TensorFlow_module.ipynb

{
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  "metadata": {
    "colab": {
      "name": "TensorFlow_module.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNJAONLMMJ3kQHwTpULFp0D",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/llcorrea/TensorFlow_module/blob/main/TensorFlow_module.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CRImUonpqSyf"
      },
      "source": [
        "#**ML model with TensorFlow2**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I532jIBVVZAd"
      },
      "source": [
        "# **Import libs**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vC2MM6I9VK3F"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.dummy import DummyRegressor\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OC_nnWx7VzxK"
      },
      "source": [
        "#@title Using TensorFlow 2.x\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U0zJbIr5ohFx"
      },
      "source": [
        "TensorFlow (TF) is an end-to-end open source platform for machine learning.\n",
        "\n",
        "Its APIs are organized hierarchically, with the high-level APIs built on the low-level APIs. \n",
        "\n",
        "Low-level APIs are used to explore new machine learning algorithms.\n",
        "\n",
        "On the contrary, the high-level API named **tf.keras** is used to train and define machine learning models.\n",
        "\n",
        "TF documentation: https://www.tensorflow.org/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "evrJ1t1YWDHc"
      },
      "source": [
        "# **Functions**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7QkAkflKbaAP"
      },
      "source": [
        "**Baseline model: Dummy Regressor using the mean values of target feature**\n",
        "\n",
        "Build, train and make predictions via the Dummy Regressor model using as input parameters the dataset entries (features and labels)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sURK5gPHn7n"
      },
      "source": [
        "def dummy_prediction(train_X, train_y, val_X, val_y):\n",
        "  ##baseline model\n",
        "  ##inner prediction function\n",
        "  def dummy_model():\n",
        "    dummy = DummyRegressor('mean')\n",
        "    dummy.fit(train_X, train_y)\n",
        "    return dummy.predict(val_X)\n",
        "\n",
        "  return np.sqrt(mean_squared_error(val_y, dummy_model())) #return RMSE"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQFL6lo2WorO"
      },
      "source": [
        "**Build the TF ML model**\n",
        "\n",
        "Simple Linear Regression using tf.keras."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqTJNAn4WJWu"
      },
      "source": [
        "def build_model(learning_rate, n_features):\n",
        "  ##the most simple keras models are sequential, which has one or more layers\n",
        "  lr_model = tf.keras.models.Sequential()\n",
        "\n",
        "  ##the topography of a simple linear regression is a single node (unit) in a single layer \n",
        "  lr_model.add(tf.keras.layers.Dense(units=1, input_shape=(n_features,)))\n",
        "\n",
        "  ##compile the model topography to minimize the model's mean squared error while training \n",
        "  lr_model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=learning_rate),\n",
        "                loss=\"mean_squared_error\",\n",
        "                metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
        "\n",
        "  return lr_model"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "44cFjc5QWx22"
      },
      "source": [
        "**Train the TF ML model with the input data (features and labels)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XCK02xkmWwyU"
      },
      "source": [
        "def train_model(model, x_df, y_df, features, epochs, batch_size):\n",
        "  history = model.fit(x=x_df[features],\n",
        "                      y=y_df,\n",
        "                      batch_size=batch_size,\n",
        "                      epochs=epochs, \n",
        "                      verbose=0)\n",
        "\n",
        "  ##trained model's parameters\n",
        "  trained_weight = model.get_weights()[0]\n",
        "  trained_bias = model.get_weights()[1]\n",
        "  epochs = history.epoch\n",
        "  \n",
        "  ##history of each epoch\n",
        "  hist = pd.DataFrame(history.history)\n",
        "\n",
        "  rmse = hist[\"root_mean_squared_error\"]\n",
        "\n",
        "  return trained_weight, trained_bias, epochs, rmse"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49mhGWBvsi2I"
      },
      "source": [
        "**Plot functions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvibu-7hsfE8"
      },
      "source": [
        "def plot_model(trained_weight, trained_bias, train_df, feature, label):\n",
        "  \"\"\"Plot the trained model against the training feature and label\"\"\"\n",
        "  \n",
        "  ##create a scatter plot from some random points\n",
        "  random_data = train_df.sample(n=200, random_state=42)\n",
        "  plt.scatter(random_data[feature], random_data[label])\n",
        "\n",
        "  plt.xlabel(feature)\n",
        "  plt.ylabel(label)\n",
        "\n",
        "  x0 = 0\n",
        "  y0 = trained_bias\n",
        "  #x1 = feature[-1]\n",
        "  x1 = 10000\n",
        "  y1 = trained_bias + (trained_weight * x1)\n",
        "  plt.plot([x0, x1], [y0, y1], c='r')\n",
        "\n",
        "  plt.show()"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7VKsg8dRshlN"
      },
      "source": [
        "def plot_loss_curve(epochs, rmse):\n",
        "  \"\"\"Plot the loss curve, which shows loss vs. epoch\"\"\"\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel(\"Epoch\")\n",
        "  plt.ylabel(\"Root Mean Squared Error\")\n",
        "\n",
        "  plt.plot(epochs, rmse, label=\"Loss\")\n",
        "  plt.legend()\n",
        "  plt.ylim([rmse.min()*0.97, rmse.max()])\n",
        "  plt.show()"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J2MXj5RtwtlI"
      },
      "source": [
        "#**Dataset and Training**\n",
        "\n",
        "**The dataset describes the 1990 census data from California.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LsOGfn7_xzfb"
      },
      "source": [
        "**Load the dataset from Google repository**\n",
        "\n",
        "https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\n",
        "\n",
        "> The dataset contains 17000 data entries with 9 features. 8 independent variables $X$ and one label (dependent variable $y$).\n",
        "\n",
        "> The goal is to predict the target feature ($y$) **median_house_value** for new data based on the train dataset.\\\n",
        "To do so, we are going to train a linear regression model. The model predicts house value based on the other selected features from the dataset.  \n",
        "\n",
        "* It has not any missing data. \n",
        "\n",
        "* All features are numerical (float64).\n",
        "\n",
        "The following parameters of the linear regression model must be adjusted to let the model converge (min loss) and better fit the data:\n",
        "* learning rate\n",
        "* number of epochs\n",
        "* batch size\n",
        "\n",
        "Training loss should steadily decrease, steeply at first, and then more slowly. The loss should stay steady at the final epochs (nearly zero slope), which indicates that model has converged.\n",
        "\n",
        "PS: One epoch covers sufficient iterations to process every entry in the dataset. Then, epoch is different of iteration. At the end of each iteration, the model's parameters are adjusted based on the reached loss value.\n",
        "\n",
        "To predict the target feature it is necessary to determine which features correlate with the label. \n",
        "\n",
        "**However, our first linear regression model with TF is going to use a single feature as independent variable. Then, it will be extended to handle multiple variables.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "-70bbOtxx2sv",
        "outputId": "8ea35702-69f8-46b4-8c51-c641a24e21c9"
      },
      "source": [
        "df = pd.read_csv(filepath_or_buffer=\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\")\n",
        "print(df.shape)\n",
        "display(df.describe())\n",
        "display(df.info())\n",
        "display(df.head())\n",
        "\n",
        "df[\"median_house_value\"] /= 1000.0 #rescale the label (y)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(17000, 9)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "      <td>17000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.562108</td>\n",
              "      <td>35.625225</td>\n",
              "      <td>28.589353</td>\n",
              "      <td>2643.664412</td>\n",
              "      <td>539.410824</td>\n",
              "      <td>1429.573941</td>\n",
              "      <td>501.221941</td>\n",
              "      <td>3.883578</td>\n",
              "      <td>207300.912353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.005166</td>\n",
              "      <td>2.137340</td>\n",
              "      <td>12.586937</td>\n",
              "      <td>2179.947071</td>\n",
              "      <td>421.499452</td>\n",
              "      <td>1147.852959</td>\n",
              "      <td>384.520841</td>\n",
              "      <td>1.908157</td>\n",
              "      <td>115983.764387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.350000</td>\n",
              "      <td>32.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>14999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.790000</td>\n",
              "      <td>33.930000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1462.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>790.000000</td>\n",
              "      <td>282.000000</td>\n",
              "      <td>2.566375</td>\n",
              "      <td>119400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.490000</td>\n",
              "      <td>34.250000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2127.000000</td>\n",
              "      <td>434.000000</td>\n",
              "      <td>1167.000000</td>\n",
              "      <td>409.000000</td>\n",
              "      <td>3.544600</td>\n",
              "      <td>180400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.000000</td>\n",
              "      <td>37.720000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3151.250000</td>\n",
              "      <td>648.250000</td>\n",
              "      <td>1721.000000</td>\n",
              "      <td>605.250000</td>\n",
              "      <td>4.767000</td>\n",
              "      <td>265000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.310000</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>37937.000000</td>\n",
              "      <td>6445.000000</td>\n",
              "      <td>35682.000000</td>\n",
              "      <td>6082.000000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>500001.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          longitude      latitude  ...  median_income  median_house_value\n",
              "count  17000.000000  17000.000000  ...   17000.000000        17000.000000\n",
              "mean    -119.562108     35.625225  ...       3.883578       207300.912353\n",
              "std        2.005166      2.137340  ...       1.908157       115983.764387\n",
              "min     -124.350000     32.540000  ...       0.499900        14999.000000\n",
              "25%     -121.790000     33.930000  ...       2.566375       119400.000000\n",
              "50%     -118.490000     34.250000  ...       3.544600       180400.000000\n",
              "75%     -118.000000     37.720000  ...       4.767000       265000.000000\n",
              "max     -114.310000     41.950000  ...      15.000100       500001.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17000 entries, 0 to 16999\n",
            "Data columns (total 9 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   longitude           17000 non-null  float64\n",
            " 1   latitude            17000 non-null  float64\n",
            " 2   housing_median_age  17000 non-null  float64\n",
            " 3   total_rooms         17000 non-null  float64\n",
            " 4   total_bedrooms      17000 non-null  float64\n",
            " 5   population          17000 non-null  float64\n",
            " 6   households          17000 non-null  float64\n",
            " 7   median_income       17000 non-null  float64\n",
            " 8   median_house_value  17000 non-null  float64\n",
            "dtypes: float64(9)\n",
            "memory usage: 1.2 MB\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
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            "tags": []
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          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.31</td>\n",
              "      <td>34.19</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5612.0</td>\n",
              "      <td>1283.0</td>\n",
              "      <td>1015.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>1.4936</td>\n",
              "      <td>66900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-114.47</td>\n",
              "      <td>34.40</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>1901.0</td>\n",
              "      <td>1129.0</td>\n",
              "      <td>463.0</td>\n",
              "      <td>1.8200</td>\n",
              "      <td>80100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-114.56</td>\n",
              "      <td>33.69</td>\n",
              "      <td>17.0</td>\n",
              "      <td>720.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>1.6509</td>\n",
              "      <td>85700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.64</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1501.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>3.1917</td>\n",
              "      <td>73400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-114.57</td>\n",
              "      <td>33.57</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1454.0</td>\n",
              "      <td>326.0</td>\n",
              "      <td>624.0</td>\n",
              "      <td>262.0</td>\n",
              "      <td>1.9250</td>\n",
              "      <td>65500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  ...  median_income  median_house_value\n",
              "0    -114.31     34.19  ...         1.4936             66900.0\n",
              "1    -114.47     34.40  ...         1.8200             80100.0\n",
              "2    -114.56     33.69  ...         1.6509             85700.0\n",
              "3    -114.57     33.64  ...         3.1917             73400.0\n",
              "4    -114.57     33.57  ...         1.9250             65500.0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NAcB8-Hbg8wf"
      },
      "source": [
        "**Dataset split: train and validation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KrN_GX2xCPA"
      },
      "source": [
        "features=['longitude', 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms',\t'population',\t'households',\t'median_income'] #independent variables\n",
        "features=['total_rooms', 'total_bedrooms']  \n",
        "\n",
        "label='median_house_value' #target variable\n",
        "\n",
        "X = df[features]\n",
        "y = df[label]\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "splitted_df=X_train.copy()\n",
        "splitted_df['median_house_value']=y_train.copy()"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCWxKEDhgiMT"
      },
      "source": [
        "**Train and Validation step:**\n",
        "\n",
        "1. Dummy Regressor model\n",
        "\n",
        "2. TF Linear Regression model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SJ_LiH3dhKLT",
        "outputId": "a488738f-6567-4121-ba67-5c133c96e97d"
      },
      "source": [
        "##call Dummy model\n",
        "print('Dummy prediction | RMSE:', dummy_prediction(X_train, y_train, X_val, y_val)) #prediction accuracy of the dummy classifier\n",
        "\n",
        "##call TF model\n",
        "learning_rate = 0.01\n",
        "lr_model=None\n",
        "lr_model=build_model(learning_rate, n_features=1)\n",
        "\n",
        "for var_x in features:\n",
        "  print(\"Feature:\", var_x)\n",
        "  batch_size = 30\n",
        "  epochs = 30\n",
        "\n",
        "  trained_weight, trained_bias, epochs, rmse=train_model(lr_model, X_train, y_train, var_x, epochs, batch_size) #train\n",
        "  print(\"Trained weight of %.4f\" % trained_weight)\n",
        "  print(\"Trained bias of %.4f\" % trained_bias)\n",
        "  print('TF regression model | Train RMSE:', rmse.min())\n",
        "\n",
        "  plot_model(trained_weight, trained_bias, splitted_df, var_x, label)\n",
        "  plot_loss_curve(epochs, rmse)\n",
        "\n",
        "  predicted_val=lr_model.predict_on_batch(x=X_val[var_x]) #prediction\n",
        "  print('TF regression model | Validation RMSE:', np.sqrt(mean_squared_error(y_val, predicted_val)), rmse.min())\n",
        "\n",
        "  ##results on batch\n",
        "  print(\"feature   label          predicted\")\n",
        "  print(\"  value   value          value\")\n",
        "  print(\"          in thousand$   in thousand$\")\n",
        "  print(\"--------------------------------------\")\n",
        "  X_index=X_val[var_x].index\n",
        "  y_index=y_val.index\n",
        "  for i in range(len(X_index)):\n",
        "    print(\"%5.0f %6.0f %15.0f\" % (X_val[var_x][X_index[i]], y_val[y_index[i]], predicted_val[i][0]))"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dummy prediction | RMSE: 117.42078641860235\n",
            "Feature: total_rooms\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:375: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Trained weight of 0.0305\n",
            "Trained bias of 108.7405\n",
            "TF regression model | Train RMSE: 129.64791870117188\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order, subok=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "TF regression model | Validation RMSE: 128.3773928102869 129.64791870117188\n",
            "feature   label          predicted\n",
            "  value   value          value\n",
            "          in thousand$   in thousand$\n",
            "--------------------------------------\n",
            " 4838    143             256\n",
            " 2742    500             192\n",
            " 3705     62             222\n",
            " 5455    163             275\n",
            " 1331     91             149\n",
            " 1616    232             158\n",
            "  674    148             129\n",
            "  349    133             119\n",
            " 1190    438             145\n",
            " 2873    188             196\n",
            "  880     68             136\n",
            " 1937    179             168\n",
            " 1424    262             152\n",
            " 2171     58             175\n",
            " 3437     53             213\n",
            "  880    119             136\n",
            " 2086    236             172\n",
            "  807     40             133\n",
            "  785     82             133\n",
            " 2978     66             200\n",
            "  440    350             122\n",
            "  894    111             136\n",
            " 1875    104             166\n",
            " 3975    500             230\n",
            " 4308    282             240\n",
            " 1632    180             158\n",
            " 1180    173             145\n",
            " 1832    186             165\n",
            " 1877    112             166\n",
            "  267     70             117\n",
            " 3111    140             204\n",
            " 1704    224             161\n",
            " 1524    112             155\n",
            " 1648    162             159\n",
            " 1010    363             140\n",
            " 2217    376             176\n",
            " 1326    108             149\n",
            " 2492    186             185\n",
            "  760    145             132\n",
            " 3389    204             212\n",
            " 1751    339             162\n",
            " 1453    247             153\n",
            " 1767    211             163\n",
            " 2344    178             180\n",
            " 2864    353             196\n",
            " 1298    137             148\n",
            " 1309    182             149\n",
            " 2698    235             191\n",
            " 2246    173             177\n",
            " 1944    112             168\n",
            " 1138    185             143\n",
            " 2215    100             176\n",
            " 1481    262             154\n",
            " 3303    340             209\n",
            "  570    417             126\n",
            " 1157     80             144\n",
            " 3637    263             220\n",
            " 2564    330             187\n",
            "  505    275             124\n",
            " 7508    500             338\n",
            " 4374    464             242\n",
            " 2438    129             183\n",
            " 3635    237             220\n",
            " 3056    300             202\n",
            " 3390    317             212\n",
            " 1271    240             147\n",
            " 3872    225             227\n",
            " 2186    257             175\n",
            " 2196    254             176\n",
            " 2865    500             196\n",
            " 2606    225             188\n",
            " 2361    392             181\n",
            " 2536     68             186\n",
            " 3562    228             217\n",
            " 1292    135             148\n",
            " 2450    375             183\n",
            " 4079    500             233\n",
            "  240     54             116\n",
            " 4232    146             238\n",
            " 3094    311             203\n",
            " 1734    163             162\n",
            " 2693    138             191\n",
            "26322    229             911\n",
            " 2117    500             173\n",
            " 1562    221             156\n",
            " 3550    271             217\n",
            " 2460    142             184\n",
            " 1666     94             160\n",
            "  652     94             129\n",
            " 2720    228             192\n",
            " 1090     98             142\n",
            " 5195    160             267\n",
            " 4427    159             244\n",
            " 1654    186             159\n",
            " 1577    116             157\n",
            "  275    158             117\n",
            " 1930    244             168\n",
            " 1784    221             163\n",
            "  696    191             130\n",
            " 1053    321             141\n",
            " 2351    102             180\n",
            " 2103     97             173\n",
            " 1296    112             148\n",
            " 3311    187             210\n",
            " 2232     88             177\n",
            " 2884    178             197\n",
            "  918    214             137\n",
            " 3652    428             220\n",
            " 3008    213             200\n",
            " 2184    294             175\n",
            "  783    127             133\n",
            "  962    126             138\n",
            " 1718    135             161\n",
            " 1686    299             160\n",
            " 4515     91             246\n",
            " 1024     47             140\n",
            " 4613    303             249\n",
            " 2994     85             200\n",
            " 2010     96             170\n",
            " 2005    269             170\n",
            " 1471     82             154\n",
            " 2672     86             190\n",
            " 1651    148             159\n",
            "  902    169             136\n",
            " 1964    192             169\n",
            " 1225    101             146\n",
            " 3354    142             211\n",
            " 1044     98             141\n",
            " 1971    262             169\n",
            " 3295    106             209\n",
            " 3614    190             219\n",
            " 1951    218             168\n",
            " 2080     98             172\n",
            " 1396    244             151\n",
            " 1492     71             154\n",
            " 2387    338             181\n",
            "  697    119             130\n",
            " 1121    174             143\n",
            " 3278    100             209\n",
            " 2063    500             172\n",
            " 1049    150             141\n",
            " 1788    186             163\n",
            " 1378    100             151\n",
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            " 1178    180             145\n",
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            " 8244    200             360\n",
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            " 1975    202             169\n",
            " 1364    414             150\n",
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            "  333    100             119\n",
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            " 1996     91             170\n",
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            " 1984    165             169\n",
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            "  116     38             112\n",
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            " 1986    135             169\n",
            "   60     68             111\n",
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            " 2031    299             171\n",
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            " 4808    280             255\n",
            " 1863    277             166\n",
            "  901    186             136\n",
            "15695    174             587\n",
            " 2808    102             194\n",
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            " 1885    173             166\n",
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            " 1982    337             169\n",
            "  493     18             124\n",
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            " 1885     93             166\n",
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            " 1861    356             165\n",
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            " 2009    500             170\n",
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            " 1963    211             169\n",
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            " 1944    127             168\n",
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            " 5320    280             271\n",
            " 4055    171             232\n",
            " 1944    166             168\n",
            " 3659    366             220\n",
            "  278     50             117\n",
            " 1495    345             154\n",
            " 3094    147             203\n",
            " 4753    288             254\n",
            "  617    111             128\n",
            " 4877    222             257\n",
            " 2144    112             174\n",
            " 1880    153             166\n",
            " 1127    220             143\n",
            " 2374    200             181\n",
            " 1036    361             140\n",
            " 2264    194             178\n",
            "  311    188             118\n",
            " 1786    431             163\n",
            " 1523    184             155\n",
            " 3009    105             200\n",
            " 4230    104             238\n",
            " 4794    213             255\n",
            "  284    138             117\n",
            " 2549     83             186\n",
            " 4030    141             232\n",
            "  966    450             138\n",
            "  917    196             137\n",
            " 2668     90             190\n",
            " 2223    279             176\n",
            "  874     58             135\n",
            " 1018    500             140\n",
            " 2994    180             200\n",
            " 3723    133             222\n",
            " 2155    148             174\n",
            " 1650     98             159\n",
            " 5738    158             284\n",
            "  782    201             133\n",
            " 7151    130             327\n",
            " 3177    191             206\n",
            "  821    209             134\n",
            " 2017     93             170\n",
            " 3296    217             209\n",
            "  401    170             121\n",
            " 3642    114             220\n",
            " 2807    234             194\n",
            " 4409    286             243\n",
            " 3058    254             202\n",
            "  777    140             132\n",
            " 1403    105             152\n",
            "  828     74             134\n",
            " 1191    179             145\n",
            " 3429    185             213\n",
            " 3814    216             225\n",
            " 1676    451             160\n",
            " 2576    130             187\n",
            " 1701     96             161\n",
            "  361     64             120\n",
            " 1810    229             164\n",
            " 4394    500             243\n",
            " 1768    161             163\n",
            " 2281     54             178\n",
            " 1812    294             164\n",
            " 1858    339             165\n",
            " 1393     44             151\n",
            " 1276    206             148\n",
            " 1428    240             152\n",
            " 1992    350             169\n",
            " 2707     92             191\n",
            " 1395    194             151\n",
            " 2894    208             197\n",
            " 1403    173             152\n",
            "  932    100             137\n",
            " 2946    256             199\n",
            "  197    200             115\n",
            " 1909    160             167\n",
            " 2787    127             194\n",
            " 2570    230             187\n",
            " 1261    225             147\n",
            " 1835    358             165\n",
            " 1497    182             154\n",
            " 2287    317             178\n",
            " 3481    158             215\n",
            " 1047     82             141\n",
            " 2734    217             192\n",
            " 2172    181             175\n",
            " 8444    240             366\n",
            " 4485    275             245\n",
            " 1937    235             168\n",
            " 1360    225             150\n",
            " 2433    159             183\n",
            " 2782    102             194\n",
            "  813     70             134\n",
            " 2370     83             181\n",
            " 1535    174             156\n",
            " 6006    322             292\n",
            " 2661    258             190\n",
            " 1485    142             154\n",
            " 3145    149             205\n",
            " 2775    496             193\n",
            " 1891     82             166\n",
            " 2558    412             187\n",
            " 2206    228             176\n",
            " 1280    132             148\n",
            "  154    275             113\n",
            " 8651    116             372\n",
            " 7357    361             333\n",
            " 1738     77             162\n",
            " 2787    500             194\n",
            " 3273    286             208\n",
            " 1716    211             161\n",
            " 1271    500             147\n",
            "  121    162             112\n",
            "  417    361             121\n",
            "  223    138             116\n",
            "Feature: total_bedrooms\n",
            "Trained weight of 0.0231\n",
            "Trained bias of 183.8863\n",
            "TF regression model | Train RMSE: 116.31726837158203\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order, subok=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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Ng2uvhSlTYMiQ4rZRSo4yWUXSFeu5esrIrAr4KKuqFJR7uBpIBIMVK8LMoeOPh1/9Cs45B+r03550nQIDmU/uV08Z2en002zqOYh0m3sYJ5g1KwSEV18NqSc++1m47LIQDPbeu9itlDKRy8BQspOdu3Nyz6aeg0iXuMPTT7eNGbzxRshQOmECTJsWgsHAgcVupZSh2IHBzPq5+6Y0T12Xg/YURXdO7h0NUncmm1rTUmG2bQtrCxLB4O23Q+2Ck06CH/4wlL4cMKDYrZQyl3UaRDMba2YvAC9Fjw8zs/9MPO/uv8998woj00k8m5P7tIkjqKlOzTOfzdTVRPdVY1MzTlv31dwljVm3W8rEtm3wt7/Bt78dMpWOHRvGCkaOhN//PiSxu/de+MpXFBSkIOLkx70GmAh8AODuy4Dj89GoQuvqyR3CAPPVU0ZSV1uDEdJiXD1lZKe//DvqvpIK0NoaktN961uhhsFxx8H118MRR8Af/gBr14YqaBdfDP37F7u1UmHipsRYaal5U9IXH4iYWV/gEaBP9F4N7n6lmQ0HbgMGAIuBL7j7FjPrA9wMHEEIQBe4+5tx2tgV3V2X0L6eQzY0NlGBtm6FRx4JA8hz5oQrgb59YdKkkJvo9NNht92K3UqRWIFhpZmNBdzMqoHLgBc7OeYTYIK7fxwd8zczuxf4DnCNu99mZtcDXwF+E/1d7+4HmNmFwM+AC2J+pi7pysm9O7ozNiElpKUl1DBoaAg1Dd5/H/r1C0Fg6tQQFHbZpditFEkRpyvpa8A3gTqgERgVPc7Ig4+jh9XRzYEJQEO0/SZCtlaAs6PHRM+faFaeqR27030lPdyWLW1jAnvvDRMnwq23hgHkhobQTXTHHXD++QoK0iPFWfn8PnBR3DcwsypCd9EBwP8FXgOa3H1rtMsqQrAh+rsyer+tZraB0N30frvXvBS4FGDo0KFxm5RTXZ1ZpLQaZeaTT0Ld44aGUPqyqSl0C511VrgyOOUUqNHVoJSGrOsxmNnPgauAZuA+4FDg8mzrMZhZLSEj678Av3f3A6LtQ4B73f3vzOw54FR3XxU99xpwdBSU0spXPYZstF8YByFV98479WZDc0vBTvaa9lokzc3wl7+EYDBvHnz4Iey+e5hSOnUqnHwy9OlT7FaKpJWregynuPv3zOwc4E1gCmFgOavA4O5NZvYgcAxQa2a9o6uGwYSuKaK/Q4BVZtYb2J1oFlRPlG5mUUur09TcAhQmPYZSchTYpk2hm6ihIcwa+vhj2GOPEAimToUTT4Sddip2K0W6JU5gSOx7OjDL3Td01v1vZgOBligo1AAnEwaUHwSmEmYmXQz8OTpkXvT48ej5BVkk78upOL++s5lBlO/0GErJUQAbN8Ldd4dgcPfdITjsuSd87nMhGIwfD9XVxW6lSM7ECQx3mdlLhK6kr0cn/c2dHDMIuCkaZ+gF3OHud0UL5W4zs6uAJcCN0f43An8ws1eBdYSiQAUT99d3pplF7eVzCqqmvebJRx+FK4KGhnCF0NwMn/oUfPGLoeTl8ceHFckiZSjO4PP0aJxhg7u3mtlGwiyijo55FhidZvvrpKn+5u6bgfOybVMuJF8h9EpTy7mjX9/pUnOnk88pqJr2mkMbNsCdd4ZgcN99YUB5773hy18OweDYY0OuIpEyF6e05xeT7ic/dXMuG1RI7a8Q2geFhHS/vhMBpbmllaoooPTvV83Hm7em1ITO9xTUzupGSCfWrw8Dx7NmhVlFW7aElNVf+1roJho7NmQxFakgca6Fj0y63xc4EXiGEg4M6frn02n/6ztdQKmpruLKMw/Z/rqFmiGUy2mvFTO76YMPwpTSWbPgr38NK5KHDoV//McQDI4+WsFAKlrW01V3ODBMP73N3U/NbZPi6c501eHT76azT19TXcW5R9Tx4Etrt58wmzZtYeOWHQNKXW1Np/Ubeqp0U29rqquyyvtUEtauDSuPGxpgwYKQq2j48LbZREceGYrdiFSIXE1XbW8jMLwbxxddpv75KjO2ubNPbQ3jDxrI7MWNKQPSmZTygG9Zzm569922YPDQQyGL6f77h1oG550Ho0crGIikEWeM4U7Y/gO7CvgMcEc+GlUomfrnk38lj5uxIKvuJijtAd+ymd30zjshQV1DQ0hY5w6f/jRccUUIBoceqmAg0ok4Vwy/SLq/FXgrsUK5VGXTPx/nxFjKA74lPbtp5cq2YLBwYQgGBx8M//qvoZvokEMUDERiiDNd9WEz24u2QehX8tOkwuosq2q2axVqqnuVbpcLJTi76a23YPbsMID8xBNh26GHwo9/DOeeGwKDiHRJnK6k84GZwEOE+s6/MrNp7t7Q4YElbvxBA7nlibc73a9vdWnPby+JpH6vv95W8vLpp8O20aPh3/4tXBl8+tPFbZ9ImYjTlfRD4Eh3XwPb0138lbb02WXpwZfWZrVf06aWPLck/wpdkyIrr7zSFgyeeSZsq6+HGTNCMNh//+K2T6QMxQkMvRJBIfIB8eo5lKRsxxhKoi++VLz0UlswWLYsbDv6aPjFL0I30bBhRW2eSLmLExjuM7P5wK3R4wuAe3LfpJ5j7pLGtGky2uvRffGlwB1eeCEEglmz4Pnnw/Zx4+Caa2DKlLAATUQKIs7g8zQzOxcYF226wd3/lJ9mFV9iwVe6oFCMmgtlxx2WLw+BoKEhXCWYwXHHwS9/GYJBnf5NRYoh1gI3d58NzM5TW3qUTOkyqsyYOfUwBYKucIelS9uCwSuvhNQTJ5wA3/oWnHMODBpU7FaKVLw4s5KmEGopfIowK8kIZZ13y1PbCqp9nqBMU1S3uSsoxOEOixa1jRm8/nrIUDp+PHz3uyEYfOpTxW6liCSJc8Xwc+BMd38xX40plnR1GAzS5lHSIHMWtm2Dp55qCwZvvRVqF5x4IvzgB3D22aHQjYj0SHECw3vlGBQgfbdRuqBQU13F+IMGMm7GAt5paqa2XzXuaKwBQjB4/PG2YLBqVahqdvLJ8KMfwVlnhRKYItLjdRoYoi4kgEVmdjswF/gk8by7z8lT2wommympNdW9OPeIupSEeuuT1i4kqr0temtdSibWjoJFyae5bm0NKShmzQqrkFevDvWOTz0VfvpTOPNMqK0tditFJKZsrhjOTLq/CTgl6bEDJR8Yskl70dyyjdmLV9Hcsq2DfVr54xNvb7/a6Kg0aNwyoj3G1q3w6KMhGMyZA++9B337wmmnhQVnZ5wBu5XFsJNIxeo0MLj7Jdm8kJld4e5Xd79JhZdtic6OgkJC+y6oTKmrSyrNdUtLSFvd0BDSWK9dCzU1cPrpIWPppEmwyy7FbqWI5Eguq5mfB5RkYEiciL97x7JOF7N1RbquqkzdV41NzYybsaD43UotLfDAAyEYzJ0bqp7tvHO4IjjvvNBdtPPOxWufiORNLgNDSec1TpyEO7py6GXQp3dV1vUZEtLNZOqo+6po3UqffBJKXTY0hNKX69fDrruGgeOpU2HixHClICJlLZe5jnL/U7sI+vTO/E/y90cP5eopI6mrrcEIgaIzmdJlTJs4gpoOMrImupXybvNmmDcPvvCFsJ7gjDNCd9GZZ4bta9bALbfA5MkKCiIVQlcMkXQ1jxOqzPjc0UO4avJIoO1X/PDpd3f4mnUdzDRKTnOd6cohb9XTmpvh3nvDlcGdd8LHH0P//iFB3dSpcNJJYXaRiFSkXAaGWTl8rYLLlAKjtqaanfv05o9PvM2DL61l/EEDefCltZ3OYqqrrWHh9Akd7pNIcz1uxoL8V0/buBHuuScEg7vvDo8HDIALLwzBYMKEsO5ARCpenJQYA4F/AIYlH+fuX47+/jTXjSukTL/Om5pbaGoO6xUam5qzKtoDocBPYp1CY1MzVVGW1nRXEXmrnvbRRyEINDSEoNDcDAMHwuc/HwaQTzghrEgWEUlinuUsHDN7DHgUWAxsP4NFifWKpr6+3hctWtTt1xn1479sDwC50L9fNZtbtmUcqO7fr5orzzxke4BIt9gNulBRbcMGuOuuEAzuuy+MIey9d1s30XHHhVxFIlLRzGyxu9enfS5GYFjq7qNy2rIcyFVgGP2Tv6SsZC6ERD6mdFcR6cY8aqqruHrKyB2DQ1NTGCieNQv+8hfYsiWkrE4Eg7FjFQxEJEVHgSFOP8JdZjbJ3cuyOE8xSnN2tEK60wVw69aF9QUNDWGKaUsLDBkC3/xmCAZjxoSU1iIiMcUJDJcBPzCzT4AWyiztdjZpMeLoV92LTVmslE5ov+o53ZhH/00bOH7pffDgz2DBgpCeYtgwuOyyEAyOOioUuxER6YY4Fdx2zWdDii3btBjZ2tSyLWPq7kySg0EiUO25cT0TX36c01YsZMzby+nt22D//UMtg/POg8MPVzAQkZyKNSXFzPoDBwJ9E9vc/ZFcN6oYsllXEJfTNo6QTZDYPj119Wr+s+lxNt92B/VvP0eVb+O1Per47djzGfGPlzD+gpMVDEQkb+IMPn+V0J00GFgKjAEed/eOJ+vnWa4Gn5MNn353TpdxJ9Y0zF3SyI/vfD7tIPew5nX8ss8bHPr4/SGVtTsfDj+QhuFjuGPY0Xx0wEFMO/WgnpdgT0RKUq4Gny8DjgSecPfxZnYQUNJrFzLJ9XhDchfR5qRxh30+XMNpKx7jrFcWctjKqAbSyJGhsM3Uqex28MF8GfhyzloiItK5OIFhs7tvNjPMrI+7v2Rm3VyB1TPlerwh0UU0c/4KBqxtZNKKhUxasZBRq18G4OVBB8BVV4UB5BFl+U8qIiUkTmBYZWa1hApu95vZeuCtjg4wsyHAzcBehC72G9z9OjPbA7idsIr6TeB8d19vZgZcB0wiFAX6krs/E+8jdV+iu+bbty/t9mvVVFfxo8/sBDNm8JvrbuTQd18F4Nm9D2DGCV/i3hFjebv/Przxw9O7/V4iIrmQ9RhDykFmJwC7A/e5+5YO9hsEDHL3Z8xsV8Kq6cnAl4B17j7DzKYD/d39+2Y2CfgWITAcDVzn7kd31JZ8jDEkDOskSV4mVWbs+/5KLnj7KS588wl2f/kFAJ4ffBBzDxjLvSPGsqp275RjOkq4JyKSa90aYzCz3dz9w+hXfsLy6O8uwLpMx7r7amB1dP8jM3sRqAPOBj4b7XYT8BDw/Wj7zR6i1RNmVmtmg6LXKbheBttixM0D3n+bC996kq++uxieey5sHDsWvvYfMGUKr6zrzS0ZuqhKprSniJS9bLqS/gc4g/BrPzHzMsGB/bJ5IzMbBowGngT2SjrZv0voaoIQNFYmHbYq2pYSGMzsUuBSgKFDh2bz9l3SUVD4/JihzF60iqGrX2fSS39j0oqFHPjBStwMjj0WrrsOpkyBwYO3HzN53/A305TYHlvaU0QqSjY1n8+I/g7v6puY2S7AbODb0dVH8uu7mcXqz3L3G4AbIHQldbVdXeLOwWve4KqnH+P7f7yNXd96nVbrxbLhI1n21f/FYZd9GQYNynh4ItV2pimxeavBICKSpWy6kg7v6PnOBofNrJoQFP7o7nOize8luoiicYg10fZGYEjS4YOjbQWTnCp7O3dGvvsqk1Ys5LQVCxnWtBp69WLX8ePhiu9RNXkyh++1V+YXTSPTlNic1mAQEemCbLqS/j362xeoB5YRupMOBRYBx2Q6MJpldCPworv/R9JT84CLgRnR3z8nbf9HM7uNMPi8oVDjC3OXNPKjec+3pd52Z9Tqlzktmlo6ZMN7bLVePLbvYdx8/AX8629/EGobdFHeajCIiHRTNl1J4wHMbA5wuLsvjx7/HfCjTg4fB3wBWG5mibmfPyAEhDvM7CuEKa/nR8/dQ5iR9CphuuolcT5MV/3z3OX88Ym3wbdxeOMKJq34G6eteIy6j9aypVdv/jZsFL8ceyH3H3g0G3fZnZlTD8s6KKSrs5DoToIu1FsQEcmzOCkxnnf3QzrbVmjdna46d9Hb/M81t3Ja1E2098fr+KSqN48MP5x7R4zjrwcczYd9d9m+/7UXjMr65B2rpoKISAHlKiXGs2b2W+CW6PFFwLPdbVzRPPkk3Hwzx/7hNiZ/tI5Pqqp5aL8juHrEOBYccBQf9dl5h0OqYiau67SmgohIDxQnMFwCfJ2QMwngEeA3OW9RocKxlW0AABB5SURBVMydC7/7HU8PGc3d44/lwf3q2dinX4eHtLrHWmuQaYaRZh6JSE8Wpx7DZjO7HrjH3VfksU2FMW0a/PM/c9WvnoyVMC/dL/5M4wiaeSQipSjrwGBmZwEzgZ2A4WY2CviJu5+Vr8bl1R5hIXdXEua909ScMq01udZCY1Mzl9++lEVvrdPMIxEpSXGKAl8JHAU0Abj7UqDLi956ismj67h6ykjqamswQknObExrWLb9aqD98L1DmOUEKa9dV1ujgWcR6fHijDG0uPsGSx2ALeyq4zxJnKhT1jF0wIGW1o4/uhMGnxdOn6BAICIlJU5geN7M/h6oMrMDgX8CHstPswpr7pJGps1aRkucjHlZ0CCziJSiOF1J3wIOAT4hJNbbQNsMpZI2c/6KnAcF0CCziJSmOIHh4OjWm5Ae42zg6Xw0qtDy8cveQIPMIlKS4nQl/RH438BzwLZO9i0pua7xDGGMQWMLIlKK4gSGte5+Z95aUkTTJo7ISRnPZHVRN1KmNQ4iIj1VrOmqZvZbM/ucmU1J3PLWsgLK9Yk60Y2UyJXU2NSM01albe6SgmYSFxGJJW5KjIOAatq6khyYk/GIElKXw+6kxFRVVWkTkVIUJzAc6e5lO5ralRXQHekoyGgaq4j0ZHG6kh4zs4Pz1pIiS14BnW+axioiPVmcwDAGWGpmK8zsWTNbbmalm3Y7jcmj61g4fQK94mXXjkW5kkSkp4vTlXRq3lpRZMkzh2r7VdPZWrea6ioG9+/LK2s2xnqf2ppqfnTWIRpfEJEeLesrBnd/K90tn40rhPYzh9Zv6jhXUiIR3qYt8ZdyfLR5K5ffvpRxMxZoZpKI9FhxrhjKUroqa5lUmfFOU3PGGUedaY3KqCamrULmqbJa/yAixRJnjKEsxZkh1Oq+fT1CdyWmraaj9Q8iUkwVHxiKOUMoU4DpqFa0iEi+VXxgmDZxBDXVVSnbqmJMS7LoVldbw+fHDN3htQCsg5cbNv3uHcYcVCtaRIqp4scYJo+uY9Fb67j1yZW0ulNlxueOGkL9vntklT/JgWsvGLW9/79+3z12GBsAuPz2pRmrGrUfc1CtaBEppoq/Ypi7pJHZixu3Dwy3ujN7cfj1nu1it+QunsRaiDdmnL69etvk0XWdlrpL7ipKdxWj9Q8iUigVHxg66s9Pd4JOJ5sunmyCTOJ12tehVq1oESmkiu9KynRSb2xq3qGbKZNsuniyycWU/DqJKw0RkUKr+MDQUZGeQ/71PrZs3dZhUKjuZRm7eNqvRTj3iDoefGktjU3NGKR0L6mrSER6iorvSpo2cQSZJg1t3NLaeS3oDAenW4swe3Ej0yaO4M0Zp3PNBaPUVSQiPZJ5B7+GS0F9fb0vWrSoW68xbPrd3Tq+rraGhdMnpGwbN2NB2iuRdPuKiBSamS129/p0z1VsV1JyN0+VWYfdRZ1JN06htQgiUqoq8ooh0c2Tq6I8EK4Exh80kAdfWss7Tc30yhBsEj1PneU/Uq4kEcknXTG0EydxXrYam5q55Ym3tz/OdAXiSftnSqTXPnBlk3RPRCRXKnLwuad052TKf6RcSSJSTBUZGHpSagmNT4hIT5PXwGBm/8/M1pjZc0nb9jCz+83slehv/2i7mdkvzezVqHTo4flqV7YrmgshXZDKFLh6UkATkfKV7yuG37NjSdDpwAPufiDwQPQY4DTgwOh2KfCbfDUqXcqJz48ZmnVupFzJtKhNuZJEpJjyOvjs7o+Y2bB2m88GPhvdvwl4CPh+tP1mD9OknjCzWjMb5O6r89G2TCknLvrvx1n42rpuv35iltJdy1bT1BzKhe68UxXVVb3Y0NzS4UyjxDbNShKRYijGrKS9kk727wJ7RffrgJVJ+62KtuUlMCRLTA3NRWU2gP79qrcvYrtq8sguvUZnuZI0nVVE8qWo01Xd3c0s9kIKM7uU0N3E0KFDu9WGfKxpWL+pJeX1OzuBxz3JazqriORTMWYlvWdmgwCiv2ui7Y3AkKT9BkfbduDuN7h7vbvXDxw4sFuNyceahoRsajd3pb6zprOKSD4VIzDMAy6O7l8M/Dlp+xej2UljgA35Gl9Ilo8poDXV4Z81mxN4V07yms4qIvmU7+mqtwKPAyPMbJWZfQWYAZxsZq8AJ0WPAe4BXgdeBf4b+EY+25aQjymgW7c5c5c0dljrIaErJ3lNZxWRfMprYHD3z7n7IHevdvfB7n6ju3/g7ie6+4HufpK7r4v2dXf/prvv7+4j3b17KVOzlI81DS2tzo/vfD7jidpge1dRV07yms4qIvlUkSufkyXWNMRhmQo4JFm/qYXxBw1MW67BoVv1nVX6U0TyqSKT6LU3eXRdrOmq2SakffCltWTaNbm+M8Rfs6DSnyKSLwoMkWxqMsf1TlMzdRlKh6q+s4j0VBXflZSQ3D2TK4lf/xoPEJFSosCQZPLoOhZOn0BtTXW3Xytx8td4gIiUGnUltTN3SSMbt2zt1msYcO4Rbd1D6ioSkVKiK4Z2Zs5fQUtr98qdOmHgWUSkFCkwtJOr1cNahSwipaqiu5LSJa+r7VedkgSvq7QKWURKVcUGhnQZSqfNWsbWbd3rRgKorjLNOhKRklWxgSFd8rqWHAQFgJ136q3BZhEpWRU7xpDPMYANzd3vihIRKZaKDQz5HAPQ+IKIlLKKDQy5yqraPkmeVjWLSKmr2MCQWJFclU2q1Azqamu45oJRWtUsImWlYgefoS2z6eW3L82YBbUj7zQ1a1WziJSdir1iSNbVuUgaSxCRclTRgSGxlqErNJYgIuWqoruS0q1lyEb/ftVceeYh6kISkbJU0YEh27UMNdW92NyyLevqaiIipayiA8M+GaqrVZmxzV2BQEQqUkUHhnTlPGuqqzTlVEQqWkUHhsTJv32GVQUFEalkFR0YQNXVRETaq+jpqiIisiMFBhERSaHAICIiKRQYREQkhQKDiIikMPfclLMsFjNbC7zVxcP3BN7PYXNKgT5zZdBnrgzd+cz7uvvAdE+UfGDoDjNb5O71xW5HIekzVwZ95sqQr8+sriQREUmhwCAiIikqPTDcUOwGFIE+c2XQZ64MefnMFT3GICIiO6r0KwYREWlHgUFERFJUbGAws1PNbIWZvWpm04vdnq4ysyFm9qCZvWBmz5vZZdH2PczsfjN7JfrbP9puZvbL6HM/a2aHJ73WxdH+r5jZxcX6TNkysyozW2Jmd0WPh5vZk9Fnu93Mdoq294kevxo9PyzpNa6Itq8ws4nF+STZMbNaM2sws5fM7EUzO6bcv2czuzz67/o5M7vVzPqW2/dsZv/PzNaY2XNJ23L2vZrZEWa2PDrml2ZmnTbK3SvuBlQBrwH7ATsBy4CDi92uLn6WQcDh0f1dgZeBg4GfA9Oj7dOBn0X3JwH3AgaMAZ6Mtu8BvB797R/d71/sz9fJZ/8O8D/AXdHjO4ALo/vXA1+P7n8DuD66fyFwe3T/4Oi77wMMj/6bqCr25+rg894EfDW6vxNQW87fM1AHvAHUJH2/Xyq37xk4HjgceC5pW86+V+CpaF+Ljj2t0zYV+x+lSF/EMcD8pMdXAFcUu105+mx/Bk4GVgCDom2DgBXR/f8CPpe0/4ro+c8B/5W0PWW/nnYDBgMPABOAu6L/6N8Herf/joH5wDHR/d7Rftb+e0/er6fdgN2jk6S1216233MUGFZGJ7ve0fc8sRy/Z2BYu8CQk+81eu6lpO0p+2W6VWpXUuI/uIRV0baSFl06jwaeBPZy99XRU+8Ce0X3M332Uvs3uRb4HrAtejwAaHL3rdHj5PZv/2zR8xui/UvpMw8H1gK/i7rPfmtmO1PG37O7NwK/AN4GVhO+t8WU9/eckKvvtS663357hyo1MJQdM9sFmA18290/TH7Ow0+FspmXbGZnAGvcfXGx21JAvQndDb9x99HARkIXw3Zl+D33B84mBMV9gJ2BU4vaqCIoxvdaqYGhERiS9HhwtK0kmVk1ISj80d3nRJvfM7NB0fODgDXR9kyfvZT+TcYBZ5nZm8BthO6k64BaM0uUq01u//bPFj2/O/ABpfWZVwGr3P3J6HEDIVCU8/d8EvCGu6919xZgDuG7L+fvOSFX32tjdL/99g5VamB4Gjgwmt2wE2Ggal6R29Ql0QyDG4EX3f0/kp6aByRmJlxMGHtIbP9iNLthDLAhumSdD5xiZv2jX2qnRNt6HHe/wt0Hu/swwne3wN0vAh4Epka7tf/MiX+LqdH+Hm2/MJrNMhw4kDBQ1+O4+7vASjMbEW06EXiBMv6eCV1IY8ysX/TfeeIzl+33nCQn32v03IdmNib6N/xi0mtlVuxBlyIO9kwizOB5DfhhsdvTjc9xLOEy81lgaXSbROhbfQB4BfgrsEe0vwH/N/rcy4H6pNf6MvBqdLuk2J8ty8//WdpmJe1H+B/+VWAW0Cfa3jd6/Gr0/H5Jx/8w+rdYQRazNYr8WUcBi6Lvei5h9klZf8/Aj4GXgOeAPxBmFpXV9wzcShhDaSFcGX4ll98rUB/9+70G/Jp2ExjS3ZQSQ0REUlRqV5KIiGSgwCAiIikUGEREJIUCg4iIpFBgEBGRFAoMIiKSQoFBSl6UjvobnewzzMz+PovXGpac/jjN818ys193pZ25OF6kEBQYpBzUElIud2QY0GlgKJakFA8iRafAIOVgBrC/mS01s5nR7bmoOMkFSfscF+1zeXRl8KiZPRPdxsZ4vyFm9lBUEOXKxEYz+7yZPRW9x3+ZWVW0/RIze9nMniLk+kns/3szu97MngR+bmajzOyJqADLn5KKs2Ta/pCZXWNmiywU7jnSzOZE7boq2mdnM7vbzJZF/yYXINKZYi8H10237t5IymUPnAvcTyjGtBch384gklJnRPv1A/pG9w8EFrV/rQzv9SVC+oIBQA0h1UA98BngTqA62u8/CXlpBkVtGEgorrMQ+HW0z+8JNQaqosfPAidE938CXNvJ9odoK+ByGfBO9H59CKkVBkT/Hv+d1P7di/196dbzb7p8lXJzLHCru7cSMlQ+DBwJfNhuv2rg12Y2CmgFPh3jPe539w8AzGxO9J5bgSOAp6PKiTWEjJhHAw+5+9po/9vbvdcsd281s92BWnd/ONp+EzAr0/ak4xPJH5cDz3uUw9/MXidk21wO/LuZ/YwQGB+N8TmlQikwSKW6HHgPOIzQpbo5xrHtE4w5IbnZTe5+RfITZja5k9faGON90/kk+rst6X7icW93f9lCXeBJwFVm9oC7/6Sb7yllTmMMUg4+ItS7BngUuMDMqsxsIKGe7lPt9oGQq3+1u28DvkDoesrWyRaKtdcAkwndQw8AU83sU7C9mPu+hGp6J5jZAAt1M85L94LuvgFYb2bHRZu+ADycaXu2DTWzfYBN7n4LMJNQw0GkQ7pikJLn7h+Y2cJomum9hD75ZYRf8t9z93fN7AOg1cyWEfr2/xOYbWZfBO4j3i/3pwiFkQYDt7j7IgAz+2fgL2bWi5BC+Zvu/oSZ/Qh4HGgipEXP5GLgejPrRyjmfkkn27MxEphpZtuiNn09xrFSoZR2W0REUqgrSUREUqgrSSQNM5sI/Kzd5jfc/ZxitEekkNSVJCIiKdSVJCIiKRQYREQkhQKDiIikUGAQEZEU/x+APgPV2de9PAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "TF regression model | Validation RMSE: 118.01871248887977 116.31726837158203\n",
            "feature   label          predicted\n",
            "  value   value          value\n",
            "          in thousand$   in thousand$\n",
            "--------------------------------------\n",
            "  920    143             205\n",
            "  422    500             194\n",
            "  739     62             201\n",
            "  882    163             204\n",
            "  346     91             192\n",
            "  292    232             191\n",
            "  153    148             187\n",
            "  124    133             187\n",
            "  182    438             188\n",
            "  480    188             195\n",
            "  187     68             188\n",
            "  286    179             190\n",
            "  289    262             191\n",
            "  480     58             195\n",
            "  696     53             200\n",
            "  451    119             194\n",
            "  575    236             197\n",
            "  199     40             188\n",
            "  151     82             187\n",
            "  694     66             200\n",
            "  202    350             189\n",
            "  232    111             189\n",
            "  361    104             192\n",
            "  716    500             200\n",
            "  717    282             200\n",
            "  295    180             191\n",
            "  212    173             189\n",
            "  415    186             193\n",
            "  519    112             196\n",
            "   57     70             185\n",
            "  623    140             198\n",
            "  263    224             190\n",
            "  414    112             193\n",
            "  484    162             195\n",
            "  244    363             190\n",
            "  447    376             194\n",
            "  300    108             191\n",
            "  611    186             198\n",
            "  219    145             189\n",
            "  704    204             200\n",
            "  328    339             191\n",
            "  306    247             191\n",
            "  467    211             195\n",
            "  435    178             194\n",
            "  495    353             195\n",
            "  294    137             191\n",
            "  477    182             195\n",
            "  623    235             198\n",
            "  437    173             194\n",
            "  394    112             193\n",
            "  217    185             189\n",
            "  544    100             196\n",
            "  277    262             190\n",
            "  584    340             197\n",
            "   76    417             186\n",
            "  276     80             190\n",
            "  933    263             205\n",
            "  502    330             195\n",
            "  124    275             187\n",
            " 1058    500             208\n",
            " 1311    464             214\n",
            "  599    129             198\n",
            "  567    237             197\n",
            " 1345    300             215\n",
            "  580    317             197\n",
            "  236    240             189\n",
            "  739    225             201\n",
            "  549    257             197\n",
            "  582    254             197\n",
            "  593    500             198\n",
            "  748    225             201\n",
            "  442    392             194\n",
            "  516     68             196\n",
            "  606    228             198\n",
            "  334    135             192\n",
            "  741    375             201\n",
            " 1112    500             210\n",
            "   46     54             185\n",
            " 1092    146             209\n",
            "  519    311             196\n",
            "  364    163             192\n",
            "  789    138             202\n",
            " 4072    229             278\n",
            "  353    500             192\n",
            "  272    221             190\n",
            "  934    271             205\n",
            "  405    142             193\n",
            "  294     94             191\n",
            "  149     94             187\n",
            "  490    228             195\n",
            "  267     98             190\n",
            " 1020    160             207\n",
            "  822    159             203\n",
            "  323    186             191\n",
            "  272    116             190\n",
            "   87    158             186\n",
            "  354    244             192\n",
            "  303    221             191\n",
            "  191    191             188\n",
            "  266    321             190\n",
            "  620    102             198\n",
            "  390     97             193\n",
            "  246    112             190\n",
            "  702    187             200\n",
            "  504     88             196\n",
            "  662    178             199\n",
            "  204    214             189\n",
            "  470    428             195\n",
            "  674    213             199\n",
            "  405    294             193\n",
            "  184    127             188\n",
            "  229    126             189\n",
            "  391    135             193\n",
            "  285    299             190\n",
            "  909     91             205\n",
            "  218     47             189\n",
            "  749    303             201\n",
            "  832     85             203\n",
            "  445     96             194\n",
            "  368    269             192\n",
            "  259     82             190\n",
            "  552     86             197\n",
            "  463    148             195\n",
            "  318    169             191\n",
            "  413    192             193\n",
            "  250    101             190\n",
            "  699    142             200\n",
            "  274     98             190\n",
            "  316    262             191\n",
            "  824    106             203\n",
            "  960    190             206\n",
            "  846    218             203\n",
            "  412     98             193\n",
            "  294    244             191\n",
            "  350     71             192\n",
            "  418    338             194\n",
            "  208    119             189\n",
            "  231    174             189\n",
            "  623    100             198\n",
            "  320    500             191\n",
            "  235    150             189\n",
            "  317    186             191\n",
            "  235    100             189\n",
            "  276    221             190\n",
            "  371    138             192\n",
            "  982    324             207\n",
            "  656    217             199\n",
            "  583    218             197\n",
            "  147    152             187\n",
            "  530     81             196\n",
            "  252    147             190\n",
            "  628    276             198\n",
            "   33    500             185\n",
            "  703    182             200\n",
            "  151    327             187\n",
            "  354     68             192\n",
            "  147    136             187\n",
            "  380    139             193\n",
            "  110     68             186\n",
            "  135    500             187\n",
            "  210    181             189\n",
            "  389    319             193\n",
            "  623     87             198\n",
            "  282    118             190\n",
            "  683    236             200\n",
            "  922    218             205\n",
            "  616    275             198\n",
            "  477    156             195\n",
            "  946    433             206\n",
            "  790    162             202\n",
            "  724    107             201\n",
            "  282    400             190\n",
            "  337     73             192\n",
            "  340    239             192\n",
            "  428    138             194\n",
            " 1341    186             215\n",
            "  239    405             189\n",
            " 1037    253             208\n",
            " 1558     99             220\n",
            "  338    152             192\n",
            "  352    500             192\n",
            " 1276    390             213\n",
            "   74    103             186\n",
            "  745    287             201\n",
            "  282     87             190\n",
            "  626    493             198\n",
            "  898    174             205\n",
            "  283    500             190\n",
            "  371    161             192\n",
            "  301    488             191\n",
            "  186     94             188\n",
            "  626    266             198\n",
            "  488    167             195\n",
            "  469    252             195\n",
            "  656    214             199\n",
            "  418    113             194\n",
            "  561    392             197\n",
            " 1268    374             213\n",
            " 1360    394             215\n",
            "  711    282             200\n",
            "  245    215             190\n",
            "  307     88             191\n",
            "  199    500             188\n",
            "  499    267             195\n",
            "  642    279             199\n",
            "  969    295             206\n",
            "  194    500             188\n",
            "   40     58             185\n",
            "  184     83             188\n",
            " 1139    113             210\n",
            "  373    223             193\n",
            "  561    500             197\n",
            "  187    335             188\n",
            "  346    147             192\n",
            "  378    255             193\n",
            "  771    146             202\n",
            "  272    164             190\n",
            "  616    157             198\n",
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            "  366    151             192\n",
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            " 1052    165             208\n",
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            "  523    209             196\n",
            " 1093     82             209\n",
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            " 1229    114             212\n",
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            " 1856    184             227\n",
            " 1080     85             209\n",
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            " 1659    223             222\n",
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            " 1003    171             207\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ecrQR5ouAXWr"
      },
      "source": [
        "**Hyperparameter tuning**\n",
        "\n",
        "In general, ML problems require a lot of hyperparameter tuning. However, there aren't concrete rules for every model. Then, it's necessary to test distinct values in order to find the best set of hyperparameters for model and dataset [[Google AI](https://ai.google/education/)].\n",
        "\n",
        "The ideal combination of hyperparameters is data dependent, so it's important to always experiment and verify like a cyclical process."
      ]
    }
  ]
}