PreProcessingLayers_TF23.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "PreProcessingLayers_TF23.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPiyGn5hoDpZci9ZOY0YcZv"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgnVQKUSt7Pf"
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow.keras as keras\n",
        "from sklearn.datasets import load_boston\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "np.set_printoptions(precision=3)\n",
        "np.set_printoptions(suppress=True)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4J4K1zMduEZ8"
      },
      "source": [
        "boston_data=load_boston()"
      ],
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwoo0DKRuHCN"
      },
      "source": [
        "data=pd.DataFrame(boston_data.data,columns=boston_data.feature_names)"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uYkOVgm1wKd7"
      },
      "source": [
        "target=pd.DataFrame(boston_data.target)"
      ],
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3eiE7I_wRbq"
      },
      "source": [
        "final_boston=pd.concat([data,target],axis=1)"
      ],
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYLqQ74fwb-K"
      },
      "source": [
        "final_boston.rename(columns={0:'TARGET'},inplace=True)"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "zvxLRVy2wTKC",
        "outputId": "c5e10155-31ea-4ad2-f0d7-c099163403a5"
      },
      "source": [
        "final_boston.head()"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>TARGET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX  ...    TAX  PTRATIO       B  LSTAT  TARGET\n",
              "0  0.00632  18.0   2.31   0.0  0.538  ...  296.0     15.3  396.90   4.98    24.0\n",
              "1  0.02731   0.0   7.07   0.0  0.469  ...  242.0     17.8  396.90   9.14    21.6\n",
              "2  0.02729   0.0   7.07   0.0  0.469  ...  242.0     17.8  392.83   4.03    34.7\n",
              "3  0.03237   0.0   2.18   0.0  0.458  ...  222.0     18.7  394.63   2.94    33.4\n",
              "4  0.06905   0.0   2.18   0.0  0.458  ...  222.0     18.7  396.90   5.33    36.2\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tabC-tuVwU2a"
      },
      "source": [
        "train, test = train_test_split(final_boston, test_size=0.2)"
      ],
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "0NuMvUWoxNfJ",
        "outputId": "16ecfe33-cfd2-4108-c296-d82aaeb9ee41"
      },
      "source": [
        "train"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>TARGET</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>304</th>\n",
              "      <td>0.05515</td>\n",
              "      <td>33.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.472</td>\n",
              "      <td>7.236</td>\n",
              "      <td>41.1</td>\n",
              "      <td>4.0220</td>\n",
              "      <td>7.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.4</td>\n",
              "      <td>393.68</td>\n",
              "      <td>6.93</td>\n",
              "      <td>36.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>247</th>\n",
              "      <td>0.19657</td>\n",
              "      <td>22.0</td>\n",
              "      <td>5.86</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.431</td>\n",
              "      <td>6.226</td>\n",
              "      <td>79.2</td>\n",
              "      <td>8.0555</td>\n",
              "      <td>7.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>19.1</td>\n",
              "      <td>376.14</td>\n",
              "      <td>10.15</td>\n",
              "      <td>20.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>0.10793</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.56</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.520</td>\n",
              "      <td>6.195</td>\n",
              "      <td>54.4</td>\n",
              "      <td>2.7778</td>\n",
              "      <td>5.0</td>\n",
              "      <td>384.0</td>\n",
              "      <td>20.9</td>\n",
              "      <td>393.49</td>\n",
              "      <td>13.00</td>\n",
              "      <td>21.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>0.07875</td>\n",
              "      <td>45.0</td>\n",
              "      <td>3.44</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.437</td>\n",
              "      <td>6.782</td>\n",
              "      <td>41.1</td>\n",
              "      <td>3.7886</td>\n",
              "      <td>5.0</td>\n",
              "      <td>398.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>393.87</td>\n",
              "      <td>6.68</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>0.29819</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.504</td>\n",
              "      <td>7.686</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3.3751</td>\n",
              "      <td>8.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>17.4</td>\n",
              "      <td>377.51</td>\n",
              "      <td>3.92</td>\n",
              "      <td>46.7</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>0.06664</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.510</td>\n",
              "      <td>6.546</td>\n",
              "      <td>33.1</td>\n",
              "      <td>3.1323</td>\n",
              "      <td>5.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>16.6</td>\n",
              "      <td>390.96</td>\n",
              "      <td>5.33</td>\n",
              "      <td>29.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0.08014</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.96</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.499</td>\n",
              "      <td>5.850</td>\n",
              "      <td>41.5</td>\n",
              "      <td>3.9342</td>\n",
              "      <td>5.0</td>\n",
              "      <td>279.0</td>\n",
              "      <td>19.2</td>\n",
              "      <td>396.90</td>\n",
              "      <td>8.77</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>0.07165</td>\n",
              "      <td>0.0</td>\n",
              "      <td>25.65</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.581</td>\n",
              "      <td>6.004</td>\n",
              "      <td>84.1</td>\n",
              "      <td>2.1974</td>\n",
              "      <td>2.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>19.1</td>\n",
              "      <td>377.67</td>\n",
              "      <td>14.27</td>\n",
              "      <td>20.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>0.33147</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.507</td>\n",
              "      <td>8.247</td>\n",
              "      <td>70.4</td>\n",
              "      <td>3.6519</td>\n",
              "      <td>8.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>17.4</td>\n",
              "      <td>378.95</td>\n",
              "      <td>3.95</td>\n",
              "      <td>48.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>0.32982</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.89</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.624</td>\n",
              "      <td>5.822</td>\n",
              "      <td>95.4</td>\n",
              "      <td>2.4699</td>\n",
              "      <td>4.0</td>\n",
              "      <td>437.0</td>\n",
              "      <td>21.2</td>\n",
              "      <td>388.69</td>\n",
              "      <td>15.03</td>\n",
              "      <td>18.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>404 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        CRIM    ZN  INDUS  CHAS    NOX  ...    TAX  PTRATIO       B  LSTAT  TARGET\n",
              "304  0.05515  33.0   2.18   0.0  0.472  ...  222.0     18.4  393.68   6.93    36.1\n",
              "247  0.19657  22.0   5.86   0.0  0.431  ...  330.0     19.1  376.14  10.15    20.5\n",
              "110  0.10793   0.0   8.56   0.0  0.520  ...  384.0     20.9  393.49  13.00    21.7\n",
              "187  0.07875  45.0   3.44   0.0  0.437  ...  398.0     15.2  393.87   6.68    32.0\n",
              "228  0.29819   0.0   6.20   0.0  0.504  ...  307.0     17.4  377.51   3.92    46.7\n",
              "..       ...   ...    ...   ...    ...  ...    ...      ...     ...    ...     ...\n",
              "175  0.06664   0.0   4.05   0.0  0.510  ...  296.0     16.6  390.96   5.33    29.4\n",
              "37   0.08014   0.0   5.96   0.0  0.499  ...  279.0     19.2  396.90   8.77    21.0\n",
              "121  0.07165   0.0  25.65   0.0  0.581  ...  188.0     19.1  377.67  14.27    20.3\n",
              "233  0.33147   0.0   6.20   0.0  0.507  ...  307.0     17.4  378.95   3.95    48.3\n",
              "133  0.32982   0.0  21.89   0.0  0.624  ...  437.0     21.2  388.69  15.03    18.4\n",
              "\n",
              "[404 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltWXPN5cxOY5"
      },
      "source": [
        "features = train.drop(columns='TARGET').columns"
      ],
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-LKI7mAuylmw"
      },
      "source": [
        "x_train = np.array(train.drop(columns='TARGET').values)\n",
        "y_train = np.array(train['TARGET'].values)"
      ],
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q5II0AvgymkY"
      },
      "source": [
        "x_test = np.array(test.drop(columns='TARGET').values)\n",
        "y_test = np.array(test['TARGET'].values)"
      ],
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-iFaS6PJ3vck"
      },
      "source": [
        "normalizer = tf.keras.layers.experimental.preprocessing.Normalization()\n",
        "normalizer.adapt(x_train)"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dgxnfBFuzRPo"
      },
      "source": [
        "tmp = normalizer(x_train)"
      ],
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9l8ZZcJfBpyE",
        "outputId": "f359fc02-d923-4e27-cb76-7df469442228"
      },
      "source": [
        "print(tmp[0])\n",
        "print(x_train[0])"
      ],
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[-0.446  0.906 -1.298 -0.262 -0.715  1.416 -0.981  0.128 -0.282 -1.111\n",
            " -0.017  0.421 -0.805], shape=(13,), dtype=float32)\n",
            "[  0.055  33.      2.18    0.      0.472   7.236  41.1     4.022   7.\n",
            " 222.     18.4   393.68    6.93 ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4ivJdBd3vjr"
      },
      "source": [
        "inputs = tf.keras.Input(shape=x_train.shape[1:])"
      ],
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mw5iAwNw3vmq"
      },
      "source": [
        "x = normalizer(inputs)\n",
        "x = tf.keras.layers.Dense(200,activation='relu') (x)\n",
        "x = tf.keras.layers.Dense(100,activation='relu') (x)\n",
        "x = tf.keras.layers.Dropout(0.25) (x)\n",
        "x = tf.keras.layers.Dense(50,activation='relu') (x)\n",
        "x = tf.keras.layers.Dense(25,activation='relu') (x)\n",
        "output = tf.keras.layers.Dense(1) (x)"
      ],
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YlxvlKo7zfV3"
      },
      "source": [
        "model = tf.keras.Model(inputs,output)"
      ],
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DMHLwjYdz_uA",
        "outputId": "a4c233c7-37db-40e7-fdc2-bf30ca0e21f3"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_10\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_8 (InputLayer)         [(None, 13)]              0         \n",
            "_________________________________________________________________\n",
            "normalization_8 (Normalizati (None, 13)                27        \n",
            "_________________________________________________________________\n",
            "dense_60 (Dense)             (None, 200)               2800      \n",
            "_________________________________________________________________\n",
            "dense_61 (Dense)             (None, 100)               20100     \n",
            "_________________________________________________________________\n",
            "dropout_12 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_62 (Dense)             (None, 50)                5050      \n",
            "_________________________________________________________________\n",
            "dense_63 (Dense)             (None, 25)                1275      \n",
            "_________________________________________________________________\n",
            "dense_64 (Dense)             (None, 1)                 26        \n",
            "=================================================================\n",
            "Total params: 29,278\n",
            "Trainable params: 29,251\n",
            "Non-trainable params: 27\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14zRegC71H3O"
      },
      "source": [
        "model.compile(optimizer='adam',loss='mean_squared_error')"
      ],
      "execution_count": 172,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6EcP76Z21SnG",
        "outputId": "638a094f-2dfc-47dc-a377-ec0cb2944108"
      },
      "source": [
        "model.fit(x_train,y_train,epochs=500,verbose=0)"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fac0ef53d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qsJ6BWoa1g92",
        "outputId": "aad7d96d-a825-4bf2-a1a9-d6a97a0e353d"
      },
      "source": [
        "model.evaluate(x_test,y_test)"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4/4 [==============================] - 0s 2ms/step - loss: 17.3414\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17.34137535095215"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XRfNEkM-1kg-",
        "outputId": "c33e066d-ef00-4705-a512-7597e1b90b0a"
      },
      "source": [
        "print(np.array(x_test[42]))"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2.0090e-02 9.5000e+01 2.6800e+00 0.0000e+00 4.1610e-01 8.0340e+00\n",
            " 3.1900e+01 5.1180e+00 4.0000e+00 2.2400e+02 1.4700e+01 3.9055e+02\n",
            " 2.8800e+00]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KNVIZlIE1l_X",
        "outputId": "c995096a-bace-4284-b87f-c2bf21982796"
      },
      "source": [
        "model.predict(np.array(x_test[42]).reshape(-1,13))"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[44.84079]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8hflGvoB2KLe",
        "outputId": "35b3a786-6a29-4d56-9a69-caa2fffdbd6d"
      },
      "source": [
        "y_test[42]"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GCre_kzc5AF8"
      },
      "source": [
        "model2 = tf.keras.Sequential([\n",
        "                            tf.keras.Input(shape=input_shape),\n",
        "                             tf.keras.layers.Dense(200,activation='relu'),\n",
        "                             tf.keras.layers.Dense(100,activation='relu'),\n",
        "                             tf.keras.layers.Dropout(0.25),\n",
        "                             tf.keras.layers.Dense(50,activation='relu'),\n",
        "                             tf.keras.layers.Dense(25,activation='relu'),\n",
        "                             tf.keras.layers.Dense(1)\n",
        "])"
      ],
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ehyRUw5m9fvn",
        "outputId": "2a59ff4b-afc9-4ee8-f5fb-1bc9b410ef4e"
      },
      "source": [
        "model2.summary()"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_65 (Dense)             (None, 200)               2800      \n",
            "_________________________________________________________________\n",
            "dense_66 (Dense)             (None, 100)               20100     \n",
            "_________________________________________________________________\n",
            "dropout_13 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_67 (Dense)             (None, 50)                5050      \n",
            "_________________________________________________________________\n",
            "dense_68 (Dense)             (None, 25)                1275      \n",
            "_________________________________________________________________\n",
            "dense_69 (Dense)             (None, 1)                 26        \n",
            "=================================================================\n",
            "Total params: 29,251\n",
            "Trainable params: 29,251\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRZomlZO9hh_"
      },
      "source": [
        "model2.compile(optimizer='adam',loss='mean_squared_error')"
      ],
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-e_bIQAV9lbP",
        "outputId": "f298233d-cca8-4d42-a2eb-24201b78b721"
      },
      "source": [
        "model2.fit(x_train,y_train,epochs=500,verbose=0)"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fac10a00e80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_JYwg7bu9okf",
        "outputId": "e1705c4c-6047-4835-fd5c-0c3b73645e40"
      },
      "source": [
        "model2.evaluate(x_test,y_test)"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4/4 [==============================] - 0s 2ms/step - loss: 65.3252\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "65.32518768310547"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J82hh07P9sRf",
        "outputId": "658e5103-fd23-4689-cc56-7970b269347c"
      },
      "source": [
        "model2.predict(np.array(x_test[42]).reshape(-1,13))"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[35.420166]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9JpGDBo9w3X"
      },
      "source": [
        ""
      ],
      "execution_count": 187,
      "outputs": []
    }
  ]
}