "INFO:tensorflow:loss = 124286296.0, step = 3801 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.513\n",
            "INFO:tensorflow:loss = 190976600.0, step = 3901 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.823\n",
            "INFO:tensorflow:loss = 108073110.0, step = 4001 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.381\n",
            "INFO:tensorflow:loss = 97439810.0, step = 4101 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.273\n",
            "INFO:tensorflow:loss = 154424770.0, step = 4201 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.684\n",
            "INFO:tensorflow:loss = 114108770.0, step = 4301 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.827\n",
            "INFO:tensorflow:loss = 133410790.0, step = 4401 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.689\n",
            "INFO:tensorflow:loss = 657074100.0, step = 4501 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.225\n",
            "INFO:tensorflow:loss = 232765540.0, step = 4601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.085\n",
            "INFO:tensorflow:loss = 92256500.0, step = 4701 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 465.83\n",
            "INFO:tensorflow:loss = 435209150.0, step = 4801 (0.213 sec)\n",
            "INFO:tensorflow:global_step/sec: 442.703\n",
            "INFO:tensorflow:loss = 114025640.0, step = 4901 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.185\n",
            "INFO:tensorflow:loss = 200459540.0, step = 5001 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.435\n",
            "INFO:tensorflow:loss = 162101170.0, step = 5101 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.533\n",
            "INFO:tensorflow:loss = 70585320.0, step = 5201 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.394\n",
            "INFO:tensorflow:loss = 87629224.0, step = 5301 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.757\n",
            "INFO:tensorflow:loss = 139883630.0, step = 5401 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.604\n",
            "INFO:tensorflow:loss = 159422030.0, step = 5501 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.767\n",
            "INFO:tensorflow:loss = 157967730.0, step = 5601 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.514\n",
            "INFO:tensorflow:loss = 131010220.0, step = 5701 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.003\n",
            "INFO:tensorflow:loss = 128217040.0, step = 5801 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 434.608\n",
            "INFO:tensorflow:loss = 129200170.0, step = 5901 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.944\n",
            "INFO:tensorflow:loss = 223516350.0, step = 6001 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.21\n",
            "INFO:tensorflow:loss = 100592810.0, step = 6101 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.639\n",
            "INFO:tensorflow:loss = 98391780.0, step = 6201 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.531\n",
            "INFO:tensorflow:loss = 382397250.0, step = 6301 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.643\n",
            "INFO:tensorflow:loss = 405923650.0, step = 6401 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 434.571\n",
            "INFO:tensorflow:loss = 390896740.0, step = 6501 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 437.339\n",
            "INFO:tensorflow:loss = 130812640.0, step = 6601 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.67\n",
            "INFO:tensorflow:loss = 162958060.0, step = 6701 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.37\n",
            "INFO:tensorflow:loss = 100495210.0, step = 6801 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.737\n",
            "INFO:tensorflow:loss = 373315500.0, step = 6901 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.052\n",
            "INFO:tensorflow:loss = 563603260.0, step = 7001 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.624\n",
            "INFO:tensorflow:loss = 172179920.0, step = 7101 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 340.056\n",
            "INFO:tensorflow:loss = 169153520.0, step = 7201 (0.294 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.823\n",
            "INFO:tensorflow:loss = 175916180.0, step = 7301 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.197\n",
            "INFO:tensorflow:loss = 109516616.0, step = 7401 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.794\n",
            "INFO:tensorflow:loss = 396481470.0, step = 7501 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.984\n",
            "INFO:tensorflow:loss = 153633460.0, step = 7601 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.067\n",
            "INFO:tensorflow:loss = 118005700.0, step = 7701 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.841\n",
            "INFO:tensorflow:loss = 127445840.0, step = 7801 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.816\n",
            "INFO:tensorflow:loss = 85603810.0, step = 7901 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.675\n",
            "INFO:tensorflow:loss = 367993000.0, step = 8001 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.158\n",
            "INFO:tensorflow:loss = 84151500.0, step = 8101 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.366\n",
            "INFO:tensorflow:loss = 359175840.0, step = 8201 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.327\n",
            "INFO:tensorflow:loss = 129478136.0, step = 8301 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.276\n",
            "INFO:tensorflow:loss = 385282750.0, step = 8401 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.766\n",
            "INFO:tensorflow:loss = 327875900.0, step = 8501 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.98\n",
            "INFO:tensorflow:loss = 164790130.0, step = 8601 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.237\n",
            "INFO:tensorflow:loss = 115014250.0, step = 8701 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.165\n",
            "INFO:tensorflow:loss = 302466080.0, step = 8801 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 349.175\n",
            "INFO:tensorflow:loss = 474604600.0, step = 8901 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.273\n",
            "INFO:tensorflow:loss = 156497340.0, step = 9001 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.517\n",
            "INFO:tensorflow:loss = 151487200.0, step = 9101 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.07\n",
            "INFO:tensorflow:loss = 322309630.0, step = 9201 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.847\n",
            "INFO:tensorflow:loss = 506575740.0, step = 9301 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.436\n",
            "INFO:tensorflow:loss = 208774400.0, step = 9401 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.225\n",
            "INFO:tensorflow:loss = 149303920.0, step = 9501 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.579\n",
            "INFO:tensorflow:loss = 94646060.0, step = 9601 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.924\n",
            "INFO:tensorflow:loss = 100909250.0, step = 9701 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.953\n",
            "INFO:tensorflow:loss = 637393860.0, step = 9801 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.058\n",
            "INFO:tensorflow:loss = 63158424.0, step = 9901 (0.259 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...\n",
            "INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...\n",
            "INFO:tensorflow:Loss for final step: 111539060.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:21:45\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-10000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.30975s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:21:45\n",
            "INFO:tensorflow:Saving dict for global step 10000: average_loss = 6323594.5, global_step = 10000, label/mean = 12949.43, loss = 185190990.0, prediction/mean = 13018.016\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tmp8o_hcd66/model.ckpt-10000\n",
            "scores {'average_loss': 6323594.5, 'label/mean': 12949.43, 'loss': 185190990.0, 'prediction/mean': 13018.016, 'global_step': 10000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-10000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...\n",
            "INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...\n",
            "INFO:tensorflow:loss = 103858690.0, step = 10001\n",
            "INFO:tensorflow:global_step/sec: 324.792\n",
            "INFO:tensorflow:loss = 245288800.0, step = 10101 (0.315 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.63\n",
            "INFO:tensorflow:loss = 430375550.0, step = 10201 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.781\n",
            "INFO:tensorflow:loss = 312561440.0, step = 10301 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.112\n",
            "INFO:tensorflow:loss = 421553020.0, step = 10401 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.503\n",
            "INFO:tensorflow:loss = 170404480.0, step = 10501 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.212\n",
            "INFO:tensorflow:loss = 374932030.0, step = 10601 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.189\n",
            "INFO:tensorflow:loss = 99536490.0, step = 10701 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.745\n",
            "INFO:tensorflow:loss = 161483620.0, step = 10801 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.731\n",
            "INFO:tensorflow:loss = 213042240.0, step = 10901 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.328\n",
            "INFO:tensorflow:loss = 125304880.0, step = 11001 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.246\n",
            "INFO:tensorflow:loss = 229713550.0, step = 11101 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.849\n",
            "INFO:tensorflow:loss = 149355060.0, step = 11201 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.151\n",
            "INFO:tensorflow:loss = 201113740.0, step = 11301 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.635\n",
            "INFO:tensorflow:loss = 88753680.0, step = 11401 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.077\n",
            "INFO:tensorflow:loss = 326083000.0, step = 11501 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.1\n",
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            "INFO:tensorflow:global_step/sec: 420.941\n",
            "INFO:tensorflow:loss = 353978180.0, step = 11701 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.576\n",
            "INFO:tensorflow:loss = 133957280.0, step = 11801 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 448.661\n",
            "INFO:tensorflow:loss = 416930880.0, step = 11901 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.03\n",
            "INFO:tensorflow:loss = 144483410.0, step = 12001 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.934\n",
            "INFO:tensorflow:loss = 130469700.0, step = 12101 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.792\n",
            "INFO:tensorflow:loss = 150050860.0, step = 12201 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.279\n",
            "INFO:tensorflow:loss = 177893570.0, step = 12301 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.271\n",
            "INFO:tensorflow:loss = 176492210.0, step = 12401 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.429\n",
            "INFO:tensorflow:loss = 298416130.0, step = 12501 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.28\n",
            "INFO:tensorflow:loss = 199091600.0, step = 12601 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 430.259\n",
            "INFO:tensorflow:loss = 110316420.0, step = 12701 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 440.438\n",
            "INFO:tensorflow:loss = 60682876.0, step = 12801 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.017\n",
            "INFO:tensorflow:loss = 430400420.0, step = 12901 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.024\n",
            "INFO:tensorflow:loss = 209786240.0, step = 13001 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.364\n",
            "INFO:tensorflow:loss = 380609570.0, step = 13101 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.403\n",
            "INFO:tensorflow:loss = 173500110.0, step = 13201 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 442.086\n",
            "INFO:tensorflow:loss = 80406550.0, step = 13301 (0.222 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.805\n",
            "INFO:tensorflow:loss = 83141060.0, step = 13401 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.934\n",
            "INFO:tensorflow:loss = 167150100.0, step = 13501 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.726\n",
            "INFO:tensorflow:loss = 139491490.0, step = 13601 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.842\n",
            "INFO:tensorflow:loss = 138918540.0, step = 13701 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.313\n",
            "INFO:tensorflow:loss = 295280300.0, step = 13801 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.484\n",
            "INFO:tensorflow:loss = 164150910.0, step = 13901 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.786\n",
            "INFO:tensorflow:loss = 181856380.0, step = 14001 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.196\n",
            "INFO:tensorflow:loss = 110163590.0, step = 14101 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.692\n",
            "INFO:tensorflow:loss = 295334900.0, step = 14201 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.77\n",
            "INFO:tensorflow:loss = 307122530.0, step = 14301 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.231\n",
            "INFO:tensorflow:loss = 122979896.0, step = 14401 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.072\n",
            "INFO:tensorflow:loss = 193290370.0, step = 14501 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.008\n",
            "INFO:tensorflow:loss = 105509600.0, step = 14601 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 351.749\n",
            "INFO:tensorflow:loss = 307879230.0, step = 14701 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.304\n",
            "INFO:tensorflow:loss = 156345810.0, step = 14801 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.019\n",
            "INFO:tensorflow:loss = 255433940.0, step = 14901 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 430.413\n",
            "INFO:tensorflow:loss = 294567200.0, step = 15001 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.966\n",
            "INFO:tensorflow:loss = 319842660.0, step = 15101 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.541\n",
            "INFO:tensorflow:loss = 192151090.0, step = 15201 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.227\n",
            "INFO:tensorflow:loss = 556079940.0, step = 15301 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.418\n",
            "INFO:tensorflow:loss = 293517660.0, step = 15401 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 437.224\n",
            "INFO:tensorflow:loss = 430511580.0, step = 15501 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.415\n",
            "INFO:tensorflow:loss = 152557170.0, step = 15601 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.553\n",
            "INFO:tensorflow:loss = 161872910.0, step = 15701 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.228\n",
            "INFO:tensorflow:loss = 361564100.0, step = 15801 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.969\n",
            "INFO:tensorflow:loss = 48483696.0, step = 15901 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.036\n",
            "INFO:tensorflow:loss = 112584584.0, step = 16001 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.749\n",
            "INFO:tensorflow:loss = 743584640.0, step = 16101 (0.291 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.1\n",
            "INFO:tensorflow:loss = 127458350.0, step = 16201 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.806\n",
            "INFO:tensorflow:loss = 372307200.0, step = 16301 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.656\n",
            "INFO:tensorflow:loss = 109530136.0, step = 16401 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.274\n",
            "INFO:tensorflow:loss = 122723380.0, step = 16501 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.801\n",
            "INFO:tensorflow:loss = 224681660.0, step = 16601 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.447\n",
            "INFO:tensorflow:loss = 100252100.0, step = 16701 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.314\n",
            "INFO:tensorflow:loss = 73518140.0, step = 16801 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.486\n",
            "INFO:tensorflow:loss = 290153000.0, step = 16901 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 432.118\n",
            "INFO:tensorflow:loss = 133503784.0, step = 17001 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.316\n",
            "INFO:tensorflow:loss = 140823900.0, step = 17101 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.604\n",
            "INFO:tensorflow:loss = 326184930.0, step = 17201 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 427.828\n",
            "INFO:tensorflow:loss = 173463660.0, step = 17301 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.449\n",
            "INFO:tensorflow:loss = 378063650.0, step = 17401 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 434.195\n",
            "INFO:tensorflow:loss = 113253210.0, step = 17501 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.131\n",
            "INFO:tensorflow:loss = 218569060.0, step = 17601 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.014\n",
            "INFO:tensorflow:loss = 256201280.0, step = 17701 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.711\n",
            "INFO:tensorflow:loss = 134929460.0, step = 17801 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.062\n",
            "INFO:tensorflow:loss = 676192700.0, step = 17901 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 326.377\n",
            "INFO:tensorflow:loss = 416503200.0, step = 18001 (0.306 sec)\n",
            "INFO:tensorflow:global_step/sec: 430.035\n",
            "INFO:tensorflow:loss = 72507860.0, step = 18101 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.217\n",
            "INFO:tensorflow:loss = 329808580.0, step = 18201 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.198\n",
            "INFO:tensorflow:loss = 127593740.0, step = 18301 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.442\n",
            "INFO:tensorflow:loss = 108991050.0, step = 18401 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 463.743\n",
            "INFO:tensorflow:loss = 324185280.0, step = 18501 (0.217 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.202\n",
            "INFO:tensorflow:loss = 182639340.0, step = 18601 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.138\n",
            "INFO:tensorflow:loss = 216802670.0, step = 18701 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.055\n",
            "INFO:tensorflow:loss = 223519260.0, step = 18801 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.824\n",
            "INFO:tensorflow:loss = 312966340.0, step = 18901 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.994\n",
            "INFO:tensorflow:loss = 101806520.0, step = 19001 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.938\n",
            "INFO:tensorflow:loss = 157935440.0, step = 19101 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 453.39\n",
            "INFO:tensorflow:loss = 128125520.0, step = 19201 (0.222 sec)\n",
            "INFO:tensorflow:global_step/sec: 442.1\n",
            "INFO:tensorflow:loss = 335767500.0, step = 19301 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 443.789\n",
            "INFO:tensorflow:loss = 86587180.0, step = 19401 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.786\n",
            "INFO:tensorflow:loss = 125275720.0, step = 19501 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.945\n",
            "INFO:tensorflow:loss = 215744700.0, step = 19601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.3\n",
            "INFO:tensorflow:loss = 303813900.0, step = 19701 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.87\n",
            "INFO:tensorflow:loss = 130435144.0, step = 19801 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.962\n",
            "INFO:tensorflow:loss = 270242880.0, step = 19901 (0.247 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20000...\n",
            "INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20000...\n",
            "INFO:tensorflow:Loss for final step: 320707420.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:22:12\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-20000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.28217s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:22:12\n",
            "INFO:tensorflow:Saving dict for global step 20000: average_loss = 6220976.5, global_step = 20000, label/mean = 12949.43, loss = 182185740.0, prediction/mean = 12991.749\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20000: /tmp/tmp8o_hcd66/model.ckpt-20000\n",
            "scores {'average_loss': 6220976.5, 'label/mean': 12949.43, 'loss': 182185740.0, 'prediction/mean': 12991.749, 'global_step': 20000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-20000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20000...\n",
            "INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20000...\n",
            "INFO:tensorflow:loss = 350672830.0, step = 20001\n",
            "INFO:tensorflow:global_step/sec: 377.214\n",
            "INFO:tensorflow:loss = 86184960.0, step = 20101 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.059\n",
            "INFO:tensorflow:loss = 72301650.0, step = 20201 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.124\n",
            "INFO:tensorflow:loss = 322841100.0, step = 20301 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.74\n",
            "INFO:tensorflow:loss = 469628320.0, step = 20401 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.42\n",
            "INFO:tensorflow:loss = 338881470.0, step = 20501 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.238\n",
            "INFO:tensorflow:loss = 310130020.0, step = 20601 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.775\n",
            "INFO:tensorflow:loss = 318959870.0, step = 20701 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.12\n",
            "INFO:tensorflow:loss = 143781860.0, step = 20801 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 444.736\n",
            "INFO:tensorflow:loss = 106639600.0, step = 20901 (0.219 sec)\n",
            "INFO:tensorflow:global_step/sec: 440.016\n",
            "INFO:tensorflow:loss = 125850350.0, step = 21001 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.662\n",
            "INFO:tensorflow:loss = 132571980.0, step = 21101 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.568\n",
            "INFO:tensorflow:loss = 674700800.0, step = 21201 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.144\n",
            "INFO:tensorflow:loss = 182766750.0, step = 21301 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.175\n",
            "INFO:tensorflow:loss = 77504130.0, step = 21401 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.668\n",
            "INFO:tensorflow:loss = 199751460.0, step = 21501 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.172\n",
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            "INFO:tensorflow:global_step/sec: 424.418\n",
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            "INFO:tensorflow:global_step/sec: 403.502\n",
            "INFO:tensorflow:loss = 96480710.0, step = 21801 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.902\n",
            "INFO:tensorflow:loss = 309288320.0, step = 21901 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.411\n",
            "INFO:tensorflow:loss = 102438360.0, step = 22001 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.748\n",
            "INFO:tensorflow:loss = 244638700.0, step = 22101 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 481.069\n",
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            "INFO:tensorflow:global_step/sec: 418.716\n",
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            "INFO:tensorflow:global_step/sec: 422.84\n",
            "INFO:tensorflow:loss = 427115330.0, step = 22401 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.709\n",
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            "INFO:tensorflow:global_step/sec: 388.836\n",
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            "INFO:tensorflow:global_step/sec: 401.668\n",
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            "INFO:tensorflow:global_step/sec: 410.36\n",
            "INFO:tensorflow:loss = 262161820.0, step = 22801 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 319.576\n",
            "INFO:tensorflow:loss = 102482960.0, step = 22901 (0.316 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.353\n",
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            "INFO:tensorflow:global_step/sec: 399.164\n",
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            "INFO:tensorflow:global_step/sec: 421.628\n",
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            "INFO:tensorflow:global_step/sec: 484.929\n",
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            "INFO:tensorflow:loss = 115489944.0, step = 25901 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.313\n",
            "INFO:tensorflow:loss = 277043100.0, step = 26001 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.836\n",
            "INFO:tensorflow:loss = 334226300.0, step = 26101 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.461\n",
            "INFO:tensorflow:loss = 406509540.0, step = 26201 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.999\n",
            "INFO:tensorflow:loss = 183290670.0, step = 26301 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.106\n",
            "INFO:tensorflow:loss = 135577860.0, step = 26401 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.305\n",
            "INFO:tensorflow:loss = 381918620.0, step = 26501 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 458.728\n",
            "INFO:tensorflow:loss = 185311230.0, step = 26601 (0.218 sec)\n",
            "INFO:tensorflow:global_step/sec: 475.49\n",
            "INFO:tensorflow:loss = 72192690.0, step = 26701 (0.210 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.882\n",
            "INFO:tensorflow:loss = 339868500.0, step = 26801 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.661\n",
            "INFO:tensorflow:loss = 139196590.0, step = 26901 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.672\n",
            "INFO:tensorflow:loss = 330742940.0, step = 27001 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.127\n",
            "INFO:tensorflow:loss = 150810350.0, step = 27101 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.627\n",
            "INFO:tensorflow:loss = 534747300.0, step = 27201 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 297.525\n",
            "INFO:tensorflow:loss = 153111400.0, step = 27301 (0.329 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.034\n",
            "INFO:tensorflow:loss = 159530460.0, step = 27401 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 431.135\n",
            "INFO:tensorflow:loss = 343315620.0, step = 27501 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.658\n",
            "INFO:tensorflow:loss = 138106260.0, step = 27601 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 444.45\n",
            "INFO:tensorflow:loss = 123490010.0, step = 27701 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 434.075\n",
            "INFO:tensorflow:loss = 108168930.0, step = 27801 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.887\n",
            "INFO:tensorflow:loss = 365926460.0, step = 27901 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.96\n",
            "INFO:tensorflow:loss = 133798990.0, step = 28001 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.182\n",
            "INFO:tensorflow:loss = 147080110.0, step = 28101 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.726\n",
            "INFO:tensorflow:loss = 180357000.0, step = 28201 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 309.35\n",
            "INFO:tensorflow:loss = 172237060.0, step = 28301 (0.332 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.402\n",
            "INFO:tensorflow:loss = 387659300.0, step = 28401 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 342.233\n",
            "INFO:tensorflow:loss = 322933340.0, step = 28501 (0.291 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.464\n",
            "INFO:tensorflow:loss = 161059570.0, step = 28601 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.827\n",
            "INFO:tensorflow:loss = 104647920.0, step = 28701 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 436.771\n",
            "INFO:tensorflow:loss = 125004650.0, step = 28801 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 338.795\n",
            "INFO:tensorflow:loss = 178217540.0, step = 28901 (0.298 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.626\n",
            "INFO:tensorflow:loss = 323582660.0, step = 29001 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 443.466\n",
            "INFO:tensorflow:loss = 320578200.0, step = 29101 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 323.36\n",
            "INFO:tensorflow:loss = 225147380.0, step = 29201 (0.316 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.223\n",
            "INFO:tensorflow:loss = 122642270.0, step = 29301 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.857\n",
            "INFO:tensorflow:loss = 209041340.0, step = 29401 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.025\n",
            "INFO:tensorflow:loss = 121002584.0, step = 29501 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.915\n",
            "INFO:tensorflow:loss = 106514150.0, step = 29601 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.851\n",
            "INFO:tensorflow:loss = 78206344.0, step = 29701 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.813\n",
            "INFO:tensorflow:loss = 321672420.0, step = 29801 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.135\n",
            "INFO:tensorflow:loss = 411183000.0, step = 29901 (0.274 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 30000...\n",
            "INFO:tensorflow:Saving checkpoints for 30000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 30000...\n",
            "INFO:tensorflow:Loss for final step: 156693070.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:22:39\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-30000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.28794s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:22:39\n",
            "INFO:tensorflow:Saving dict for global step 30000: average_loss = 6196823.5, global_step = 30000, label/mean = 12949.43, loss = 181478400.0, prediction/mean = 12899.57\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 30000: /tmp/tmp8o_hcd66/model.ckpt-30000\n",
            "scores {'average_loss': 6196823.5, 'label/mean': 12949.43, 'loss': 181478400.0, 'prediction/mean': 12899.57, 'global_step': 30000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-30000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 30000...\n",
            "INFO:tensorflow:Saving checkpoints for 30000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 30000...\n",
            "INFO:tensorflow:loss = 128865220.0, step = 30001\n",
            "INFO:tensorflow:global_step/sec: 356.598\n",
            "INFO:tensorflow:loss = 178485380.0, step = 30101 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.382\n",
            "INFO:tensorflow:loss = 107345290.0, step = 30201 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.231\n",
            "INFO:tensorflow:loss = 80039240.0, step = 30301 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.756\n",
            "INFO:tensorflow:loss = 115542210.0, step = 30401 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.775\n",
            "INFO:tensorflow:loss = 82662620.0, step = 30501 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.336\n",
            "INFO:tensorflow:loss = 151782110.0, step = 30601 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.621\n",
            "INFO:tensorflow:loss = 569889300.0, step = 30701 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.854\n",
            "INFO:tensorflow:loss = 132311600.0, step = 30801 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 466.774\n",
            "INFO:tensorflow:loss = 299663070.0, step = 30901 (0.220 sec)\n",
            "INFO:tensorflow:global_step/sec: 455.539\n",
            "INFO:tensorflow:loss = 158668860.0, step = 31001 (0.213 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.23\n",
            "INFO:tensorflow:loss = 348183300.0, step = 31101 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.406\n",
            "INFO:tensorflow:loss = 142576670.0, step = 31201 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.578\n",
            "INFO:tensorflow:loss = 190428180.0, step = 31301 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.379\n",
            "INFO:tensorflow:loss = 113486540.0, step = 31401 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.493\n",
            "INFO:tensorflow:loss = 148410850.0, step = 31501 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.993\n",
            "INFO:tensorflow:loss = 144480420.0, step = 31601 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.672\n",
            "INFO:tensorflow:loss = 320811500.0, step = 31701 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 458.492\n",
            "INFO:tensorflow:loss = 83381570.0, step = 31801 (0.216 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.662\n",
            "INFO:tensorflow:loss = 149554060.0, step = 31901 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.618\n",
            "INFO:tensorflow:loss = 107277660.0, step = 32001 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.551\n",
            "INFO:tensorflow:loss = 534858940.0, step = 32101 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 443.583\n",
            "INFO:tensorflow:loss = 185362160.0, step = 32201 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.546\n",
            "INFO:tensorflow:loss = 62397636.0, step = 32301 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.198\n",
            "INFO:tensorflow:loss = 85995784.0, step = 32401 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 354.992\n",
            "INFO:tensorflow:loss = 125906910.0, step = 32501 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.993\n",
            "INFO:tensorflow:loss = 98537140.0, step = 32601 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 436.417\n",
            "INFO:tensorflow:loss = 116023490.0, step = 32701 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 442.626\n",
            "INFO:tensorflow:loss = 152924590.0, step = 32801 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.43\n",
            "INFO:tensorflow:loss = 110492264.0, step = 32901 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 335.497\n",
            "INFO:tensorflow:loss = 209523250.0, step = 33001 (0.303 sec)\n",
            "INFO:tensorflow:global_step/sec: 321.25\n",
            "INFO:tensorflow:loss = 505780100.0, step = 33101 (0.308 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.71\n",
            "INFO:tensorflow:loss = 69619730.0, step = 33201 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.302\n",
            "INFO:tensorflow:loss = 286350500.0, step = 33301 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 449.705\n",
            "INFO:tensorflow:loss = 165954300.0, step = 33401 (0.219 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.403\n",
            "INFO:tensorflow:loss = 173843950.0, step = 33501 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.183\n",
            "INFO:tensorflow:loss = 348944770.0, step = 33601 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.368\n",
            "INFO:tensorflow:loss = 339097630.0, step = 33701 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.155\n",
            "INFO:tensorflow:loss = 397836300.0, step = 33801 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.421\n",
            "INFO:tensorflow:loss = 98220800.0, step = 33901 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.451\n",
            "INFO:tensorflow:loss = 345554850.0, step = 34001 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.584\n",
            "INFO:tensorflow:loss = 144604460.0, step = 34101 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.43\n",
            "INFO:tensorflow:loss = 380662100.0, step = 34201 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.974\n",
            "INFO:tensorflow:loss = 128053810.0, step = 34301 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.804\n",
            "INFO:tensorflow:loss = 391810140.0, step = 34401 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.966\n",
            "INFO:tensorflow:loss = 140376060.0, step = 34501 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 462.138\n",
            "INFO:tensorflow:loss = 146293200.0, step = 34601 (0.221 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.831\n",
            "INFO:tensorflow:loss = 228256110.0, step = 34701 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.591\n",
            "INFO:tensorflow:loss = 105218530.0, step = 34801 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.492\n",
            "INFO:tensorflow:loss = 116852080.0, step = 34901 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.112\n",
            "INFO:tensorflow:loss = 79460470.0, step = 35001 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.502\n",
            "INFO:tensorflow:loss = 578336400.0, step = 35101 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.721\n",
            "INFO:tensorflow:loss = 521215300.0, step = 35201 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.382\n",
            "INFO:tensorflow:loss = 151463940.0, step = 35301 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 354.682\n",
            "INFO:tensorflow:loss = 333501120.0, step = 35401 (0.295 sec)\n",
            "INFO:tensorflow:global_step/sec: 439.947\n",
            "INFO:tensorflow:loss = 97254800.0, step = 35501 (0.215 sec)\n",
            "INFO:tensorflow:global_step/sec: 445.661\n",
            "INFO:tensorflow:loss = 70834080.0, step = 35601 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.361\n",
            "INFO:tensorflow:loss = 400765800.0, step = 35701 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.624\n",
            "INFO:tensorflow:loss = 341171680.0, step = 35801 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.737\n",
            "INFO:tensorflow:loss = 100892296.0, step = 35901 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 343.475\n",
            "INFO:tensorflow:loss = 171022080.0, step = 36001 (0.294 sec)\n",
            "INFO:tensorflow:global_step/sec: 434.82\n",
            "INFO:tensorflow:loss = 94244430.0, step = 36101 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 485.13\n",
            "INFO:tensorflow:loss = 138854320.0, step = 36201 (0.200 sec)\n",
            "INFO:tensorflow:global_step/sec: 449.195\n",
            "INFO:tensorflow:loss = 493785800.0, step = 36301 (0.223 sec)\n",
            "INFO:tensorflow:global_step/sec: 492.411\n",
            "INFO:tensorflow:loss = 82085740.0, step = 36401 (0.210 sec)\n",
            "INFO:tensorflow:global_step/sec: 346.012\n",
            "INFO:tensorflow:loss = 599467500.0, step = 36501 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.184\n",
            "INFO:tensorflow:loss = 136890980.0, step = 36601 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.181\n",
            "INFO:tensorflow:loss = 261201200.0, step = 36701 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 436.71\n",
            "INFO:tensorflow:loss = 90258860.0, step = 36801 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.424\n",
            "INFO:tensorflow:loss = 84117840.0, step = 36901 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 453.564\n",
            "INFO:tensorflow:loss = 321530600.0, step = 37001 (0.219 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.729\n",
            "INFO:tensorflow:loss = 147808370.0, step = 37101 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 442.376\n",
            "INFO:tensorflow:loss = 147143490.0, step = 37201 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.948\n",
            "INFO:tensorflow:loss = 312881380.0, step = 37301 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.82\n",
            "INFO:tensorflow:loss = 121628770.0, step = 37401 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.496\n",
            "INFO:tensorflow:loss = 107520190.0, step = 37501 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.813\n",
            "INFO:tensorflow:loss = 171139580.0, step = 37601 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.423\n",
            "INFO:tensorflow:loss = 152424980.0, step = 37701 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.126\n",
            "INFO:tensorflow:loss = 375908740.0, step = 37801 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.314\n",
            "INFO:tensorflow:loss = 92736090.0, step = 37901 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 310.846\n",
            "INFO:tensorflow:loss = 345584300.0, step = 38001 (0.328 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.034\n",
            "INFO:tensorflow:loss = 197825200.0, step = 38101 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.467\n",
            "INFO:tensorflow:loss = 316242340.0, step = 38201 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.749\n",
            "INFO:tensorflow:loss = 93729860.0, step = 38301 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.196\n",
            "INFO:tensorflow:loss = 135900030.0, step = 38401 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.937\n",
            "INFO:tensorflow:loss = 142583680.0, step = 38501 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.426\n",
            "INFO:tensorflow:loss = 204541330.0, step = 38601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.108\n",
            "INFO:tensorflow:loss = 186879970.0, step = 38701 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 451.523\n",
            "INFO:tensorflow:loss = 207949280.0, step = 38801 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 480.21\n",
            "INFO:tensorflow:loss = 297098180.0, step = 38901 (0.204 sec)\n",
            "INFO:tensorflow:global_step/sec: 454.422\n",
            "INFO:tensorflow:loss = 104319176.0, step = 39001 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 325.483\n",
            "INFO:tensorflow:loss = 172173550.0, step = 39101 (0.308 sec)\n",
            "INFO:tensorflow:global_step/sec: 327.162\n",
            "INFO:tensorflow:loss = 91651870.0, step = 39201 (0.304 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.127\n",
            "INFO:tensorflow:loss = 413568420.0, step = 39301 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.174\n",
            "INFO:tensorflow:loss = 86684660.0, step = 39401 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.231\n",
            "INFO:tensorflow:loss = 82286240.0, step = 39501 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.036\n",
            "INFO:tensorflow:loss = 142757700.0, step = 39601 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.634\n",
            "INFO:tensorflow:loss = 300432830.0, step = 39701 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.452\n",
            "INFO:tensorflow:loss = 570954050.0, step = 39801 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 338.001\n",
            "INFO:tensorflow:loss = 309291040.0, step = 39901 (0.297 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 40000...\n",
            "INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 40000...\n",
            "INFO:tensorflow:Loss for final step: 274838460.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:23:06\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-40000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.29688s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:23:06\n",
            "INFO:tensorflow:Saving dict for global step 40000: average_loss = 6186621.5, global_step = 40000, label/mean = 12949.43, loss = 181179630.0, prediction/mean = 12967.353\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 40000: /tmp/tmp8o_hcd66/model.ckpt-40000\n",
            "scores {'average_loss': 6186621.5, 'label/mean': 12949.43, 'loss': 181179630.0, 'prediction/mean': 12967.353, 'global_step': 40000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-40000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 40000...\n",
            "INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 40000...\n",
            "INFO:tensorflow:loss = 153133490.0, step = 40001\n",
            "INFO:tensorflow:global_step/sec: 351.631\n",
            "INFO:tensorflow:loss = 148461730.0, step = 40101 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.635\n",
            "INFO:tensorflow:loss = 229694130.0, step = 40201 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 349.78\n",
            "INFO:tensorflow:loss = 86862380.0, step = 40301 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 308.318\n",
            "INFO:tensorflow:loss = 106898850.0, step = 40401 (0.327 sec)\n",
            "INFO:tensorflow:global_step/sec: 328.46\n",
            "INFO:tensorflow:loss = 340539900.0, step = 40501 (0.301 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.411\n",
            "INFO:tensorflow:loss = 125734984.0, step = 40601 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.072\n",
            "INFO:tensorflow:loss = 160675090.0, step = 40701 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.03\n",
            "INFO:tensorflow:loss = 288981630.0, step = 40801 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.003\n",
            "INFO:tensorflow:loss = 202859070.0, step = 40901 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.394\n",
            "INFO:tensorflow:loss = 382675260.0, step = 41001 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.738\n",
            "INFO:tensorflow:loss = 96555390.0, step = 41101 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.879\n",
            "INFO:tensorflow:loss = 176057730.0, step = 41201 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.617\n",
            "INFO:tensorflow:loss = 413048900.0, step = 41301 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.036\n",
            "INFO:tensorflow:loss = 177990620.0, step = 41401 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.469\n",
            "INFO:tensorflow:loss = 55007300.0, step = 41501 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 330.922\n",
            "INFO:tensorflow:loss = 101619530.0, step = 41601 (0.302 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.699\n",
            "INFO:tensorflow:loss = 132743460.0, step = 41701 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.267\n",
            "INFO:tensorflow:loss = 98901736.0, step = 41801 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 296.302\n",
            "INFO:tensorflow:loss = 215431380.0, step = 41901 (0.340 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.483\n",
            "INFO:tensorflow:loss = 117245330.0, step = 42001 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.609\n",
            "INFO:tensorflow:loss = 102239530.0, step = 42101 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 297.09\n",
            "INFO:tensorflow:loss = 131339770.0, step = 42201 (0.342 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.349\n",
            "INFO:tensorflow:loss = 495891260.0, step = 42301 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.635\n",
            "INFO:tensorflow:loss = 248759790.0, step = 42401 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.78\n",
            "INFO:tensorflow:loss = 373026500.0, step = 42501 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.584\n",
            "INFO:tensorflow:loss = 104467410.0, step = 42601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.258\n",
            "INFO:tensorflow:loss = 169711040.0, step = 42701 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.521\n",
            "INFO:tensorflow:loss = 171653100.0, step = 42801 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.303\n",
            "INFO:tensorflow:loss = 230789600.0, step = 42901 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.62\n",
            "INFO:tensorflow:loss = 134666050.0, step = 43001 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 467.16\n",
            "INFO:tensorflow:loss = 318247330.0, step = 43101 (0.217 sec)\n",
            "INFO:tensorflow:global_step/sec: 456.36\n",
            "INFO:tensorflow:loss = 440642140.0, step = 43201 (0.217 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.083\n",
            "INFO:tensorflow:loss = 108598410.0, step = 43301 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.523\n",
            "INFO:tensorflow:loss = 84194040.0, step = 43401 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.462\n",
            "INFO:tensorflow:loss = 330994000.0, step = 43501 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 451.539\n",
            "INFO:tensorflow:loss = 173455800.0, step = 43601 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.349\n",
            "INFO:tensorflow:loss = 176149230.0, step = 43701 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.785\n",
            "INFO:tensorflow:loss = 88195270.0, step = 43801 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.607\n",
            "INFO:tensorflow:loss = 107603400.0, step = 43901 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.436\n",
            "INFO:tensorflow:loss = 417793660.0, step = 44001 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.836\n",
            "INFO:tensorflow:loss = 357941150.0, step = 44101 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 444.119\n",
            "INFO:tensorflow:loss = 62260700.0, step = 44201 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.675\n",
            "INFO:tensorflow:loss = 124576136.0, step = 44301 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.285\n",
            "INFO:tensorflow:loss = 132500650.0, step = 44401 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 448.117\n",
            "INFO:tensorflow:loss = 109986520.0, step = 44501 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.459\n",
            "INFO:tensorflow:loss = 240934500.0, step = 44601 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.667\n",
            "INFO:tensorflow:loss = 115311176.0, step = 44701 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.002\n",
            "INFO:tensorflow:loss = 119971730.0, step = 44801 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 484.276\n",
            "INFO:tensorflow:loss = 604539600.0, step = 44901 (0.211 sec)\n",
            "INFO:tensorflow:global_step/sec: 451.671\n",
            "INFO:tensorflow:loss = 663162300.0, step = 45001 (0.213 sec)\n",
            "INFO:tensorflow:global_step/sec: 461.355\n",
            "INFO:tensorflow:loss = 101865530.0, step = 45101 (0.218 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.632\n",
            "INFO:tensorflow:loss = 347401000.0, step = 45201 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 455.043\n",
            "INFO:tensorflow:loss = 327082530.0, step = 45301 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.842\n",
            "INFO:tensorflow:loss = 154318350.0, step = 45401 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.625\n",
            "INFO:tensorflow:loss = 267347580.0, step = 45501 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 328.793\n",
            "INFO:tensorflow:loss = 93517240.0, step = 45601 (0.299 sec)\n",
            "INFO:tensorflow:global_step/sec: 444.709\n",
            "INFO:tensorflow:loss = 545497300.0, step = 45701 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.792\n",
            "INFO:tensorflow:loss = 240964430.0, step = 45801 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.168\n",
            "INFO:tensorflow:loss = 441981760.0, step = 45901 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.865\n",
            "INFO:tensorflow:loss = 114316580.0, step = 46001 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.901\n",
            "INFO:tensorflow:loss = 125207410.0, step = 46101 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 427.102\n",
            "INFO:tensorflow:loss = 308127700.0, step = 46201 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 436.114\n",
            "INFO:tensorflow:loss = 302520320.0, step = 46301 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.194\n",
            "INFO:tensorflow:loss = 118555816.0, step = 46401 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 427.584\n",
            "INFO:tensorflow:loss = 488868580.0, step = 46501 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.468\n",
            "INFO:tensorflow:loss = 123502640.0, step = 46601 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.648\n",
            "INFO:tensorflow:loss = 92775580.0, step = 46701 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.924\n",
            "INFO:tensorflow:loss = 392325280.0, step = 46801 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.994\n",
            "INFO:tensorflow:loss = 130420936.0, step = 46901 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.839\n",
            "INFO:tensorflow:loss = 269485540.0, step = 47001 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.819\n",
            "INFO:tensorflow:loss = 134933970.0, step = 47101 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.958\n",
            "INFO:tensorflow:loss = 164258140.0, step = 47201 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 451.267\n",
            "INFO:tensorflow:loss = 84694664.0, step = 47301 (0.220 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.964\n",
            "INFO:tensorflow:loss = 131589620.0, step = 47401 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.453\n",
            "INFO:tensorflow:loss = 348602530.0, step = 47501 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.477\n",
            "INFO:tensorflow:loss = 377897380.0, step = 47601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.668\n",
            "INFO:tensorflow:loss = 97635780.0, step = 47701 (0.375 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.318\n",
            "INFO:tensorflow:loss = 168736910.0, step = 47801 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.645\n",
            "INFO:tensorflow:loss = 184574540.0, step = 47901 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.85\n",
            "INFO:tensorflow:loss = 258656910.0, step = 48001 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 452.963\n",
            "INFO:tensorflow:loss = 171476940.0, step = 48101 (0.218 sec)\n",
            "INFO:tensorflow:global_step/sec: 460.682\n",
            "INFO:tensorflow:loss = 315011740.0, step = 48201 (0.217 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.445\n",
            "INFO:tensorflow:loss = 152274820.0, step = 48301 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 438.19\n",
            "INFO:tensorflow:loss = 119261670.0, step = 48401 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.559\n",
            "INFO:tensorflow:loss = 94351630.0, step = 48501 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.028\n",
            "INFO:tensorflow:loss = 80626610.0, step = 48601 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.605\n",
            "INFO:tensorflow:loss = 115443780.0, step = 48701 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.065\n",
            "INFO:tensorflow:loss = 340031400.0, step = 48801 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 445.962\n",
            "INFO:tensorflow:loss = 108480590.0, step = 48901 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.371\n",
            "INFO:tensorflow:loss = 201173890.0, step = 49001 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.045\n",
            "INFO:tensorflow:loss = 259112100.0, step = 49101 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.773\n",
            "INFO:tensorflow:loss = 297113920.0, step = 49201 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.359\n",
            "INFO:tensorflow:loss = 172867890.0, step = 49301 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.62\n",
            "INFO:tensorflow:loss = 158737440.0, step = 49401 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.262\n",
            "INFO:tensorflow:loss = 443828160.0, step = 49501 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.948\n",
            "INFO:tensorflow:loss = 304219940.0, step = 49601 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.785\n",
            "INFO:tensorflow:loss = 348121630.0, step = 49701 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.506\n",
            "INFO:tensorflow:loss = 334883870.0, step = 49801 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.143\n",
            "INFO:tensorflow:loss = 392782000.0, step = 49901 (0.232 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50000...\n",
            "INFO:tensorflow:Saving checkpoints for 50000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50000...\n",
            "INFO:tensorflow:Loss for final step: 183162800.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:23:33\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-50000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.26571s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:23:33\n",
            "INFO:tensorflow:Saving dict for global step 50000: average_loss = 6183408.5, global_step = 50000, label/mean = 12949.43, loss = 181085540.0, prediction/mean = 12927.155\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 50000: /tmp/tmp8o_hcd66/model.ckpt-50000\n",
            "scores {'average_loss': 6183408.5, 'label/mean': 12949.43, 'loss': 181085540.0, 'prediction/mean': 12927.155, 'global_step': 50000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-50000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50000...\n",
            "INFO:tensorflow:Saving checkpoints for 50000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50000...\n",
            "INFO:tensorflow:loss = 235915660.0, step = 50001\n",
            "INFO:tensorflow:global_step/sec: 320.911\n",
            "INFO:tensorflow:loss = 282767460.0, step = 50101 (0.316 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.884\n",
            "INFO:tensorflow:loss = 108516950.0, step = 50201 (0.290 sec)\n",
            "INFO:tensorflow:global_step/sec: 431.605\n",
            "INFO:tensorflow:loss = 329685200.0, step = 50301 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 434.683\n",
            "INFO:tensorflow:loss = 377229980.0, step = 50401 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.933\n",
            "INFO:tensorflow:loss = 96581944.0, step = 50501 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.207\n",
            "INFO:tensorflow:loss = 116893730.0, step = 50601 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.858\n",
            "INFO:tensorflow:loss = 109123720.0, step = 50701 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.947\n",
            "INFO:tensorflow:loss = 144317380.0, step = 50801 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.01\n",
            "INFO:tensorflow:loss = 120218130.0, step = 50901 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.763\n",
            "INFO:tensorflow:loss = 166673940.0, step = 51001 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.983\n",
            "INFO:tensorflow:loss = 326651800.0, step = 51101 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.858\n",
            "INFO:tensorflow:loss = 103164830.0, step = 51201 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.057\n",
            "INFO:tensorflow:loss = 151535170.0, step = 51301 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.75\n",
            "INFO:tensorflow:loss = 103301870.0, step = 51401 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.792\n",
            "INFO:tensorflow:loss = 140682880.0, step = 51501 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.597\n",
            "INFO:tensorflow:loss = 193811490.0, step = 51601 (0.298 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.656\n",
            "INFO:tensorflow:loss = 123225610.0, step = 51701 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.851\n",
            "INFO:tensorflow:loss = 315807970.0, step = 51801 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.531\n",
            "INFO:tensorflow:loss = 397338780.0, step = 51901 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.533\n",
            "INFO:tensorflow:loss = 181101090.0, step = 52001 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.644\n",
            "INFO:tensorflow:loss = 145959680.0, step = 52101 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.608\n",
            "INFO:tensorflow:loss = 119172040.0, step = 52201 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.215\n",
            "INFO:tensorflow:loss = 254630530.0, step = 52301 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.395\n",
            "INFO:tensorflow:loss = 89635080.0, step = 52401 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.836\n",
            "INFO:tensorflow:loss = 89584400.0, step = 52501 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.235\n",
            "INFO:tensorflow:loss = 173407180.0, step = 52601 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.247\n",
            "INFO:tensorflow:loss = 294879970.0, step = 52701 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 337.784\n",
            "INFO:tensorflow:loss = 386947620.0, step = 52801 (0.304 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.959\n",
            "INFO:tensorflow:loss = 109935336.0, step = 52901 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.343\n",
            "INFO:tensorflow:loss = 113253010.0, step = 53001 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.833\n",
            "INFO:tensorflow:loss = 345075940.0, step = 53101 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.751\n",
            "INFO:tensorflow:loss = 343612380.0, step = 53201 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 335.672\n",
            "INFO:tensorflow:loss = 61017720.0, step = 53301 (0.303 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.401\n",
            "INFO:tensorflow:loss = 110195160.0, step = 53401 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 331.013\n",
            "INFO:tensorflow:loss = 100550290.0, step = 53501 (0.301 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.98\n",
            "INFO:tensorflow:loss = 137511840.0, step = 53601 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.399\n",
            "INFO:tensorflow:loss = 111183580.0, step = 53701 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.345\n",
            "INFO:tensorflow:loss = 152893000.0, step = 53801 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 431.98\n",
            "INFO:tensorflow:loss = 246957300.0, step = 53901 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.112\n",
            "INFO:tensorflow:loss = 109623490.0, step = 54001 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.105\n",
            "INFO:tensorflow:loss = 176408160.0, step = 54101 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.904\n",
            "INFO:tensorflow:loss = 138857120.0, step = 54201 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.838\n",
            "INFO:tensorflow:loss = 538600200.0, step = 54301 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.318\n",
            "INFO:tensorflow:loss = 301158940.0, step = 54401 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.444\n",
            "INFO:tensorflow:loss = 78161060.0, step = 54501 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.526\n",
            "INFO:tensorflow:loss = 93206984.0, step = 54601 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 346.515\n",
            "INFO:tensorflow:loss = 97333496.0, step = 54701 (0.292 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.877\n",
            "INFO:tensorflow:loss = 384789150.0, step = 54801 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.026\n",
            "INFO:tensorflow:loss = 198825950.0, step = 54901 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.807\n",
            "INFO:tensorflow:loss = 348858750.0, step = 55001 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.177\n",
            "INFO:tensorflow:loss = 147500130.0, step = 55101 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.485\n",
            "INFO:tensorflow:loss = 353212160.0, step = 55201 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.233\n",
            "INFO:tensorflow:loss = 483616260.0, step = 55301 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 312.041\n",
            "INFO:tensorflow:loss = 146904430.0, step = 55401 (0.318 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.267\n",
            "INFO:tensorflow:loss = 230563400.0, step = 55501 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.086\n",
            "INFO:tensorflow:loss = 76792830.0, step = 55601 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.428\n",
            "INFO:tensorflow:loss = 319323140.0, step = 55701 (0.298 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.86\n",
            "INFO:tensorflow:loss = 111799040.0, step = 55801 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 437.491\n",
            "INFO:tensorflow:loss = 138524140.0, step = 55901 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.51\n",
            "INFO:tensorflow:loss = 131881430.0, step = 56001 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.989\n",
            "INFO:tensorflow:loss = 87960880.0, step = 56101 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.014\n",
            "INFO:tensorflow:loss = 142765490.0, step = 56201 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.917\n",
            "INFO:tensorflow:loss = 289252380.0, step = 56301 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.282\n",
            "INFO:tensorflow:loss = 413994500.0, step = 56401 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 337.207\n",
            "INFO:tensorflow:loss = 122004550.0, step = 56501 (0.302 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.094\n",
            "INFO:tensorflow:loss = 167911230.0, step = 56601 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.104\n",
            "INFO:tensorflow:loss = 89210130.0, step = 56701 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.056\n",
            "INFO:tensorflow:loss = 106168600.0, step = 56801 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 351.197\n",
            "INFO:tensorflow:loss = 65219640.0, step = 56901 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.433\n",
            "INFO:tensorflow:loss = 81407970.0, step = 57001 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.073\n",
            "INFO:tensorflow:loss = 255226220.0, step = 57101 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.99\n",
            "INFO:tensorflow:loss = 152897090.0, step = 57201 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.284\n",
            "INFO:tensorflow:loss = 150823340.0, step = 57301 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.25\n",
            "INFO:tensorflow:loss = 311428100.0, step = 57401 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.091\n",
            "INFO:tensorflow:loss = 181756260.0, step = 57501 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.818\n",
            "INFO:tensorflow:loss = 133547570.0, step = 57601 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.3\n",
            "INFO:tensorflow:loss = 145453020.0, step = 57701 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 328.048\n",
            "INFO:tensorflow:loss = 58976620.0, step = 57801 (0.297 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.335\n",
            "INFO:tensorflow:loss = 296557020.0, step = 57901 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.758\n",
            "INFO:tensorflow:loss = 306913730.0, step = 58001 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.033\n",
            "INFO:tensorflow:loss = 387331700.0, step = 58101 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.67\n",
            "INFO:tensorflow:loss = 141081420.0, step = 58201 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.617\n",
            "INFO:tensorflow:loss = 160477740.0, step = 58301 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.673\n",
            "INFO:tensorflow:loss = 428565540.0, step = 58401 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.135\n",
            "INFO:tensorflow:loss = 123801760.0, step = 58501 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.625\n",
            "INFO:tensorflow:loss = 197588830.0, step = 58601 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.743\n",
            "INFO:tensorflow:loss = 143606430.0, step = 58701 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.629\n",
            "INFO:tensorflow:loss = 144304180.0, step = 58801 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.569\n",
            "INFO:tensorflow:loss = 179091680.0, step = 58901 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.533\n",
            "INFO:tensorflow:loss = 97606020.0, step = 59001 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.751\n",
            "INFO:tensorflow:loss = 94752140.0, step = 59101 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.764\n",
            "INFO:tensorflow:loss = 120552710.0, step = 59201 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.799\n",
            "INFO:tensorflow:loss = 101481010.0, step = 59301 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.81\n",
            "INFO:tensorflow:loss = 199109630.0, step = 59401 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.614\n",
            "INFO:tensorflow:loss = 673762000.0, step = 59501 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.769\n",
            "INFO:tensorflow:loss = 137715700.0, step = 59601 (0.261 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.933\n",
            "INFO:tensorflow:loss = 405230200.0, step = 59701 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.697\n",
            "INFO:tensorflow:loss = 296496600.0, step = 59801 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.779\n",
            "INFO:tensorflow:loss = 136892620.0, step = 59901 (0.253 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 60000...\n",
            "INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 60000...\n",
            "INFO:tensorflow:Loss for final step: 316761200.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:24:01\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-60000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.27954s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:24:01\n",
            "INFO:tensorflow:Saving dict for global step 60000: average_loss = 6183419.0, global_step = 60000, label/mean = 12949.43, loss = 181085840.0, prediction/mean = 12993.71\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 60000: /tmp/tmp8o_hcd66/model.ckpt-60000\n",
            "scores {'average_loss': 6183419.0, 'label/mean': 12949.43, 'loss': 181085840.0, 'prediction/mean': 12993.71, 'global_step': 60000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-60000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 60000...\n",
            "INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 60000...\n",
            "INFO:tensorflow:loss = 183925980.0, step = 60001\n",
            "INFO:tensorflow:global_step/sec: 348.58\n",
            "INFO:tensorflow:loss = 84123010.0, step = 60101 (0.289 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.922\n",
            "INFO:tensorflow:loss = 321493340.0, step = 60201 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.814\n",
            "INFO:tensorflow:loss = 323510240.0, step = 60301 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.022\n",
            "INFO:tensorflow:loss = 326206980.0, step = 60401 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.217\n",
            "INFO:tensorflow:loss = 175195630.0, step = 60501 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.242\n",
            "INFO:tensorflow:loss = 125055540.0, step = 60601 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.418\n",
            "INFO:tensorflow:loss = 154515540.0, step = 60701 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.929\n",
            "INFO:tensorflow:loss = 196806530.0, step = 60801 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.064\n",
            "INFO:tensorflow:loss = 403789700.0, step = 60901 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.776\n",
            "INFO:tensorflow:loss = 246744110.0, step = 61001 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.178\n",
            "INFO:tensorflow:loss = 64763284.0, step = 61101 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.41\n",
            "INFO:tensorflow:loss = 145138740.0, step = 61201 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.496\n",
            "INFO:tensorflow:loss = 329359870.0, step = 61301 (0.292 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.156\n",
            "INFO:tensorflow:loss = 75094160.0, step = 61401 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.627\n",
            "INFO:tensorflow:loss = 159919220.0, step = 61501 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 446.101\n",
            "INFO:tensorflow:loss = 308585120.0, step = 61601 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.21\n",
            "INFO:tensorflow:loss = 88717256.0, step = 61701 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.553\n",
            "INFO:tensorflow:loss = 175063410.0, step = 61801 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 421.151\n",
            "INFO:tensorflow:loss = 329207420.0, step = 61901 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.849\n",
            "INFO:tensorflow:loss = 139950140.0, step = 62001 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.504\n",
            "INFO:tensorflow:loss = 99064750.0, step = 62101 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.08\n",
            "INFO:tensorflow:loss = 123010770.0, step = 62201 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 454.146\n",
            "INFO:tensorflow:loss = 188443140.0, step = 62301 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 449.12\n",
            "INFO:tensorflow:loss = 289639500.0, step = 62401 (0.220 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.552\n",
            "INFO:tensorflow:loss = 232646370.0, step = 62501 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.99\n",
            "INFO:tensorflow:loss = 367341760.0, step = 62601 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.76\n",
            "INFO:tensorflow:loss = 363057860.0, step = 62701 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.448\n",
            "INFO:tensorflow:loss = 349163500.0, step = 62801 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.832\n",
            "INFO:tensorflow:loss = 140362670.0, step = 62901 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.825\n",
            "INFO:tensorflow:loss = 133574370.0, step = 63001 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 452.743\n",
            "INFO:tensorflow:loss = 212966320.0, step = 63101 (0.221 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.312\n",
            "INFO:tensorflow:loss = 119240160.0, step = 63201 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.78\n",
            "INFO:tensorflow:loss = 485626500.0, step = 63301 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.914\n",
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            "INFO:tensorflow:global_step/sec: 437.692\n",
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            "INFO:tensorflow:global_step/sec: 419.212\n",
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            "INFO:tensorflow:global_step/sec: 417.514\n",
            "INFO:tensorflow:loss = 207828200.0, step = 63701 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.564\n",
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            "INFO:tensorflow:global_step/sec: 387.729\n",
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            "INFO:tensorflow:global_step/sec: 309.943\n",
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            "INFO:tensorflow:global_step/sec: 381.089\n",
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            "INFO:tensorflow:loss = 283543100.0, step = 67101 (0.256 sec)\n",
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            "INFO:tensorflow:global_step/sec: 405.874\n",
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            "INFO:tensorflow:global_step/sec: 362.625\n",
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            "INFO:tensorflow:global_step/sec: 378.286\n",
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            "INFO:tensorflow:global_step/sec: 373.655\n",
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            "INFO:tensorflow:global_step/sec: 416.849\n",
            "INFO:tensorflow:loss = 539338600.0, step = 68101 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.293\n",
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            "INFO:tensorflow:global_step/sec: 396.192\n",
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            "INFO:tensorflow:global_step/sec: 384.076\n",
            "INFO:tensorflow:loss = 80791290.0, step = 68401 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.26\n",
            "INFO:tensorflow:loss = 115008990.0, step = 68501 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.428\n",
            "INFO:tensorflow:loss = 64029840.0, step = 68601 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.712\n",
            "INFO:tensorflow:loss = 134680500.0, step = 68701 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.919\n",
            "INFO:tensorflow:loss = 418087400.0, step = 68801 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.873\n",
            "INFO:tensorflow:loss = 301310080.0, step = 68901 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.881\n",
            "INFO:tensorflow:loss = 154641840.0, step = 69001 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 440.308\n",
            "INFO:tensorflow:loss = 79797030.0, step = 69101 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 440.234\n",
            "INFO:tensorflow:loss = 193120600.0, step = 69201 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.2\n",
            "INFO:tensorflow:loss = 142477280.0, step = 69301 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.847\n",
            "INFO:tensorflow:loss = 270973250.0, step = 69401 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 397.076\n",
            "INFO:tensorflow:loss = 164865840.0, step = 69501 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.794\n",
            "INFO:tensorflow:loss = 179204370.0, step = 69601 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.376\n",
            "INFO:tensorflow:loss = 213590140.0, step = 69701 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.449\n",
            "INFO:tensorflow:loss = 133510220.0, step = 69801 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 350.317\n",
            "INFO:tensorflow:loss = 262146320.0, step = 69901 (0.292 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 70000...\n",
            "INFO:tensorflow:Saving checkpoints for 70000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 70000...\n",
            "INFO:tensorflow:Loss for final step: 169840770.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:24:29\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-70000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.30786s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:24:29\n",
            "INFO:tensorflow:Saving dict for global step 70000: average_loss = 6180962.0, global_step = 70000, label/mean = 12949.43, loss = 181013890.0, prediction/mean = 12935.067\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 70000: /tmp/tmp8o_hcd66/model.ckpt-70000\n",
            "scores {'average_loss': 6180962.0, 'label/mean': 12949.43, 'loss': 181013890.0, 'prediction/mean': 12935.067, 'global_step': 70000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-70000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 70000...\n",
            "INFO:tensorflow:Saving checkpoints for 70000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 70000...\n",
            "INFO:tensorflow:loss = 145501920.0, step = 70001\n",
            "INFO:tensorflow:global_step/sec: 348.864\n",
            "INFO:tensorflow:loss = 90192264.0, step = 70101 (0.293 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.711\n",
            "INFO:tensorflow:loss = 283945500.0, step = 70201 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.719\n",
            "INFO:tensorflow:loss = 157721620.0, step = 70301 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.208\n",
            "INFO:tensorflow:loss = 359548930.0, step = 70401 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 372.624\n",
            "INFO:tensorflow:loss = 114824540.0, step = 70501 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.869\n",
            "INFO:tensorflow:loss = 50227160.0, step = 70601 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.316\n",
            "INFO:tensorflow:loss = 332613950.0, step = 70701 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.04\n",
            "INFO:tensorflow:loss = 329024400.0, step = 70801 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.139\n",
            "INFO:tensorflow:loss = 166572430.0, step = 70901 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.267\n",
            "INFO:tensorflow:loss = 125591840.0, step = 71001 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.686\n",
            "INFO:tensorflow:loss = 145817340.0, step = 71101 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.383\n",
            "INFO:tensorflow:loss = 103158470.0, step = 71201 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.414\n",
            "INFO:tensorflow:loss = 546937540.0, step = 71301 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.838\n",
            "INFO:tensorflow:loss = 94294040.0, step = 71401 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.708\n",
            "INFO:tensorflow:loss = 291541950.0, step = 71501 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.443\n",
            "INFO:tensorflow:loss = 209137870.0, step = 71601 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.659\n",
            "INFO:tensorflow:loss = 149237440.0, step = 71701 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.473\n",
            "INFO:tensorflow:loss = 407206050.0, step = 71801 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.545\n",
            "INFO:tensorflow:loss = 96070504.0, step = 71901 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.106\n",
            "INFO:tensorflow:loss = 171374080.0, step = 72001 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 428.967\n",
            "INFO:tensorflow:loss = 362515700.0, step = 72101 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.062\n",
            "INFO:tensorflow:loss = 301840400.0, step = 72201 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.373\n",
            "INFO:tensorflow:loss = 328419200.0, step = 72301 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.947\n",
            "INFO:tensorflow:loss = 117297624.0, step = 72401 (0.277 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.048\n",
            "INFO:tensorflow:loss = 122879336.0, step = 72501 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 354.555\n",
            "INFO:tensorflow:loss = 92228710.0, step = 72601 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.274\n",
            "INFO:tensorflow:loss = 594374660.0, step = 72701 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.021\n",
            "INFO:tensorflow:loss = 510612540.0, step = 72801 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.378\n",
            "INFO:tensorflow:loss = 81196730.0, step = 72901 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.027\n",
            "INFO:tensorflow:loss = 93604024.0, step = 73001 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 435.056\n",
            "INFO:tensorflow:loss = 135595070.0, step = 73101 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.025\n",
            "INFO:tensorflow:loss = 79122760.0, step = 73201 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.917\n",
            "INFO:tensorflow:loss = 356140450.0, step = 73301 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.619\n",
            "INFO:tensorflow:loss = 201755940.0, step = 73401 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.937\n",
            "INFO:tensorflow:loss = 114274696.0, step = 73501 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 418.66\n",
            "INFO:tensorflow:loss = 493820480.0, step = 73601 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 439.91\n",
            "INFO:tensorflow:loss = 133357040.0, step = 73701 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.764\n",
            "INFO:tensorflow:loss = 223854850.0, step = 73801 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.553\n",
            "INFO:tensorflow:loss = 153543740.0, step = 73901 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.352\n",
            "INFO:tensorflow:loss = 220684320.0, step = 74001 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.996\n",
            "INFO:tensorflow:loss = 128918640.0, step = 74101 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.192\n",
            "INFO:tensorflow:loss = 531833570.0, step = 74201 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.602\n",
            "INFO:tensorflow:loss = 123746856.0, step = 74301 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.857\n",
            "INFO:tensorflow:loss = 146345540.0, step = 74401 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 445.259\n",
            "INFO:tensorflow:loss = 193442240.0, step = 74501 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 427.713\n",
            "INFO:tensorflow:loss = 180815230.0, step = 74601 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.517\n",
            "INFO:tensorflow:loss = 194339540.0, step = 74701 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.341\n",
            "INFO:tensorflow:loss = 156251500.0, step = 74801 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.36\n",
            "INFO:tensorflow:loss = 362495100.0, step = 74901 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.811\n",
            "INFO:tensorflow:loss = 176514820.0, step = 75001 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.975\n",
            "INFO:tensorflow:loss = 383952350.0, step = 75101 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.638\n",
            "INFO:tensorflow:loss = 122126424.0, step = 75201 (0.299 sec)\n",
            "INFO:tensorflow:global_step/sec: 433.158\n",
            "INFO:tensorflow:loss = 160922850.0, step = 75301 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.671\n",
            "INFO:tensorflow:loss = 160703620.0, step = 75401 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.286\n",
            "INFO:tensorflow:loss = 142711400.0, step = 75501 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 353.544\n",
            "INFO:tensorflow:loss = 167379460.0, step = 75601 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.5\n",
            "INFO:tensorflow:loss = 204499420.0, step = 75701 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.421\n",
            "INFO:tensorflow:loss = 321952540.0, step = 75801 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.076\n",
            "INFO:tensorflow:loss = 156889820.0, step = 75901 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 362.07\n",
            "INFO:tensorflow:loss = 276083970.0, step = 76001 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.661\n",
            "INFO:tensorflow:loss = 138284420.0, step = 76101 (0.254 sec)\n",
            "INFO:tensorflow:global_step/sec: 452.569\n",
            "INFO:tensorflow:loss = 104084600.0, step = 76201 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.568\n",
            "INFO:tensorflow:loss = 176825420.0, step = 76301 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.713\n",
            "INFO:tensorflow:loss = 91346970.0, step = 76401 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.108\n",
            "INFO:tensorflow:loss = 119169930.0, step = 76501 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.201\n",
            "INFO:tensorflow:loss = 341663300.0, step = 76601 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.605\n",
            "INFO:tensorflow:loss = 342976220.0, step = 76701 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.575\n",
            "INFO:tensorflow:loss = 135784290.0, step = 76801 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.283\n",
            "INFO:tensorflow:loss = 112634860.0, step = 76901 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.003\n",
            "INFO:tensorflow:loss = 120173390.0, step = 77001 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.589\n",
            "INFO:tensorflow:loss = 131681070.0, step = 77101 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.74\n",
            "INFO:tensorflow:loss = 122306500.0, step = 77201 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.03\n",
            "INFO:tensorflow:loss = 417188800.0, step = 77301 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.466\n",
            "INFO:tensorflow:loss = 518748740.0, step = 77401 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.449\n",
            "INFO:tensorflow:loss = 186098060.0, step = 77501 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.348\n",
            "INFO:tensorflow:loss = 169382340.0, step = 77601 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.391\n",
            "INFO:tensorflow:loss = 138378080.0, step = 77701 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.506\n",
            "INFO:tensorflow:loss = 72756700.0, step = 77801 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 412.18\n",
            "INFO:tensorflow:loss = 177653140.0, step = 77901 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.137\n",
            "INFO:tensorflow:loss = 82645660.0, step = 78001 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.795\n",
            "INFO:tensorflow:loss = 173516460.0, step = 78101 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.488\n",
            "INFO:tensorflow:loss = 381031500.0, step = 78201 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 356.537\n",
            "INFO:tensorflow:loss = 143295140.0, step = 78301 (0.283 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.708\n",
            "INFO:tensorflow:loss = 90166456.0, step = 78401 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.625\n",
            "INFO:tensorflow:loss = 113866504.0, step = 78501 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.369\n",
            "INFO:tensorflow:loss = 108690024.0, step = 78601 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.616\n",
            "INFO:tensorflow:loss = 115466570.0, step = 78701 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 369.49\n",
            "INFO:tensorflow:loss = 345404540.0, step = 78801 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.256\n",
            "INFO:tensorflow:loss = 534032130.0, step = 78901 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.276\n",
            "INFO:tensorflow:loss = 293084060.0, step = 79001 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.418\n",
            "INFO:tensorflow:loss = 352841660.0, step = 79101 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.072\n",
            "INFO:tensorflow:loss = 91887176.0, step = 79201 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.26\n",
            "INFO:tensorflow:loss = 189935260.0, step = 79301 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.543\n",
            "INFO:tensorflow:loss = 134209140.0, step = 79401 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 363.433\n",
            "INFO:tensorflow:loss = 112863260.0, step = 79501 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.022\n",
            "INFO:tensorflow:loss = 183131820.0, step = 79601 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.825\n",
            "INFO:tensorflow:loss = 144448460.0, step = 79701 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 454.033\n",
            "INFO:tensorflow:loss = 143688930.0, step = 79801 (0.225 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.969\n",
            "INFO:tensorflow:loss = 136993500.0, step = 79901 (0.256 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 80000...\n",
            "INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 80000...\n",
            "INFO:tensorflow:Loss for final step: 148819760.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:24:56\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-80000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.30658s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:24:56\n",
            "INFO:tensorflow:Saving dict for global step 80000: average_loss = 6180505.5, global_step = 80000, label/mean = 12949.43, loss = 181000530.0, prediction/mean = 12948.496\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 80000: /tmp/tmp8o_hcd66/model.ckpt-80000\n",
            "scores {'average_loss': 6180505.5, 'label/mean': 12949.43, 'loss': 181000530.0, 'prediction/mean': 12948.496, 'global_step': 80000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-80000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 80000...\n",
            "INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 80000...\n",
            "INFO:tensorflow:loss = 91794820.0, step = 80001\n",
            "INFO:tensorflow:global_step/sec: 357.739\n",
            "INFO:tensorflow:loss = 532828900.0, step = 80101 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.118\n",
            "INFO:tensorflow:loss = 520835300.0, step = 80201 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.658\n",
            "INFO:tensorflow:loss = 99054570.0, step = 80301 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.23\n",
            "INFO:tensorflow:loss = 354615460.0, step = 80401 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 338.827\n",
            "INFO:tensorflow:loss = 353333700.0, step = 80501 (0.295 sec)\n",
            "INFO:tensorflow:global_step/sec: 345.843\n",
            "INFO:tensorflow:loss = 506545020.0, step = 80601 (0.287 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.799\n",
            "INFO:tensorflow:loss = 145579330.0, step = 80701 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.264\n",
            "INFO:tensorflow:loss = 99968140.0, step = 80801 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.764\n",
            "INFO:tensorflow:loss = 229200020.0, step = 80901 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 455.35\n",
            "INFO:tensorflow:loss = 66266670.0, step = 81001 (0.223 sec)\n",
            "INFO:tensorflow:global_step/sec: 432.133\n",
            "INFO:tensorflow:loss = 131924910.0, step = 81101 (0.227 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.512\n",
            "INFO:tensorflow:loss = 118115180.0, step = 81201 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 394.566\n",
            "INFO:tensorflow:loss = 165178720.0, step = 81301 (0.259 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.874\n",
            "INFO:tensorflow:loss = 184363460.0, step = 81401 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.711\n",
            "INFO:tensorflow:loss = 235369570.0, step = 81501 (0.279 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.183\n",
            "INFO:tensorflow:loss = 140401060.0, step = 81601 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.702\n",
            "INFO:tensorflow:loss = 128494220.0, step = 81701 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.792\n",
            "INFO:tensorflow:loss = 144622420.0, step = 81801 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.797\n",
            "INFO:tensorflow:loss = 318092030.0, step = 81901 (0.239 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.643\n",
            "INFO:tensorflow:loss = 321330240.0, step = 82001 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.801\n",
            "INFO:tensorflow:loss = 119719780.0, step = 82101 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.853\n",
            "INFO:tensorflow:loss = 356584540.0, step = 82201 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.163\n",
            "INFO:tensorflow:loss = 552205760.0, step = 82301 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.96\n",
            "INFO:tensorflow:loss = 597159500.0, step = 82401 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.982\n",
            "INFO:tensorflow:loss = 122447080.0, step = 82501 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 390.147\n",
            "INFO:tensorflow:loss = 66432628.0, step = 82601 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.867\n",
            "INFO:tensorflow:loss = 103551150.0, step = 82701 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.035\n",
            "INFO:tensorflow:loss = 306535500.0, step = 82801 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 393.84\n",
            "INFO:tensorflow:loss = 336716930.0, step = 82901 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.554\n",
            "INFO:tensorflow:loss = 123996104.0, step = 83001 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.299\n",
            "INFO:tensorflow:loss = 108285720.0, step = 83101 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.877\n",
            "INFO:tensorflow:loss = 285137180.0, step = 83201 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.745\n",
            "INFO:tensorflow:loss = 106783780.0, step = 83301 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.013\n",
            "INFO:tensorflow:loss = 642919800.0, step = 83401 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.752\n",
            "INFO:tensorflow:loss = 136977730.0, step = 83501 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.588\n",
            "INFO:tensorflow:loss = 68567890.0, step = 83601 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.321\n",
            "INFO:tensorflow:loss = 274342300.0, step = 83701 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.893\n",
            "INFO:tensorflow:loss = 164834460.0, step = 83801 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.056\n",
            "INFO:tensorflow:loss = 133440150.0, step = 83901 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.73\n",
            "INFO:tensorflow:loss = 179444860.0, step = 84001 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.184\n",
            "INFO:tensorflow:loss = 125757976.0, step = 84101 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 431.226\n",
            "INFO:tensorflow:loss = 108442630.0, step = 84201 (0.231 sec)\n",
            "INFO:tensorflow:global_step/sec: 405.183\n",
            "INFO:tensorflow:loss = 468165120.0, step = 84301 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 457.686\n",
            "INFO:tensorflow:loss = 71767944.0, step = 84401 (0.221 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.751\n",
            "INFO:tensorflow:loss = 164976670.0, step = 84501 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 408.711\n",
            "INFO:tensorflow:loss = 312639600.0, step = 84601 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.958\n",
            "INFO:tensorflow:loss = 106098424.0, step = 84701 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.748\n",
            "INFO:tensorflow:loss = 171904130.0, step = 84801 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 344.909\n",
            "INFO:tensorflow:loss = 69037220.0, step = 84901 (0.296 sec)\n",
            "INFO:tensorflow:global_step/sec: 371.153\n",
            "INFO:tensorflow:loss = 141850400.0, step = 85001 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.189\n",
            "INFO:tensorflow:loss = 142336590.0, step = 85101 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.732\n",
            "INFO:tensorflow:loss = 88107680.0, step = 85201 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.566\n",
            "INFO:tensorflow:loss = 267843970.0, step = 85301 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 328.779\n",
            "INFO:tensorflow:loss = 129946200.0, step = 85401 (0.302 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.205\n",
            "INFO:tensorflow:loss = 515101150.0, step = 85501 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 325.219\n",
            "INFO:tensorflow:loss = 144929630.0, step = 85601 (0.312 sec)\n",
            "INFO:tensorflow:global_step/sec: 298.198\n",
            "INFO:tensorflow:loss = 131067340.0, step = 85701 (0.332 sec)\n",
            "INFO:tensorflow:global_step/sec: 411.877\n",
            "INFO:tensorflow:loss = 364248030.0, step = 85801 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.255\n",
            "INFO:tensorflow:loss = 92867140.0, step = 85901 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.14\n",
            "INFO:tensorflow:loss = 155838560.0, step = 86001 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 344.919\n",
            "INFO:tensorflow:loss = 328956400.0, step = 86101 (0.291 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.296\n",
            "INFO:tensorflow:loss = 175917800.0, step = 86201 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 378.402\n",
            "INFO:tensorflow:loss = 121408300.0, step = 86301 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.175\n",
            "INFO:tensorflow:loss = 137399140.0, step = 86401 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.449\n",
            "INFO:tensorflow:loss = 138855330.0, step = 86501 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 454.062\n",
            "INFO:tensorflow:loss = 271348100.0, step = 86601 (0.217 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.453\n",
            "INFO:tensorflow:loss = 148349680.0, step = 86701 (0.249 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.923\n",
            "INFO:tensorflow:loss = 177747330.0, step = 86801 (0.273 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.657\n",
            "INFO:tensorflow:loss = 125480760.0, step = 86901 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.62\n",
            "INFO:tensorflow:loss = 124262920.0, step = 87001 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.153\n",
            "INFO:tensorflow:loss = 309956300.0, step = 87101 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.2\n",
            "INFO:tensorflow:loss = 86580550.0, step = 87201 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 359.678\n",
            "INFO:tensorflow:loss = 50703870.0, step = 87301 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 376.019\n",
            "INFO:tensorflow:loss = 163272000.0, step = 87401 (0.262 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.351\n",
            "INFO:tensorflow:loss = 365036500.0, step = 87501 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.484\n",
            "INFO:tensorflow:loss = 82352970.0, step = 87601 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.721\n",
            "INFO:tensorflow:loss = 430175800.0, step = 87701 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.599\n",
            "INFO:tensorflow:loss = 162158720.0, step = 87801 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 400.334\n",
            "INFO:tensorflow:loss = 100415060.0, step = 87901 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 419.768\n",
            "INFO:tensorflow:loss = 188201470.0, step = 88001 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 375.834\n",
            "INFO:tensorflow:loss = 80201520.0, step = 88101 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.259\n",
            "INFO:tensorflow:loss = 288683000.0, step = 88201 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.359\n",
            "INFO:tensorflow:loss = 361430530.0, step = 88301 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.843\n",
            "INFO:tensorflow:loss = 89621110.0, step = 88401 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.012\n",
            "INFO:tensorflow:loss = 378321950.0, step = 88501 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.516\n",
            "INFO:tensorflow:loss = 96754660.0, step = 88601 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 384.692\n",
            "INFO:tensorflow:loss = 174120300.0, step = 88701 (0.263 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.744\n",
            "INFO:tensorflow:loss = 154035740.0, step = 88801 (0.250 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.045\n",
            "INFO:tensorflow:loss = 99841310.0, step = 88901 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.645\n",
            "INFO:tensorflow:loss = 229465260.0, step = 89001 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.483\n",
            "INFO:tensorflow:loss = 192567620.0, step = 89101 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.325\n",
            "INFO:tensorflow:loss = 292524000.0, step = 89201 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.683\n",
            "INFO:tensorflow:loss = 333774600.0, step = 89301 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.166\n",
            "INFO:tensorflow:loss = 70950990.0, step = 89401 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.2\n",
            "INFO:tensorflow:loss = 99958136.0, step = 89501 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.9\n",
            "INFO:tensorflow:loss = 192716130.0, step = 89601 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.516\n",
            "INFO:tensorflow:loss = 194879140.0, step = 89701 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.411\n",
            "INFO:tensorflow:loss = 290895740.0, step = 89801 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.051\n",
            "INFO:tensorflow:loss = 309985300.0, step = 89901 (0.235 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 90000...\n",
            "INFO:tensorflow:Saving checkpoints for 90000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 90000...\n",
            "INFO:tensorflow:Loss for final step: 325560700.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:25:23\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-90000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.29308s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:25:24\n",
            "INFO:tensorflow:Saving dict for global step 90000: average_loss = 6180675.0, global_step = 90000, label/mean = 12949.43, loss = 181005470.0, prediction/mean = 12971.317\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 90000: /tmp/tmp8o_hcd66/model.ckpt-90000\n",
            "scores {'average_loss': 6180675.0, 'label/mean': 12949.43, 'loss': 181005470.0, 'prediction/mean': 12971.317, 'global_step': 90000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-90000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 90000...\n",
            "INFO:tensorflow:Saving checkpoints for 90000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 90000...\n",
            "INFO:tensorflow:loss = 169543650.0, step = 90001\n",
            "INFO:tensorflow:global_step/sec: 320.674\n",
            "INFO:tensorflow:loss = 109742560.0, step = 90101 (0.315 sec)\n",
            "INFO:tensorflow:global_step/sec: 401.699\n",
            "INFO:tensorflow:loss = 315547330.0, step = 90201 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.123\n",
            "INFO:tensorflow:loss = 81610690.0, step = 90301 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.421\n",
            "INFO:tensorflow:loss = 106902184.0, step = 90401 (0.270 sec)\n",
            "INFO:tensorflow:global_step/sec: 410.665\n",
            "INFO:tensorflow:loss = 102806776.0, step = 90501 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.61\n",
            "INFO:tensorflow:loss = 258815020.0, step = 90601 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 435.818\n",
            "INFO:tensorflow:loss = 341596960.0, step = 90701 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.222\n",
            "INFO:tensorflow:loss = 118808744.0, step = 90801 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 377.926\n",
            "INFO:tensorflow:loss = 174138670.0, step = 90901 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.535\n",
            "INFO:tensorflow:loss = 152570260.0, step = 91001 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 416.504\n",
            "INFO:tensorflow:loss = 145408100.0, step = 91101 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.638\n",
            "INFO:tensorflow:loss = 124296584.0, step = 91201 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 315.988\n",
            "INFO:tensorflow:loss = 498844740.0, step = 91301 (0.319 sec)\n",
            "INFO:tensorflow:global_step/sec: 395.948\n",
            "INFO:tensorflow:loss = 130121040.0, step = 91401 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 380.831\n",
            "INFO:tensorflow:loss = 348144770.0, step = 91501 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 365.894\n",
            "INFO:tensorflow:loss = 272371260.0, step = 91601 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.86\n",
            "INFO:tensorflow:loss = 131889590.0, step = 91701 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.12\n",
            "INFO:tensorflow:loss = 178221800.0, step = 91801 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.868\n",
            "INFO:tensorflow:loss = 97990904.0, step = 91901 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.761\n",
            "INFO:tensorflow:loss = 134355140.0, step = 92001 (0.238 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.053\n",
            "INFO:tensorflow:loss = 209040260.0, step = 92101 (0.267 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.179\n",
            "INFO:tensorflow:loss = 361568770.0, step = 92201 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 403.439\n",
            "INFO:tensorflow:loss = 93289416.0, step = 92301 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.004\n",
            "INFO:tensorflow:loss = 132862210.0, step = 92401 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 396.156\n",
            "INFO:tensorflow:loss = 115844020.0, step = 92501 (0.251 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.709\n",
            "INFO:tensorflow:loss = 204695260.0, step = 92601 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.472\n",
            "INFO:tensorflow:loss = 173831570.0, step = 92701 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 402.726\n",
            "INFO:tensorflow:loss = 111179900.0, step = 92801 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.302\n",
            "INFO:tensorflow:loss = 462103870.0, step = 92901 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.564\n",
            "INFO:tensorflow:loss = 153636130.0, step = 93001 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.003\n",
            "INFO:tensorflow:loss = 583484350.0, step = 93101 (0.258 sec)\n",
            "INFO:tensorflow:global_step/sec: 428.557\n",
            "INFO:tensorflow:loss = 240719860.0, step = 93201 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.926\n",
            "INFO:tensorflow:loss = 87013980.0, step = 93301 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 417.792\n",
            "INFO:tensorflow:loss = 70386960.0, step = 93401 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 368.787\n",
            "INFO:tensorflow:loss = 382059740.0, step = 93501 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 345.246\n",
            "INFO:tensorflow:loss = 352436960.0, step = 93601 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 330.631\n",
            "INFO:tensorflow:loss = 251683620.0, step = 93701 (0.295 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.06\n",
            "INFO:tensorflow:loss = 174522980.0, step = 93801 (0.295 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.751\n",
            "INFO:tensorflow:loss = 403142080.0, step = 93901 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 422.737\n",
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            "INFO:tensorflow:global_step/sec: 419.256\n",
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            "INFO:tensorflow:global_step/sec: 379.035\n",
            "INFO:tensorflow:loss = 400859620.0, step = 94201 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 373.461\n",
            "INFO:tensorflow:loss = 137386050.0, step = 94301 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.81\n",
            "INFO:tensorflow:loss = 117554800.0, step = 94401 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.237\n",
            "INFO:tensorflow:loss = 343570270.0, step = 94501 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.477\n",
            "INFO:tensorflow:loss = 92948880.0, step = 94601 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.42\n",
            "INFO:tensorflow:loss = 252307090.0, step = 94701 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 387.623\n",
            "INFO:tensorflow:loss = 84201590.0, step = 94801 (0.268 sec)\n",
            "INFO:tensorflow:global_step/sec: 386.528\n",
            "INFO:tensorflow:loss = 122893310.0, step = 94901 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.267\n",
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            "INFO:tensorflow:global_step/sec: 405.74\n",
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            "INFO:tensorflow:global_step/sec: 418.388\n",
            "INFO:tensorflow:loss = 290710400.0, step = 95201 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.779\n",
            "INFO:tensorflow:loss = 356422300.0, step = 95301 (0.285 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.409\n",
            "INFO:tensorflow:loss = 110760424.0, step = 95401 (0.266 sec)\n",
            "INFO:tensorflow:global_step/sec: 344.879\n",
            "INFO:tensorflow:loss = 382583550.0, step = 95501 (0.290 sec)\n",
            "INFO:tensorflow:global_step/sec: 382.131\n",
            "INFO:tensorflow:loss = 152251230.0, step = 95601 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.522\n",
            "INFO:tensorflow:loss = 329151040.0, step = 95701 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.841\n",
            "INFO:tensorflow:loss = 135521860.0, step = 95801 (0.248 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.856\n",
            "INFO:tensorflow:loss = 89791600.0, step = 95901 (0.242 sec)\n",
            "INFO:tensorflow:global_step/sec: 425.612\n",
            "INFO:tensorflow:loss = 332957920.0, step = 96001 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 409.951\n",
            "INFO:tensorflow:loss = 122732040.0, step = 96101 (0.244 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.069\n",
            "INFO:tensorflow:loss = 137163890.0, step = 96201 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 473.785\n",
            "INFO:tensorflow:loss = 119851176.0, step = 96301 (0.215 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.495\n",
            "INFO:tensorflow:loss = 105167620.0, step = 96401 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 379.669\n",
            "INFO:tensorflow:loss = 198882220.0, step = 96501 (0.269 sec)\n",
            "INFO:tensorflow:global_step/sec: 398.75\n",
            "INFO:tensorflow:loss = 416374340.0, step = 96601 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 434.704\n",
            "INFO:tensorflow:loss = 485647740.0, step = 96701 (0.229 sec)\n",
            "INFO:tensorflow:global_step/sec: 428.504\n",
            "INFO:tensorflow:loss = 322281950.0, step = 96801 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 360.058\n",
            "INFO:tensorflow:loss = 109431470.0, step = 96901 (0.278 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.206\n",
            "INFO:tensorflow:loss = 157249520.0, step = 97001 (0.281 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.651\n",
            "INFO:tensorflow:loss = 196645360.0, step = 97101 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 374.297\n",
            "INFO:tensorflow:loss = 149862380.0, step = 97201 (0.264 sec)\n",
            "INFO:tensorflow:global_step/sec: 423.026\n",
            "INFO:tensorflow:loss = 79143060.0, step = 97301 (0.245 sec)\n",
            "INFO:tensorflow:global_step/sec: 426.52\n",
            "INFO:tensorflow:loss = 104354670.0, step = 97401 (0.230 sec)\n",
            "INFO:tensorflow:global_step/sec: 333.307\n",
            "INFO:tensorflow:loss = 100010300.0, step = 97501 (0.301 sec)\n",
            "INFO:tensorflow:global_step/sec: 344.881\n",
            "INFO:tensorflow:loss = 106303220.0, step = 97601 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 415.355\n",
            "INFO:tensorflow:loss = 237839000.0, step = 97701 (0.243 sec)\n",
            "INFO:tensorflow:global_step/sec: 388.663\n",
            "INFO:tensorflow:loss = 336460500.0, step = 97801 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 420.753\n",
            "INFO:tensorflow:loss = 292756160.0, step = 97901 (0.235 sec)\n",
            "INFO:tensorflow:global_step/sec: 399.431\n",
            "INFO:tensorflow:loss = 112004824.0, step = 98001 (0.253 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.523\n",
            "INFO:tensorflow:loss = 137107470.0, step = 98101 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 413.606\n",
            "INFO:tensorflow:loss = 89206136.0, step = 98201 (0.240 sec)\n",
            "INFO:tensorflow:global_step/sec: 358.163\n",
            "INFO:tensorflow:loss = 133662800.0, step = 98301 (0.286 sec)\n",
            "INFO:tensorflow:global_step/sec: 389.548\n",
            "INFO:tensorflow:loss = 160774190.0, step = 98401 (0.257 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.222\n",
            "INFO:tensorflow:loss = 350766100.0, step = 98501 (0.236 sec)\n",
            "INFO:tensorflow:global_step/sec: 443.801\n",
            "INFO:tensorflow:loss = 184898980.0, step = 98601 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 392.225\n",
            "INFO:tensorflow:loss = 72022730.0, step = 98701 (0.255 sec)\n",
            "INFO:tensorflow:global_step/sec: 366.019\n",
            "INFO:tensorflow:loss = 161584740.0, step = 98801 (0.274 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.12\n",
            "INFO:tensorflow:loss = 270505100.0, step = 98901 (0.288 sec)\n",
            "INFO:tensorflow:global_step/sec: 407.288\n",
            "INFO:tensorflow:loss = 124432080.0, step = 99001 (0.246 sec)\n",
            "INFO:tensorflow:global_step/sec: 385.296\n",
            "INFO:tensorflow:loss = 107224136.0, step = 99101 (0.256 sec)\n",
            "INFO:tensorflow:global_step/sec: 406.006\n",
            "INFO:tensorflow:loss = 71747530.0, step = 99201 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 370.721\n",
            "INFO:tensorflow:loss = 311157760.0, step = 99301 (0.271 sec)\n",
            "INFO:tensorflow:global_step/sec: 381.07\n",
            "INFO:tensorflow:loss = 118908790.0, step = 99401 (0.260 sec)\n",
            "INFO:tensorflow:global_step/sec: 404.443\n",
            "INFO:tensorflow:loss = 186348540.0, step = 99501 (0.247 sec)\n",
            "INFO:tensorflow:global_step/sec: 471.741\n",
            "INFO:tensorflow:loss = 342415700.0, step = 99601 (0.215 sec)\n",
            "INFO:tensorflow:global_step/sec: 391.343\n",
            "INFO:tensorflow:loss = 410564640.0, step = 99701 (0.252 sec)\n",
            "INFO:tensorflow:global_step/sec: 383.561\n",
            "INFO:tensorflow:loss = 74424424.0, step = 99801 (0.265 sec)\n",
            "INFO:tensorflow:global_step/sec: 355.44\n",
            "INFO:tensorflow:loss = 192172900.0, step = 99901 (0.284 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 100000...\n",
            "INFO:tensorflow:Saving checkpoints for 100000 into /tmp/tmp8o_hcd66/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 100000...\n",
            "INFO:tensorflow:Loss for final step: 374414370.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:25:51\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp8o_hcd66/model.ckpt-100000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.29449s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:25:51\n",
            "INFO:tensorflow:Saving dict for global step 100000: average_loss = 6181963.0, global_step = 100000, label/mean = 12949.43, loss = 181043200.0, prediction/mean = 12992.5\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 100000: /tmp/tmp8o_hcd66/model.ckpt-100000\n",
            "scores {'average_loss': 6181963.0, 'label/mean': 12949.43, 'loss': 181043200.0, 'prediction/mean': 12992.5, 'global_step': 100000}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EftIzPAI9RJj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d8a8391-fbaf-40a2-8c3b-5d28d320e08c"
      },
      "source": [
        "#@title Possible solution\n",
        "# We have the full list of values that each feature takes on, and the list is\n",
        "# relatively small so we use categorical_column_with_vocabulary_list.\n",
        "\n",
        "batch_size = 16\n",
        "\n",
        "x_df = car_data[categorical_feature_names]\n",
        "y_series = car_data['price']\n",
        "\n",
        "train_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    num_epochs=None,\n",
        "    shuffle=True)\n",
        "\n",
        "eval_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "predict_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "model_feature_columns = [\n",
        "    tf.feature_column.indicator_column(\n",
        "        tf.feature_column.categorical_column_with_vocabulary_list(\n",
        "            feature_name, vocabulary_list=car_data[feature_name].unique()))\n",
        "    for feature_name in categorical_feature_names\n",
        "]\n",
        "print('model_feature_columns', model_feature_columns)\n",
        "\n",
        "est = tf.estimator.DNNRegressor(\n",
        "    feature_columns=model_feature_columns,\n",
        "    hidden_units=[64],\n",
        "    optimizer=tf.train.AdagradOptimizer(learning_rate=0.01),\n",
        "  )\n",
        "\n",
        "# TRAIN\n",
        "num_print_statements = 10\n",
        "num_training_steps = 10000\n",
        "for _ in range(num_print_statements):\n",
        "  est.train(train_input_fn, steps=num_training_steps // num_print_statements)\n",
        "  scores = est.evaluate(eval_input_fn)\n",
        "  \n",
        "  # The `scores` dictionary has several metrics automatically generated by the\n",
        "  # canned Estimator.\n",
        "  # `average_loss` is the average loss for an individual example.\n",
        "  # `loss` is the summed loss for the batch.\n",
        "  # In addition to these scalar losses, you may find the visualization functions\n",
        "  # in the next cell helpful for debugging model quality.\n",
        "  print('scores', scores)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model_feature_columns [IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='fuel-system', vocabulary_list=('mpfi', 'idi', '2bbl', 'mfi', '1bbl', 'spdi', '4bbl', 'spfi'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='drive-wheels', vocabulary_list=('fwd', 'rwd', '4wd'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='aspiration', vocabulary_list=('std', 'turbo'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='engine-location', vocabulary_list=('front', 'rear'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='engine-type', vocabulary_list=('ohc', 'ohcv', 'l', 'dohc', 'rotor', 'ohcf', 'dohcv'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='make', vocabulary_list=('volkswagen', 'nissan', 'mazda', 'mitsubishi', 'peugot', 'saab', 'mercedes-benz', 'dodge', 'toyota', 'chevrolet', 'honda', 'volvo', 'plymouth', 'bmw', 'renault', 'mercury', 'subaru', 'audi', 'alfa-romero', 'porsche', 'jaguar', 'isuzu'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='num-doors', vocabulary_list=('two', 'four', '?'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='fuel-type', vocabulary_list=('gas', 'diesel'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='body-style', vocabulary_list=('sedan', 'hatchback', 'wagon', 'hardtop', 'convertible'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='num-cylinders', vocabulary_list=('four', 'six', 'five', 'three', 'two', 'eight', 'twelve'), dtype=tf.string, default_value=-1, num_oov_buckets=0))]\n",
            "INFO:tensorflow:Using default config.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp5jbdb63w\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp5jbdb63w', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...\n",
            "INFO:tensorflow:loss = 1858137000.0, step = 1\n",
            "INFO:tensorflow:global_step/sec: 428.28\n",
            "INFO:tensorflow:loss = 6036601000.0, step = 101 (0.237 sec)\n",
            "INFO:tensorflow:global_step/sec: 513.82\n",
            "INFO:tensorflow:loss = 2814857500.0, step = 201 (0.201 sec)\n",
            "INFO:tensorflow:global_step/sec: 480.969\n",
            "INFO:tensorflow:loss = 4834247700.0, step = 301 (0.203 sec)\n",
            "INFO:tensorflow:global_step/sec: 511.557\n",
            "INFO:tensorflow:loss = 5584386600.0, step = 401 (0.199 sec)\n",
            "INFO:tensorflow:global_step/sec: 486.038\n",
            "INFO:tensorflow:loss = 3752542000.0, step = 501 (0.200 sec)\n",
            "INFO:tensorflow:global_step/sec: 424.389\n",
            "INFO:tensorflow:loss = 3820437500.0, step = 601 (0.241 sec)\n",
            "INFO:tensorflow:global_step/sec: 450.544\n",
            "INFO:tensorflow:loss = 2681629000.0, step = 701 (0.219 sec)\n",
            "INFO:tensorflow:global_step/sec: 460.479\n",
            "INFO:tensorflow:loss = 2506742300.0, step = 801 (0.217 sec)\n",
            "INFO:tensorflow:global_step/sec: 452.641\n",
            "INFO:tensorflow:loss = 8213048300.0, step = 901 (0.222 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...\n",
            "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...\n",
            "INFO:tensorflow:Loss for final step: 1450086100.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:56:23\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-1000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.26994s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:56:23\n",
            "INFO:tensorflow:Saving dict for global step 1000: average_loss = 225563920.0, global_step = 1000, label/mean = 12949.43, loss = 3556969500.0, prediction/mean = 279.57803\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: /tmp/tmp5jbdb63w/model.ckpt-1000\n",
            "scores {'average_loss': 225563920.0, 'label/mean': 12949.43, 'loss': 3556969500.0, 'prediction/mean': 279.57803, 'global_step': 1000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-1000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...\n",
            "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...\n",
            "INFO:tensorflow:loss = 2910354000.0, step = 1001\n",
            "INFO:tensorflow:global_step/sec: 449.908\n",
            "INFO:tensorflow:loss = 4042476300.0, step = 1101 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 505.877\n",
            "INFO:tensorflow:loss = 3826256100.0, step = 1201 (0.197 sec)\n",
            "INFO:tensorflow:global_step/sec: 485.747\n",
            "INFO:tensorflow:loss = 4703122400.0, step = 1301 (0.210 sec)\n",
            "INFO:tensorflow:global_step/sec: 477.106\n",
            "INFO:tensorflow:loss = 3814988000.0, step = 1401 (0.210 sec)\n",
            "INFO:tensorflow:global_step/sec: 440.31\n",
            "INFO:tensorflow:loss = 4374028300.0, step = 1501 (0.226 sec)\n",
            "INFO:tensorflow:global_step/sec: 436.039\n",
            "INFO:tensorflow:loss = 3826275300.0, step = 1601 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 478.114\n",
            "INFO:tensorflow:loss = 2123785300.0, step = 1701 (0.206 sec)\n",
            "INFO:tensorflow:global_step/sec: 451.862\n",
            "INFO:tensorflow:loss = 1461290200.0, step = 1801 (0.221 sec)\n",
            "INFO:tensorflow:global_step/sec: 437.991\n",
            "INFO:tensorflow:loss = 1441287400.0, step = 1901 (0.234 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2000...\n",
            "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2000...\n",
            "INFO:tensorflow:Loss for final step: 1972647600.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:56:26\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-2000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.26834s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:56:27\n",
            "INFO:tensorflow:Saving dict for global step 2000: average_loss = 218900600.0, global_step = 2000, label/mean = 12949.43, loss = 3451894300.0, prediction/mean = 548.6162\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: /tmp/tmp5jbdb63w/model.ckpt-2000\n",
            "scores {'average_loss': 218900600.0, 'label/mean': 12949.43, 'loss': 3451894300.0, 'prediction/mean': 548.6162, 'global_step': 2000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-2000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2000...\n",
            "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2000...\n",
            "INFO:tensorflow:loss = 5173291000.0, step = 2001\n",
            "INFO:tensorflow:global_step/sec: 437.133\n",
            "INFO:tensorflow:loss = 5257619500.0, step = 2101 (0.232 sec)\n",
            "INFO:tensorflow:global_step/sec: 492.559\n",
            "INFO:tensorflow:loss = 3555145700.0, step = 2201 (0.206 sec)\n",
            "INFO:tensorflow:global_step/sec: 466.56\n",
            "INFO:tensorflow:loss = 1677213400.0, step = 2301 (0.220 sec)\n",
            "INFO:tensorflow:global_step/sec: 460.524\n",
            "INFO:tensorflow:loss = 6583065600.0, step = 2401 (0.214 sec)\n",
            "INFO:tensorflow:global_step/sec: 467.924\n",
            "INFO:tensorflow:loss = 4755992600.0, step = 2501 (0.212 sec)\n",
            "INFO:tensorflow:global_step/sec: 505.479\n",
            "INFO:tensorflow:loss = 3244461800.0, step = 2601 (0.194 sec)\n",
            "INFO:tensorflow:global_step/sec: 505.628\n",
            "INFO:tensorflow:loss = 2337262800.0, step = 2701 (0.204 sec)\n",
            "INFO:tensorflow:global_step/sec: 464.636\n",
            "INFO:tensorflow:loss = 2583722500.0, step = 2801 (0.213 sec)\n",
            "INFO:tensorflow:global_step/sec: 502.011\n",
            "INFO:tensorflow:loss = 3266697200.0, step = 2901 (0.214 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3000...\n",
            "INFO:tensorflow:Saving checkpoints for 3000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3000...\n",
            "INFO:tensorflow:Loss for final step: 4169618400.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:56:30\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-3000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.58359s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:56:31\n",
            "INFO:tensorflow:Saving dict for global step 3000: average_loss = 212484500.0, global_step = 3000, label/mean = 12949.43, loss = 3350717000.0, prediction/mean = 813.29236\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3000: /tmp/tmp5jbdb63w/model.ckpt-3000\n",
            "scores {'average_loss': 212484500.0, 'label/mean': 12949.43, 'loss': 3350717000.0, 'prediction/mean': 813.29236, 'global_step': 3000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-3000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3000...\n",
            "INFO:tensorflow:Saving checkpoints for 3000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3000...\n",
            "INFO:tensorflow:loss = 3974221600.0, step = 3001\n",
            "INFO:tensorflow:global_step/sec: 446.732\n",
            "INFO:tensorflow:loss = 5609368000.0, step = 3101 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 500.828\n",
            "INFO:tensorflow:loss = 2616192000.0, step = 3201 (0.202 sec)\n",
            "INFO:tensorflow:global_step/sec: 485.275\n",
            "INFO:tensorflow:loss = 3462139000.0, step = 3301 (0.210 sec)\n",
            "INFO:tensorflow:global_step/sec: 467.374\n",
            "INFO:tensorflow:loss = 2937880000.0, step = 3401 (0.214 sec)\n",
            "INFO:tensorflow:global_step/sec: 429.809\n",
            "INFO:tensorflow:loss = 3536646100.0, step = 3501 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 487.878\n",
            "INFO:tensorflow:loss = 3045844000.0, step = 3601 (0.200 sec)\n",
            "INFO:tensorflow:global_step/sec: 489.271\n",
            "INFO:tensorflow:loss = 3218855000.0, step = 3701 (0.213 sec)\n",
            "INFO:tensorflow:global_step/sec: 496.597\n",
            "INFO:tensorflow:loss = 3822640600.0, step = 3801 (0.195 sec)\n",
            "INFO:tensorflow:global_step/sec: 449.719\n",
            "INFO:tensorflow:loss = 3382381600.0, step = 3901 (0.229 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4000...\n",
            "INFO:tensorflow:Saving checkpoints for 4000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4000...\n",
            "INFO:tensorflow:Loss for final step: 3101479000.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:56:34\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-4000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.26277s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:56:34\n",
            "INFO:tensorflow:Saving dict for global step 4000: average_loss = 206285940.0, global_step = 4000, label/mean = 12949.43, loss = 3252970500.0, prediction/mean = 1074.5312\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4000: /tmp/tmp5jbdb63w/model.ckpt-4000\n",
            "scores {'average_loss': 206285940.0, 'label/mean': 12949.43, 'loss': 3252970500.0, 'prediction/mean': 1074.5312, 'global_step': 4000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-4000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4000...\n",
            "INFO:tensorflow:Saving checkpoints for 4000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4000...\n",
            "INFO:tensorflow:loss = 4890667000.0, step = 4001\n",
            "INFO:tensorflow:global_step/sec: 477.669\n",
            "INFO:tensorflow:loss = 3050542300.0, step = 4101 (0.211 sec)\n",
            "INFO:tensorflow:global_step/sec: 537.618\n",
            "INFO:tensorflow:loss = 4811402000.0, step = 4201 (0.194 sec)\n",
            "INFO:tensorflow:global_step/sec: 457.497\n",
            "INFO:tensorflow:loss = 3767527400.0, step = 4301 (0.215 sec)\n",
            "INFO:tensorflow:global_step/sec: 470.238\n",
            "INFO:tensorflow:loss = 6898264000.0, step = 4401 (0.213 sec)\n",
            "INFO:tensorflow:global_step/sec: 507.775\n",
            "INFO:tensorflow:loss = 3134128000.0, step = 4501 (0.205 sec)\n",
            "INFO:tensorflow:global_step/sec: 449.629\n",
            "INFO:tensorflow:loss = 5279863000.0, step = 4601 (0.215 sec)\n",
            "INFO:tensorflow:global_step/sec: 451.237\n",
            "INFO:tensorflow:loss = 1891226600.0, step = 4701 (0.223 sec)\n",
            "INFO:tensorflow:global_step/sec: 432.365\n",
            "INFO:tensorflow:loss = 1304792000.0, step = 4801 (0.228 sec)\n",
            "INFO:tensorflow:global_step/sec: 414.463\n",
            "INFO:tensorflow:loss = 3674510300.0, step = 4901 (0.240 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5000...\n",
            "INFO:tensorflow:Saving checkpoints for 5000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5000...\n",
            "INFO:tensorflow:Loss for final step: 4160411100.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:56:38\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-5000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.27953s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:56:38\n",
            "INFO:tensorflow:Saving dict for global step 5000: average_loss = 200301330.0, global_step = 5000, label/mean = 12949.43, loss = 3158598000.0, prediction/mean = 1332.2576\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 5000: /tmp/tmp5jbdb63w/model.ckpt-5000\n",
            "scores {'average_loss': 200301330.0, 'label/mean': 12949.43, 'loss': 3158598000.0, 'prediction/mean': 1332.2576, 'global_step': 5000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-5000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5000...\n",
            "INFO:tensorflow:Saving checkpoints for 5000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5000...\n",
            "INFO:tensorflow:loss = 3016693500.0, step = 5001\n",
            "INFO:tensorflow:global_step/sec: 454.532\n",
            "INFO:tensorflow:loss = 2328325600.0, step = 5101 (0.224 sec)\n",
            "INFO:tensorflow:global_step/sec: 467.686\n",
            "INFO:tensorflow:loss = 1457607300.0, step = 5201 (0.217 sec)\n",
            "INFO:tensorflow:global_step/sec: 460.509\n",
            "INFO:tensorflow:loss = 4636481500.0, step = 5301 (0.216 sec)\n",
            "INFO:tensorflow:global_step/sec: 444.487\n",
            "INFO:tensorflow:loss = 4319220000.0, step = 5401 (0.221 sec)\n",
            "INFO:tensorflow:global_step/sec: 480.03\n",
            "INFO:tensorflow:loss = 4140792300.0, step = 5501 (0.212 sec)\n",
            "INFO:tensorflow:global_step/sec: 471.573\n",
            "INFO:tensorflow:loss = 5668935700.0, step = 5601 (0.215 sec)\n",
            "INFO:tensorflow:global_step/sec: 458.174\n",
            "INFO:tensorflow:loss = 3155124000.0, step = 5701 (0.210 sec)\n",
            "INFO:tensorflow:global_step/sec: 471.676\n",
            "INFO:tensorflow:loss = 2247057700.0, step = 5801 (0.214 sec)\n",
            "INFO:tensorflow:global_step/sec: 507.121\n",
            "INFO:tensorflow:loss = 4383863300.0, step = 5901 (0.194 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6000...\n",
            "INFO:tensorflow:Saving checkpoints for 6000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6000...\n",
            "INFO:tensorflow:Loss for final step: 2089359200.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:56:42\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-6000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.26464s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:56:42\n",
            "INFO:tensorflow:Saving dict for global step 6000: average_loss = 194529060.0, global_step = 6000, label/mean = 12949.43, loss = 3067573500.0, prediction/mean = 1586.3431\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 6000: /tmp/tmp5jbdb63w/model.ckpt-6000\n",
            "scores {'average_loss': 194529060.0, 'label/mean': 12949.43, 'loss': 3067573500.0, 'prediction/mean': 1586.3431, 'global_step': 6000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-6000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6000...\n",
            "INFO:tensorflow:Saving checkpoints for 6000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6000...\n",
            "INFO:tensorflow:loss = 2979843000.0, step = 6001\n",
            "INFO:tensorflow:global_step/sec: 432.605\n",
            "INFO:tensorflow:loss = 2253333000.0, step = 6101 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 483.095\n",
            "INFO:tensorflow:loss = 5252758500.0, step = 6201 (0.210 sec)\n",
            "INFO:tensorflow:global_step/sec: 499.24\n",
            "INFO:tensorflow:loss = 3787212800.0, step = 6301 (0.203 sec)\n",
            "INFO:tensorflow:global_step/sec: 457.315\n",
            "INFO:tensorflow:loss = 3506506200.0, step = 6401 (0.214 sec)\n",
            "INFO:tensorflow:global_step/sec: 481.241\n",
            "INFO:tensorflow:loss = 1733055100.0, step = 6501 (0.209 sec)\n",
            "INFO:tensorflow:global_step/sec: 508.577\n",
            "INFO:tensorflow:loss = 4369085000.0, step = 6601 (0.198 sec)\n",
            "INFO:tensorflow:global_step/sec: 462.344\n",
            "INFO:tensorflow:loss = 2297636400.0, step = 6701 (0.215 sec)\n",
            "INFO:tensorflow:global_step/sec: 528.227\n",
            "INFO:tensorflow:loss = 2242396700.0, step = 6801 (0.195 sec)\n",
            "INFO:tensorflow:global_step/sec: 476.615\n",
            "INFO:tensorflow:loss = 4270981600.0, step = 6901 (0.201 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7000...\n",
            "INFO:tensorflow:Saving checkpoints for 7000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7000...\n",
            "INFO:tensorflow:Loss for final step: 6568661500.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:56:45\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-7000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.26401s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:56:46\n",
            "INFO:tensorflow:Saving dict for global step 7000: average_loss = 188959400.0, global_step = 7000, label/mean = 12949.43, loss = 2979744300.0, prediction/mean = 1836.9692\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 7000: /tmp/tmp5jbdb63w/model.ckpt-7000\n",
            "scores {'average_loss': 188959400.0, 'label/mean': 12949.43, 'loss': 2979744300.0, 'prediction/mean': 1836.9692, 'global_step': 7000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-7000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7000...\n",
            "INFO:tensorflow:Saving checkpoints for 7000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7000...\n",
            "INFO:tensorflow:loss = 1527213800.0, step = 7001\n",
            "INFO:tensorflow:global_step/sec: 442.891\n",
            "INFO:tensorflow:loss = 3595753000.0, step = 7101 (0.234 sec)\n",
            "INFO:tensorflow:global_step/sec: 489.693\n",
            "INFO:tensorflow:loss = 3529610800.0, step = 7201 (0.203 sec)\n",
            "INFO:tensorflow:global_step/sec: 482.314\n",
            "INFO:tensorflow:loss = 1439726500.0, step = 7301 (0.202 sec)\n",
            "INFO:tensorflow:global_step/sec: 516.172\n",
            "INFO:tensorflow:loss = 2738320100.0, step = 7401 (0.194 sec)\n",
            "INFO:tensorflow:global_step/sec: 479.164\n",
            "INFO:tensorflow:loss = 1218305800.0, step = 7501 (0.215 sec)\n",
            "INFO:tensorflow:global_step/sec: 462.112\n",
            "INFO:tensorflow:loss = 2192363500.0, step = 7601 (0.214 sec)\n",
            "INFO:tensorflow:global_step/sec: 498.321\n",
            "INFO:tensorflow:loss = 2769514800.0, step = 7701 (0.203 sec)\n",
            "INFO:tensorflow:global_step/sec: 470.213\n",
            "INFO:tensorflow:loss = 4185983500.0, step = 7801 (0.210 sec)\n",
            "INFO:tensorflow:global_step/sec: 490.972\n",
            "INFO:tensorflow:loss = 1482270000.0, step = 7901 (0.201 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8000...\n",
            "INFO:tensorflow:Saving checkpoints for 8000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8000...\n",
            "INFO:tensorflow:Loss for final step: 1714257400.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:56:49\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-8000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.26972s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:56:49\n",
            "INFO:tensorflow:Saving dict for global step 8000: average_loss = 183586700.0, global_step = 8000, label/mean = 12949.43, loss = 2895021000.0, prediction/mean = 2084.146\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 8000: /tmp/tmp5jbdb63w/model.ckpt-8000\n",
            "scores {'average_loss': 183586700.0, 'label/mean': 12949.43, 'loss': 2895021000.0, 'prediction/mean': 2084.146, 'global_step': 8000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-8000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8000...\n",
            "INFO:tensorflow:Saving checkpoints for 8000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8000...\n",
            "INFO:tensorflow:loss = 6226214400.0, step = 8001\n",
            "INFO:tensorflow:global_step/sec: 474.479\n",
            "INFO:tensorflow:loss = 1826900500.0, step = 8101 (0.221 sec)\n",
            "INFO:tensorflow:global_step/sec: 478.746\n",
            "INFO:tensorflow:loss = 5034413600.0, step = 8201 (0.202 sec)\n",
            "INFO:tensorflow:global_step/sec: 461.846\n",
            "INFO:tensorflow:loss = 4681057300.0, step = 8301 (0.221 sec)\n",
            "INFO:tensorflow:global_step/sec: 468.453\n",
            "INFO:tensorflow:loss = 2667333600.0, step = 8401 (0.213 sec)\n",
            "INFO:tensorflow:global_step/sec: 465.712\n",
            "INFO:tensorflow:loss = 2949424600.0, step = 8501 (0.213 sec)\n",
            "INFO:tensorflow:global_step/sec: 517.288\n",
            "INFO:tensorflow:loss = 4185060900.0, step = 8601 (0.195 sec)\n",
            "INFO:tensorflow:global_step/sec: 492.63\n",
            "INFO:tensorflow:loss = 2302916900.0, step = 8701 (0.198 sec)\n",
            "INFO:tensorflow:global_step/sec: 487.34\n",
            "INFO:tensorflow:loss = 2858485500.0, step = 8801 (0.211 sec)\n",
            "INFO:tensorflow:global_step/sec: 496.577\n",
            "INFO:tensorflow:loss = 2215028700.0, step = 8901 (0.197 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9000...\n",
            "INFO:tensorflow:Saving checkpoints for 9000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9000...\n",
            "INFO:tensorflow:Loss for final step: 3487950800.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:56:53\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-9000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.29168s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:56:53\n",
            "INFO:tensorflow:Saving dict for global step 9000: average_loss = 178406800.0, global_step = 9000, label/mean = 12949.43, loss = 2813337900.0, prediction/mean = 2327.8027\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 9000: /tmp/tmp5jbdb63w/model.ckpt-9000\n",
            "scores {'average_loss': 178406800.0, 'label/mean': 12949.43, 'loss': 2813337900.0, 'prediction/mean': 2327.8027, 'global_step': 9000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-9000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9000...\n",
            "INFO:tensorflow:Saving checkpoints for 9000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9000...\n",
            "INFO:tensorflow:loss = 2207583000.0, step = 9001\n",
            "INFO:tensorflow:global_step/sec: 446.771\n",
            "INFO:tensorflow:loss = 2733416000.0, step = 9101 (0.233 sec)\n",
            "INFO:tensorflow:global_step/sec: 474.309\n",
            "INFO:tensorflow:loss = 1718234600.0, step = 9201 (0.214 sec)\n",
            "INFO:tensorflow:global_step/sec: 503.021\n",
            "INFO:tensorflow:loss = 2081816000.0, step = 9301 (0.192 sec)\n",
            "INFO:tensorflow:global_step/sec: 497.296\n",
            "INFO:tensorflow:loss = 2818233300.0, step = 9401 (0.205 sec)\n",
            "INFO:tensorflow:global_step/sec: 488.098\n",
            "INFO:tensorflow:loss = 935180200.0, step = 9501 (0.202 sec)\n",
            "INFO:tensorflow:global_step/sec: 474.692\n",
            "INFO:tensorflow:loss = 2920892000.0, step = 9601 (0.208 sec)\n",
            "INFO:tensorflow:global_step/sec: 493.402\n",
            "INFO:tensorflow:loss = 3929816800.0, step = 9701 (0.201 sec)\n",
            "INFO:tensorflow:global_step/sec: 474.961\n",
            "INFO:tensorflow:loss = 2864985600.0, step = 9801 (0.214 sec)\n",
            "INFO:tensorflow:global_step/sec: 447.831\n",
            "INFO:tensorflow:loss = 2165451500.0, step = 9901 (0.227 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...\n",
            "INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tmp5jbdb63w/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...\n",
            "INFO:tensorflow:Loss for final step: 1502316800.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:56:57\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmp5jbdb63w/model.ckpt-10000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.28289s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:56:57\n",
            "INFO:tensorflow:Saving dict for global step 10000: average_loss = 173411700.0, global_step = 10000, label/mean = 12949.43, loss = 2734569000.0, prediction/mean = 2568.1033\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tmp5jbdb63w/model.ckpt-10000\n",
            "scores {'average_loss': 173411700.0, 'label/mean': 12949.43, 'loss': 2734569000.0, 'prediction/mean': 2568.1033, 'global_step': 10000}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gyBH5Wai_HTD"
      },
      "source": [
        "## Task 4: Using all the features, make the best model that you can make\n",
        "\n",
        "With all the features combined, your model should perform better than your earlier models using numerical and categorical models alone. Tune your model until that is the case."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNfCzC-q8edv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c90f5700-972a-445e-8c71-3abe2a041278"
      },
      "source": [
        "# Code\n",
        "\n",
        "batch_size = 34\n",
        "\n",
        "x_df = car_data[numeric_feature_names + categorical_feature_names]\n",
        "y_series = car_data['price']\n",
        "\n",
        "train_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    num_epochs=None,\n",
        "    shuffle=True)\n",
        "\n",
        "eval_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "predict_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "epsilon = 0.000001\n",
        "model_feature_columns = [\n",
        "    tf.feature_column.indicator_column(\n",
        "        tf.feature_column.categorical_column_with_vocabulary_list(\n",
        "            feature_name, vocabulary_list=car_data[feature_name].unique()))\n",
        "    for feature_name in categorical_feature_names\n",
        "] + [\n",
        "    tf.feature_column.numeric_column(feature_name,\n",
        "                                     normalizer_fn=lambda val: (val - x_df.mean()[feature_name]) / (epsilon + x_df.std()[feature_name]))\n",
        "    for feature_name in numeric_feature_names\n",
        "]\n",
        "\n",
        "\n",
        "print('model_feature_columns', model_feature_columns)\n",
        "\n",
        "est = tf.estimator.DNNRegressor(\n",
        "    feature_columns=model_feature_columns,\n",
        "    hidden_units=[84],\n",
        "    optimizer=tf.train.AdagradOptimizer(learning_rate=0.9),\n",
        "  )\n",
        "\n",
        "# Training\n",
        "num_print_statements = 10\n",
        "num_training_steps = 100000\n",
        "for _ in range(num_print_statements):\n",
        "  est.train(train_input_fn, steps=num_training_steps // num_print_statements)\n",
        "  scores = est.evaluate(eval_input_fn)\n",
        "  \n",
        "\n",
        "  print('scores', scores)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model_feature_columns [IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='fuel-system', vocabulary_list=('1bbl', 'mpfi', '2bbl', '4bbl', 'idi', 'spdi', 'mfi', 'spfi'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='drive-wheels', vocabulary_list=('fwd', 'rwd', '4wd'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='aspiration', vocabulary_list=('std', 'turbo'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='engine-location', vocabulary_list=('front', 'rear'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='engine-type', vocabulary_list=('ohc', 'ohcv', 'ohcf', 'l', 'dohc', 'rotor', 'dohcv'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='make', vocabulary_list=('honda', 'nissan', 'toyota', 'mitsubishi', 'mazda', 'subaru', 'dodge', 'peugot', 'porsche', 'alfa-romero', 'plymouth', 'audi', 'volkswagen', 'isuzu', 'volvo', 'saab', 'chevrolet', 'bmw', 'mercedes-benz', 'jaguar', 'renault', 'mercury'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='num-doors', vocabulary_list=('two', 'four', '?'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='fuel-type', vocabulary_list=('gas', 'diesel'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='body-style', vocabulary_list=('hatchback', 'sedan', 'wagon', 'hardtop', 'convertible'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='num-cylinders', vocabulary_list=('four', 'six', 'five', 'two', 'eight', 'twelve', 'three'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), NumericColumn(key='symboling', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f85472ae8c0>), NumericColumn(key='normalized-losses', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f85472ae680>), NumericColumn(key='wheel-base', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f85472aea70>), NumericColumn(key='length', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8546daaef0>), NumericColumn(key='width', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8546daaf80>), NumericColumn(key='height', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547098560>), NumericColumn(key='weight', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f85470984d0>), NumericColumn(key='engine-size', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547098200>), NumericColumn(key='horsepower', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f85470983b0>), NumericColumn(key='peak-rpm', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547098320>), NumericColumn(key='city-mpg', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547098170>), NumericColumn(key='highway-mpg', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f85470980e0>), NumericColumn(key='bore', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547098050>), NumericColumn(key='stroke', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8547098440>), NumericColumn(key='compression-ratio', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f85470985f0>)]\n",
            "INFO:tensorflow:Using default config.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpdiqxge0c\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpdiqxge0c', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "INFO:tensorflow:Calling model_fn.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:34: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...\n",
            "INFO:tensorflow:loss = 10668493000.0, step = 1\n",
            "INFO:tensorflow:global_step/sec: 242.98\n",
            "INFO:tensorflow:loss = 1230072300.0, step = 101 (0.414 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.968\n",
            "INFO:tensorflow:loss = 329856700.0, step = 201 (0.366 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.025\n",
            "INFO:tensorflow:loss = 1886941000.0, step = 301 (0.372 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.835\n",
            "INFO:tensorflow:loss = 448430430.0, step = 401 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.919\n",
            "INFO:tensorflow:loss = 408817540.0, step = 501 (0.366 sec)\n",
            "INFO:tensorflow:global_step/sec: 243.117\n",
            "INFO:tensorflow:loss = 232239570.0, step = 601 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.754\n",
            "INFO:tensorflow:loss = 584499400.0, step = 701 (0.394 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.878\n",
            "INFO:tensorflow:loss = 154381650.0, step = 801 (0.362 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.435\n",
            "INFO:tensorflow:loss = 120590810.0, step = 901 (0.370 sec)\n",
            "INFO:tensorflow:global_step/sec: 282.443\n",
            "INFO:tensorflow:loss = 298909730.0, step = 1001 (0.355 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.413\n",
            "INFO:tensorflow:loss = 275110140.0, step = 1101 (0.374 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.674\n",
            "INFO:tensorflow:loss = 180892690.0, step = 1201 (0.371 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.244\n",
            "INFO:tensorflow:loss = 317699620.0, step = 1301 (0.357 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.293\n",
            "INFO:tensorflow:loss = 393164480.0, step = 1401 (0.368 sec)\n",
            "INFO:tensorflow:global_step/sec: 271.864\n",
            "INFO:tensorflow:loss = 843964860.0, step = 1501 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.874\n",
            "INFO:tensorflow:loss = 248768000.0, step = 1601 (0.382 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.124\n",
            "INFO:tensorflow:loss = 536965400.0, step = 1701 (0.375 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.281\n",
            "INFO:tensorflow:loss = 1304856300.0, step = 1801 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 289.118\n",
            "INFO:tensorflow:loss = 148822690.0, step = 1901 (0.347 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.324\n",
            "INFO:tensorflow:loss = 773134000.0, step = 2001 (0.378 sec)\n",
            "INFO:tensorflow:global_step/sec: 271.164\n",
            "INFO:tensorflow:loss = 175278480.0, step = 2101 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 243.124\n",
            "INFO:tensorflow:loss = 120198410.0, step = 2201 (0.416 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.109\n",
            "INFO:tensorflow:loss = 124678690.0, step = 2301 (0.368 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.375\n",
            "INFO:tensorflow:loss = 374533440.0, step = 2401 (0.391 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.322\n",
            "INFO:tensorflow:loss = 112909496.0, step = 2501 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.785\n",
            "INFO:tensorflow:loss = 114276376.0, step = 2601 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.779\n",
            "INFO:tensorflow:loss = 184761440.0, step = 2701 (0.360 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.46\n",
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            "INFO:tensorflow:global_step/sec: 269.107\n",
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            "INFO:tensorflow:global_step/sec: 299.842\n",
            "INFO:tensorflow:loss = 298027070.0, step = 4201 (0.335 sec)\n",
            "INFO:tensorflow:global_step/sec: 296.888\n",
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            "INFO:tensorflow:global_step/sec: 266.772\n",
            "INFO:tensorflow:loss = 165863520.0, step = 4401 (0.375 sec)\n",
            "INFO:tensorflow:global_step/sec: 292.62\n",
            "INFO:tensorflow:loss = 110089740.0, step = 4501 (0.342 sec)\n",
            "INFO:tensorflow:global_step/sec: 299.383\n",
            "INFO:tensorflow:loss = 322821340.0, step = 4601 (0.340 sec)\n",
            "INFO:tensorflow:global_step/sec: 268.014\n",
            "INFO:tensorflow:loss = 232549400.0, step = 4701 (0.367 sec)\n",
            "INFO:tensorflow:global_step/sec: 270.902\n",
            "INFO:tensorflow:loss = 403563200.0, step = 4801 (0.370 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.29\n",
            "INFO:tensorflow:loss = 510941730.0, step = 4901 (0.374 sec)\n",
            "INFO:tensorflow:global_step/sec: 272.948\n",
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            "INFO:tensorflow:global_step/sec: 298.114\n",
            "INFO:tensorflow:loss = 45828000.0, step = 5101 (0.334 sec)\n",
            "INFO:tensorflow:global_step/sec: 288.832\n",
            "INFO:tensorflow:loss = 77057160.0, step = 5201 (0.346 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.878\n",
            "INFO:tensorflow:loss = 285531400.0, step = 5301 (0.373 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.589\n",
            "INFO:tensorflow:loss = 127019880.0, step = 5401 (0.393 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.668\n",
            "INFO:tensorflow:loss = 156846560.0, step = 5501 (0.396 sec)\n",
            "INFO:tensorflow:global_step/sec: 261.731\n",
            "INFO:tensorflow:loss = 161509170.0, step = 5601 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 270.693\n",
            "INFO:tensorflow:loss = 284155360.0, step = 5701 (0.370 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.179\n",
            "INFO:tensorflow:loss = 241056460.0, step = 5801 (0.359 sec)\n",
            "INFO:tensorflow:global_step/sec: 276.365\n",
            "INFO:tensorflow:loss = 40685668.0, step = 5901 (0.363 sec)\n",
            "INFO:tensorflow:global_step/sec: 236.075\n",
            "INFO:tensorflow:loss = 113855500.0, step = 6001 (0.422 sec)\n",
            "INFO:tensorflow:global_step/sec: 286.951\n",
            "INFO:tensorflow:loss = 212813360.0, step = 6101 (0.348 sec)\n",
            "INFO:tensorflow:global_step/sec: 282.567\n",
            "INFO:tensorflow:loss = 183800510.0, step = 6201 (0.354 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.469\n",
            "INFO:tensorflow:loss = 399644300.0, step = 6301 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 279.899\n",
            "INFO:tensorflow:loss = 155902880.0, step = 6401 (0.353 sec)\n",
            "INFO:tensorflow:global_step/sec: 296.307\n",
            "INFO:tensorflow:loss = 122692750.0, step = 6501 (0.340 sec)\n",
            "INFO:tensorflow:global_step/sec: 290.872\n",
            "INFO:tensorflow:loss = 129599250.0, step = 6601 (0.343 sec)\n",
            "INFO:tensorflow:global_step/sec: 294.831\n",
            "INFO:tensorflow:loss = 195932670.0, step = 6701 (0.338 sec)\n",
            "INFO:tensorflow:global_step/sec: 286.874\n",
            "INFO:tensorflow:loss = 309097760.0, step = 6801 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.644\n",
            "INFO:tensorflow:loss = 102853120.0, step = 6901 (0.363 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.788\n",
            "INFO:tensorflow:loss = 92532856.0, step = 7001 (0.384 sec)\n",
            "INFO:tensorflow:global_step/sec: 222.533\n",
            "INFO:tensorflow:loss = 321640400.0, step = 7101 (0.451 sec)\n",
            "INFO:tensorflow:global_step/sec: 306.657\n",
            "INFO:tensorflow:loss = 179467800.0, step = 7201 (0.321 sec)\n",
            "INFO:tensorflow:global_step/sec: 297.194\n",
            "INFO:tensorflow:loss = 125149660.0, step = 7301 (0.336 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.685\n",
            "INFO:tensorflow:loss = 206904480.0, step = 7401 (0.401 sec)\n",
            "INFO:tensorflow:global_step/sec: 266.016\n",
            "INFO:tensorflow:loss = 193158740.0, step = 7501 (0.376 sec)\n",
            "INFO:tensorflow:global_step/sec: 280.349\n",
            "INFO:tensorflow:loss = 269178300.0, step = 7601 (0.357 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.672\n",
            "INFO:tensorflow:loss = 51525590.0, step = 7701 (0.373 sec)\n",
            "INFO:tensorflow:global_step/sec: 272.523\n",
            "INFO:tensorflow:loss = 104948780.0, step = 7801 (0.368 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.195\n",
            "INFO:tensorflow:loss = 53553944.0, step = 7901 (0.384 sec)\n",
            "INFO:tensorflow:global_step/sec: 259.431\n",
            "INFO:tensorflow:loss = 282358240.0, step = 8001 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.572\n",
            "INFO:tensorflow:loss = 139317780.0, step = 8101 (0.376 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.502\n",
            "INFO:tensorflow:loss = 59501590.0, step = 8201 (0.382 sec)\n",
            "INFO:tensorflow:global_step/sec: 292.374\n",
            "INFO:tensorflow:loss = 148025500.0, step = 8301 (0.344 sec)\n",
            "INFO:tensorflow:global_step/sec: 299.956\n",
            "INFO:tensorflow:loss = 146854030.0, step = 8401 (0.331 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.345\n",
            "INFO:tensorflow:loss = 163730720.0, step = 8501 (0.365 sec)\n",
            "INFO:tensorflow:global_step/sec: 298.364\n",
            "INFO:tensorflow:loss = 95697380.0, step = 8601 (0.337 sec)\n",
            "INFO:tensorflow:global_step/sec: 257.042\n",
            "INFO:tensorflow:loss = 83076040.0, step = 8701 (0.391 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.52\n",
            "INFO:tensorflow:loss = 103315420.0, step = 8801 (0.360 sec)\n",
            "INFO:tensorflow:global_step/sec: 279.153\n",
            "INFO:tensorflow:loss = 213370290.0, step = 8901 (0.359 sec)\n",
            "INFO:tensorflow:global_step/sec: 257.785\n",
            "INFO:tensorflow:loss = 34098616.0, step = 9001 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.733\n",
            "INFO:tensorflow:loss = 37426976.0, step = 9101 (0.397 sec)\n",
            "INFO:tensorflow:global_step/sec: 257.9\n",
            "INFO:tensorflow:loss = 132746800.0, step = 9201 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.823\n",
            "INFO:tensorflow:loss = 64679296.0, step = 9301 (0.419 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.967\n",
            "INFO:tensorflow:loss = 126515140.0, step = 9401 (0.384 sec)\n",
            "INFO:tensorflow:global_step/sec: 276.748\n",
            "INFO:tensorflow:loss = 80368680.0, step = 9501 (0.362 sec)\n",
            "INFO:tensorflow:global_step/sec: 229.974\n",
            "INFO:tensorflow:loss = 65510976.0, step = 9601 (0.435 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.715\n",
            "INFO:tensorflow:loss = 48883190.0, step = 9701 (0.371 sec)\n",
            "INFO:tensorflow:global_step/sec: 270.999\n",
            "INFO:tensorflow:loss = 64826588.0, step = 9801 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.499\n",
            "INFO:tensorflow:loss = 111510700.0, step = 9901 (0.376 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...\n",
            "INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...\n",
            "INFO:tensorflow:Loss for final step: 75667630.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:36:10\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-10000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.31843s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:36:10\n",
            "INFO:tensorflow:Saving dict for global step 10000: average_loss = 2931792.0, global_step = 10000, label/mean = 12949.43, loss = 85859624.0, prediction/mean = 12673.226\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tmpdiqxge0c/model.ckpt-10000\n",
            "scores {'average_loss': 2931792.0, 'label/mean': 12949.43, 'loss': 85859624.0, 'prediction/mean': 12673.226, 'global_step': 10000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-10000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...\n",
            "INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...\n",
            "INFO:tensorflow:loss = 178588430.0, step = 10001\n",
            "INFO:tensorflow:global_step/sec: 253.176\n",
            "INFO:tensorflow:loss = 93144400.0, step = 10101 (0.399 sec)\n",
            "INFO:tensorflow:global_step/sec: 287.081\n",
            "INFO:tensorflow:loss = 105344936.0, step = 10201 (0.346 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.334\n",
            "INFO:tensorflow:loss = 106457416.0, step = 10301 (0.367 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.756\n",
            "INFO:tensorflow:loss = 77614760.0, step = 10401 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.358\n",
            "INFO:tensorflow:loss = 86488150.0, step = 10501 (0.415 sec)\n",
            "INFO:tensorflow:global_step/sec: 281.18\n",
            "INFO:tensorflow:loss = 77178250.0, step = 10601 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 292.333\n",
            "INFO:tensorflow:loss = 45642270.0, step = 10701 (0.348 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.476\n",
            "INFO:tensorflow:loss = 93430296.0, step = 10801 (0.361 sec)\n",
            "INFO:tensorflow:global_step/sec: 246.128\n",
            "INFO:tensorflow:loss = 67990460.0, step = 10901 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.895\n",
            "INFO:tensorflow:loss = 150348690.0, step = 11001 (0.367 sec)\n",
            "INFO:tensorflow:global_step/sec: 287.641\n",
            "INFO:tensorflow:loss = 32675660.0, step = 11101 (0.349 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.361\n",
            "INFO:tensorflow:loss = 105267410.0, step = 11201 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 290.236\n",
            "INFO:tensorflow:loss = 41971020.0, step = 11301 (0.345 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.957\n",
            "INFO:tensorflow:loss = 47390510.0, step = 11401 (0.375 sec)\n",
            "INFO:tensorflow:global_step/sec: 279.804\n",
            "INFO:tensorflow:loss = 86425390.0, step = 11501 (0.354 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.232\n",
            "INFO:tensorflow:loss = 43957120.0, step = 11601 (0.385 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.452\n",
            "INFO:tensorflow:loss = 83921530.0, step = 11701 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 277.081\n",
            "INFO:tensorflow:loss = 254641090.0, step = 11801 (0.358 sec)\n",
            "INFO:tensorflow:global_step/sec: 288.646\n",
            "INFO:tensorflow:loss = 121553560.0, step = 11901 (0.344 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.03\n",
            "INFO:tensorflow:loss = 77723730.0, step = 12001 (0.360 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.293\n",
            "INFO:tensorflow:loss = 132080540.0, step = 12101 (0.382 sec)\n",
            "INFO:tensorflow:global_step/sec: 259.898\n",
            "INFO:tensorflow:loss = 221734530.0, step = 12201 (0.387 sec)\n",
            "INFO:tensorflow:global_step/sec: 271.674\n",
            "INFO:tensorflow:loss = 112665544.0, step = 12301 (0.365 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.138\n",
            "INFO:tensorflow:loss = 56062240.0, step = 12401 (0.368 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.676\n",
            "INFO:tensorflow:loss = 95998260.0, step = 12501 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 261.673\n",
            "INFO:tensorflow:loss = 50442436.0, step = 12601 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.493\n",
            "INFO:tensorflow:loss = 78168080.0, step = 12701 (0.355 sec)\n",
            "INFO:tensorflow:global_step/sec: 272.62\n",
            "INFO:tensorflow:loss = 53855036.0, step = 12801 (0.368 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.455\n",
            "INFO:tensorflow:loss = 125577720.0, step = 12901 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 257.989\n",
            "INFO:tensorflow:loss = 111824770.0, step = 13001 (0.382 sec)\n",
            "INFO:tensorflow:global_step/sec: 271.188\n",
            "INFO:tensorflow:loss = 92050550.0, step = 13101 (0.368 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.751\n",
            "INFO:tensorflow:loss = 143719660.0, step = 13201 (0.414 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.908\n",
            "INFO:tensorflow:loss = 102357850.0, step = 13301 (0.363 sec)\n",
            "INFO:tensorflow:global_step/sec: 259.824\n",
            "INFO:tensorflow:loss = 75320296.0, step = 13401 (0.386 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.36\n",
            "INFO:tensorflow:loss = 115689800.0, step = 13501 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 239.164\n",
            "INFO:tensorflow:loss = 68618030.0, step = 13601 (0.414 sec)\n",
            "INFO:tensorflow:global_step/sec: 259.886\n",
            "INFO:tensorflow:loss = 106779480.0, step = 13701 (0.384 sec)\n",
            "INFO:tensorflow:global_step/sec: 302.478\n",
            "INFO:tensorflow:loss = 103091464.0, step = 13801 (0.336 sec)\n",
            "INFO:tensorflow:global_step/sec: 223.637\n",
            "INFO:tensorflow:loss = 45462092.0, step = 13901 (0.445 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.928\n",
            "INFO:tensorflow:loss = 54394972.0, step = 14001 (0.400 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.207\n",
            "INFO:tensorflow:loss = 60062772.0, step = 14101 (0.391 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.662\n",
            "INFO:tensorflow:loss = 114882630.0, step = 14201 (0.376 sec)\n",
            "INFO:tensorflow:global_step/sec: 293.964\n",
            "INFO:tensorflow:loss = 47509580.0, step = 14301 (0.342 sec)\n",
            "INFO:tensorflow:global_step/sec: 285.602\n",
            "INFO:tensorflow:loss = 45711012.0, step = 14401 (0.349 sec)\n",
            "INFO:tensorflow:global_step/sec: 244.989\n",
            "INFO:tensorflow:loss = 78316456.0, step = 14501 (0.405 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.572\n",
            "INFO:tensorflow:loss = 68506664.0, step = 14601 (0.405 sec)\n",
            "INFO:tensorflow:global_step/sec: 291.913\n",
            "INFO:tensorflow:loss = 81621370.0, step = 14701 (0.337 sec)\n",
            "INFO:tensorflow:global_step/sec: 282.782\n",
            "INFO:tensorflow:loss = 115556400.0, step = 14801 (0.360 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.689\n",
            "INFO:tensorflow:loss = 42117130.0, step = 14901 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.141\n",
            "INFO:tensorflow:loss = 110392890.0, step = 15001 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.608\n",
            "INFO:tensorflow:loss = 57855284.0, step = 15101 (0.355 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.649\n",
            "INFO:tensorflow:loss = 85578320.0, step = 15201 (0.366 sec)\n",
            "INFO:tensorflow:global_step/sec: 266.781\n",
            "INFO:tensorflow:loss = 118306720.0, step = 15301 (0.376 sec)\n",
            "INFO:tensorflow:global_step/sec: 287.66\n",
            "INFO:tensorflow:loss = 89125160.0, step = 15401 (0.349 sec)\n",
            "INFO:tensorflow:global_step/sec: 293.533\n",
            "INFO:tensorflow:loss = 72986984.0, step = 15501 (0.336 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.774\n",
            "INFO:tensorflow:loss = 64704896.0, step = 15601 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 290.621\n",
            "INFO:tensorflow:loss = 66703776.0, step = 15701 (0.344 sec)\n",
            "INFO:tensorflow:global_step/sec: 314.986\n",
            "INFO:tensorflow:loss = 80553720.0, step = 15801 (0.321 sec)\n",
            "INFO:tensorflow:global_step/sec: 286.904\n",
            "INFO:tensorflow:loss = 110922180.0, step = 15901 (0.347 sec)\n",
            "INFO:tensorflow:global_step/sec: 291.698\n",
            "INFO:tensorflow:loss = 93151880.0, step = 16001 (0.346 sec)\n",
            "INFO:tensorflow:global_step/sec: 268.481\n",
            "INFO:tensorflow:loss = 38472990.0, step = 16101 (0.367 sec)\n",
            "INFO:tensorflow:global_step/sec: 296.643\n",
            "INFO:tensorflow:loss = 44517500.0, step = 16201 (0.341 sec)\n",
            "INFO:tensorflow:global_step/sec: 271.304\n",
            "INFO:tensorflow:loss = 62712000.0, step = 16301 (0.370 sec)\n",
            "INFO:tensorflow:global_step/sec: 248.733\n",
            "INFO:tensorflow:loss = 35585788.0, step = 16401 (0.397 sec)\n",
            "INFO:tensorflow:global_step/sec: 270.248\n",
            "INFO:tensorflow:loss = 66987410.0, step = 16501 (0.372 sec)\n",
            "INFO:tensorflow:global_step/sec: 237.225\n",
            "INFO:tensorflow:loss = 65013520.0, step = 16601 (0.419 sec)\n",
            "INFO:tensorflow:global_step/sec: 239.302\n",
            "INFO:tensorflow:loss = 16677664.0, step = 16701 (0.425 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.037\n",
            "INFO:tensorflow:loss = 95037140.0, step = 16801 (0.392 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.093\n",
            "INFO:tensorflow:loss = 131180680.0, step = 16901 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 244.215\n",
            "INFO:tensorflow:loss = 26852210.0, step = 17001 (0.407 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.301\n",
            "INFO:tensorflow:loss = 53995336.0, step = 17101 (0.396 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.277\n",
            "INFO:tensorflow:loss = 186774660.0, step = 17201 (0.399 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.215\n",
            "INFO:tensorflow:loss = 118406720.0, step = 17301 (0.348 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.539\n",
            "INFO:tensorflow:loss = 75898376.0, step = 17401 (0.412 sec)\n",
            "INFO:tensorflow:global_step/sec: 272.365\n",
            "INFO:tensorflow:loss = 80217680.0, step = 17501 (0.374 sec)\n",
            "INFO:tensorflow:global_step/sec: 233.291\n",
            "INFO:tensorflow:loss = 40786788.0, step = 17601 (0.426 sec)\n",
            "INFO:tensorflow:global_step/sec: 239.756\n",
            "INFO:tensorflow:loss = 69979496.0, step = 17701 (0.417 sec)\n",
            "INFO:tensorflow:global_step/sec: 277.916\n",
            "INFO:tensorflow:loss = 120002340.0, step = 17801 (0.363 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.908\n",
            "INFO:tensorflow:loss = 34967820.0, step = 17901 (0.356 sec)\n",
            "INFO:tensorflow:global_step/sec: 279.018\n",
            "INFO:tensorflow:loss = 54203624.0, step = 18001 (0.364 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.145\n",
            "INFO:tensorflow:loss = 40530028.0, step = 18101 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 248.078\n",
            "INFO:tensorflow:loss = 50242180.0, step = 18201 (0.407 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.662\n",
            "INFO:tensorflow:loss = 136184770.0, step = 18301 (0.376 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.43\n",
            "INFO:tensorflow:loss = 33073198.0, step = 18401 (0.414 sec)\n",
            "INFO:tensorflow:global_step/sec: 248.361\n",
            "INFO:tensorflow:loss = 39164588.0, step = 18501 (0.398 sec)\n",
            "INFO:tensorflow:global_step/sec: 287.15\n",
            "INFO:tensorflow:loss = 48050812.0, step = 18601 (0.348 sec)\n",
            "INFO:tensorflow:global_step/sec: 279.011\n",
            "INFO:tensorflow:loss = 51895804.0, step = 18701 (0.366 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.747\n",
            "INFO:tensorflow:loss = 48093390.0, step = 18801 (0.367 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.11\n",
            "INFO:tensorflow:loss = 99423730.0, step = 18901 (0.396 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.999\n",
            "INFO:tensorflow:loss = 108901690.0, step = 19001 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 257.373\n",
            "INFO:tensorflow:loss = 37158144.0, step = 19101 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.581\n",
            "INFO:tensorflow:loss = 75661770.0, step = 19201 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 291.351\n",
            "INFO:tensorflow:loss = 126595970.0, step = 19301 (0.340 sec)\n",
            "INFO:tensorflow:global_step/sec: 309.075\n",
            "INFO:tensorflow:loss = 57238104.0, step = 19401 (0.325 sec)\n",
            "INFO:tensorflow:global_step/sec: 303.684\n",
            "INFO:tensorflow:loss = 95066620.0, step = 19501 (0.327 sec)\n",
            "INFO:tensorflow:global_step/sec: 308.847\n",
            "INFO:tensorflow:loss = 44899850.0, step = 19601 (0.325 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.618\n",
            "INFO:tensorflow:loss = 58815010.0, step = 19701 (0.362 sec)\n",
            "INFO:tensorflow:global_step/sec: 281.065\n",
            "INFO:tensorflow:loss = 80002216.0, step = 19801 (0.352 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.276\n",
            "INFO:tensorflow:loss = 53705790.0, step = 19901 (0.360 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20000...\n",
            "INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20000...\n",
            "INFO:tensorflow:Loss for final step: 46744830.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:36:50\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-20000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.37068s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:36:51\n",
            "INFO:tensorflow:Saving dict for global step 20000: average_loss = 1767984.2, global_step = 20000, label/mean = 12949.43, loss = 51776680.0, prediction/mean = 12623.858\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20000: /tmp/tmpdiqxge0c/model.ckpt-20000\n",
            "scores {'average_loss': 1767984.2, 'label/mean': 12949.43, 'loss': 51776680.0, 'prediction/mean': 12623.858, 'global_step': 20000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-20000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20000...\n",
            "INFO:tensorflow:Saving checkpoints for 20000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20000...\n",
            "INFO:tensorflow:loss = 109024800.0, step = 20001\n",
            "INFO:tensorflow:global_step/sec: 198.497\n",
            "INFO:tensorflow:loss = 54854450.0, step = 20101 (0.508 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.533\n",
            "INFO:tensorflow:loss = 41337456.0, step = 20201 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.464\n",
            "INFO:tensorflow:loss = 75693576.0, step = 20301 (0.350 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.024\n",
            "INFO:tensorflow:loss = 38955390.0, step = 20401 (0.422 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.201\n",
            "INFO:tensorflow:loss = 79950880.0, step = 20501 (0.412 sec)\n",
            "INFO:tensorflow:global_step/sec: 255.57\n",
            "INFO:tensorflow:loss = 32566830.0, step = 20601 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.884\n",
            "INFO:tensorflow:loss = 39971650.0, step = 20701 (0.401 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.929\n",
            "INFO:tensorflow:loss = 77962450.0, step = 20801 (0.414 sec)\n",
            "INFO:tensorflow:global_step/sec: 279.296\n",
            "INFO:tensorflow:loss = 61556184.0, step = 20901 (0.356 sec)\n",
            "INFO:tensorflow:global_step/sec: 225.979\n",
            "INFO:tensorflow:loss = 93535520.0, step = 21001 (0.440 sec)\n",
            "INFO:tensorflow:global_step/sec: 255.878\n",
            "INFO:tensorflow:loss = 41027910.0, step = 21101 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.599\n",
            "INFO:tensorflow:loss = 48827440.0, step = 21201 (0.413 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.011\n",
            "INFO:tensorflow:loss = 47944476.0, step = 21301 (0.387 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.649\n",
            "INFO:tensorflow:loss = 78916680.0, step = 21401 (0.412 sec)\n",
            "INFO:tensorflow:global_step/sec: 281.147\n",
            "INFO:tensorflow:loss = 58804428.0, step = 21501 (0.358 sec)\n",
            "INFO:tensorflow:global_step/sec: 246.353\n",
            "INFO:tensorflow:loss = 49764332.0, step = 21601 (0.404 sec)\n",
            "INFO:tensorflow:global_step/sec: 233.414\n",
            "INFO:tensorflow:loss = 40519736.0, step = 21701 (0.428 sec)\n",
            "INFO:tensorflow:global_step/sec: 235.582\n",
            "INFO:tensorflow:loss = 45072390.0, step = 21801 (0.430 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.655\n",
            "INFO:tensorflow:loss = 41670930.0, step = 21901 (0.393 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.342\n",
            "INFO:tensorflow:loss = 143391140.0, step = 22001 (0.376 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.468\n",
            "INFO:tensorflow:loss = 27012712.0, step = 22101 (0.367 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.752\n",
            "INFO:tensorflow:loss = 38447810.0, step = 22201 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 230.439\n",
            "INFO:tensorflow:loss = 22685170.0, step = 22301 (0.435 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.66\n",
            "INFO:tensorflow:loss = 42090170.0, step = 22401 (0.391 sec)\n",
            "INFO:tensorflow:global_step/sec: 280.289\n",
            "INFO:tensorflow:loss = 44428804.0, step = 22501 (0.357 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.07\n",
            "INFO:tensorflow:loss = 76348460.0, step = 22601 (0.373 sec)\n",
            "INFO:tensorflow:global_step/sec: 268.517\n",
            "INFO:tensorflow:loss = 34088744.0, step = 22701 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 272.834\n",
            "INFO:tensorflow:loss = 50100256.0, step = 22801 (0.364 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.568\n",
            "INFO:tensorflow:loss = 46902516.0, step = 22901 (0.386 sec)\n",
            "INFO:tensorflow:global_step/sec: 243.33\n",
            "INFO:tensorflow:loss = 100336720.0, step = 23001 (0.407 sec)\n",
            "INFO:tensorflow:global_step/sec: 280.652\n",
            "INFO:tensorflow:loss = 33736396.0, step = 23101 (0.356 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.723\n",
            "INFO:tensorflow:loss = 31897504.0, step = 23201 (0.364 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.783\n",
            "INFO:tensorflow:loss = 59278560.0, step = 23301 (0.420 sec)\n",
            "INFO:tensorflow:global_step/sec: 288.27\n",
            "INFO:tensorflow:loss = 44686796.0, step = 23401 (0.344 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.166\n",
            "INFO:tensorflow:loss = 50475536.0, step = 23501 (0.386 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.912\n",
            "INFO:tensorflow:loss = 29006080.0, step = 23601 (0.398 sec)\n",
            "INFO:tensorflow:global_step/sec: 272.584\n",
            "INFO:tensorflow:loss = 32615366.0, step = 23701 (0.373 sec)\n",
            "INFO:tensorflow:global_step/sec: 237.994\n",
            "INFO:tensorflow:loss = 29567248.0, step = 23801 (0.415 sec)\n",
            "INFO:tensorflow:global_step/sec: 234.768\n",
            "INFO:tensorflow:loss = 85147100.0, step = 23901 (0.427 sec)\n",
            "INFO:tensorflow:global_step/sec: 231.935\n",
            "INFO:tensorflow:loss = 68341256.0, step = 24001 (0.430 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.47\n",
            "INFO:tensorflow:loss = 70679064.0, step = 24101 (0.384 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.845\n",
            "INFO:tensorflow:loss = 57260090.0, step = 24201 (0.416 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.591\n",
            "INFO:tensorflow:loss = 74117800.0, step = 24301 (0.380 sec)\n",
            "INFO:tensorflow:global_step/sec: 266.755\n",
            "INFO:tensorflow:loss = 67647920.0, step = 24401 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.983\n",
            "INFO:tensorflow:loss = 40687256.0, step = 24501 (0.374 sec)\n",
            "INFO:tensorflow:global_step/sec: 284.344\n",
            "INFO:tensorflow:loss = 51079396.0, step = 24601 (0.352 sec)\n",
            "INFO:tensorflow:global_step/sec: 288.145\n",
            "INFO:tensorflow:loss = 42320676.0, step = 24701 (0.348 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.88\n",
            "INFO:tensorflow:loss = 45736536.0, step = 24801 (0.365 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.482\n",
            "INFO:tensorflow:loss = 39160012.0, step = 24901 (0.353 sec)\n",
            "INFO:tensorflow:global_step/sec: 276.02\n",
            "INFO:tensorflow:loss = 52200704.0, step = 25001 (0.364 sec)\n",
            "INFO:tensorflow:global_step/sec: 279.003\n",
            "INFO:tensorflow:loss = 34340510.0, step = 25101 (0.358 sec)\n",
            "INFO:tensorflow:global_step/sec: 309.801\n",
            "INFO:tensorflow:loss = 64210870.0, step = 25201 (0.326 sec)\n",
            "INFO:tensorflow:global_step/sec: 292.861\n",
            "INFO:tensorflow:loss = 28669868.0, step = 25301 (0.339 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.764\n",
            "INFO:tensorflow:loss = 44378816.0, step = 25401 (0.409 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.405\n",
            "INFO:tensorflow:loss = 44379132.0, step = 25501 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 261.558\n",
            "INFO:tensorflow:loss = 31458656.0, step = 25601 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.771\n",
            "INFO:tensorflow:loss = 25396270.0, step = 25701 (0.373 sec)\n",
            "INFO:tensorflow:global_step/sec: 295.401\n",
            "INFO:tensorflow:loss = 28232434.0, step = 25801 (0.341 sec)\n",
            "INFO:tensorflow:global_step/sec: 266.166\n",
            "INFO:tensorflow:loss = 44037190.0, step = 25901 (0.378 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.81\n",
            "INFO:tensorflow:loss = 72051320.0, step = 26001 (0.384 sec)\n",
            "INFO:tensorflow:global_step/sec: 304.071\n",
            "INFO:tensorflow:loss = 55219960.0, step = 26101 (0.329 sec)\n",
            "INFO:tensorflow:global_step/sec: 259.871\n",
            "INFO:tensorflow:loss = 44343060.0, step = 26201 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 268.155\n",
            "INFO:tensorflow:loss = 39457480.0, step = 26301 (0.378 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.363\n",
            "INFO:tensorflow:loss = 51316020.0, step = 26401 (0.359 sec)\n",
            "INFO:tensorflow:global_step/sec: 259.433\n",
            "INFO:tensorflow:loss = 48471956.0, step = 26501 (0.393 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.282\n",
            "INFO:tensorflow:loss = 30371274.0, step = 26601 (0.380 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.762\n",
            "INFO:tensorflow:loss = 28164128.0, step = 26701 (0.429 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.623\n",
            "INFO:tensorflow:loss = 30112084.0, step = 26801 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 216.168\n",
            "INFO:tensorflow:loss = 52262116.0, step = 26901 (0.461 sec)\n",
            "INFO:tensorflow:global_step/sec: 257.883\n",
            "INFO:tensorflow:loss = 29562036.0, step = 27001 (0.393 sec)\n",
            "INFO:tensorflow:global_step/sec: 286.673\n",
            "INFO:tensorflow:loss = 48614324.0, step = 27101 (0.347 sec)\n",
            "INFO:tensorflow:global_step/sec: 296.215\n",
            "INFO:tensorflow:loss = 37309820.0, step = 27201 (0.338 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.845\n",
            "INFO:tensorflow:loss = 53140892.0, step = 27301 (0.358 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.611\n",
            "INFO:tensorflow:loss = 29858200.0, step = 27401 (0.366 sec)\n",
            "INFO:tensorflow:global_step/sec: 226.733\n",
            "INFO:tensorflow:loss = 51348220.0, step = 27501 (0.440 sec)\n",
            "INFO:tensorflow:global_step/sec: 236.433\n",
            "INFO:tensorflow:loss = 50456132.0, step = 27601 (0.418 sec)\n",
            "INFO:tensorflow:global_step/sec: 232.382\n",
            "INFO:tensorflow:loss = 30369458.0, step = 27701 (0.430 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.155\n",
            "INFO:tensorflow:loss = 79700760.0, step = 27801 (0.404 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.96\n",
            "INFO:tensorflow:loss = 79373224.0, step = 27901 (0.385 sec)\n",
            "INFO:tensorflow:global_step/sec: 259.28\n",
            "INFO:tensorflow:loss = 38307110.0, step = 28001 (0.380 sec)\n",
            "INFO:tensorflow:global_step/sec: 276.384\n",
            "INFO:tensorflow:loss = 51135070.0, step = 28101 (0.367 sec)\n",
            "INFO:tensorflow:global_step/sec: 255.666\n",
            "INFO:tensorflow:loss = 66077360.0, step = 28201 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.196\n",
            "INFO:tensorflow:loss = 60710532.0, step = 28301 (0.398 sec)\n",
            "INFO:tensorflow:global_step/sec: 293.143\n",
            "INFO:tensorflow:loss = 53048630.0, step = 28401 (0.349 sec)\n",
            "INFO:tensorflow:global_step/sec: 295.703\n",
            "INFO:tensorflow:loss = 43284556.0, step = 28501 (0.329 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.286\n",
            "INFO:tensorflow:loss = 54263852.0, step = 28601 (0.378 sec)\n",
            "INFO:tensorflow:global_step/sec: 270.521\n",
            "INFO:tensorflow:loss = 47420652.0, step = 28701 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.582\n",
            "INFO:tensorflow:loss = 42339620.0, step = 28801 (0.374 sec)\n",
            "INFO:tensorflow:global_step/sec: 271.38\n",
            "INFO:tensorflow:loss = 56324480.0, step = 28901 (0.359 sec)\n",
            "INFO:tensorflow:global_step/sec: 285.065\n",
            "INFO:tensorflow:loss = 35513044.0, step = 29001 (0.355 sec)\n",
            "INFO:tensorflow:global_step/sec: 231.891\n",
            "INFO:tensorflow:loss = 79105630.0, step = 29101 (0.432 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.908\n",
            "INFO:tensorflow:loss = 71745070.0, step = 29201 (0.385 sec)\n",
            "INFO:tensorflow:global_step/sec: 248.129\n",
            "INFO:tensorflow:loss = 40768310.0, step = 29301 (0.400 sec)\n",
            "INFO:tensorflow:global_step/sec: 294.632\n",
            "INFO:tensorflow:loss = 30813404.0, step = 29401 (0.343 sec)\n",
            "INFO:tensorflow:global_step/sec: 289.5\n",
            "INFO:tensorflow:loss = 68962856.0, step = 29501 (0.341 sec)\n",
            "INFO:tensorflow:global_step/sec: 277.088\n",
            "INFO:tensorflow:loss = 34826820.0, step = 29601 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.06\n",
            "INFO:tensorflow:loss = 55262544.0, step = 29701 (0.389 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.348\n",
            "INFO:tensorflow:loss = 52308450.0, step = 29801 (0.372 sec)\n",
            "INFO:tensorflow:global_step/sec: 248.498\n",
            "INFO:tensorflow:loss = 35102356.0, step = 29901 (0.407 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 30000...\n",
            "INFO:tensorflow:Saving checkpoints for 30000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 30000...\n",
            "INFO:tensorflow:Loss for final step: 48361056.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:37:31\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-30000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.33387s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:37:31\n",
            "INFO:tensorflow:Saving dict for global step 30000: average_loss = 1215026.8, global_step = 30000, label/mean = 12949.43, loss = 35582930.0, prediction/mean = 12849.192\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 30000: /tmp/tmpdiqxge0c/model.ckpt-30000\n",
            "scores {'average_loss': 1215026.8, 'label/mean': 12949.43, 'loss': 35582930.0, 'prediction/mean': 12849.192, 'global_step': 30000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-30000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 30000...\n",
            "INFO:tensorflow:Saving checkpoints for 30000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 30000...\n",
            "INFO:tensorflow:loss = 26152644.0, step = 30001\n",
            "INFO:tensorflow:global_step/sec: 218.917\n",
            "INFO:tensorflow:loss = 34009108.0, step = 30101 (0.466 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.28\n",
            "INFO:tensorflow:loss = 63410244.0, step = 30201 (0.399 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.386\n",
            "INFO:tensorflow:loss = 16472846.0, step = 30301 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.056\n",
            "INFO:tensorflow:loss = 27571720.0, step = 30401 (0.392 sec)\n",
            "INFO:tensorflow:global_step/sec: 271.662\n",
            "INFO:tensorflow:loss = 41913396.0, step = 30501 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.002\n",
            "INFO:tensorflow:loss = 16860130.0, step = 30601 (0.394 sec)\n",
            "INFO:tensorflow:global_step/sec: 244.265\n",
            "INFO:tensorflow:loss = 37937650.0, step = 30701 (0.412 sec)\n",
            "INFO:tensorflow:global_step/sec: 221.579\n",
            "INFO:tensorflow:loss = 45363230.0, step = 30801 (0.450 sec)\n",
            "INFO:tensorflow:global_step/sec: 226.801\n",
            "INFO:tensorflow:loss = 50712228.0, step = 30901 (0.440 sec)\n",
            "INFO:tensorflow:global_step/sec: 221.371\n",
            "INFO:tensorflow:loss = 33323420.0, step = 31001 (0.451 sec)\n",
            "INFO:tensorflow:global_step/sec: 246.491\n",
            "INFO:tensorflow:loss = 74102220.0, step = 31101 (0.406 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.766\n",
            "INFO:tensorflow:loss = 45893856.0, step = 31201 (0.378 sec)\n",
            "INFO:tensorflow:global_step/sec: 172.48\n",
            "INFO:tensorflow:loss = 40874356.0, step = 31301 (0.575 sec)\n",
            "INFO:tensorflow:global_step/sec: 239.969\n",
            "INFO:tensorflow:loss = 24970038.0, step = 31401 (0.417 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.752\n",
            "INFO:tensorflow:loss = 57033092.0, step = 31501 (0.422 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.801\n",
            "INFO:tensorflow:loss = 44199292.0, step = 31601 (0.397 sec)\n",
            "INFO:tensorflow:global_step/sec: 244.532\n",
            "INFO:tensorflow:loss = 33061776.0, step = 31701 (0.412 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.427\n",
            "INFO:tensorflow:loss = 36692490.0, step = 31801 (0.395 sec)\n",
            "INFO:tensorflow:global_step/sec: 233.864\n",
            "INFO:tensorflow:loss = 38210656.0, step = 31901 (0.428 sec)\n",
            "INFO:tensorflow:global_step/sec: 243\n",
            "INFO:tensorflow:loss = 34627910.0, step = 32001 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 230.067\n",
            "INFO:tensorflow:loss = 34836584.0, step = 32101 (0.435 sec)\n",
            "INFO:tensorflow:global_step/sec: 285.881\n",
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            "INFO:tensorflow:global_step/sec: 285.265\n",
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            "INFO:tensorflow:global_step/sec: 276.361\n",
            "INFO:tensorflow:loss = 46978736.0, step = 32401 (0.357 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.778\n",
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            "INFO:tensorflow:global_step/sec: 275.087\n",
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            "INFO:tensorflow:global_step/sec: 285.067\n",
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            "INFO:tensorflow:global_step/sec: 257.808\n",
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            "INFO:tensorflow:global_step/sec: 250.423\n",
            "INFO:tensorflow:loss = 42302870.0, step = 32901 (0.399 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.275\n",
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            "INFO:tensorflow:global_step/sec: 244.023\n",
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            "INFO:tensorflow:global_step/sec: 264.287\n",
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            "INFO:tensorflow:global_step/sec: 249.308\n",
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            "INFO:tensorflow:global_step/sec: 262.855\n",
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            "INFO:tensorflow:global_step/sec: 278.944\n",
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            "INFO:tensorflow:global_step/sec: 257.938\n",
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            "INFO:tensorflow:global_step/sec: 232.688\n",
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            "INFO:tensorflow:global_step/sec: 240\n",
            "INFO:tensorflow:loss = 25190010.0, step = 37901 (0.420 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.195\n",
            "INFO:tensorflow:loss = 41687580.0, step = 38001 (0.415 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.324\n",
            "INFO:tensorflow:loss = 19899862.0, step = 38101 (0.384 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.973\n",
            "INFO:tensorflow:loss = 25237964.0, step = 38201 (0.426 sec)\n",
            "INFO:tensorflow:global_step/sec: 226.717\n",
            "INFO:tensorflow:loss = 22261602.0, step = 38301 (0.434 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.313\n",
            "INFO:tensorflow:loss = 26378172.0, step = 38401 (0.404 sec)\n",
            "INFO:tensorflow:global_step/sec: 230.21\n",
            "INFO:tensorflow:loss = 53213740.0, step = 38501 (0.430 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.165\n",
            "INFO:tensorflow:loss = 46508536.0, step = 38601 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 222.975\n",
            "INFO:tensorflow:loss = 43576850.0, step = 38701 (0.455 sec)\n",
            "INFO:tensorflow:global_step/sec: 224.817\n",
            "INFO:tensorflow:loss = 62727410.0, step = 38801 (0.440 sec)\n",
            "INFO:tensorflow:global_step/sec: 235.426\n",
            "INFO:tensorflow:loss = 45031484.0, step = 38901 (0.423 sec)\n",
            "INFO:tensorflow:global_step/sec: 244.309\n",
            "INFO:tensorflow:loss = 21925110.0, step = 39001 (0.410 sec)\n",
            "INFO:tensorflow:global_step/sec: 246.813\n",
            "INFO:tensorflow:loss = 44451296.0, step = 39101 (0.413 sec)\n",
            "INFO:tensorflow:global_step/sec: 237.358\n",
            "INFO:tensorflow:loss = 21320334.0, step = 39201 (0.415 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.713\n",
            "INFO:tensorflow:loss = 56370156.0, step = 39301 (0.406 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.275\n",
            "INFO:tensorflow:loss = 36184268.0, step = 39401 (0.418 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.854\n",
            "INFO:tensorflow:loss = 24542986.0, step = 39501 (0.401 sec)\n",
            "INFO:tensorflow:global_step/sec: 227.731\n",
            "INFO:tensorflow:loss = 22540926.0, step = 39601 (0.449 sec)\n",
            "INFO:tensorflow:global_step/sec: 237.763\n",
            "INFO:tensorflow:loss = 38910924.0, step = 39701 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.462\n",
            "INFO:tensorflow:loss = 43600890.0, step = 39801 (0.419 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.644\n",
            "INFO:tensorflow:loss = 37196856.0, step = 39901 (0.397 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 40000...\n",
            "INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 40000...\n",
            "INFO:tensorflow:Loss for final step: 41924144.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:38:15\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-40000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.38062s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:38:15\n",
            "INFO:tensorflow:Saving dict for global step 40000: average_loss = 977339.7, global_step = 40000, label/mean = 12949.43, loss = 28622092.0, prediction/mean = 13048.004\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 40000: /tmp/tmpdiqxge0c/model.ckpt-40000\n",
            "scores {'average_loss': 977339.7, 'label/mean': 12949.43, 'loss': 28622092.0, 'prediction/mean': 13048.004, 'global_step': 40000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-40000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 40000...\n",
            "INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 40000...\n",
            "INFO:tensorflow:loss = 23897664.0, step = 40001\n",
            "INFO:tensorflow:global_step/sec: 256.896\n",
            "INFO:tensorflow:loss = 32948754.0, step = 40101 (0.398 sec)\n",
            "INFO:tensorflow:global_step/sec: 232.852\n",
            "INFO:tensorflow:loss = 40551680.0, step = 40201 (0.427 sec)\n",
            "INFO:tensorflow:global_step/sec: 227.768\n",
            "INFO:tensorflow:loss = 46840864.0, step = 40301 (0.440 sec)\n",
            "INFO:tensorflow:global_step/sec: 225.833\n",
            "INFO:tensorflow:loss = 39201504.0, step = 40401 (0.444 sec)\n",
            "INFO:tensorflow:global_step/sec: 233.564\n",
            "INFO:tensorflow:loss = 42590136.0, step = 40501 (0.428 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.479\n",
            "INFO:tensorflow:loss = 36788636.0, step = 40601 (0.420 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.96\n",
            "INFO:tensorflow:loss = 33240864.0, step = 40701 (0.397 sec)\n",
            "INFO:tensorflow:global_step/sec: 257.325\n",
            "INFO:tensorflow:loss = 36022988.0, step = 40801 (0.392 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.055\n",
            "INFO:tensorflow:loss = 50666540.0, step = 40901 (0.402 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.293\n",
            "INFO:tensorflow:loss = 61999500.0, step = 41001 (0.382 sec)\n",
            "INFO:tensorflow:global_step/sec: 237.111\n",
            "INFO:tensorflow:loss = 21693332.0, step = 41101 (0.431 sec)\n",
            "INFO:tensorflow:global_step/sec: 266.346\n",
            "INFO:tensorflow:loss = 48506468.0, step = 41201 (0.371 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.545\n",
            "INFO:tensorflow:loss = 21691680.0, step = 41301 (0.361 sec)\n",
            "INFO:tensorflow:global_step/sec: 279.646\n",
            "INFO:tensorflow:loss = 44668424.0, step = 41401 (0.359 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.341\n",
            "INFO:tensorflow:loss = 24344020.0, step = 41501 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 268.272\n",
            "INFO:tensorflow:loss = 17777818.0, step = 41601 (0.371 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.231\n",
            "INFO:tensorflow:loss = 27469058.0, step = 41701 (0.371 sec)\n",
            "INFO:tensorflow:global_step/sec: 226.25\n",
            "INFO:tensorflow:loss = 33482400.0, step = 41801 (0.441 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.421\n",
            "INFO:tensorflow:loss = 22794838.0, step = 41901 (0.398 sec)\n",
            "INFO:tensorflow:global_step/sec: 280.408\n",
            "INFO:tensorflow:loss = 27714536.0, step = 42001 (0.361 sec)\n",
            "INFO:tensorflow:global_step/sec: 284.272\n",
            "INFO:tensorflow:loss = 46801150.0, step = 42101 (0.357 sec)\n",
            "INFO:tensorflow:global_step/sec: 235.325\n",
            "INFO:tensorflow:loss = 38321844.0, step = 42201 (0.421 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.995\n",
            "INFO:tensorflow:loss = 33303030.0, step = 42301 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.45\n",
            "INFO:tensorflow:loss = 40802456.0, step = 42401 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.29\n",
            "INFO:tensorflow:loss = 55729170.0, step = 42501 (0.374 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.907\n",
            "INFO:tensorflow:loss = 23337400.0, step = 42601 (0.399 sec)\n",
            "INFO:tensorflow:global_step/sec: 230.592\n",
            "INFO:tensorflow:loss = 27337452.0, step = 42701 (0.438 sec)\n",
            "INFO:tensorflow:global_step/sec: 277\n",
            "INFO:tensorflow:loss = 36376210.0, step = 42801 (0.358 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.656\n",
            "INFO:tensorflow:loss = 34444316.0, step = 42901 (0.413 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.766\n",
            "INFO:tensorflow:loss = 47410540.0, step = 43001 (0.428 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.958\n",
            "INFO:tensorflow:loss = 26010144.0, step = 43101 (0.412 sec)\n",
            "INFO:tensorflow:global_step/sec: 243.926\n",
            "INFO:tensorflow:loss = 25361004.0, step = 43201 (0.404 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.171\n",
            "INFO:tensorflow:loss = 26479752.0, step = 43301 (0.384 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.75\n",
            "INFO:tensorflow:loss = 32773652.0, step = 43401 (0.409 sec)\n",
            "INFO:tensorflow:global_step/sec: 227.444\n",
            "INFO:tensorflow:loss = 39902570.0, step = 43501 (0.435 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.759\n",
            "INFO:tensorflow:loss = 50600736.0, step = 43601 (0.403 sec)\n",
            "INFO:tensorflow:global_step/sec: 271.66\n",
            "INFO:tensorflow:loss = 35957000.0, step = 43701 (0.361 sec)\n",
            "INFO:tensorflow:global_step/sec: 298.987\n",
            "INFO:tensorflow:loss = 38119060.0, step = 43801 (0.336 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.818\n",
            "INFO:tensorflow:loss = 26311384.0, step = 43901 (0.357 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.436\n",
            "INFO:tensorflow:loss = 17319660.0, step = 44001 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.132\n",
            "INFO:tensorflow:loss = 48683690.0, step = 44101 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 235.822\n",
            "INFO:tensorflow:loss = 40193056.0, step = 44201 (0.428 sec)\n",
            "INFO:tensorflow:global_step/sec: 228.033\n",
            "INFO:tensorflow:loss = 17358542.0, step = 44301 (0.438 sec)\n",
            "INFO:tensorflow:global_step/sec: 288.383\n",
            "INFO:tensorflow:loss = 27604054.0, step = 44401 (0.343 sec)\n",
            "INFO:tensorflow:global_step/sec: 270.744\n",
            "INFO:tensorflow:loss = 28795608.0, step = 44501 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 297.359\n",
            "INFO:tensorflow:loss = 23927780.0, step = 44601 (0.339 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.143\n",
            "INFO:tensorflow:loss = 22962160.0, step = 44701 (0.382 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.858\n",
            "INFO:tensorflow:loss = 58073612.0, step = 44801 (0.376 sec)\n",
            "INFO:tensorflow:global_step/sec: 261.751\n",
            "INFO:tensorflow:loss = 33639856.0, step = 44901 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 237.645\n",
            "INFO:tensorflow:loss = 35981920.0, step = 45001 (0.420 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.099\n",
            "INFO:tensorflow:loss = 34461976.0, step = 45101 (0.394 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.323\n",
            "INFO:tensorflow:loss = 30150378.0, step = 45201 (0.379 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.533\n",
            "INFO:tensorflow:loss = 29452214.0, step = 45301 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 248.732\n",
            "INFO:tensorflow:loss = 64310100.0, step = 45401 (0.396 sec)\n",
            "INFO:tensorflow:global_step/sec: 219.12\n",
            "INFO:tensorflow:loss = 31649490.0, step = 45501 (0.462 sec)\n",
            "INFO:tensorflow:global_step/sec: 231.364\n",
            "INFO:tensorflow:loss = 36505260.0, step = 45601 (0.439 sec)\n",
            "INFO:tensorflow:global_step/sec: 231.074\n",
            "INFO:tensorflow:loss = 29695082.0, step = 45701 (0.420 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.566\n",
            "INFO:tensorflow:loss = 37041164.0, step = 45801 (0.399 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.401\n",
            "INFO:tensorflow:loss = 35725748.0, step = 45901 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.12\n",
            "INFO:tensorflow:loss = 42416548.0, step = 46001 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.704\n",
            "INFO:tensorflow:loss = 31711756.0, step = 46101 (0.389 sec)\n",
            "INFO:tensorflow:global_step/sec: 239.343\n",
            "INFO:tensorflow:loss = 25371164.0, step = 46201 (0.415 sec)\n",
            "INFO:tensorflow:global_step/sec: 230.44\n",
            "INFO:tensorflow:loss = 36625570.0, step = 46301 (0.434 sec)\n",
            "INFO:tensorflow:global_step/sec: 226.691\n",
            "INFO:tensorflow:loss = 25469450.0, step = 46401 (0.448 sec)\n",
            "INFO:tensorflow:global_step/sec: 289.928\n",
            "INFO:tensorflow:loss = 25585658.0, step = 46501 (0.335 sec)\n",
            "INFO:tensorflow:global_step/sec: 282.739\n",
            "INFO:tensorflow:loss = 41903636.0, step = 46601 (0.358 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.174\n",
            "INFO:tensorflow:loss = 43188690.0, step = 46701 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.591\n",
            "INFO:tensorflow:loss = 18242680.0, step = 46801 (0.391 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.627\n",
            "INFO:tensorflow:loss = 49343356.0, step = 46901 (0.379 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.486\n",
            "INFO:tensorflow:loss = 27137222.0, step = 47001 (0.392 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.819\n",
            "INFO:tensorflow:loss = 29818816.0, step = 47101 (0.400 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.447\n",
            "INFO:tensorflow:loss = 27673668.0, step = 47201 (0.414 sec)\n",
            "INFO:tensorflow:global_step/sec: 219.977\n",
            "INFO:tensorflow:loss = 24515136.0, step = 47301 (0.455 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.172\n",
            "INFO:tensorflow:loss = 36415696.0, step = 47401 (0.392 sec)\n",
            "INFO:tensorflow:global_step/sec: 291.526\n",
            "INFO:tensorflow:loss = 36214040.0, step = 47501 (0.345 sec)\n",
            "INFO:tensorflow:global_step/sec: 291.653\n",
            "INFO:tensorflow:loss = 15736957.0, step = 47601 (0.343 sec)\n",
            "INFO:tensorflow:global_step/sec: 246.088\n",
            "INFO:tensorflow:loss = 36735484.0, step = 47701 (0.401 sec)\n",
            "INFO:tensorflow:global_step/sec: 244.107\n",
            "INFO:tensorflow:loss = 32164104.0, step = 47801 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 255.427\n",
            "INFO:tensorflow:loss = 20442024.0, step = 47901 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.411\n",
            "INFO:tensorflow:loss = 38797756.0, step = 48001 (0.401 sec)\n",
            "INFO:tensorflow:global_step/sec: 284.438\n",
            "INFO:tensorflow:loss = 25478202.0, step = 48101 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 268.416\n",
            "INFO:tensorflow:loss = 18901686.0, step = 48201 (0.374 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.938\n",
            "INFO:tensorflow:loss = 27351256.0, step = 48301 (0.399 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.299\n",
            "INFO:tensorflow:loss = 44550690.0, step = 48401 (0.401 sec)\n",
            "INFO:tensorflow:global_step/sec: 284.189\n",
            "INFO:tensorflow:loss = 19902864.0, step = 48501 (0.356 sec)\n",
            "INFO:tensorflow:global_step/sec: 285.306\n",
            "INFO:tensorflow:loss = 39776130.0, step = 48601 (0.350 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.288\n",
            "INFO:tensorflow:loss = 19659960.0, step = 48701 (0.380 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.872\n",
            "INFO:tensorflow:loss = 46347170.0, step = 48801 (0.382 sec)\n",
            "INFO:tensorflow:global_step/sec: 255.411\n",
            "INFO:tensorflow:loss = 19232608.0, step = 48901 (0.391 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.628\n",
            "INFO:tensorflow:loss = 35712572.0, step = 49001 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.414\n",
            "INFO:tensorflow:loss = 44063900.0, step = 49101 (0.412 sec)\n",
            "INFO:tensorflow:global_step/sec: 233.836\n",
            "INFO:tensorflow:loss = 29278814.0, step = 49201 (0.425 sec)\n",
            "INFO:tensorflow:global_step/sec: 246.878\n",
            "INFO:tensorflow:loss = 30216878.0, step = 49301 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.795\n",
            "INFO:tensorflow:loss = 30866416.0, step = 49401 (0.399 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.511\n",
            "INFO:tensorflow:loss = 39072984.0, step = 49501 (0.426 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.985\n",
            "INFO:tensorflow:loss = 39969616.0, step = 49601 (0.392 sec)\n",
            "INFO:tensorflow:global_step/sec: 239.115\n",
            "INFO:tensorflow:loss = 16056713.0, step = 49701 (0.421 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.069\n",
            "INFO:tensorflow:loss = 32654728.0, step = 49801 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.198\n",
            "INFO:tensorflow:loss = 31874492.0, step = 49901 (0.387 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50000...\n",
            "INFO:tensorflow:Saving checkpoints for 50000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50000...\n",
            "INFO:tensorflow:Loss for final step: 18413378.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:38:56\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-50000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.38474s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:38:57\n",
            "INFO:tensorflow:Saving dict for global step 50000: average_loss = 844656.3, global_step = 50000, label/mean = 12949.43, loss = 24736364.0, prediction/mean = 12998.462\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 50000: /tmp/tmpdiqxge0c/model.ckpt-50000\n",
            "scores {'average_loss': 844656.3, 'label/mean': 12949.43, 'loss': 24736364.0, 'prediction/mean': 12998.462, 'global_step': 50000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-50000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50000...\n",
            "INFO:tensorflow:Saving checkpoints for 50000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50000...\n",
            "INFO:tensorflow:loss = 29750662.0, step = 50001\n",
            "INFO:tensorflow:global_step/sec: 249.257\n",
            "INFO:tensorflow:loss = 41658036.0, step = 50101 (0.401 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.025\n",
            "INFO:tensorflow:loss = 18439664.0, step = 50201 (0.379 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.522\n",
            "INFO:tensorflow:loss = 43305788.0, step = 50301 (0.378 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.009\n",
            "INFO:tensorflow:loss = 42847244.0, step = 50401 (0.404 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.816\n",
            "INFO:tensorflow:loss = 28915578.0, step = 50501 (0.413 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.624\n",
            "INFO:tensorflow:loss = 23716656.0, step = 50601 (0.360 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.164\n",
            "INFO:tensorflow:loss = 18809418.0, step = 50701 (0.386 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.66\n",
            "INFO:tensorflow:loss = 10922822.0, step = 50801 (0.396 sec)\n",
            "INFO:tensorflow:global_step/sec: 244.639\n",
            "INFO:tensorflow:loss = 17232628.0, step = 50901 (0.407 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.599\n",
            "INFO:tensorflow:loss = 20972112.0, step = 51001 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.374\n",
            "INFO:tensorflow:loss = 28023344.0, step = 51101 (0.371 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.355\n",
            "INFO:tensorflow:loss = 17736306.0, step = 51201 (0.399 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.015\n",
            "INFO:tensorflow:loss = 45894144.0, step = 51301 (0.398 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.498\n",
            "INFO:tensorflow:loss = 45720630.0, step = 51401 (0.372 sec)\n",
            "INFO:tensorflow:global_step/sec: 284.945\n",
            "INFO:tensorflow:loss = 31028898.0, step = 51501 (0.350 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.407\n",
            "INFO:tensorflow:loss = 33928748.0, step = 51601 (0.405 sec)\n",
            "INFO:tensorflow:global_step/sec: 155.228\n",
            "INFO:tensorflow:loss = 13557122.0, step = 51701 (0.649 sec)\n",
            "INFO:tensorflow:global_step/sec: 244.175\n",
            "INFO:tensorflow:loss = 10932193.0, step = 51801 (0.401 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.479\n",
            "INFO:tensorflow:loss = 25635734.0, step = 51901 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.942\n",
            "INFO:tensorflow:loss = 39794404.0, step = 52001 (0.418 sec)\n",
            "INFO:tensorflow:global_step/sec: 276.454\n",
            "INFO:tensorflow:loss = 25540948.0, step = 52101 (0.363 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.97\n",
            "INFO:tensorflow:loss = 18107650.0, step = 52201 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 287.037\n",
            "INFO:tensorflow:loss = 34815560.0, step = 52301 (0.350 sec)\n",
            "INFO:tensorflow:global_step/sec: 227.398\n",
            "INFO:tensorflow:loss = 42918116.0, step = 52401 (0.438 sec)\n",
            "INFO:tensorflow:global_step/sec: 259.374\n",
            "INFO:tensorflow:loss = 24532342.0, step = 52501 (0.391 sec)\n",
            "INFO:tensorflow:global_step/sec: 284.157\n",
            "INFO:tensorflow:loss = 26758984.0, step = 52601 (0.352 sec)\n",
            "INFO:tensorflow:global_step/sec: 210.98\n",
            "INFO:tensorflow:loss = 23000822.0, step = 52701 (0.475 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.516\n",
            "INFO:tensorflow:loss = 36326560.0, step = 52801 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 257.032\n",
            "INFO:tensorflow:loss = 32013866.0, step = 52901 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.013\n",
            "INFO:tensorflow:loss = 27761422.0, step = 53001 (0.384 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.074\n",
            "INFO:tensorflow:loss = 25355006.0, step = 53101 (0.357 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.976\n",
            "INFO:tensorflow:loss = 21584264.0, step = 53201 (0.382 sec)\n",
            "INFO:tensorflow:global_step/sec: 272.615\n",
            "INFO:tensorflow:loss = 35273636.0, step = 53301 (0.367 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.657\n",
            "INFO:tensorflow:loss = 20589576.0, step = 53401 (0.374 sec)\n",
            "INFO:tensorflow:global_step/sec: 272.866\n",
            "INFO:tensorflow:loss = 43072196.0, step = 53501 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 279.421\n",
            "INFO:tensorflow:loss = 24923630.0, step = 53601 (0.356 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.209\n",
            "INFO:tensorflow:loss = 11845408.0, step = 53701 (0.366 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.41\n",
            "INFO:tensorflow:loss = 23217762.0, step = 53801 (0.394 sec)\n",
            "INFO:tensorflow:global_step/sec: 271.41\n",
            "INFO:tensorflow:loss = 42397580.0, step = 53901 (0.372 sec)\n",
            "INFO:tensorflow:global_step/sec: 276.026\n",
            "INFO:tensorflow:loss = 24649426.0, step = 54001 (0.362 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.444\n",
            "INFO:tensorflow:loss = 31997018.0, step = 54101 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 313.112\n",
            "INFO:tensorflow:loss = 16817674.0, step = 54201 (0.322 sec)\n",
            "INFO:tensorflow:global_step/sec: 259.231\n",
            "INFO:tensorflow:loss = 28645828.0, step = 54301 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.384\n",
            "INFO:tensorflow:loss = 16039538.0, step = 54401 (0.398 sec)\n",
            "INFO:tensorflow:global_step/sec: 248.811\n",
            "INFO:tensorflow:loss = 29994862.0, step = 54501 (0.402 sec)\n",
            "INFO:tensorflow:global_step/sec: 243.786\n",
            "INFO:tensorflow:loss = 39875948.0, step = 54601 (0.410 sec)\n",
            "INFO:tensorflow:global_step/sec: 266.607\n",
            "INFO:tensorflow:loss = 23423416.0, step = 54701 (0.375 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.615\n",
            "INFO:tensorflow:loss = 29508144.0, step = 54801 (0.393 sec)\n",
            "INFO:tensorflow:global_step/sec: 255.84\n",
            "INFO:tensorflow:loss = 23954930.0, step = 54901 (0.391 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.131\n",
            "INFO:tensorflow:loss = 33012752.0, step = 55001 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.926\n",
            "INFO:tensorflow:loss = 29046492.0, step = 55101 (0.397 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.461\n",
            "INFO:tensorflow:loss = 37486404.0, step = 55201 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 259.061\n",
            "INFO:tensorflow:loss = 29541816.0, step = 55301 (0.384 sec)\n",
            "INFO:tensorflow:global_step/sec: 284.893\n",
            "INFO:tensorflow:loss = 43203750.0, step = 55401 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.336\n",
            "INFO:tensorflow:loss = 41119870.0, step = 55501 (0.363 sec)\n",
            "INFO:tensorflow:global_step/sec: 233.991\n",
            "INFO:tensorflow:loss = 39225080.0, step = 55601 (0.427 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.88\n",
            "INFO:tensorflow:loss = 44544500.0, step = 55701 (0.400 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.11\n",
            "INFO:tensorflow:loss = 33223176.0, step = 55801 (0.405 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.352\n",
            "INFO:tensorflow:loss = 26995952.0, step = 55901 (0.404 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.467\n",
            "INFO:tensorflow:loss = 24106752.0, step = 56001 (0.353 sec)\n",
            "INFO:tensorflow:global_step/sec: 266.085\n",
            "INFO:tensorflow:loss = 24545110.0, step = 56101 (0.378 sec)\n",
            "INFO:tensorflow:global_step/sec: 289.752\n",
            "INFO:tensorflow:loss = 26368842.0, step = 56201 (0.343 sec)\n",
            "INFO:tensorflow:global_step/sec: 295.089\n",
            "INFO:tensorflow:loss = 19849926.0, step = 56301 (0.339 sec)\n",
            "INFO:tensorflow:global_step/sec: 279.71\n",
            "INFO:tensorflow:loss = 43551932.0, step = 56401 (0.359 sec)\n",
            "INFO:tensorflow:global_step/sec: 295.657\n",
            "INFO:tensorflow:loss = 24514426.0, step = 56501 (0.337 sec)\n",
            "INFO:tensorflow:global_step/sec: 268.643\n",
            "INFO:tensorflow:loss = 44348960.0, step = 56601 (0.373 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.754\n",
            "INFO:tensorflow:loss = 25669082.0, step = 56701 (0.358 sec)\n",
            "INFO:tensorflow:global_step/sec: 270.582\n",
            "INFO:tensorflow:loss = 30149848.0, step = 56801 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 290.97\n",
            "INFO:tensorflow:loss = 36360284.0, step = 56901 (0.344 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.361\n",
            "INFO:tensorflow:loss = 40871950.0, step = 57001 (0.401 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.027\n",
            "INFO:tensorflow:loss = 10756318.0, step = 57101 (0.382 sec)\n",
            "INFO:tensorflow:global_step/sec: 261.874\n",
            "INFO:tensorflow:loss = 25190160.0, step = 57201 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.188\n",
            "INFO:tensorflow:loss = 27502854.0, step = 57301 (0.413 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.233\n",
            "INFO:tensorflow:loss = 20900746.0, step = 57401 (0.387 sec)\n",
            "INFO:tensorflow:global_step/sec: 246.149\n",
            "INFO:tensorflow:loss = 25987812.0, step = 57501 (0.399 sec)\n",
            "INFO:tensorflow:global_step/sec: 276.843\n",
            "INFO:tensorflow:loss = 20443812.0, step = 57601 (0.361 sec)\n",
            "INFO:tensorflow:global_step/sec: 287.212\n",
            "INFO:tensorflow:loss = 18659024.0, step = 57701 (0.348 sec)\n",
            "INFO:tensorflow:global_step/sec: 266.246\n",
            "INFO:tensorflow:loss = 26400206.0, step = 57801 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.643\n",
            "INFO:tensorflow:loss = 40162390.0, step = 57901 (0.380 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.294\n",
            "INFO:tensorflow:loss = 17692384.0, step = 58001 (0.399 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.113\n",
            "INFO:tensorflow:loss = 43302344.0, step = 58101 (0.397 sec)\n",
            "INFO:tensorflow:global_step/sec: 248.794\n",
            "INFO:tensorflow:loss = 34925240.0, step = 58201 (0.403 sec)\n",
            "INFO:tensorflow:global_step/sec: 239.875\n",
            "INFO:tensorflow:loss = 19512968.0, step = 58301 (0.416 sec)\n",
            "INFO:tensorflow:global_step/sec: 239.682\n",
            "INFO:tensorflow:loss = 22622412.0, step = 58401 (0.415 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.842\n",
            "INFO:tensorflow:loss = 29259932.0, step = 58501 (0.373 sec)\n",
            "INFO:tensorflow:global_step/sec: 284.802\n",
            "INFO:tensorflow:loss = 32524288.0, step = 58601 (0.344 sec)\n",
            "INFO:tensorflow:global_step/sec: 277.855\n",
            "INFO:tensorflow:loss = 17331798.0, step = 58701 (0.360 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.184\n",
            "INFO:tensorflow:loss = 28619540.0, step = 58801 (0.384 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.234\n",
            "INFO:tensorflow:loss = 50855932.0, step = 58901 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 292.885\n",
            "INFO:tensorflow:loss = 12536743.0, step = 59001 (0.347 sec)\n",
            "INFO:tensorflow:global_step/sec: 237.198\n",
            "INFO:tensorflow:loss = 31848744.0, step = 59101 (0.419 sec)\n",
            "INFO:tensorflow:global_step/sec: 243.789\n",
            "INFO:tensorflow:loss = 15378897.0, step = 59201 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 270.864\n",
            "INFO:tensorflow:loss = 19272630.0, step = 59301 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 271.243\n",
            "INFO:tensorflow:loss = 44079636.0, step = 59401 (0.371 sec)\n",
            "INFO:tensorflow:global_step/sec: 277.717\n",
            "INFO:tensorflow:loss = 17053384.0, step = 59501 (0.358 sec)\n",
            "INFO:tensorflow:global_step/sec: 234.275\n",
            "INFO:tensorflow:loss = 21548594.0, step = 59601 (0.428 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.826\n",
            "INFO:tensorflow:loss = 18232382.0, step = 59701 (0.398 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.647\n",
            "INFO:tensorflow:loss = 18924652.0, step = 59801 (0.402 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.147\n",
            "INFO:tensorflow:loss = 26590754.0, step = 59901 (0.399 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 60000...\n",
            "INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 60000...\n",
            "INFO:tensorflow:Loss for final step: 21758386.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:39:37\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-60000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.36315s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:39:38\n",
            "INFO:tensorflow:Saving dict for global step 60000: average_loss = 758848.7, global_step = 60000, label/mean = 12949.43, loss = 22223426.0, prediction/mean = 12919.78\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 60000: /tmp/tmpdiqxge0c/model.ckpt-60000\n",
            "scores {'average_loss': 758848.7, 'label/mean': 12949.43, 'loss': 22223426.0, 'prediction/mean': 12919.78, 'global_step': 60000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-60000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 60000...\n",
            "INFO:tensorflow:Saving checkpoints for 60000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 60000...\n",
            "INFO:tensorflow:loss = 18541294.0, step = 60001\n",
            "INFO:tensorflow:global_step/sec: 215.372\n",
            "INFO:tensorflow:loss = 20011852.0, step = 60101 (0.469 sec)\n",
            "INFO:tensorflow:global_step/sec: 243.899\n",
            "INFO:tensorflow:loss = 29309678.0, step = 60201 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 236.085\n",
            "INFO:tensorflow:loss = 26415978.0, step = 60301 (0.425 sec)\n",
            "INFO:tensorflow:global_step/sec: 235.133\n",
            "INFO:tensorflow:loss = 18839582.0, step = 60401 (0.431 sec)\n",
            "INFO:tensorflow:global_step/sec: 213.881\n",
            "INFO:tensorflow:loss = 29714540.0, step = 60501 (0.463 sec)\n",
            "INFO:tensorflow:global_step/sec: 234.849\n",
            "INFO:tensorflow:loss = 27571448.0, step = 60601 (0.423 sec)\n",
            "INFO:tensorflow:global_step/sec: 233.53\n",
            "INFO:tensorflow:loss = 27617494.0, step = 60701 (0.431 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.123\n",
            "INFO:tensorflow:loss = 35465496.0, step = 60801 (0.370 sec)\n",
            "INFO:tensorflow:global_step/sec: 221.089\n",
            "INFO:tensorflow:loss = 21463750.0, step = 60901 (0.454 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.32\n",
            "INFO:tensorflow:loss = 13573255.0, step = 61001 (0.420 sec)\n",
            "INFO:tensorflow:global_step/sec: 235.412\n",
            "INFO:tensorflow:loss = 26335244.0, step = 61101 (0.418 sec)\n",
            "INFO:tensorflow:global_step/sec: 235.909\n",
            "INFO:tensorflow:loss = 29091746.0, step = 61201 (0.429 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.841\n",
            "INFO:tensorflow:loss = 39844380.0, step = 61301 (0.396 sec)\n",
            "INFO:tensorflow:global_step/sec: 235.194\n",
            "INFO:tensorflow:loss = 20139236.0, step = 61401 (0.429 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.781\n",
            "INFO:tensorflow:loss = 21012726.0, step = 61501 (0.371 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.706\n",
            "INFO:tensorflow:loss = 13182984.0, step = 61601 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.462\n",
            "INFO:tensorflow:loss = 18365922.0, step = 61701 (0.416 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.556\n",
            "INFO:tensorflow:loss = 47057616.0, step = 61801 (0.399 sec)\n",
            "INFO:tensorflow:global_step/sec: 236.816\n",
            "INFO:tensorflow:loss = 17596100.0, step = 61901 (0.421 sec)\n",
            "INFO:tensorflow:global_step/sec: 244.438\n",
            "INFO:tensorflow:loss = 21599506.0, step = 62001 (0.414 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.456\n",
            "INFO:tensorflow:loss = 27096776.0, step = 62101 (0.393 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.954\n",
            "INFO:tensorflow:loss = 19051080.0, step = 62201 (0.420 sec)\n",
            "INFO:tensorflow:global_step/sec: 157.053\n",
            "INFO:tensorflow:loss = 44542224.0, step = 62301 (0.642 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.331\n",
            "INFO:tensorflow:loss = 20918094.0, step = 62401 (0.386 sec)\n",
            "INFO:tensorflow:global_step/sec: 248.721\n",
            "INFO:tensorflow:loss = 25798012.0, step = 62501 (0.402 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.941\n",
            "INFO:tensorflow:loss = 24381738.0, step = 62601 (0.391 sec)\n",
            "INFO:tensorflow:global_step/sec: 228.335\n",
            "INFO:tensorflow:loss = 26266576.0, step = 62701 (0.433 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.739\n",
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            "INFO:tensorflow:global_step/sec: 265.33\n",
            "INFO:tensorflow:loss = 28151362.0, step = 62901 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 272.366\n",
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            "INFO:tensorflow:global_step/sec: 247.963\n",
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            "INFO:tensorflow:global_step/sec: 248.852\n",
            "INFO:tensorflow:loss = 25496178.0, step = 63201 (0.402 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.6\n",
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            "INFO:tensorflow:global_step/sec: 257.939\n",
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            "INFO:tensorflow:global_step/sec: 227.507\n",
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            "INFO:tensorflow:global_step/sec: 218.465\n",
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            "INFO:tensorflow:global_step/sec: 249.78\n",
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            "INFO:tensorflow:global_step/sec: 286.549\n",
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            "INFO:tensorflow:global_step/sec: 251.871\n",
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            "INFO:tensorflow:global_step/sec: 264.303\n",
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            "INFO:tensorflow:global_step/sec: 275.299\n",
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            "INFO:tensorflow:global_step/sec: 276.529\n",
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            "INFO:tensorflow:global_step/sec: 270.13\n",
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            "INFO:tensorflow:global_step/sec: 233.044\n",
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            "INFO:tensorflow:global_step/sec: 261.201\n",
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            "INFO:tensorflow:global_step/sec: 244.676\n",
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            "INFO:tensorflow:global_step/sec: 259.804\n",
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            "INFO:tensorflow:global_step/sec: 239.453\n",
            "INFO:tensorflow:loss = 25915256.0, step = 65601 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.193\n",
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            "INFO:tensorflow:global_step/sec: 262.255\n",
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            "INFO:tensorflow:global_step/sec: 250.041\n",
            "INFO:tensorflow:loss = 25003686.0, step = 66201 (0.400 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.248\n",
            "INFO:tensorflow:loss = 24351824.0, step = 66301 (0.389 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.155\n",
            "INFO:tensorflow:loss = 24081994.0, step = 66401 (0.396 sec)\n",
            "INFO:tensorflow:global_step/sec: 246.699\n",
            "INFO:tensorflow:loss = 24042924.0, step = 66501 (0.407 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.814\n",
            "INFO:tensorflow:loss = 36366516.0, step = 66601 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 239.428\n",
            "INFO:tensorflow:loss = 28862782.0, step = 66701 (0.418 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.479\n",
            "INFO:tensorflow:loss = 38540276.0, step = 66801 (0.398 sec)\n",
            "INFO:tensorflow:global_step/sec: 219.453\n",
            "INFO:tensorflow:loss = 19083058.0, step = 66901 (0.457 sec)\n",
            "INFO:tensorflow:global_step/sec: 236.054\n",
            "INFO:tensorflow:loss = 20992330.0, step = 67001 (0.422 sec)\n",
            "INFO:tensorflow:global_step/sec: 237.681\n",
            "INFO:tensorflow:loss = 18002646.0, step = 67101 (0.421 sec)\n",
            "INFO:tensorflow:global_step/sec: 214.21\n",
            "INFO:tensorflow:loss = 32771262.0, step = 67201 (0.473 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.161\n",
            "INFO:tensorflow:loss = 25917834.0, step = 67301 (0.398 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.581\n",
            "INFO:tensorflow:loss = 17031516.0, step = 67401 (0.412 sec)\n",
            "INFO:tensorflow:global_step/sec: 257.77\n",
            "INFO:tensorflow:loss = 13873365.0, step = 67501 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 248.824\n",
            "INFO:tensorflow:loss = 28904910.0, step = 67601 (0.402 sec)\n",
            "INFO:tensorflow:global_step/sec: 236.087\n",
            "INFO:tensorflow:loss = 47870910.0, step = 67701 (0.427 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.097\n",
            "INFO:tensorflow:loss = 19697214.0, step = 67801 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.8\n",
            "INFO:tensorflow:loss = 18467930.0, step = 67901 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 243.19\n",
            "INFO:tensorflow:loss = 15793181.0, step = 68001 (0.407 sec)\n",
            "INFO:tensorflow:global_step/sec: 236.713\n",
            "INFO:tensorflow:loss = 26412980.0, step = 68101 (0.424 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.021\n",
            "INFO:tensorflow:loss = 28115374.0, step = 68201 (0.348 sec)\n",
            "INFO:tensorflow:global_step/sec: 231.331\n",
            "INFO:tensorflow:loss = 33563892.0, step = 68301 (0.433 sec)\n",
            "INFO:tensorflow:global_step/sec: 291.148\n",
            "INFO:tensorflow:loss = 20046760.0, step = 68401 (0.343 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.296\n",
            "INFO:tensorflow:loss = 19675754.0, step = 68501 (0.358 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.246\n",
            "INFO:tensorflow:loss = 28085896.0, step = 68601 (0.402 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.716\n",
            "INFO:tensorflow:loss = 41218228.0, step = 68701 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.656\n",
            "INFO:tensorflow:loss = 23424240.0, step = 68801 (0.386 sec)\n",
            "INFO:tensorflow:global_step/sec: 287.658\n",
            "INFO:tensorflow:loss = 27259850.0, step = 68901 (0.346 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.692\n",
            "INFO:tensorflow:loss = 22724214.0, step = 69001 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.595\n",
            "INFO:tensorflow:loss = 15814931.0, step = 69101 (0.415 sec)\n",
            "INFO:tensorflow:global_step/sec: 255.266\n",
            "INFO:tensorflow:loss = 26932482.0, step = 69201 (0.392 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.711\n",
            "INFO:tensorflow:loss = 20325596.0, step = 69301 (0.416 sec)\n",
            "INFO:tensorflow:global_step/sec: 244.984\n",
            "INFO:tensorflow:loss = 33128188.0, step = 69401 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.783\n",
            "INFO:tensorflow:loss = 19371286.0, step = 69501 (0.403 sec)\n",
            "INFO:tensorflow:global_step/sec: 234.573\n",
            "INFO:tensorflow:loss = 22472684.0, step = 69601 (0.424 sec)\n",
            "INFO:tensorflow:global_step/sec: 270.881\n",
            "INFO:tensorflow:loss = 22668518.0, step = 69701 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.403\n",
            "INFO:tensorflow:loss = 32984518.0, step = 69801 (0.404 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.257\n",
            "INFO:tensorflow:loss = 29095964.0, step = 69901 (0.395 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 70000...\n",
            "INFO:tensorflow:Saving checkpoints for 70000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 70000...\n",
            "INFO:tensorflow:Loss for final step: 28030202.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:40:21\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-70000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.36468s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:40:21\n",
            "INFO:tensorflow:Saving dict for global step 70000: average_loss = 867262.7, global_step = 70000, label/mean = 12949.43, loss = 25398406.0, prediction/mean = 13309.517\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 70000: /tmp/tmpdiqxge0c/model.ckpt-70000\n",
            "scores {'average_loss': 867262.7, 'label/mean': 12949.43, 'loss': 25398406.0, 'prediction/mean': 13309.517, 'global_step': 70000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-70000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 70000...\n",
            "INFO:tensorflow:Saving checkpoints for 70000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 70000...\n",
            "INFO:tensorflow:loss = 31571860.0, step = 70001\n",
            "INFO:tensorflow:global_step/sec: 228.421\n",
            "INFO:tensorflow:loss = 18875614.0, step = 70101 (0.447 sec)\n",
            "INFO:tensorflow:global_step/sec: 226.634\n",
            "INFO:tensorflow:loss = 21623408.0, step = 70201 (0.440 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.839\n",
            "INFO:tensorflow:loss = 20759614.0, step = 70301 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.319\n",
            "INFO:tensorflow:loss = 28672412.0, step = 70401 (0.353 sec)\n",
            "INFO:tensorflow:global_step/sec: 299.243\n",
            "INFO:tensorflow:loss = 19585380.0, step = 70501 (0.334 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.517\n",
            "INFO:tensorflow:loss = 32618200.0, step = 70601 (0.396 sec)\n",
            "INFO:tensorflow:global_step/sec: 231.39\n",
            "INFO:tensorflow:loss = 20432448.0, step = 70701 (0.429 sec)\n",
            "INFO:tensorflow:global_step/sec: 204.035\n",
            "INFO:tensorflow:loss = 21755846.0, step = 70801 (0.491 sec)\n",
            "INFO:tensorflow:global_step/sec: 231.479\n",
            "INFO:tensorflow:loss = 27094484.0, step = 70901 (0.429 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.313\n",
            "INFO:tensorflow:loss = 19257512.0, step = 71001 (0.420 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.671\n",
            "INFO:tensorflow:loss = 35085660.0, step = 71101 (0.410 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.42\n",
            "INFO:tensorflow:loss = 45295276.0, step = 71201 (0.382 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.346\n",
            "INFO:tensorflow:loss = 26269260.0, step = 71301 (0.374 sec)\n",
            "INFO:tensorflow:global_step/sec: 156.062\n",
            "INFO:tensorflow:loss = 18065112.0, step = 71401 (0.635 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.333\n",
            "INFO:tensorflow:loss = 29135284.0, step = 71501 (0.363 sec)\n",
            "INFO:tensorflow:global_step/sec: 230.21\n",
            "INFO:tensorflow:loss = 15715683.0, step = 71601 (0.435 sec)\n",
            "INFO:tensorflow:global_step/sec: 255.615\n",
            "INFO:tensorflow:loss = 30580124.0, step = 71701 (0.392 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.458\n",
            "INFO:tensorflow:loss = 23858706.0, step = 71801 (0.360 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.245\n",
            "INFO:tensorflow:loss = 36264120.0, step = 71901 (0.415 sec)\n",
            "INFO:tensorflow:global_step/sec: 230.733\n",
            "INFO:tensorflow:loss = 20019510.0, step = 72001 (0.436 sec)\n",
            "INFO:tensorflow:global_step/sec: 228.678\n",
            "INFO:tensorflow:loss = 24756138.0, step = 72101 (0.435 sec)\n",
            "INFO:tensorflow:global_step/sec: 235.764\n",
            "INFO:tensorflow:loss = 19370332.0, step = 72201 (0.424 sec)\n",
            "INFO:tensorflow:global_step/sec: 229.509\n",
            "INFO:tensorflow:loss = 20539744.0, step = 72301 (0.436 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.87\n",
            "INFO:tensorflow:loss = 27057386.0, step = 72401 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.233\n",
            "INFO:tensorflow:loss = 23189914.0, step = 72501 (0.393 sec)\n",
            "INFO:tensorflow:global_step/sec: 227.86\n",
            "INFO:tensorflow:loss = 50440916.0, step = 72601 (0.436 sec)\n",
            "INFO:tensorflow:global_step/sec: 232.298\n",
            "INFO:tensorflow:loss = 23950626.0, step = 72701 (0.439 sec)\n",
            "INFO:tensorflow:global_step/sec: 237.498\n",
            "INFO:tensorflow:loss = 15228180.0, step = 72801 (0.413 sec)\n",
            "INFO:tensorflow:global_step/sec: 234.535\n",
            "INFO:tensorflow:loss = 25422420.0, step = 72901 (0.426 sec)\n",
            "INFO:tensorflow:global_step/sec: 233.246\n",
            "INFO:tensorflow:loss = 15498892.0, step = 73001 (0.429 sec)\n",
            "INFO:tensorflow:global_step/sec: 234.572\n",
            "INFO:tensorflow:loss = 19544030.0, step = 73101 (0.428 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.191\n",
            "INFO:tensorflow:loss = 29208860.0, step = 73201 (0.378 sec)\n",
            "INFO:tensorflow:global_step/sec: 296.068\n",
            "INFO:tensorflow:loss = 13866132.0, step = 73301 (0.336 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.418\n",
            "INFO:tensorflow:loss = 19992314.0, step = 73401 (0.360 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.439\n",
            "INFO:tensorflow:loss = 17426654.0, step = 73501 (0.407 sec)\n",
            "INFO:tensorflow:global_step/sec: 266.402\n",
            "INFO:tensorflow:loss = 22132964.0, step = 73601 (0.375 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.183\n",
            "INFO:tensorflow:loss = 28450950.0, step = 73701 (0.364 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.926\n",
            "INFO:tensorflow:loss = 26456390.0, step = 73801 (0.413 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.681\n",
            "INFO:tensorflow:loss = 28044286.0, step = 73901 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 266.826\n",
            "INFO:tensorflow:loss = 21499772.0, step = 74001 (0.373 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.938\n",
            "INFO:tensorflow:loss = 24847280.0, step = 74101 (0.403 sec)\n",
            "INFO:tensorflow:global_step/sec: 228.578\n",
            "INFO:tensorflow:loss = 16336402.0, step = 74201 (0.441 sec)\n",
            "INFO:tensorflow:global_step/sec: 234.601\n",
            "INFO:tensorflow:loss = 22478226.0, step = 74301 (0.426 sec)\n",
            "INFO:tensorflow:global_step/sec: 233.202\n",
            "INFO:tensorflow:loss = 25803862.0, step = 74401 (0.432 sec)\n",
            "INFO:tensorflow:global_step/sec: 246.708\n",
            "INFO:tensorflow:loss = 25543784.0, step = 74501 (0.404 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.029\n",
            "INFO:tensorflow:loss = 18201286.0, step = 74601 (0.403 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.84\n",
            "INFO:tensorflow:loss = 24037092.0, step = 74701 (0.380 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.522\n",
            "INFO:tensorflow:loss = 36968228.0, step = 74801 (0.361 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.471\n",
            "INFO:tensorflow:loss = 16616779.0, step = 74901 (0.391 sec)\n",
            "INFO:tensorflow:global_step/sec: 232.266\n",
            "INFO:tensorflow:loss = 40136150.0, step = 75001 (0.431 sec)\n",
            "INFO:tensorflow:global_step/sec: 243.407\n",
            "INFO:tensorflow:loss = 24413472.0, step = 75101 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 272.577\n",
            "INFO:tensorflow:loss = 19972060.0, step = 75201 (0.367 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.908\n",
            "INFO:tensorflow:loss = 7123163.5, step = 75301 (0.395 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.813\n",
            "INFO:tensorflow:loss = 30062012.0, step = 75401 (0.376 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.452\n",
            "INFO:tensorflow:loss = 34914060.0, step = 75501 (0.418 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.979\n",
            "INFO:tensorflow:loss = 18092010.0, step = 75601 (0.417 sec)\n",
            "INFO:tensorflow:global_step/sec: 229.918\n",
            "INFO:tensorflow:loss = 25863756.0, step = 75701 (0.430 sec)\n",
            "INFO:tensorflow:global_step/sec: 210.067\n",
            "INFO:tensorflow:loss = 19578506.0, step = 75801 (0.476 sec)\n",
            "INFO:tensorflow:global_step/sec: 222.908\n",
            "INFO:tensorflow:loss = 12373136.0, step = 75901 (0.448 sec)\n",
            "INFO:tensorflow:global_step/sec: 219.115\n",
            "INFO:tensorflow:loss = 35655536.0, step = 76001 (0.456 sec)\n",
            "INFO:tensorflow:global_step/sec: 210.444\n",
            "INFO:tensorflow:loss = 31605266.0, step = 76101 (0.475 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.214\n",
            "INFO:tensorflow:loss = 37848772.0, step = 76201 (0.385 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.525\n",
            "INFO:tensorflow:loss = 32664772.0, step = 76301 (0.418 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.203\n",
            "INFO:tensorflow:loss = 16322884.0, step = 76401 (0.397 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.232\n",
            "INFO:tensorflow:loss = 15724208.0, step = 76501 (0.353 sec)\n",
            "INFO:tensorflow:global_step/sec: 292.532\n",
            "INFO:tensorflow:loss = 10237210.0, step = 76601 (0.344 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.13\n",
            "INFO:tensorflow:loss = 20115158.0, step = 76701 (0.375 sec)\n",
            "INFO:tensorflow:global_step/sec: 224.653\n",
            "INFO:tensorflow:loss = 26346360.0, step = 76801 (0.450 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.535\n",
            "INFO:tensorflow:loss = 11283939.0, step = 76901 (0.397 sec)\n",
            "INFO:tensorflow:global_step/sec: 228.719\n",
            "INFO:tensorflow:loss = 35918816.0, step = 77001 (0.437 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.858\n",
            "INFO:tensorflow:loss = 25697554.0, step = 77101 (0.363 sec)\n",
            "INFO:tensorflow:global_step/sec: 276.526\n",
            "INFO:tensorflow:loss = 16073094.0, step = 77201 (0.362 sec)\n",
            "INFO:tensorflow:global_step/sec: 292.34\n",
            "INFO:tensorflow:loss = 27192878.0, step = 77301 (0.342 sec)\n",
            "INFO:tensorflow:global_step/sec: 268.116\n",
            "INFO:tensorflow:loss = 27638714.0, step = 77401 (0.379 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.244\n",
            "INFO:tensorflow:loss = 36808730.0, step = 77501 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.373\n",
            "INFO:tensorflow:loss = 16949974.0, step = 77601 (0.418 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.75\n",
            "INFO:tensorflow:loss = 26133972.0, step = 77701 (0.394 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.607\n",
            "INFO:tensorflow:loss = 22712532.0, step = 77801 (0.398 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.002\n",
            "INFO:tensorflow:loss = 41364400.0, step = 77901 (0.415 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.12\n",
            "INFO:tensorflow:loss = 26483276.0, step = 78001 (0.396 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.5\n",
            "INFO:tensorflow:loss = 22464260.0, step = 78101 (0.415 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.643\n",
            "INFO:tensorflow:loss = 17803576.0, step = 78201 (0.419 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.84\n",
            "INFO:tensorflow:loss = 16696239.0, step = 78301 (0.394 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.232\n",
            "INFO:tensorflow:loss = 22985514.0, step = 78401 (0.364 sec)\n",
            "INFO:tensorflow:global_step/sec: 268.486\n",
            "INFO:tensorflow:loss = 18453004.0, step = 78501 (0.379 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.052\n",
            "INFO:tensorflow:loss = 26756330.0, step = 78601 (0.375 sec)\n",
            "INFO:tensorflow:global_step/sec: 265.386\n",
            "INFO:tensorflow:loss = 25581442.0, step = 78701 (0.380 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.966\n",
            "INFO:tensorflow:loss = 22399304.0, step = 78801 (0.387 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.294\n",
            "INFO:tensorflow:loss = 30366222.0, step = 78901 (0.407 sec)\n",
            "INFO:tensorflow:global_step/sec: 234.315\n",
            "INFO:tensorflow:loss = 25664712.0, step = 79001 (0.424 sec)\n",
            "INFO:tensorflow:global_step/sec: 229.319\n",
            "INFO:tensorflow:loss = 16016719.0, step = 79101 (0.436 sec)\n",
            "INFO:tensorflow:global_step/sec: 280.417\n",
            "INFO:tensorflow:loss = 22040472.0, step = 79201 (0.356 sec)\n",
            "INFO:tensorflow:global_step/sec: 285.476\n",
            "INFO:tensorflow:loss = 33876588.0, step = 79301 (0.350 sec)\n",
            "INFO:tensorflow:global_step/sec: 280.669\n",
            "INFO:tensorflow:loss = 22085366.0, step = 79401 (0.356 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.703\n",
            "INFO:tensorflow:loss = 16197163.0, step = 79501 (0.419 sec)\n",
            "INFO:tensorflow:global_step/sec: 223.427\n",
            "INFO:tensorflow:loss = 30560334.0, step = 79601 (0.451 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.968\n",
            "INFO:tensorflow:loss = 35420540.0, step = 79701 (0.405 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.26\n",
            "INFO:tensorflow:loss = 27811120.0, step = 79801 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.849\n",
            "INFO:tensorflow:loss = 19557204.0, step = 79901 (0.401 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 80000...\n",
            "INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 80000...\n",
            "INFO:tensorflow:Loss for final step: 23497942.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:41:03\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-80000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.38925s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:41:04\n",
            "INFO:tensorflow:Saving dict for global step 80000: average_loss = 687944.44, global_step = 80000, label/mean = 12949.43, loss = 20146944.0, prediction/mean = 12836.366\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 80000: /tmp/tmpdiqxge0c/model.ckpt-80000\n",
            "scores {'average_loss': 687944.44, 'label/mean': 12949.43, 'loss': 20146944.0, 'prediction/mean': 12836.366, 'global_step': 80000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-80000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 80000...\n",
            "INFO:tensorflow:Saving checkpoints for 80000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 80000...\n",
            "INFO:tensorflow:loss = 13526534.0, step = 80001\n",
            "INFO:tensorflow:global_step/sec: 219.196\n",
            "INFO:tensorflow:loss = 18960248.0, step = 80101 (0.466 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.691\n",
            "INFO:tensorflow:loss = 21287722.0, step = 80201 (0.362 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.203\n",
            "INFO:tensorflow:loss = 31529682.0, step = 80301 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 279.421\n",
            "INFO:tensorflow:loss = 28202594.0, step = 80401 (0.358 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.096\n",
            "INFO:tensorflow:loss = 20821822.0, step = 80501 (0.394 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.289\n",
            "INFO:tensorflow:loss = 18459554.0, step = 80601 (0.403 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.759\n",
            "INFO:tensorflow:loss = 38350372.0, step = 80701 (0.414 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.311\n",
            "INFO:tensorflow:loss = 20743360.0, step = 80801 (0.371 sec)\n",
            "INFO:tensorflow:global_step/sec: 233.395\n",
            "INFO:tensorflow:loss = 33206632.0, step = 80901 (0.429 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.445\n",
            "INFO:tensorflow:loss = 31302504.0, step = 81001 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 255.01\n",
            "INFO:tensorflow:loss = 26682554.0, step = 81101 (0.393 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.617\n",
            "INFO:tensorflow:loss = 24090828.0, step = 81201 (0.364 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.333\n",
            "INFO:tensorflow:loss = 30614686.0, step = 81301 (0.394 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.339\n",
            "INFO:tensorflow:loss = 25200628.0, step = 81401 (0.394 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.3\n",
            "INFO:tensorflow:loss = 21068600.0, step = 81501 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 219.361\n",
            "INFO:tensorflow:loss = 30485726.0, step = 81601 (0.454 sec)\n",
            "INFO:tensorflow:global_step/sec: 284.612\n",
            "INFO:tensorflow:loss = 22438582.0, step = 81701 (0.360 sec)\n",
            "INFO:tensorflow:global_step/sec: 282.228\n",
            "INFO:tensorflow:loss = 37125100.0, step = 81801 (0.346 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.391\n",
            "INFO:tensorflow:loss = 18671656.0, step = 81901 (0.422 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.32\n",
            "INFO:tensorflow:loss = 13954712.0, step = 82001 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 248.824\n",
            "INFO:tensorflow:loss = 12549990.0, step = 82101 (0.403 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.935\n",
            "INFO:tensorflow:loss = 33938508.0, step = 82201 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.127\n",
            "INFO:tensorflow:loss = 34105520.0, step = 82301 (0.397 sec)\n",
            "INFO:tensorflow:global_step/sec: 243.758\n",
            "INFO:tensorflow:loss = 25383536.0, step = 82401 (0.412 sec)\n",
            "INFO:tensorflow:global_step/sec: 232.893\n",
            "INFO:tensorflow:loss = 21615740.0, step = 82501 (0.421 sec)\n",
            "INFO:tensorflow:global_step/sec: 288.264\n",
            "INFO:tensorflow:loss = 20727838.0, step = 82601 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.664\n",
            "INFO:tensorflow:loss = 46341944.0, step = 82701 (0.400 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.215\n",
            "INFO:tensorflow:loss = 18463620.0, step = 82801 (0.398 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.427\n",
            "INFO:tensorflow:loss = 23954678.0, step = 82901 (0.385 sec)\n",
            "INFO:tensorflow:global_step/sec: 255.508\n",
            "INFO:tensorflow:loss = 25903776.0, step = 83001 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 282.471\n",
            "INFO:tensorflow:loss = 25877440.0, step = 83101 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 255.744\n",
            "INFO:tensorflow:loss = 23087144.0, step = 83201 (0.396 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.303\n",
            "INFO:tensorflow:loss = 23180308.0, step = 83301 (0.392 sec)\n",
            "INFO:tensorflow:global_step/sec: 227.618\n",
            "INFO:tensorflow:loss = 21087638.0, step = 83401 (0.444 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.981\n",
            "INFO:tensorflow:loss = 32508888.0, step = 83501 (0.395 sec)\n",
            "INFO:tensorflow:global_step/sec: 268.341\n",
            "INFO:tensorflow:loss = 22886308.0, step = 83601 (0.375 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.099\n",
            "INFO:tensorflow:loss = 30796670.0, step = 83701 (0.396 sec)\n",
            "INFO:tensorflow:global_step/sec: 246.22\n",
            "INFO:tensorflow:loss = 18968850.0, step = 83801 (0.412 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.99\n",
            "INFO:tensorflow:loss = 22160272.0, step = 83901 (0.402 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.832\n",
            "INFO:tensorflow:loss = 14723307.0, step = 84001 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 239.735\n",
            "INFO:tensorflow:loss = 33728536.0, step = 84101 (0.412 sec)\n",
            "INFO:tensorflow:global_step/sec: 233.811\n",
            "INFO:tensorflow:loss = 17342174.0, step = 84201 (0.433 sec)\n",
            "INFO:tensorflow:global_step/sec: 225.412\n",
            "INFO:tensorflow:loss = 13566626.0, step = 84301 (0.437 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.652\n",
            "INFO:tensorflow:loss = 22563058.0, step = 84401 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.992\n",
            "INFO:tensorflow:loss = 12775753.0, step = 84501 (0.425 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.052\n",
            "INFO:tensorflow:loss = 16946286.0, step = 84601 (0.404 sec)\n",
            "INFO:tensorflow:global_step/sec: 242.62\n",
            "INFO:tensorflow:loss = 24487206.0, step = 84701 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 261.253\n",
            "INFO:tensorflow:loss = 21686592.0, step = 84801 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.3\n",
            "INFO:tensorflow:loss = 29377292.0, step = 84901 (0.404 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.339\n",
            "INFO:tensorflow:loss = 14377130.0, step = 85001 (0.418 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.812\n",
            "INFO:tensorflow:loss = 28915430.0, step = 85101 (0.421 sec)\n",
            "INFO:tensorflow:global_step/sec: 233.988\n",
            "INFO:tensorflow:loss = 26600432.0, step = 85201 (0.425 sec)\n",
            "INFO:tensorflow:global_step/sec: 255.709\n",
            "INFO:tensorflow:loss = 26522400.0, step = 85301 (0.386 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.197\n",
            "INFO:tensorflow:loss = 29070494.0, step = 85401 (0.366 sec)\n",
            "INFO:tensorflow:global_step/sec: 261.735\n",
            "INFO:tensorflow:loss = 29243046.0, step = 85501 (0.385 sec)\n",
            "INFO:tensorflow:global_step/sec: 304.808\n",
            "INFO:tensorflow:loss = 23936592.0, step = 85601 (0.330 sec)\n",
            "INFO:tensorflow:global_step/sec: 291.158\n",
            "INFO:tensorflow:loss = 31892366.0, step = 85701 (0.339 sec)\n",
            "INFO:tensorflow:global_step/sec: 261.508\n",
            "INFO:tensorflow:loss = 24080254.0, step = 85801 (0.387 sec)\n",
            "INFO:tensorflow:global_step/sec: 277.023\n",
            "INFO:tensorflow:loss = 16981698.0, step = 85901 (0.359 sec)\n",
            "INFO:tensorflow:global_step/sec: 256.698\n",
            "INFO:tensorflow:loss = 20446882.0, step = 86001 (0.391 sec)\n",
            "INFO:tensorflow:global_step/sec: 230.696\n",
            "INFO:tensorflow:loss = 23196576.0, step = 86101 (0.428 sec)\n",
            "INFO:tensorflow:global_step/sec: 246.564\n",
            "INFO:tensorflow:loss = 33172574.0, step = 86201 (0.414 sec)\n",
            "INFO:tensorflow:global_step/sec: 237.941\n",
            "INFO:tensorflow:loss = 32961744.0, step = 86301 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.158\n",
            "INFO:tensorflow:loss = 11305574.0, step = 86401 (0.401 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.814\n",
            "INFO:tensorflow:loss = 37920320.0, step = 86501 (0.418 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.288\n",
            "INFO:tensorflow:loss = 20313000.0, step = 86601 (0.348 sec)\n",
            "INFO:tensorflow:global_step/sec: 266.403\n",
            "INFO:tensorflow:loss = 17153876.0, step = 86701 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 229.852\n",
            "INFO:tensorflow:loss = 23287012.0, step = 86801 (0.433 sec)\n",
            "INFO:tensorflow:global_step/sec: 252.397\n",
            "INFO:tensorflow:loss = 15223267.0, step = 86901 (0.397 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.1\n",
            "INFO:tensorflow:loss = 21849700.0, step = 87001 (0.421 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.606\n",
            "INFO:tensorflow:loss = 25191500.0, step = 87101 (0.391 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.608\n",
            "INFO:tensorflow:loss = 17994012.0, step = 87201 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 268.867\n",
            "INFO:tensorflow:loss = 35351650.0, step = 87301 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 244.157\n",
            "INFO:tensorflow:loss = 24231520.0, step = 87401 (0.409 sec)\n",
            "INFO:tensorflow:global_step/sec: 243.558\n",
            "INFO:tensorflow:loss = 33093438.0, step = 87501 (0.415 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.185\n",
            "INFO:tensorflow:loss = 22574244.0, step = 87601 (0.408 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.741\n",
            "INFO:tensorflow:loss = 8871006.0, step = 87701 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.082\n",
            "INFO:tensorflow:loss = 18902920.0, step = 87801 (0.401 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.934\n",
            "INFO:tensorflow:loss = 24643312.0, step = 87901 (0.400 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.34\n",
            "INFO:tensorflow:loss = 15774784.0, step = 88001 (0.362 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.008\n",
            "INFO:tensorflow:loss = 28674802.0, step = 88101 (0.397 sec)\n",
            "INFO:tensorflow:global_step/sec: 235.349\n",
            "INFO:tensorflow:loss = 21234886.0, step = 88201 (0.422 sec)\n",
            "INFO:tensorflow:global_step/sec: 231.873\n",
            "INFO:tensorflow:loss = 16086647.0, step = 88301 (0.434 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.502\n",
            "INFO:tensorflow:loss = 37363916.0, step = 88401 (0.424 sec)\n",
            "INFO:tensorflow:global_step/sec: 248.692\n",
            "INFO:tensorflow:loss = 15145291.0, step = 88501 (0.394 sec)\n",
            "INFO:tensorflow:global_step/sec: 235.871\n",
            "INFO:tensorflow:loss = 16846782.0, step = 88601 (0.423 sec)\n",
            "INFO:tensorflow:global_step/sec: 234.876\n",
            "INFO:tensorflow:loss = 25172454.0, step = 88701 (0.424 sec)\n",
            "INFO:tensorflow:global_step/sec: 287.827\n",
            "INFO:tensorflow:loss = 24026716.0, step = 88801 (0.348 sec)\n",
            "INFO:tensorflow:global_step/sec: 286.063\n",
            "INFO:tensorflow:loss = 16637062.0, step = 88901 (0.353 sec)\n",
            "INFO:tensorflow:global_step/sec: 243.849\n",
            "INFO:tensorflow:loss = 21117486.0, step = 89001 (0.411 sec)\n",
            "INFO:tensorflow:global_step/sec: 260.028\n",
            "INFO:tensorflow:loss = 16539407.0, step = 89101 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 281.183\n",
            "INFO:tensorflow:loss = 20847516.0, step = 89201 (0.348 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.257\n",
            "INFO:tensorflow:loss = 19916456.0, step = 89301 (0.352 sec)\n",
            "INFO:tensorflow:global_step/sec: 272.382\n",
            "INFO:tensorflow:loss = 21477148.0, step = 89401 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 246.673\n",
            "INFO:tensorflow:loss = 9654702.0, step = 89501 (0.403 sec)\n",
            "INFO:tensorflow:global_step/sec: 276.578\n",
            "INFO:tensorflow:loss = 16515162.0, step = 89601 (0.355 sec)\n",
            "INFO:tensorflow:global_step/sec: 276.209\n",
            "INFO:tensorflow:loss = 12900945.0, step = 89701 (0.361 sec)\n",
            "INFO:tensorflow:global_step/sec: 297.952\n",
            "INFO:tensorflow:loss = 23106950.0, step = 89801 (0.335 sec)\n",
            "INFO:tensorflow:global_step/sec: 240.831\n",
            "INFO:tensorflow:loss = 18403006.0, step = 89901 (0.416 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 90000...\n",
            "INFO:tensorflow:Saving checkpoints for 90000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 90000...\n",
            "INFO:tensorflow:Loss for final step: 25750308.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:41:45\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-90000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.36644s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:41:46\n",
            "INFO:tensorflow:Saving dict for global step 90000: average_loss = 639604.25, global_step = 90000, label/mean = 12949.43, loss = 18731268.0, prediction/mean = 13060.318\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 90000: /tmp/tmpdiqxge0c/model.ckpt-90000\n",
            "scores {'average_loss': 639604.25, 'label/mean': 12949.43, 'loss': 18731268.0, 'prediction/mean': 13060.318, 'global_step': 90000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-90000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 90000...\n",
            "INFO:tensorflow:Saving checkpoints for 90000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 90000...\n",
            "INFO:tensorflow:loss = 21109990.0, step = 90001\n",
            "INFO:tensorflow:global_step/sec: 264.65\n",
            "INFO:tensorflow:loss = 20036954.0, step = 90101 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.677\n",
            "INFO:tensorflow:loss = 18501940.0, step = 90201 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 261.965\n",
            "INFO:tensorflow:loss = 31969528.0, step = 90301 (0.379 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.249\n",
            "INFO:tensorflow:loss = 20857890.0, step = 90401 (0.375 sec)\n",
            "INFO:tensorflow:global_step/sec: 292.014\n",
            "INFO:tensorflow:loss = 24857056.0, step = 90501 (0.340 sec)\n",
            "INFO:tensorflow:global_step/sec: 233.794\n",
            "INFO:tensorflow:loss = 25889120.0, step = 90601 (0.426 sec)\n",
            "INFO:tensorflow:global_step/sec: 293.513\n",
            "INFO:tensorflow:loss = 21131030.0, step = 90701 (0.341 sec)\n",
            "INFO:tensorflow:global_step/sec: 293.708\n",
            "INFO:tensorflow:loss = 18606422.0, step = 90801 (0.342 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.162\n",
            "INFO:tensorflow:loss = 25675020.0, step = 90901 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 276.386\n",
            "INFO:tensorflow:loss = 22982414.0, step = 91001 (0.368 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.594\n",
            "INFO:tensorflow:loss = 28195126.0, step = 91101 (0.371 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.722\n",
            "INFO:tensorflow:loss = 25231494.0, step = 91201 (0.400 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.301\n",
            "INFO:tensorflow:loss = 18673194.0, step = 91301 (0.357 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.327\n",
            "INFO:tensorflow:loss = 23989628.0, step = 91401 (0.376 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.091\n",
            "INFO:tensorflow:loss = 22779122.0, step = 91501 (0.386 sec)\n",
            "INFO:tensorflow:global_step/sec: 295.436\n",
            "INFO:tensorflow:loss = 17628292.0, step = 91601 (0.339 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.257\n",
            "INFO:tensorflow:loss = 31322772.0, step = 91701 (0.402 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.434\n",
            "INFO:tensorflow:loss = 25208928.0, step = 91801 (0.387 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.703\n",
            "INFO:tensorflow:loss = 23156050.0, step = 91901 (0.367 sec)\n",
            "INFO:tensorflow:global_step/sec: 249.161\n",
            "INFO:tensorflow:loss = 20465374.0, step = 92001 (0.405 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.07\n",
            "INFO:tensorflow:loss = 14655483.0, step = 92101 (0.364 sec)\n",
            "INFO:tensorflow:global_step/sec: 261.579\n",
            "INFO:tensorflow:loss = 22527880.0, step = 92201 (0.384 sec)\n",
            "INFO:tensorflow:global_step/sec: 280.126\n",
            "INFO:tensorflow:loss = 18732280.0, step = 92301 (0.353 sec)\n",
            "INFO:tensorflow:global_step/sec: 292.795\n",
            "INFO:tensorflow:loss = 26315574.0, step = 92401 (0.342 sec)\n",
            "INFO:tensorflow:global_step/sec: 244.919\n",
            "INFO:tensorflow:loss = 18168050.0, step = 92501 (0.412 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.728\n",
            "INFO:tensorflow:loss = 23858356.0, step = 92601 (0.354 sec)\n",
            "INFO:tensorflow:global_step/sec: 261.564\n",
            "INFO:tensorflow:loss = 24791292.0, step = 92701 (0.387 sec)\n",
            "INFO:tensorflow:global_step/sec: 281.757\n",
            "INFO:tensorflow:loss = 25674130.0, step = 92801 (0.350 sec)\n",
            "INFO:tensorflow:global_step/sec: 286.5\n",
            "INFO:tensorflow:loss = 16644012.0, step = 92901 (0.358 sec)\n",
            "INFO:tensorflow:global_step/sec: 295.749\n",
            "INFO:tensorflow:loss = 9504600.0, step = 93001 (0.338 sec)\n",
            "INFO:tensorflow:global_step/sec: 275.854\n",
            "INFO:tensorflow:loss = 20670524.0, step = 93101 (0.359 sec)\n",
            "INFO:tensorflow:global_step/sec: 289.528\n",
            "INFO:tensorflow:loss = 35459530.0, step = 93201 (0.341 sec)\n",
            "INFO:tensorflow:global_step/sec: 259.189\n",
            "INFO:tensorflow:loss = 22859448.0, step = 93301 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 215.338\n",
            "INFO:tensorflow:loss = 30502144.0, step = 93401 (0.464 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.679\n",
            "INFO:tensorflow:loss = 16627797.0, step = 93501 (0.365 sec)\n",
            "INFO:tensorflow:global_step/sec: 288.395\n",
            "INFO:tensorflow:loss = 23421280.0, step = 93601 (0.350 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.216\n",
            "INFO:tensorflow:loss = 26128122.0, step = 93701 (0.346 sec)\n",
            "INFO:tensorflow:global_step/sec: 266.336\n",
            "INFO:tensorflow:loss = 22710596.0, step = 93801 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 276.959\n",
            "INFO:tensorflow:loss = 26553486.0, step = 93901 (0.358 sec)\n",
            "INFO:tensorflow:global_step/sec: 301.933\n",
            "INFO:tensorflow:loss = 19286002.0, step = 94001 (0.332 sec)\n",
            "INFO:tensorflow:global_step/sec: 287.738\n",
            "INFO:tensorflow:loss = 20957906.0, step = 94101 (0.341 sec)\n",
            "INFO:tensorflow:global_step/sec: 279.527\n",
            "INFO:tensorflow:loss = 17523730.0, step = 94201 (0.364 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.67\n",
            "INFO:tensorflow:loss = 20160904.0, step = 94301 (0.353 sec)\n",
            "INFO:tensorflow:global_step/sec: 281.534\n",
            "INFO:tensorflow:loss = 11875099.0, step = 94401 (0.362 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.584\n",
            "INFO:tensorflow:loss = 20630468.0, step = 94501 (0.417 sec)\n",
            "INFO:tensorflow:global_step/sec: 247.295\n",
            "INFO:tensorflow:loss = 19414108.0, step = 94601 (0.401 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.574\n",
            "INFO:tensorflow:loss = 27852502.0, step = 94701 (0.370 sec)\n",
            "INFO:tensorflow:global_step/sec: 279.823\n",
            "INFO:tensorflow:loss = 27463556.0, step = 94801 (0.355 sec)\n",
            "INFO:tensorflow:global_step/sec: 239.237\n",
            "INFO:tensorflow:loss = 33907996.0, step = 94901 (0.419 sec)\n",
            "INFO:tensorflow:global_step/sec: 266.194\n",
            "INFO:tensorflow:loss = 16800264.0, step = 95001 (0.371 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.979\n",
            "INFO:tensorflow:loss = 17905882.0, step = 95101 (0.369 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.049\n",
            "INFO:tensorflow:loss = 28090004.0, step = 95201 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 259.653\n",
            "INFO:tensorflow:loss = 20565026.0, step = 95301 (0.395 sec)\n",
            "INFO:tensorflow:global_step/sec: 270.032\n",
            "INFO:tensorflow:loss = 25199412.0, step = 95401 (0.362 sec)\n",
            "INFO:tensorflow:global_step/sec: 285.247\n",
            "INFO:tensorflow:loss = 17625768.0, step = 95501 (0.352 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.368\n",
            "INFO:tensorflow:loss = 21939468.0, step = 95601 (0.372 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.815\n",
            "INFO:tensorflow:loss = 17566900.0, step = 95701 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 287.657\n",
            "INFO:tensorflow:loss = 12278112.0, step = 95801 (0.349 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.66\n",
            "INFO:tensorflow:loss = 12467890.0, step = 95901 (0.393 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.061\n",
            "INFO:tensorflow:loss = 33186750.0, step = 96001 (0.370 sec)\n",
            "INFO:tensorflow:global_step/sec: 269.944\n",
            "INFO:tensorflow:loss = 16409165.0, step = 96101 (0.364 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.871\n",
            "INFO:tensorflow:loss = 27238300.0, step = 96201 (0.378 sec)\n",
            "INFO:tensorflow:global_step/sec: 273.811\n",
            "INFO:tensorflow:loss = 14249817.0, step = 96301 (0.373 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.133\n",
            "INFO:tensorflow:loss = 21868626.0, step = 96401 (0.365 sec)\n",
            "INFO:tensorflow:global_step/sec: 282.519\n",
            "INFO:tensorflow:loss = 19408210.0, step = 96501 (0.359 sec)\n",
            "INFO:tensorflow:global_step/sec: 270.891\n",
            "INFO:tensorflow:loss = 27519916.0, step = 96601 (0.370 sec)\n",
            "INFO:tensorflow:global_step/sec: 285.029\n",
            "INFO:tensorflow:loss = 26470512.0, step = 96701 (0.353 sec)\n",
            "INFO:tensorflow:global_step/sec: 272.255\n",
            "INFO:tensorflow:loss = 35618236.0, step = 96801 (0.367 sec)\n",
            "INFO:tensorflow:global_step/sec: 286.416\n",
            "INFO:tensorflow:loss = 19978232.0, step = 96901 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 262.17\n",
            "INFO:tensorflow:loss = 22843832.0, step = 97001 (0.373 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.352\n",
            "INFO:tensorflow:loss = 27632540.0, step = 97101 (0.380 sec)\n",
            "INFO:tensorflow:global_step/sec: 237.143\n",
            "INFO:tensorflow:loss = 5994258.0, step = 97201 (0.424 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.776\n",
            "INFO:tensorflow:loss = 30032648.0, step = 97301 (0.395 sec)\n",
            "INFO:tensorflow:global_step/sec: 250.985\n",
            "INFO:tensorflow:loss = 18103766.0, step = 97401 (0.402 sec)\n",
            "INFO:tensorflow:global_step/sec: 309.324\n",
            "INFO:tensorflow:loss = 16980626.0, step = 97501 (0.319 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.143\n",
            "INFO:tensorflow:loss = 18719570.0, step = 97601 (0.381 sec)\n",
            "INFO:tensorflow:global_step/sec: 278.805\n",
            "INFO:tensorflow:loss = 24075442.0, step = 97701 (0.359 sec)\n",
            "INFO:tensorflow:global_step/sec: 286.294\n",
            "INFO:tensorflow:loss = 13034160.0, step = 97801 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 277.139\n",
            "INFO:tensorflow:loss = 26803574.0, step = 97901 (0.359 sec)\n",
            "INFO:tensorflow:global_step/sec: 277.399\n",
            "INFO:tensorflow:loss = 25515296.0, step = 98001 (0.367 sec)\n",
            "INFO:tensorflow:global_step/sec: 253.679\n",
            "INFO:tensorflow:loss = 16557456.0, step = 98101 (0.393 sec)\n",
            "INFO:tensorflow:global_step/sec: 274.37\n",
            "INFO:tensorflow:loss = 21539510.0, step = 98201 (0.362 sec)\n",
            "INFO:tensorflow:global_step/sec: 263.431\n",
            "INFO:tensorflow:loss = 8256664.5, step = 98301 (0.382 sec)\n",
            "INFO:tensorflow:global_step/sec: 303.175\n",
            "INFO:tensorflow:loss = 27858640.0, step = 98401 (0.336 sec)\n",
            "INFO:tensorflow:global_step/sec: 245.792\n",
            "INFO:tensorflow:loss = 16710625.0, step = 98501 (0.397 sec)\n",
            "INFO:tensorflow:global_step/sec: 297.924\n",
            "INFO:tensorflow:loss = 26850350.0, step = 98601 (0.339 sec)\n",
            "INFO:tensorflow:global_step/sec: 289.68\n",
            "INFO:tensorflow:loss = 11490068.0, step = 98701 (0.341 sec)\n",
            "INFO:tensorflow:global_step/sec: 255.742\n",
            "INFO:tensorflow:loss = 18035634.0, step = 98801 (0.393 sec)\n",
            "INFO:tensorflow:global_step/sec: 238.34\n",
            "INFO:tensorflow:loss = 23962470.0, step = 98901 (0.426 sec)\n",
            "INFO:tensorflow:global_step/sec: 241.552\n",
            "INFO:tensorflow:loss = 19244252.0, step = 99001 (0.405 sec)\n",
            "INFO:tensorflow:global_step/sec: 264.175\n",
            "INFO:tensorflow:loss = 11813059.0, step = 99101 (0.383 sec)\n",
            "INFO:tensorflow:global_step/sec: 254.601\n",
            "INFO:tensorflow:loss = 11337062.0, step = 99201 (0.388 sec)\n",
            "INFO:tensorflow:global_step/sec: 261.821\n",
            "INFO:tensorflow:loss = 27705080.0, step = 99301 (0.390 sec)\n",
            "INFO:tensorflow:global_step/sec: 285.332\n",
            "INFO:tensorflow:loss = 19492720.0, step = 99401 (0.342 sec)\n",
            "INFO:tensorflow:global_step/sec: 290.671\n",
            "INFO:tensorflow:loss = 18066748.0, step = 99501 (0.345 sec)\n",
            "INFO:tensorflow:global_step/sec: 258.078\n",
            "INFO:tensorflow:loss = 30372668.0, step = 99601 (0.391 sec)\n",
            "INFO:tensorflow:global_step/sec: 251.912\n",
            "INFO:tensorflow:loss = 29845178.0, step = 99701 (0.392 sec)\n",
            "INFO:tensorflow:global_step/sec: 307.02\n",
            "INFO:tensorflow:loss = 16740676.0, step = 99801 (0.327 sec)\n",
            "INFO:tensorflow:global_step/sec: 267.268\n",
            "INFO:tensorflow:loss = 18274400.0, step = 99901 (0.377 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 100000...\n",
            "INFO:tensorflow:Saving checkpoints for 100000 into /tmp/tmpdiqxge0c/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 100000...\n",
            "INFO:tensorflow:Loss for final step: 23931110.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T21:42:25\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpdiqxge0c/model.ckpt-100000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.35189s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-21:42:25\n",
            "INFO:tensorflow:Saving dict for global step 100000: average_loss = 659111.2, global_step = 100000, label/mean = 12949.43, loss = 19302542.0, prediction/mean = 12725.627\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 100000: /tmp/tmpdiqxge0c/model.ckpt-100000\n",
            "scores {'average_loss': 659111.2, 'label/mean': 12949.43, 'loss': 19302542.0, 'prediction/mean': 12725.627, 'global_step': 100000}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CmaGHWFVGKMr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01bed8af-977a-44b5-e799-f31bf0ede0b3"
      },
      "source": [
        "# @title Possible solution\n",
        "# This is a first pass at a model that uses all the features.\n",
        "# Do you have any improvements?\n",
        "\n",
        "batch_size = 16\n",
        "\n",
        "x_df = car_data[numeric_feature_names + categorical_feature_names]\n",
        "y_series = car_data['price']\n",
        "\n",
        "train_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    num_epochs=None,\n",
        "    shuffle=True)\n",
        "\n",
        "eval_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    y=y_series,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "predict_input_fn = tf.estimator.inputs.pandas_input_fn(\n",
        "    x=x_df,\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False)\n",
        "\n",
        "epsilon = 0.000001\n",
        "model_feature_columns = [\n",
        "    tf.feature_column.indicator_column(\n",
        "        tf.feature_column.categorical_column_with_vocabulary_list(\n",
        "            feature_name, vocabulary_list=car_data[feature_name].unique()))\n",
        "    for feature_name in categorical_feature_names\n",
        "] + [\n",
        "    tf.feature_column.numeric_column(feature_name,\n",
        "                                     normalizer_fn=lambda val: (val - x_df.mean()[feature_name]) / (epsilon + x_df.std()[feature_name]))\n",
        "    for feature_name in numeric_feature_names\n",
        "]\n",
        "\n",
        "\n",
        "print('model_feature_columns', model_feature_columns)\n",
        "\n",
        "est = tf.estimator.DNNRegressor(\n",
        "    feature_columns=model_feature_columns,\n",
        "    hidden_units=[64],\n",
        "    optimizer=tf.train.AdagradOptimizer(learning_rate=0.01),\n",
        "  )\n",
        "\n",
        "# TRAIN\n",
        "num_print_statements = 10\n",
        "num_training_steps = 10000\n",
        "for _ in range(num_print_statements):\n",
        "  est.train(train_input_fn, steps=num_training_steps // num_print_statements)\n",
        "  scores = est.evaluate(eval_input_fn)\n",
        "  \n",
        "  # The `scores` dictionary has several metrics automatically generated by the \n",
        "  # canned Estimator.\n",
        "  # `average_loss` is the average loss for an individual example.\n",
        "  # `loss` is the summed loss for the batch.\n",
        "  # In addition to these scalar losses, you may find the visualization functions\n",
        "  # in the next cell helpful for debugging model quality.\n",
        "  print('scores', scores)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model_feature_columns [IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='fuel-system', vocabulary_list=('mpfi', 'idi', '2bbl', 'mfi', '1bbl', 'spdi', '4bbl', 'spfi'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='drive-wheels', vocabulary_list=('fwd', 'rwd', '4wd'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='aspiration', vocabulary_list=('std', 'turbo'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='engine-location', vocabulary_list=('front', 'rear'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='engine-type', vocabulary_list=('ohc', 'ohcv', 'l', 'dohc', 'rotor', 'ohcf', 'dohcv'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='make', vocabulary_list=('volkswagen', 'nissan', 'mazda', 'mitsubishi', 'peugot', 'saab', 'mercedes-benz', 'dodge', 'toyota', 'chevrolet', 'honda', 'volvo', 'plymouth', 'bmw', 'renault', 'mercury', 'subaru', 'audi', 'alfa-romero', 'porsche', 'jaguar', 'isuzu'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='num-doors', vocabulary_list=('two', 'four', '?'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='fuel-type', vocabulary_list=('gas', 'diesel'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='body-style', vocabulary_list=('sedan', 'hatchback', 'wagon', 'hardtop', 'convertible'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='num-cylinders', vocabulary_list=('four', 'six', 'five', 'three', 'two', 'eight', 'twelve'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), NumericColumn(key='symboling', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8546647b00>), NumericColumn(key='normalized-losses', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8546647320>), NumericColumn(key='wheel-base', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f85466477a0>), NumericColumn(key='length', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8546647e60>), NumericColumn(key='width', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8546647830>), NumericColumn(key='height', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8546647050>), NumericColumn(key='weight', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8546647950>), NumericColumn(key='engine-size', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8548289b90>), NumericColumn(key='horsepower', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8548289ef0>), NumericColumn(key='peak-rpm', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8548289680>), NumericColumn(key='city-mpg', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8548289050>), NumericColumn(key='highway-mpg', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f85482898c0>), NumericColumn(key='bore', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8548e8ddd0>), NumericColumn(key='stroke', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8548e8d8c0>), NumericColumn(key='compression-ratio', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=<function <listcomp>.<lambda> at 0x7f8548e8d200>)]\n",
            "INFO:tensorflow:Using default config.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmptxv87rwr\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmptxv87rwr', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
            "INFO:tensorflow:Calling model_fn.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:36: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...\n",
            "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...\n",
            "INFO:tensorflow:loss = 3411909400.0, step = 1\n",
            "INFO:tensorflow:global_step/sec: 271.614\n",
            "INFO:tensorflow:loss = 1536491100.0, step = 101 (0.379 sec)\n",
            "INFO:tensorflow:global_step/sec: 323.395\n",
            "INFO:tensorflow:loss = 1320954600.0, step = 201 (0.307 sec)\n",
            "INFO:tensorflow:global_step/sec: 304.303\n",
            "INFO:tensorflow:loss = 292738100.0, step = 301 (0.333 sec)\n",
            "INFO:tensorflow:global_step/sec: 303.323\n",
            "INFO:tensorflow:loss = 2560005000.0, step = 401 (0.328 sec)\n",
            "INFO:tensorflow:global_step/sec: 317.138\n",
            "INFO:tensorflow:loss = 1424271200.0, step = 501 (0.314 sec)\n",
            "INFO:tensorflow:global_step/sec: 305.578\n",
            "INFO:tensorflow:loss = 916933100.0, step = 601 (0.319 sec)\n",
            "INFO:tensorflow:global_step/sec: 311.91\n",
            "INFO:tensorflow:loss = 278011500.0, step = 701 (0.321 sec)\n",
            "INFO:tensorflow:global_step/sec: 322.547\n",
            "INFO:tensorflow:loss = 880183740.0, step = 801 (0.320 sec)\n",
            "INFO:tensorflow:global_step/sec: 304.315\n",
            "INFO:tensorflow:loss = 1207775200.0, step = 901 (0.319 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...\n",
            "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...\n",
            "INFO:tensorflow:Loss for final step: 1092579200.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:57:02\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-1000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.33383s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:57:03\n",
            "INFO:tensorflow:Saving dict for global step 1000: average_loss = 50132476.0, global_step = 1000, label/mean = 12949.43, loss = 790550600.0, prediction/mean = 13203.582\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: /tmp/tmptxv87rwr/model.ckpt-1000\n",
            "scores {'average_loss': 50132476.0, 'label/mean': 12949.43, 'loss': 790550600.0, 'prediction/mean': 13203.582, 'global_step': 1000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-1000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1000...\n",
            "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1000...\n",
            "INFO:tensorflow:loss = 415170270.0, step = 1001\n",
            "INFO:tensorflow:global_step/sec: 287.731\n",
            "INFO:tensorflow:loss = 349178180.0, step = 1101 (0.357 sec)\n",
            "INFO:tensorflow:global_step/sec: 310.166\n",
            "INFO:tensorflow:loss = 452729950.0, step = 1201 (0.320 sec)\n",
            "INFO:tensorflow:global_step/sec: 318.738\n",
            "INFO:tensorflow:loss = 2324670000.0, step = 1301 (0.311 sec)\n",
            "INFO:tensorflow:global_step/sec: 328.029\n",
            "INFO:tensorflow:loss = 1146908500.0, step = 1401 (0.310 sec)\n",
            "INFO:tensorflow:global_step/sec: 347.835\n",
            "INFO:tensorflow:loss = 385501900.0, step = 1501 (0.280 sec)\n",
            "INFO:tensorflow:global_step/sec: 323.576\n",
            "INFO:tensorflow:loss = 1030790900.0, step = 1601 (0.316 sec)\n",
            "INFO:tensorflow:global_step/sec: 315.478\n",
            "INFO:tensorflow:loss = 807106200.0, step = 1701 (0.314 sec)\n",
            "INFO:tensorflow:global_step/sec: 324.748\n",
            "INFO:tensorflow:loss = 796814700.0, step = 1801 (0.312 sec)\n",
            "INFO:tensorflow:global_step/sec: 324.786\n",
            "INFO:tensorflow:loss = 595847740.0, step = 1901 (0.306 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2000...\n",
            "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2000...\n",
            "INFO:tensorflow:Loss for final step: 306994240.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:57:08\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-2000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.33042s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:57:08\n",
            "INFO:tensorflow:Saving dict for global step 2000: average_loss = 45084670.0, global_step = 2000, label/mean = 12949.43, loss = 710950600.0, prediction/mean = 13190.304\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: /tmp/tmptxv87rwr/model.ckpt-2000\n",
            "scores {'average_loss': 45084670.0, 'label/mean': 12949.43, 'loss': 710950600.0, 'prediction/mean': 13190.304, 'global_step': 2000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-2000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2000...\n",
            "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2000...\n",
            "INFO:tensorflow:loss = 414956930.0, step = 2001\n",
            "INFO:tensorflow:global_step/sec: 302.059\n",
            "INFO:tensorflow:loss = 958993000.0, step = 2101 (0.340 sec)\n",
            "INFO:tensorflow:global_step/sec: 302.134\n",
            "INFO:tensorflow:loss = 986148100.0, step = 2201 (0.330 sec)\n",
            "INFO:tensorflow:global_step/sec: 301.846\n",
            "INFO:tensorflow:loss = 437696200.0, step = 2301 (0.332 sec)\n",
            "INFO:tensorflow:global_step/sec: 312.728\n",
            "INFO:tensorflow:loss = 285059520.0, step = 2401 (0.321 sec)\n",
            "INFO:tensorflow:global_step/sec: 283.619\n",
            "INFO:tensorflow:loss = 487578430.0, step = 2501 (0.347 sec)\n",
            "INFO:tensorflow:global_step/sec: 309.414\n",
            "INFO:tensorflow:loss = 171532380.0, step = 2601 (0.324 sec)\n",
            "INFO:tensorflow:global_step/sec: 335.191\n",
            "INFO:tensorflow:loss = 1132408000.0, step = 2701 (0.295 sec)\n",
            "INFO:tensorflow:global_step/sec: 343.15\n",
            "INFO:tensorflow:loss = 322145300.0, step = 2801 (0.296 sec)\n",
            "INFO:tensorflow:global_step/sec: 337.443\n",
            "INFO:tensorflow:loss = 1002229760.0, step = 2901 (0.297 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3000...\n",
            "INFO:tensorflow:Saving checkpoints for 3000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3000...\n",
            "INFO:tensorflow:Loss for final step: 481848960.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:57:13\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-3000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.32813s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:57:13\n",
            "INFO:tensorflow:Saving dict for global step 3000: average_loss = 40051376.0, global_step = 3000, label/mean = 12949.43, loss = 631579400.0, prediction/mean = 13147.03\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3000: /tmp/tmptxv87rwr/model.ckpt-3000\n",
            "scores {'average_loss': 40051376.0, 'label/mean': 12949.43, 'loss': 631579400.0, 'prediction/mean': 13147.03, 'global_step': 3000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-3000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3000...\n",
            "INFO:tensorflow:Saving checkpoints for 3000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3000...\n",
            "INFO:tensorflow:loss = 952976500.0, step = 3001\n",
            "INFO:tensorflow:global_step/sec: 295.949\n",
            "INFO:tensorflow:loss = 256385120.0, step = 3101 (0.341 sec)\n",
            "INFO:tensorflow:global_step/sec: 339.238\n",
            "INFO:tensorflow:loss = 511764450.0, step = 3201 (0.295 sec)\n",
            "INFO:tensorflow:global_step/sec: 346.769\n",
            "INFO:tensorflow:loss = 165952180.0, step = 3301 (0.289 sec)\n",
            "INFO:tensorflow:global_step/sec: 300.697\n",
            "INFO:tensorflow:loss = 244409950.0, step = 3401 (0.332 sec)\n",
            "INFO:tensorflow:global_step/sec: 297.391\n",
            "INFO:tensorflow:loss = 380775230.0, step = 3501 (0.342 sec)\n",
            "INFO:tensorflow:global_step/sec: 293.323\n",
            "INFO:tensorflow:loss = 1246721500.0, step = 3601 (0.342 sec)\n",
            "INFO:tensorflow:global_step/sec: 305.145\n",
            "INFO:tensorflow:loss = 1108577800.0, step = 3701 (0.325 sec)\n",
            "INFO:tensorflow:global_step/sec: 348.622\n",
            "INFO:tensorflow:loss = 1934639700.0, step = 3801 (0.292 sec)\n",
            "INFO:tensorflow:global_step/sec: 308.55\n",
            "INFO:tensorflow:loss = 211205890.0, step = 3901 (0.318 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4000...\n",
            "INFO:tensorflow:Saving checkpoints for 4000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4000...\n",
            "INFO:tensorflow:Loss for final step: 1408652300.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:57:19\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-4000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.36510s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:57:19\n",
            "INFO:tensorflow:Saving dict for global step 4000: average_loss = 36379450.0, global_step = 4000, label/mean = 12949.43, loss = 573675900.0, prediction/mean = 13288.256\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4000: /tmp/tmptxv87rwr/model.ckpt-4000\n",
            "scores {'average_loss': 36379450.0, 'label/mean': 12949.43, 'loss': 573675900.0, 'prediction/mean': 13288.256, 'global_step': 4000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-4000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4000...\n",
            "INFO:tensorflow:Saving checkpoints for 4000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4000...\n",
            "INFO:tensorflow:loss = 1655342600.0, step = 4001\n",
            "INFO:tensorflow:global_step/sec: 287.847\n",
            "INFO:tensorflow:loss = 293192770.0, step = 4101 (0.356 sec)\n",
            "INFO:tensorflow:global_step/sec: 303.773\n",
            "INFO:tensorflow:loss = 1221708800.0, step = 4201 (0.327 sec)\n",
            "INFO:tensorflow:global_step/sec: 295.853\n",
            "INFO:tensorflow:loss = 252880480.0, step = 4301 (0.341 sec)\n",
            "INFO:tensorflow:global_step/sec: 296.546\n",
            "INFO:tensorflow:loss = 405845300.0, step = 4401 (0.335 sec)\n",
            "INFO:tensorflow:global_step/sec: 304.442\n",
            "INFO:tensorflow:loss = 697320900.0, step = 4501 (0.327 sec)\n",
            "INFO:tensorflow:global_step/sec: 303.906\n",
            "INFO:tensorflow:loss = 399235170.0, step = 4601 (0.335 sec)\n",
            "INFO:tensorflow:global_step/sec: 298.804\n",
            "INFO:tensorflow:loss = 557707300.0, step = 4701 (0.334 sec)\n",
            "INFO:tensorflow:global_step/sec: 312.493\n",
            "INFO:tensorflow:loss = 131980110.0, step = 4801 (0.316 sec)\n",
            "INFO:tensorflow:global_step/sec: 325.255\n",
            "INFO:tensorflow:loss = 499706500.0, step = 4901 (0.309 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5000...\n",
            "INFO:tensorflow:Saving checkpoints for 5000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5000...\n",
            "INFO:tensorflow:Loss for final step: 867963800.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:57:24\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-5000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.32813s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:57:25\n",
            "INFO:tensorflow:Saving dict for global step 5000: average_loss = 33515348.0, global_step = 5000, label/mean = 12949.43, loss = 528511260.0, prediction/mean = 13227.743\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 5000: /tmp/tmptxv87rwr/model.ckpt-5000\n",
            "scores {'average_loss': 33515348.0, 'label/mean': 12949.43, 'loss': 528511260.0, 'prediction/mean': 13227.743, 'global_step': 5000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-5000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5000...\n",
            "INFO:tensorflow:Saving checkpoints for 5000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5000...\n",
            "INFO:tensorflow:loss = 139290940.0, step = 5001\n",
            "INFO:tensorflow:global_step/sec: 276.627\n",
            "INFO:tensorflow:loss = 622299700.0, step = 5101 (0.363 sec)\n",
            "INFO:tensorflow:global_step/sec: 336.492\n",
            "INFO:tensorflow:loss = 702134100.0, step = 5201 (0.304 sec)\n",
            "INFO:tensorflow:global_step/sec: 306.435\n",
            "INFO:tensorflow:loss = 1060075600.0, step = 5301 (0.326 sec)\n",
            "INFO:tensorflow:global_step/sec: 296.348\n",
            "INFO:tensorflow:loss = 337065150.0, step = 5401 (0.339 sec)\n",
            "INFO:tensorflow:global_step/sec: 322.943\n",
            "INFO:tensorflow:loss = 459210460.0, step = 5501 (0.314 sec)\n",
            "INFO:tensorflow:global_step/sec: 346.573\n",
            "INFO:tensorflow:loss = 343718800.0, step = 5601 (0.275 sec)\n",
            "INFO:tensorflow:global_step/sec: 357.117\n",
            "INFO:tensorflow:loss = 163999950.0, step = 5701 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 352.54\n",
            "INFO:tensorflow:loss = 928868860.0, step = 5801 (0.284 sec)\n",
            "INFO:tensorflow:global_step/sec: 293.576\n",
            "INFO:tensorflow:loss = 136902030.0, step = 5901 (0.345 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6000...\n",
            "INFO:tensorflow:Saving checkpoints for 6000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6000...\n",
            "INFO:tensorflow:Loss for final step: 500261440.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:57:30\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-6000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.31294s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:57:30\n",
            "INFO:tensorflow:Saving dict for global step 6000: average_loss = 31273060.0, global_step = 6000, label/mean = 12949.43, loss = 493152100.0, prediction/mean = 13221.449\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 6000: /tmp/tmptxv87rwr/model.ckpt-6000\n",
            "scores {'average_loss': 31273060.0, 'label/mean': 12949.43, 'loss': 493152100.0, 'prediction/mean': 13221.449, 'global_step': 6000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-6000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6000...\n",
            "INFO:tensorflow:Saving checkpoints for 6000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6000...\n",
            "INFO:tensorflow:loss = 137511580.0, step = 6001\n",
            "INFO:tensorflow:global_step/sec: 250.397\n",
            "INFO:tensorflow:loss = 952402750.0, step = 6101 (0.402 sec)\n",
            "INFO:tensorflow:global_step/sec: 311.873\n",
            "INFO:tensorflow:loss = 188986690.0, step = 6201 (0.319 sec)\n",
            "INFO:tensorflow:global_step/sec: 329.436\n",
            "INFO:tensorflow:loss = 800356300.0, step = 6301 (0.305 sec)\n",
            "INFO:tensorflow:global_step/sec: 303.75\n",
            "INFO:tensorflow:loss = 232807040.0, step = 6401 (0.334 sec)\n",
            "INFO:tensorflow:global_step/sec: 311.076\n",
            "INFO:tensorflow:loss = 642896260.0, step = 6501 (0.319 sec)\n",
            "INFO:tensorflow:global_step/sec: 320.649\n",
            "INFO:tensorflow:loss = 382234750.0, step = 6601 (0.316 sec)\n",
            "INFO:tensorflow:global_step/sec: 320.194\n",
            "INFO:tensorflow:loss = 339252260.0, step = 6701 (0.305 sec)\n",
            "INFO:tensorflow:global_step/sec: 271.273\n",
            "INFO:tensorflow:loss = 235019500.0, step = 6801 (0.377 sec)\n",
            "INFO:tensorflow:global_step/sec: 315.526\n",
            "INFO:tensorflow:loss = 113802830.0, step = 6901 (0.310 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7000...\n",
            "INFO:tensorflow:Saving checkpoints for 7000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7000...\n",
            "INFO:tensorflow:Loss for final step: 542880100.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:57:35\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-7000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.30355s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:57:35\n",
            "INFO:tensorflow:Saving dict for global step 7000: average_loss = 29515476.0, global_step = 7000, label/mean = 12949.43, loss = 465436350.0, prediction/mean = 13169.856\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 7000: /tmp/tmptxv87rwr/model.ckpt-7000\n",
            "scores {'average_loss': 29515476.0, 'label/mean': 12949.43, 'loss': 465436350.0, 'prediction/mean': 13169.856, 'global_step': 7000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-7000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7000...\n",
            "INFO:tensorflow:Saving checkpoints for 7000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7000...\n",
            "INFO:tensorflow:loss = 690995140.0, step = 7001\n",
            "INFO:tensorflow:global_step/sec: 277.902\n",
            "INFO:tensorflow:loss = 117852320.0, step = 7101 (0.371 sec)\n",
            "INFO:tensorflow:global_step/sec: 299.256\n",
            "INFO:tensorflow:loss = 1498398500.0, step = 7201 (0.327 sec)\n",
            "INFO:tensorflow:global_step/sec: 321.363\n",
            "INFO:tensorflow:loss = 752465300.0, step = 7301 (0.316 sec)\n",
            "INFO:tensorflow:global_step/sec: 322.412\n",
            "INFO:tensorflow:loss = 489055500.0, step = 7401 (0.304 sec)\n",
            "INFO:tensorflow:global_step/sec: 322.866\n",
            "INFO:tensorflow:loss = 702731700.0, step = 7501 (0.312 sec)\n",
            "INFO:tensorflow:global_step/sec: 286.86\n",
            "INFO:tensorflow:loss = 817810800.0, step = 7601 (0.351 sec)\n",
            "INFO:tensorflow:global_step/sec: 319.792\n",
            "INFO:tensorflow:loss = 834116100.0, step = 7701 (0.308 sec)\n",
            "INFO:tensorflow:global_step/sec: 320.713\n",
            "INFO:tensorflow:loss = 210616750.0, step = 7801 (0.312 sec)\n",
            "INFO:tensorflow:global_step/sec: 325.344\n",
            "INFO:tensorflow:loss = 79036130.0, step = 7901 (0.308 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8000...\n",
            "INFO:tensorflow:Saving checkpoints for 8000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8000...\n",
            "INFO:tensorflow:Loss for final step: 925087600.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:57:40\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-8000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.32606s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:57:41\n",
            "INFO:tensorflow:Saving dict for global step 8000: average_loss = 28142824.0, global_step = 8000, label/mean = 12949.43, loss = 443790700.0, prediction/mean = 13169.278\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 8000: /tmp/tmptxv87rwr/model.ckpt-8000\n",
            "scores {'average_loss': 28142824.0, 'label/mean': 12949.43, 'loss': 443790700.0, 'prediction/mean': 13169.278, 'global_step': 8000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-8000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8000...\n",
            "INFO:tensorflow:Saving checkpoints for 8000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8000...\n",
            "INFO:tensorflow:loss = 609634800.0, step = 8001\n",
            "INFO:tensorflow:global_step/sec: 285.312\n",
            "INFO:tensorflow:loss = 1259240400.0, step = 8101 (0.360 sec)\n",
            "INFO:tensorflow:global_step/sec: 313.282\n",
            "INFO:tensorflow:loss = 309114140.0, step = 8201 (0.311 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.02\n",
            "INFO:tensorflow:loss = 835109760.0, step = 8301 (0.294 sec)\n",
            "INFO:tensorflow:global_step/sec: 345.066\n",
            "INFO:tensorflow:loss = 158987760.0, step = 8401 (0.295 sec)\n",
            "INFO:tensorflow:global_step/sec: 340.335\n",
            "INFO:tensorflow:loss = 373908540.0, step = 8501 (0.294 sec)\n",
            "INFO:tensorflow:global_step/sec: 324.15\n",
            "INFO:tensorflow:loss = 541521000.0, step = 8601 (0.307 sec)\n",
            "INFO:tensorflow:global_step/sec: 343.158\n",
            "INFO:tensorflow:loss = 598437800.0, step = 8701 (0.289 sec)\n",
            "INFO:tensorflow:global_step/sec: 351.595\n",
            "INFO:tensorflow:loss = 899224600.0, step = 8801 (0.282 sec)\n",
            "INFO:tensorflow:global_step/sec: 318.349\n",
            "INFO:tensorflow:loss = 1072766600.0, step = 8901 (0.320 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9000...\n",
            "INFO:tensorflow:Saving checkpoints for 9000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9000...\n",
            "INFO:tensorflow:Loss for final step: 905526340.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:57:46\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-9000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.32124s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:57:46\n",
            "INFO:tensorflow:Saving dict for global step 9000: average_loss = 27072836.0, global_step = 9000, label/mean = 12949.43, loss = 426917820.0, prediction/mean = 13165.144\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 9000: /tmp/tmptxv87rwr/model.ckpt-9000\n",
            "scores {'average_loss': 27072836.0, 'label/mean': 12949.43, 'loss': 426917820.0, 'prediction/mean': 13165.144, 'global_step': 9000}\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Create CheckpointSaverHook.\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-9000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9000...\n",
            "INFO:tensorflow:Saving checkpoints for 9000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9000...\n",
            "INFO:tensorflow:loss = 748659100.0, step = 9001\n",
            "INFO:tensorflow:global_step/sec: 273.819\n",
            "INFO:tensorflow:loss = 832359940.0, step = 9101 (0.372 sec)\n",
            "INFO:tensorflow:global_step/sec: 311.806\n",
            "INFO:tensorflow:loss = 193352420.0, step = 9201 (0.319 sec)\n",
            "INFO:tensorflow:global_step/sec: 310.354\n",
            "INFO:tensorflow:loss = 631254460.0, step = 9301 (0.323 sec)\n",
            "INFO:tensorflow:global_step/sec: 319.303\n",
            "INFO:tensorflow:loss = 81208700.0, step = 9401 (0.313 sec)\n",
            "INFO:tensorflow:global_step/sec: 361.042\n",
            "INFO:tensorflow:loss = 547023170.0, step = 9501 (0.276 sec)\n",
            "INFO:tensorflow:global_step/sec: 364.079\n",
            "INFO:tensorflow:loss = 615027840.0, step = 9601 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 367.103\n",
            "INFO:tensorflow:loss = 125510220.0, step = 9701 (0.272 sec)\n",
            "INFO:tensorflow:global_step/sec: 341.806\n",
            "INFO:tensorflow:loss = 359205220.0, step = 9801 (0.298 sec)\n",
            "INFO:tensorflow:global_step/sec: 345.481\n",
            "INFO:tensorflow:loss = 94683180.0, step = 9901 (0.287 sec)\n",
            "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10000...\n",
            "INFO:tensorflow:Saving checkpoints for 10000 into /tmp/tmptxv87rwr/model.ckpt.\n",
            "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10000...\n",
            "INFO:tensorflow:Loss for final step: 903862000.0.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Starting evaluation at 2022-04-12T17:57:51\n",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmptxv87rwr/model.ckpt-10000\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "INFO:tensorflow:Inference Time : 0.33290s\n",
            "INFO:tensorflow:Finished evaluation at 2022-04-12-17:57:52\n",
            "INFO:tensorflow:Saving dict for global step 10000: average_loss = 26237036.0, global_step = 10000, label/mean = 12949.43, loss = 413737860.0, prediction/mean = 13136.9375\n",
            "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10000: /tmp/tmptxv87rwr/model.ckpt-10000\n",
            "scores {'average_loss': 26237036.0, 'label/mean': 12949.43, 'loss': 413737860.0, 'prediction/mean': 13136.9375, 'global_step': 10000}\n"
          ]
        }
      ]
    }
  ]
}