Deep_Learning_Regression_with_Admissions_Data.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **PREDICTING ADMISSION USING DL REGRESSION**\n",
        "## BAIM MOHAMED JALAL\n"
      ],
      "metadata": {
        "id": "zFH-Fcw6ms25"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Objectives:\n",
        "- We will create a deep learning regression model that predicts the likelihood\n",
        "that a student applying to graduate school will be accepted based on various application factors (such as test scores).\n",
        "- By analyzing the parameters in this graduate admissions dataset, we will use TensorFlow with Keras to create a regression model that can evaluate the chances of an applicant being admitted."
      ],
      "metadata": {
        "id": "PvGE0SKDhWTw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1- Importing Libraries**"
      ],
      "metadata": {
        "id": "vfUduhb2Q-SL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "_KvXo1DWQFKB"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade tensorflow"
      ],
      "metadata": {
        "colab": {
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        "id": "D54ulrbKsZLu",
        "outputId": "cba32536-86ca-407c-cc91-b8b34b6844e3"
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      "execution_count": 105,
      "outputs": [
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --user keras"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RYIhq4tnuJmm",
        "outputId": "50be489e-9c55-4ce2-eba5-d5e2ac323233"
      },
      "execution_count": 106,
      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (2.12.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow\timport keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras import layers"
      ],
      "metadata": {
        "id": "Nto6YGLtRNBg"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import Normalizer\n",
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "5IUUm6QgRS10"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown 1FlEtcEYmopQmIwSG6fLZZcbXkv7UexQD"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4z_DiuU_RztS",
        "outputId": "08a911bd-e65a-47d3-dae1-73ec299f7947"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1FlEtcEYmopQmIwSG6fLZZcbXkv7UexQD\n",
            "To: /content/admissions_data.csv\n",
            "\r  0% 0.00/16.2k [00:00<?, ?B/s]\r100% 16.2k/16.2k [00:00<00:00, 50.8MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2- Loading Data**"
      ],
      "metadata": {
        "id": "V46xZQN6YRcO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/admissions_data.csv')"
      ],
      "metadata": {
        "id": "MyY0jH2ISBwK"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VADE1bCWSec-",
        "outputId": "3607087f-b4fa-4434-8995-dd43b011c858"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
              "0           1        337          118                  4  4.5   4.5  9.65   \n",
              "1           2        324          107                  4  4.0   4.5  8.87   \n",
              "2           3        316          104                  3  3.0   3.5  8.00   \n",
              "3           4        322          110                  3  3.5   2.5  8.67   \n",
              "4           5        314          103                  2  2.0   3.0  8.21   \n",
              "\n",
              "   Research  Chance of Admit   \n",
              "0         1              0.92  \n",
              "1         1              0.76  \n",
              "2         1              0.72  \n",
              "3         1              0.80  \n",
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        "df.tail()"
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        "id": "ihTZSomrg4Mf",
        "outputId": "cd79e628-e1dd-4acd-d8a7-d2e5c612708a"
      },
      "execution_count": 112,
      "outputs": [
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          "data": {
            "text/plain": [
              "     Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
              "495         496        332          108                  5  4.5   4.0  9.02   \n",
              "496         497        337          117                  5  5.0   5.0  9.87   \n",
              "497         498        330          120                  5  4.5   5.0  9.56   \n",
              "498         499        312          103                  4  4.0   5.0  8.43   \n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDHTaPqFmcEd",
        "outputId": "8e908e5d-4bdf-42aa-fbd7-71e4554f8d2d"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Serial No.', 'GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
              "       'LOR ', 'CGPA', 'Research', 'Chance of Admit '],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['University Rating'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pi6jDmQxnxwR",
        "outputId": "76b77bf2-a2d4-463e-b867-f540bf648909"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    162\n",
              "2    126\n",
              "4    105\n",
              "5     73\n",
              "1     34\n",
              "Name: University Rating, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3- Model**\n",
        "## 1- Split features and label\n",
        "- In our dataset, all variables are numerical.\n",
        "\n",
        "- Our features are : GRE Score, TOEFL Score, University Rating, SOP: Statement of purpose, LOR: Letter of recommendation, CGPA: Undegraduate GPA, Research\n",
        "\n",
        "- Our Label will be : Chance of admit\n"
      ],
      "metadata": {
        "id": "rl7DSZkUoj2x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label = df.iloc[:,-1]\n",
        "label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GwsX51xOpiig",
        "outputId": "1866ade3-27f4-46b1-fe55-b2ff3bb5ca3c"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0.92\n",
              "1      0.76\n",
              "2      0.72\n",
              "3      0.80\n",
              "4      0.65\n",
              "       ... \n",
              "495    0.87\n",
              "496    0.96\n",
              "497    0.93\n",
              "498    0.73\n",
              "499    0.84\n",
              "Name: Chance of Admit , Length: 500, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
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    {
      "cell_type": "code",
      "source": [
        "features = df.iloc[:,:-1]\n",
        "features"
      ],
      "metadata": {
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          "height": 424
        },
        "id": "1rLZYz0vqwp9",
        "outputId": "19cd573e-b825-4177-b49a-70f17ca9e0fd"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
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              "     Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
              "0             1        337          118                  4  4.5   4.5  9.65   \n",
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              "..          ...        ...          ...                ...  ...   ...   ...   \n",
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              "499         500        327          113                  4  4.5   4.5  9.04   \n",
              "\n",
              "     Research  \n",
              "0           1  \n",
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              "4           0  \n",
              "..        ...  \n",
              "495         1  \n",
              "496         1  \n",
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              "499         0  \n",
              "\n",
              "[500 rows x 8 columns]"
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9c6ee9f0-3f5a-43f1-9826-b53e54c684c8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(features,label, train_size=0.2,random_state = 42)"
      ],
      "metadata": {
        "id": "sb9HHmQtrTAv"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### NB:\n",
        "If we look through our dataset, we may notice that there are many different scales being used. For example, the GRE Score is out of 340 while the University Rating is out of 5.\n",
        "\n",
        "So we need to use StandardScaler over the training set only so as to avoid data leakage.\n"
      ],
      "metadata": {
        "id": "JLlohcM79ZgJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_train = pd.DataFrame(scaler.fit_transform(X_train))\n",
        "X_test =  pd.DataFrame(scaler.transform(X_test))\n",
        "type(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HsFX-EdktCqK",
        "outputId": "29120aa3-ea65-4796-a903-2a525fb924c8"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train\n",
        "# Training set well scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "txrpsPcFrREO",
        "outputId": "acfc8f2d-d7d6-4e00-c111-a8a49d6525b0"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           0         1         2         3         4         5         6  \\\n",
              "0  -0.328003  1.771239  1.980961  0.683288  1.578740  1.474356  1.711906   \n",
              "1   0.249761 -0.156016 -1.221886 -0.924448 -0.445286 -1.033052 -0.000314   \n",
              "2   1.377444 -2.175044 -1.382028 -1.728316 -1.457299 -2.036015 -1.571159   \n",
              "3  -0.056523 -0.247790 -1.382028 -0.924448 -0.951292 -0.531570  0.675149   \n",
              "4   1.593235  0.302854  0.699822 -0.120580 -0.445286  0.471393  0.078228   \n",
              "..       ...       ...       ...       ...       ...       ...       ...   \n",
              "95 -1.065869  0.945272  0.539680  0.683288  1.072734  0.972874  0.957901   \n",
              "96  0.075736 -1.165530 -0.421174 -0.924448 -0.951292 -0.531570 -0.550110   \n",
              "97  0.618694 -1.532626 -1.382028 -1.728316 -1.457299 -1.534534 -2.073829   \n",
              "98  1.224302 -0.890208 -0.421174 -0.924448 -0.951292  0.471393 -1.398366   \n",
              "99 -1.093713 -0.431338 -0.261032 -0.924448  0.566727 -0.030089 -0.502984   \n",
              "\n",
              "           7  \n",
              "0   0.717741  \n",
              "1  -1.393261  \n",
              "2  -1.393261  \n",
              "3  -1.393261  \n",
              "4   0.717741  \n",
              "..       ...  \n",
              "95  0.717741  \n",
              "96  0.717741  \n",
              "97 -1.393261  \n",
              "98 -1.393261  \n",
              "99 -1.393261  \n",
              "\n",
              "[100 rows x 8 columns]"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.328003</td>\n",
              "      <td>1.771239</td>\n",
              "      <td>1.980961</td>\n",
              "      <td>0.683288</td>\n",
              "      <td>1.578740</td>\n",
              "      <td>1.474356</td>\n",
              "      <td>1.711906</td>\n",
              "      <td>0.717741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.249761</td>\n",
              "      <td>-0.156016</td>\n",
              "      <td>-1.221886</td>\n",
              "      <td>-0.924448</td>\n",
              "      <td>-0.445286</td>\n",
              "      <td>-1.033052</td>\n",
              "      <td>-0.000314</td>\n",
              "      <td>-1.393261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.377444</td>\n",
              "      <td>-2.175044</td>\n",
              "      <td>-1.382028</td>\n",
              "      <td>-1.728316</td>\n",
              "      <td>-1.457299</td>\n",
              "      <td>-2.036015</td>\n",
              "      <td>-1.571159</td>\n",
              "      <td>-1.393261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.056523</td>\n",
              "      <td>-0.247790</td>\n",
              "      <td>-1.382028</td>\n",
              "      <td>-0.924448</td>\n",
              "      <td>-0.951292</td>\n",
              "      <td>-0.531570</td>\n",
              "      <td>0.675149</td>\n",
              "      <td>-1.393261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.593235</td>\n",
              "      <td>0.302854</td>\n",
              "      <td>0.699822</td>\n",
              "      <td>-0.120580</td>\n",
              "      <td>-0.445286</td>\n",
              "      <td>0.471393</td>\n",
              "      <td>0.078228</td>\n",
              "      <td>0.717741</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",
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              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>-1.065869</td>\n",
              "      <td>0.945272</td>\n",
              "      <td>0.539680</td>\n",
              "      <td>0.683288</td>\n",
              "      <td>1.072734</td>\n",
              "      <td>0.972874</td>\n",
              "      <td>0.957901</td>\n",
              "      <td>0.717741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>0.075736</td>\n",
              "      <td>-1.165530</td>\n",
              "      <td>-0.421174</td>\n",
              "      <td>-0.924448</td>\n",
              "      <td>-0.951292</td>\n",
              "      <td>-0.531570</td>\n",
              "      <td>-0.550110</td>\n",
              "      <td>0.717741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>0.618694</td>\n",
              "      <td>-1.532626</td>\n",
              "      <td>-1.382028</td>\n",
              "      <td>-1.728316</td>\n",
              "      <td>-1.457299</td>\n",
              "      <td>-1.534534</td>\n",
              "      <td>-2.073829</td>\n",
              "      <td>-1.393261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>1.224302</td>\n",
              "      <td>-0.890208</td>\n",
              "      <td>-0.421174</td>\n",
              "      <td>-0.924448</td>\n",
              "      <td>-0.951292</td>\n",
              "      <td>0.471393</td>\n",
              "      <td>-1.398366</td>\n",
              "      <td>-1.393261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>-1.093713</td>\n",
              "      <td>-0.431338</td>\n",
              "      <td>-0.261032</td>\n",
              "      <td>-0.924448</td>\n",
              "      <td>0.566727</td>\n",
              "      <td>-0.030089</td>\n",
              "      <td>-0.502984</td>\n",
              "      <td>-1.393261</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1ae3f60-3d18-46bf-9353-7ce465e78cdf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d1ae3f60-3d18-46bf-9353-7ce465e78cdf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d1ae3f60-3d18-46bf-9353-7ce465e78cdf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2- Creating our NN model\n",
        "Our model will perform a regression analysis on the admission data, it will include:\n",
        "- Hidden layers as how many neurons we have\n",
        "- Activation functions\n",
        "- Loss functions, and adequat metrics\n",
        "- Gradient descent optimizer\n",
        "- Learning rate"
      ],
      "metadata": {
        "id": "HZuG4U3brmfd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Activation, Dense\n",
        "from tensorflow.keras.optimizers import Adam"
      ],
      "metadata": {
        "id": "a0oIetdJvU56"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the neural network model\n",
        "model = Sequential()\n",
        "model.add(Dense(64,activation = 'relu', input_shape=(X_train.shape[1],)))\n",
        "model.add(Dense(32, activation = 'relu'))\n",
        "model.add(Dense(1))\n",
        "# Compile the model\n",
        "model.compile(optimizer = Adam(learning_rate = 0.001), loss='mean_squared_error')"
      ],
      "metadata": {
        "id": "xWw0NCnVrd0X"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "model.fit(X_train,y_train,epochs = 100, batch_size=32,verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2kgkGpYvv06E",
        "outputId": "78f3d2fe-0ae1-4ca8-daca-0a29778193d4"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.7722\n",
            "Epoch 2/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.4405\n",
            "Epoch 3/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.2447\n",
            "Epoch 4/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.1395\n",
            "Epoch 5/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0833\n",
            "Epoch 6/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0570\n",
            "Epoch 7/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0476\n",
            "Epoch 8/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0466\n",
            "Epoch 9/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0439\n",
            "Epoch 10/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0383\n",
            "Epoch 11/100\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 0.0333\n",
            "Epoch 12/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0291\n",
            "Epoch 13/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0260\n",
            "Epoch 14/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0240\n",
            "Epoch 15/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0224\n",
            "Epoch 16/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0211\n",
            "Epoch 17/100\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0200\n",
            "Epoch 18/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0189\n",
            "Epoch 19/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0178\n",
            "Epoch 20/100\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 0.0169\n",
            "Epoch 21/100\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 0.0161\n",
            "Epoch 22/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0153\n",
            "Epoch 23/100\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 0.0144\n",
            "Epoch 24/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0137\n",
            "Epoch 25/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0127\n",
            "Epoch 26/100\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 0.0120\n",
            "Epoch 27/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0113\n",
            "Epoch 28/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0107\n",
            "Epoch 29/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0101\n",
            "Epoch 30/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0097\n",
            "Epoch 31/100\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 0.0091\n",
            "Epoch 32/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0089\n",
            "Epoch 33/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0086\n",
            "Epoch 34/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0081\n",
            "Epoch 35/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0075\n",
            "Epoch 36/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0072\n",
            "Epoch 37/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0071\n",
            "Epoch 38/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0068\n",
            "Epoch 39/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0064\n",
            "Epoch 40/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0067\n",
            "Epoch 41/100\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 0.0064\n",
            "Epoch 42/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0055\n",
            "Epoch 43/100\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 0.0051\n",
            "Epoch 44/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0054\n",
            "Epoch 45/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0055\n",
            "Epoch 46/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0046\n",
            "Epoch 47/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0046\n",
            "Epoch 48/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0046\n",
            "Epoch 49/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0042\n",
            "Epoch 50/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0043\n",
            "Epoch 51/100\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 0.0039\n",
            "Epoch 52/100\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 0.0036\n",
            "Epoch 53/100\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 0.0035\n",
            "Epoch 54/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0034\n",
            "Epoch 55/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0031\n",
            "Epoch 56/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0030\n",
            "Epoch 57/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0029\n",
            "Epoch 58/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0027\n",
            "Epoch 59/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0028\n",
            "Epoch 60/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0030\n",
            "Epoch 61/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0027\n",
            "Epoch 62/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0024\n",
            "Epoch 63/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0025\n",
            "Epoch 64/100\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 0.0023\n",
            "Epoch 65/100\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 0.0022\n",
            "Epoch 66/100\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 0.0021\n",
            "Epoch 67/100\n",
            "4/4 [==============================] - 0s 16ms/step - loss: 0.0020\n",
            "Epoch 68/100\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 0.0019\n",
            "Epoch 69/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0019\n",
            "Epoch 70/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0019\n",
            "Epoch 71/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0018\n",
            "Epoch 72/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0017\n",
            "Epoch 73/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0016\n",
            "Epoch 74/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0016\n",
            "Epoch 75/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0015\n",
            "Epoch 76/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0015\n",
            "Epoch 77/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0014\n",
            "Epoch 78/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0014\n",
            "Epoch 79/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0013\n",
            "Epoch 80/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0013\n",
            "Epoch 81/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0012\n",
            "Epoch 82/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0012\n",
            "Epoch 83/100\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 0.0012\n",
            "Epoch 84/100\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 0.0011\n",
            "Epoch 85/100\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 0.0011\n",
            "Epoch 86/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0011\n",
            "Epoch 87/100\n",
            "4/4 [==============================] - 0s 13ms/step - loss: 0.0011\n",
            "Epoch 88/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0011\n",
            "Epoch 89/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0011\n",
            "Epoch 90/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0010\n",
            "Epoch 91/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 9.6388e-04\n",
            "Epoch 92/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 9.6436e-04\n",
            "Epoch 93/100\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 8.8175e-04\n",
            "Epoch 94/100\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 8.7787e-04\n",
            "Epoch 95/100\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 9.2280e-04\n",
            "Epoch 96/100\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 8.4773e-04\n",
            "Epoch 97/100\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 8.2434e-04\n",
            "Epoch 98/100\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 8.8095e-04\n",
            "Epoch 99/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 8.0762e-04\n",
            "Epoch 100/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 7.4645e-04\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc30e6cc610>"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test set\n",
        "loss_score = model.evaluate(X_test, y_test)\n",
        "print(\"Test loss\",loss_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lx_KfTsfwn98",
        "outputId": "57029c0e-0546-4642-8e16-754198bc9f90"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/13 [==============================] - 0s 2ms/step - loss: 0.0144\n",
            "Test loss 0.014398499391973019\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion:\n",
        "Great! A test loss of 0.01296 indicates that our neural network model performed well on the regression task.\n",
        "\n",
        "Let's adjust our model's parameters and try to make our model more accurate."
      ],
      "metadata": {
        "id": "AsfY5HBfxfEt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Nb of epochs\n",
        "nb_of_epoch=[50,300,500,800]\n",
        "test_loss=[]\n",
        "for n in nb_of_epoch:\n",
        "  model.fit(X_train,y_train,epochs = n, batch_size=32,verbose=1)\n",
        "  loss_score = model.evaluate(X_test, y_test)\n",
        "  test_loss.append(loss_score)\n",
        "print(test_loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i4QbVjWCx8XE",
        "outputId": "b2cd812d-d6e0-43fa-e23a-dc64a7113655"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 7.6874e-04\n",
            "Epoch 2/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.9034e-04\n",
            "Epoch 3/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 6.5436e-04\n",
            "Epoch 4/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.5591e-04\n",
            "Epoch 5/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.2274e-04\n",
            "Epoch 6/50\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 6.3685e-04\n",
            "Epoch 7/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 6.6624e-04\n",
            "Epoch 8/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 5.8144e-04\n",
            "Epoch 9/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 7.2747e-04\n",
            "Epoch 10/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.7049e-04\n",
            "Epoch 11/50\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 6.0277e-04\n",
            "Epoch 12/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.7599e-04\n",
            "Epoch 13/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.9731e-04\n",
            "Epoch 14/50\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 5.3234e-04\n",
            "Epoch 15/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.4378e-04\n",
            "Epoch 16/50\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 4.8031e-04\n",
            "Epoch 17/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.4239e-04\n",
            "Epoch 18/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.5226e-04\n",
            "Epoch 19/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.2735e-04\n",
            "Epoch 20/50\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 4.4340e-04\n",
            "Epoch 21/50\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 4.4741e-04\n",
            "Epoch 22/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.8751e-04\n",
            "Epoch 23/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.8928e-04\n",
            "Epoch 24/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.9619e-04\n",
            "Epoch 25/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.6458e-04\n",
            "Epoch 26/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.5879e-04\n",
            "Epoch 27/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.3750e-04\n",
            "Epoch 28/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.2691e-04\n",
            "Epoch 29/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.2210e-04\n",
            "Epoch 30/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.2142e-04\n",
            "Epoch 31/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.0898e-04\n",
            "Epoch 32/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.0814e-04\n",
            "Epoch 33/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.9296e-04\n",
            "Epoch 34/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.8566e-04\n",
            "Epoch 35/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.7298e-04\n",
            "Epoch 36/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.6521e-04\n",
            "Epoch 37/50\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.6333e-04\n",
            "Epoch 38/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.5475e-04\n",
            "Epoch 39/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.4489e-04\n",
            "Epoch 40/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.4854e-04\n",
            "Epoch 41/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.4223e-04\n",
            "Epoch 42/50\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 2.2827e-04\n",
            "Epoch 43/50\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.3415e-04\n",
            "Epoch 44/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.2204e-04\n",
            "Epoch 45/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.2267e-04\n",
            "Epoch 46/50\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.1521e-04\n",
            "Epoch 47/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.0465e-04\n",
            "Epoch 48/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.0071e-04\n",
            "Epoch 49/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.9259e-04\n",
            "Epoch 50/50\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.8926e-04\n",
            "13/13 [==============================] - 0s 2ms/step - loss: 0.0134\n",
            "Epoch 1/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.8096e-04\n",
            "Epoch 2/300\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 2.0102e-04\n",
            "Epoch 3/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.0095e-04\n",
            "Epoch 4/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.8385e-04\n",
            "Epoch 5/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.1053e-04\n",
            "Epoch 6/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.8197e-04\n",
            "Epoch 7/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.9644e-04\n",
            "Epoch 8/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.7322e-04\n",
            "Epoch 9/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.8893e-04\n",
            "Epoch 10/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.6603e-04\n",
            "Epoch 11/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.4778e-04\n",
            "Epoch 12/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.4132e-04\n",
            "Epoch 13/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.3524e-04\n",
            "Epoch 14/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.3670e-04\n",
            "Epoch 15/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.2998e-04\n",
            "Epoch 16/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.2895e-04\n",
            "Epoch 17/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.5109e-04\n",
            "Epoch 18/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.4359e-04\n",
            "Epoch 19/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.7083e-04\n",
            "Epoch 20/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.6853e-04\n",
            "Epoch 21/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.3031e-04\n",
            "Epoch 22/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.5464e-04\n",
            "Epoch 23/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.2278e-04\n",
            "Epoch 24/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.3625e-04\n",
            "Epoch 25/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.1951e-04\n",
            "Epoch 26/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.6979e-04\n",
            "Epoch 27/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.1589e-04\n",
            "Epoch 28/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.2269e-04\n",
            "Epoch 29/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.1240e-04\n",
            "Epoch 30/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.3007e-04\n",
            "Epoch 31/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.0044e-04\n",
            "Epoch 32/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.1059e-04\n",
            "Epoch 33/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 8.7651e-05\n",
            "Epoch 34/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.0092e-04\n",
            "Epoch 35/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 8.3318e-05\n",
            "Epoch 36/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 7.4962e-05\n",
            "Epoch 37/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 7.9025e-05\n",
            "Epoch 38/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 7.4443e-05\n",
            "Epoch 39/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 7.0487e-05\n",
            "Epoch 40/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.7487e-05\n",
            "Epoch 41/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 6.6784e-05\n",
            "Epoch 42/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.5348e-05\n",
            "Epoch 43/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.4640e-05\n",
            "Epoch 44/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 6.4192e-05\n",
            "Epoch 45/300\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 6.6166e-05\n",
            "Epoch 46/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.2263e-05\n",
            "Epoch 47/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.9645e-05\n",
            "Epoch 48/300\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 5.9782e-05\n",
            "Epoch 49/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.7475e-05\n",
            "Epoch 50/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.9670e-05\n",
            "Epoch 51/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.6240e-05\n",
            "Epoch 52/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.9226e-05\n",
            "Epoch 53/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.5855e-05\n",
            "Epoch 54/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.8008e-05\n",
            "Epoch 55/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.2398e-05\n",
            "Epoch 56/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.7007e-05\n",
            "Epoch 57/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.7545e-05\n",
            "Epoch 58/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 6.0703e-05\n",
            "Epoch 59/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.9665e-05\n",
            "Epoch 60/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.5998e-05\n",
            "Epoch 61/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 4.7768e-05\n",
            "Epoch 62/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 4.6575e-05\n",
            "Epoch 63/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 4.3667e-05\n",
            "Epoch 64/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 4.6869e-05\n",
            "Epoch 65/300\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 4.9243e-05\n",
            "Epoch 66/300\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.5070e-05\n",
            "Epoch 67/300\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 4.8432e-05\n",
            "Epoch 68/300\n",
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            "Epoch 626/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.0502e-06\n",
            "Epoch 627/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.6930e-06\n",
            "Epoch 628/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 8.1534e-06\n",
            "Epoch 629/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 8.4169e-06\n",
            "Epoch 630/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.2269e-05\n",
            "Epoch 631/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.1649e-05\n",
            "Epoch 632/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.8883e-05\n",
            "Epoch 633/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.1591e-05\n",
            "Epoch 634/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.7262e-06\n",
            "Epoch 635/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.0027e-06\n",
            "Epoch 636/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.2045e-06\n",
            "Epoch 637/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.9888e-06\n",
            "Epoch 638/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.2906e-06\n",
            "Epoch 639/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.4390e-06\n",
            "Epoch 640/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.5119e-06\n",
            "Epoch 641/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.5420e-06\n",
            "Epoch 642/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.4672e-06\n",
            "Epoch 643/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 8.4592e-07\n",
            "Epoch 644/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.7971e-06\n",
            "Epoch 645/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 8.0507e-07\n",
            "Epoch 646/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.5454e-07\n",
            "Epoch 647/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 7.5916e-07\n",
            "Epoch 648/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 8.3451e-07\n",
            "Epoch 649/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 1.5732e-06\n",
            "Epoch 650/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.2956e-06\n",
            "Epoch 651/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.0395e-06\n",
            "Epoch 652/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.1598e-07\n",
            "Epoch 653/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 8.4798e-07\n",
            "Epoch 654/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 8.7247e-07\n",
            "Epoch 655/800\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 5.4634e-07\n",
            "Epoch 656/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 5.3185e-07\n",
            "Epoch 657/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.4694e-07\n",
            "Epoch 658/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.9457e-07\n",
            "Epoch 659/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.4337e-07\n",
            "Epoch 660/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.3342e-07\n",
            "Epoch 661/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 3.2500e-07\n",
            "Epoch 662/800\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 3.9147e-07\n",
            "Epoch 663/800\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 3.6534e-07\n",
            "Epoch 664/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 5.3252e-07\n",
            "Epoch 665/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 8.8681e-07\n",
            "Epoch 666/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.1126e-06\n",
            "Epoch 667/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.3254e-06\n",
            "Epoch 668/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.4715e-06\n",
            "Epoch 669/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 8.1049e-07\n",
            "Epoch 670/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 1.5178e-06\n",
            "Epoch 671/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 2.4501e-06\n",
            "Epoch 672/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.0476e-06\n",
            "Epoch 673/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.1674e-06\n",
            "Epoch 674/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 7.4485e-07\n",
            "Epoch 675/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.0015e-06\n",
            "Epoch 676/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 8.3168e-07\n",
            "Epoch 677/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 9.4718e-07\n",
            "Epoch 678/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.2361e-06\n",
            "Epoch 679/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.1622e-07\n",
            "Epoch 680/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 9.6685e-07\n",
            "Epoch 681/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.8568e-06\n",
            "Epoch 682/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.2401e-06\n",
            "Epoch 683/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 8.6213e-07\n",
            "Epoch 684/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.0925e-06\n",
            "Epoch 685/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.7974e-05\n",
            "Epoch 686/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.0205e-05\n",
            "Epoch 687/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.1547e-05\n",
            "Epoch 688/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 9.1915e-06\n",
            "Epoch 689/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.3021e-05\n",
            "Epoch 690/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.5713e-05\n",
            "Epoch 691/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.1571e-05\n",
            "Epoch 692/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.1664e-05\n",
            "Epoch 693/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.8229e-06\n",
            "Epoch 694/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.1877e-05\n",
            "Epoch 695/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.1716e-05\n",
            "Epoch 696/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.2503e-05\n",
            "Epoch 697/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.0652e-05\n",
            "Epoch 698/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 4.1141e-05\n",
            "Epoch 699/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 3.5992e-05\n",
            "Epoch 700/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 4.9414e-05\n",
            "Epoch 701/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.4012e-05\n",
            "Epoch 702/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 6.2422e-05\n",
            "Epoch 703/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 4.1589e-05\n",
            "Epoch 704/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 4.7880e-05\n",
            "Epoch 705/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.2384e-05\n",
            "Epoch 706/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 7.1192e-05\n",
            "Epoch 707/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.1362e-04\n",
            "Epoch 708/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.2383e-04\n",
            "Epoch 709/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 8.6682e-05\n",
            "Epoch 710/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.0273e-04\n",
            "Epoch 711/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 7.0210e-05\n",
            "Epoch 712/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 6.7024e-05\n",
            "Epoch 713/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.9517e-05\n",
            "Epoch 714/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.4639e-05\n",
            "Epoch 715/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.0922e-05\n",
            "Epoch 716/800\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 4.7547e-05\n",
            "Epoch 717/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.1987e-05\n",
            "Epoch 718/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 3.0520e-05\n",
            "Epoch 719/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 2.6640e-05\n",
            "Epoch 720/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.3209e-05\n",
            "Epoch 721/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.6664e-05\n",
            "Epoch 722/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 3.7159e-05\n",
            "Epoch 723/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.0536e-05\n",
            "Epoch 724/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 4.0285e-05\n",
            "Epoch 725/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.9435e-05\n",
            "Epoch 726/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.8921e-05\n",
            "Epoch 727/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 5.1273e-05\n",
            "Epoch 728/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 9.8376e-05\n",
            "Epoch 729/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 7.8942e-05\n",
            "Epoch 730/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.1332e-04\n",
            "Epoch 731/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.2567e-04\n",
            "Epoch 732/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 1.5384e-04\n",
            "Epoch 733/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.2857e-04\n",
            "Epoch 734/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.0673e-04\n",
            "Epoch 735/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 2.2595e-04\n",
            "Epoch 736/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.2070e-04\n",
            "Epoch 737/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 3.9992e-04\n",
            "Epoch 738/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 3.6662e-04\n",
            "Epoch 739/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 3.4169e-04\n",
            "Epoch 740/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.6569e-04\n",
            "Epoch 741/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.9094e-04\n",
            "Epoch 742/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 4.7435e-04\n",
            "Epoch 743/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.5435e-04\n",
            "Epoch 744/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.2538e-04\n",
            "Epoch 745/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.5136e-04\n",
            "Epoch 746/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.0372e-04\n",
            "Epoch 747/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.8672e-04\n",
            "Epoch 748/800\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 2.6844e-04\n",
            "Epoch 749/800\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 4.6988e-04\n",
            "Epoch 750/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 8.3831e-04\n",
            "Epoch 751/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.4569e-04\n",
            "Epoch 752/800\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0011\n",
            "Epoch 753/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 7.8885e-04\n",
            "Epoch 754/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.7700e-04\n",
            "Epoch 755/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.6228e-04\n",
            "Epoch 756/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.4375e-04\n",
            "Epoch 757/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.7956e-04\n",
            "Epoch 758/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.2109e-04\n",
            "Epoch 759/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.4126e-04\n",
            "Epoch 760/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.9960e-04\n",
            "Epoch 761/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.3661e-04\n",
            "Epoch 762/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.0657e-04\n",
            "Epoch 763/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 2.2744e-04\n",
            "Epoch 764/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.6797e-04\n",
            "Epoch 765/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 2.1523e-04\n",
            "Epoch 766/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.3372e-04\n",
            "Epoch 767/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 8.6563e-05\n",
            "Epoch 768/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.0181e-04\n",
            "Epoch 769/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.9612e-05\n",
            "Epoch 770/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.5772e-05\n",
            "Epoch 771/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 8.5962e-05\n",
            "Epoch 772/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.9913e-05\n",
            "Epoch 773/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 6.1971e-05\n",
            "Epoch 774/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 6.3567e-05\n",
            "Epoch 775/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 2.8468e-05\n",
            "Epoch 776/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.9380e-05\n",
            "Epoch 777/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.5256e-05\n",
            "Epoch 778/800\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 2.2485e-05\n",
            "Epoch 779/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.8807e-05\n",
            "Epoch 780/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 2.2629e-05\n",
            "Epoch 781/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.8672e-05\n",
            "Epoch 782/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.2452e-05\n",
            "Epoch 783/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.2942e-05\n",
            "Epoch 784/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.5704e-05\n",
            "Epoch 785/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.8506e-05\n",
            "Epoch 786/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.3410e-05\n",
            "Epoch 787/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 9.4677e-06\n",
            "Epoch 788/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.8748e-05\n",
            "Epoch 789/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.0047e-05\n",
            "Epoch 790/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.4794e-05\n",
            "Epoch 791/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.5499e-05\n",
            "Epoch 792/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.1903e-05\n",
            "Epoch 793/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 8.1641e-06\n",
            "Epoch 794/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 1.8664e-05\n",
            "Epoch 795/800\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 1.5904e-05\n",
            "Epoch 796/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 9.2451e-06\n",
            "Epoch 797/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 7.3539e-06\n",
            "Epoch 798/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 9.9233e-06\n",
            "Epoch 799/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 8.4386e-06\n",
            "Epoch 800/800\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 7.5096e-06\n",
            "13/13 [==============================] - 0s 3ms/step - loss: 0.0115\n",
            "[0.013371093198657036, 0.012526030652225018, 0.01214996911585331, 0.011518879793584347]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Nb of epochs:\n",
        "As we can see, More epochs require too much time in the training, but give better results."
      ],
      "metadata": {
        "id": "zk8j8rVvzaLK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# batch size :\n",
        "batch = [32,64,128,256]\n",
        "test_loss = []\n",
        "for b in batch :\n",
        "  model.fit(X_train,y_train,epochs = 200, batch_size=b,verbose=1)\n",
        "  loss_score = model.evaluate(X_test, y_test)\n",
        "  test_loss.append(loss_score)\n",
        "print(test_loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "18ZDuf85z0tx",
        "outputId": "2e7e6663-4fd4-4ee4-8272-ae3a2a0230da"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 4.3053e-06\n",
            "Epoch 2/200\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 3.0566e-06\n",
            "Epoch 3/200\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 3.3112e-06\n",
            "Epoch 4/200\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.1063e-06\n",
            "Epoch 5/200\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 3.7760e-06\n",
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            "1/1 [==============================] - 0s 10ms/step - loss: 2.4202e-10\n",
            "Epoch 179/200\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.4161e-10\n",
            "Epoch 180/200\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.4086e-10\n",
            "Epoch 181/200\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.4030e-10\n",
            "Epoch 182/200\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.3948e-10\n",
            "Epoch 183/200\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 2.3872e-10\n",
            "Epoch 184/200\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.3823e-10\n",
            "Epoch 185/200\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 2.3748e-10\n",
            "Epoch 186/200\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 2.3695e-10\n",
            "Epoch 187/200\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.3674e-10\n",
            "Epoch 188/200\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 2.3581e-10\n",
            "Epoch 189/200\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 2.3517e-10\n",
            "Epoch 190/200\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 2.3437e-10\n",
            "Epoch 191/200\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 2.3400e-10\n",
            "Epoch 192/200\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.3306e-10\n",
            "Epoch 193/200\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.3266e-10\n",
            "Epoch 194/200\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.3200e-10\n",
            "Epoch 195/200\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 2.3161e-10\n",
            "Epoch 196/200\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.3083e-10\n",
            "Epoch 197/200\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 2.2994e-10\n",
            "Epoch 198/200\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 2.2943e-10\n",
            "Epoch 199/200\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.2878e-10\n",
            "Epoch 200/200\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 2.2850e-10\n",
            "13/13 [==============================] - 0s 5ms/step - loss: 0.0112\n",
            "[0.011177301406860352, 0.011191761121153831, 0.011192340403795242, 0.011192846111953259]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Batch size:\n",
        "Batch size refers to the nb of samples processed together in each iteration,As we can see after 64 the loss doesn't change a lot, which means 64 can be the best batch size."
      ],
      "metadata": {
        "id": "ZaqTBhLQ2qdq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model\n",
        "model.fit(X_train,y_train,epochs = 300, batch_size=64,verbose=1)\n",
        "# Evaluate the model on the test set\n",
        "loss_score = model.evaluate(X_test, y_test)\n",
        "print(\"Test loss\",loss_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TujWIENu3PVW",
        "outputId": "5dca0178-27e3-41d7-daea-2561188c15cd"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 2.3809e-10\n",
            "Epoch 2/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 2.5508e-10\n",
            "Epoch 3/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 2.6919e-10\n",
            "Epoch 4/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.2986e-10\n",
            "Epoch 5/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.6053e-10\n",
            "Epoch 6/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 2.3779e-10\n",
            "Epoch 7/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.3656e-10\n",
            "Epoch 8/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.3241e-10\n",
            "Epoch 9/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.2021e-10\n",
            "Epoch 10/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 2.3051e-10\n",
            "Epoch 11/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 2.2523e-10\n",
            "Epoch 12/300\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 2.2828e-10\n",
            "Epoch 13/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.3315e-10\n",
            "Epoch 14/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 2.1966e-10\n",
            "Epoch 15/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.2448e-10\n",
            "Epoch 16/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.3654e-10\n",
            "Epoch 17/300\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 2.1493e-10\n",
            "Epoch 18/300\n",
            "2/2 [==============================] - 0s 37ms/step - loss: 2.3738e-10\n",
            "Epoch 19/300\n",
            "2/2 [==============================] - 0s 33ms/step - loss: 2.2132e-10\n",
            "Epoch 20/300\n",
            "2/2 [==============================] - 0s 46ms/step - loss: 2.1361e-10\n",
            "Epoch 21/300\n",
            "2/2 [==============================] - 0s 37ms/step - loss: 2.3454e-10\n",
            "Epoch 22/300\n",
            "2/2 [==============================] - 0s 34ms/step - loss: 2.0740e-10\n",
            "Epoch 23/300\n",
            "2/2 [==============================] - 0s 29ms/step - loss: 2.0874e-10\n",
            "Epoch 24/300\n",
            "2/2 [==============================] - 0s 27ms/step - loss: 2.0513e-10\n",
            "Epoch 25/300\n",
            "2/2 [==============================] - 0s 19ms/step - loss: 2.1225e-10\n",
            "Epoch 26/300\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 2.3582e-10\n",
            "Epoch 27/300\n",
            "2/2 [==============================] - 0s 34ms/step - loss: 2.0311e-10\n",
            "Epoch 28/300\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 2.3287e-10\n",
            "Epoch 29/300\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 2.1908e-10\n",
            "Epoch 30/300\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0673e-10\n",
            "Epoch 31/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.2653e-10\n",
            "Epoch 32/300\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 2.0652e-10\n",
            "Epoch 33/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 2.1114e-10\n",
            "Epoch 34/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 2.0119e-10\n",
            "Epoch 35/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 2.1243e-10\n",
            "Epoch 36/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 2.4977e-10\n",
            "Epoch 37/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 1.9853e-10\n",
            "Epoch 38/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 2.3228e-10\n",
            "Epoch 39/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 2.4774e-10\n",
            "Epoch 40/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 1.9248e-10\n",
            "Epoch 41/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 2.5686e-10\n",
            "Epoch 42/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 2.2551e-10\n",
            "Epoch 43/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.9860e-10\n",
            "Epoch 44/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 2.5059e-10\n",
            "Epoch 45/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.9949e-10\n",
            "Epoch 46/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 2.2281e-10\n",
            "Epoch 47/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 2.5547e-10\n",
            "Epoch 48/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 2.6408e-10\n",
            "Epoch 49/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 2.5280e-10\n",
            "Epoch 50/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 2.4071e-10\n",
            "Epoch 51/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 2.0527e-10\n",
            "Epoch 52/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 2.4144e-10\n",
            "Epoch 53/300\n",
            "2/2 [==============================] - 0s 41ms/step - loss: 1.8207e-10\n",
            "Epoch 54/300\n",
            "2/2 [==============================] - 0s 39ms/step - loss: 1.7981e-10\n",
            "Epoch 55/300\n",
            "2/2 [==============================] - 0s 38ms/step - loss: 2.0462e-10\n",
            "Epoch 56/300\n",
            "2/2 [==============================] - 0s 32ms/step - loss: 1.8224e-10\n",
            "Epoch 57/300\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 1.8798e-10\n",
            "Epoch 58/300\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 1.8408e-10\n",
            "Epoch 59/300\n",
            "2/2 [==============================] - 0s 17ms/step - loss: 1.7040e-10\n",
            "Epoch 60/300\n",
            "2/2 [==============================] - 0s 32ms/step - loss: 1.8669e-10\n",
            "Epoch 61/300\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 1.7014e-10\n",
            "Epoch 62/300\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 1.7725e-10\n",
            "Epoch 63/300\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 1.8645e-10\n",
            "Epoch 64/300\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 1.7319e-10\n",
            "Epoch 65/300\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 1.9202e-10\n",
            "Epoch 66/300\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 1.7936e-10\n",
            "Epoch 67/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.6149e-10\n",
            "Epoch 68/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.6100e-10\n",
            "Epoch 69/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.5890e-10\n",
            "Epoch 70/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.5800e-10\n",
            "Epoch 71/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.5557e-10\n",
            "Epoch 72/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.6419e-10\n",
            "Epoch 73/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.5249e-10\n",
            "Epoch 74/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.8088e-10\n",
            "Epoch 75/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 2.0808e-10\n",
            "Epoch 76/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.5859e-10\n",
            "Epoch 77/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.9040e-10\n",
            "Epoch 78/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.7601e-10\n",
            "Epoch 79/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.6025e-10\n",
            "Epoch 80/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.8299e-10\n",
            "Epoch 81/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.6105e-10\n",
            "Epoch 82/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.6929e-10\n",
            "Epoch 83/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.0272e-10\n",
            "Epoch 84/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.4363e-10\n",
            "Epoch 85/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 1.9993e-10\n",
            "Epoch 86/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.8481e-10\n",
            "Epoch 87/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.6431e-10\n",
            "Epoch 88/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 2.0029e-10\n",
            "Epoch 89/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.5188e-10\n",
            "Epoch 90/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 1.9354e-10\n",
            "Epoch 91/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 1.6540e-10\n",
            "Epoch 92/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.4710e-10\n",
            "Epoch 93/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.5911e-10\n",
            "Epoch 94/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.5425e-10\n",
            "Epoch 95/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.4454e-10\n",
            "Epoch 96/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.6481e-10\n",
            "Epoch 97/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.5652e-10\n",
            "Epoch 98/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.6602e-10\n",
            "Epoch 99/300\n",
            "2/2 [==============================] - 0s 38ms/step - loss: 1.3921e-10\n",
            "Epoch 100/300\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 1.5694e-10\n",
            "Epoch 101/300\n",
            "2/2 [==============================] - 0s 34ms/step - loss: 1.3234e-10\n",
            "Epoch 102/300\n",
            "2/2 [==============================] - 0s 18ms/step - loss: 1.4057e-10\n",
            "Epoch 103/300\n",
            "2/2 [==============================] - 0s 33ms/step - loss: 1.3796e-10\n",
            "Epoch 104/300\n",
            "2/2 [==============================] - 0s 28ms/step - loss: 1.2903e-10\n",
            "Epoch 105/300\n",
            "2/2 [==============================] - 0s 30ms/step - loss: 1.3267e-10\n",
            "Epoch 106/300\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 1.2996e-10\n",
            "Epoch 107/300\n",
            "2/2 [==============================] - 0s 36ms/step - loss: 1.3241e-10\n",
            "Epoch 108/300\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 1.3514e-10\n",
            "Epoch 109/300\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 1.3567e-10\n",
            "Epoch 110/300\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 1.2806e-10\n",
            "Epoch 111/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 1.4896e-10\n",
            "Epoch 112/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 1.2655e-10\n",
            "Epoch 113/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 1.3487e-10\n",
            "Epoch 114/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 1.3097e-10\n",
            "Epoch 115/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 1.2496e-10\n",
            "Epoch 116/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 1.3260e-10\n",
            "Epoch 117/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.2303e-10\n",
            "Epoch 118/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 1.3553e-10\n",
            "Epoch 119/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.6449e-10\n",
            "Epoch 120/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.6648e-10\n",
            "Epoch 121/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.3582e-10\n",
            "Epoch 122/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.5775e-10\n",
            "Epoch 123/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.3143e-10\n",
            "Epoch 124/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.2717e-10\n",
            "Epoch 125/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.3442e-10\n",
            "Epoch 126/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.1923e-10\n",
            "Epoch 127/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.2097e-10\n",
            "Epoch 128/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.1700e-10\n",
            "Epoch 129/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.2245e-10\n",
            "Epoch 130/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.1938e-10\n",
            "Epoch 131/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.2228e-10\n",
            "Epoch 132/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.1602e-10\n",
            "Epoch 133/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.5107e-10\n",
            "Epoch 134/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.4751e-10\n",
            "Epoch 135/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.5896e-10\n",
            "Epoch 136/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.4998e-10\n",
            "Epoch 137/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.1886e-10\n",
            "Epoch 138/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.2492e-10\n",
            "Epoch 139/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.1173e-10\n",
            "Epoch 140/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.1028e-10\n",
            "Epoch 141/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.2134e-10\n",
            "Epoch 142/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.1469e-10\n",
            "Epoch 143/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.0754e-10\n",
            "Epoch 144/300\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 1.6568e-10\n",
            "Epoch 145/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 1.5529e-10\n",
            "Epoch 146/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.5391e-10\n",
            "Epoch 147/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.2844e-10\n",
            "Epoch 148/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.4033e-10\n",
            "Epoch 149/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.2345e-10\n",
            "Epoch 150/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.3468e-10\n",
            "Epoch 151/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.1134e-10\n",
            "Epoch 152/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.1353e-10\n",
            "Epoch 153/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.1712e-10\n",
            "Epoch 154/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.9963e-11\n",
            "Epoch 155/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.1822e-10\n",
            "Epoch 156/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.0532e-10\n",
            "Epoch 157/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.0157e-10\n",
            "Epoch 158/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.1485e-10\n",
            "Epoch 159/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.4210e-11\n",
            "Epoch 160/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.9384e-11\n",
            "Epoch 161/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.0377e-10\n",
            "Epoch 162/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.0965e-10\n",
            "Epoch 163/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.0956e-11\n",
            "Epoch 164/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.1335e-10\n",
            "Epoch 165/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.0139e-11\n",
            "Epoch 166/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.5731e-11\n",
            "Epoch 167/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.6553e-11\n",
            "Epoch 168/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 8.7926e-11\n",
            "Epoch 169/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 1.0058e-10\n",
            "Epoch 170/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.1249e-11\n",
            "Epoch 171/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.0213e-11\n",
            "Epoch 172/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.0768e-10\n",
            "Epoch 173/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 9.4677e-11\n",
            "Epoch 174/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 8.6113e-11\n",
            "Epoch 175/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.0647e-10\n",
            "Epoch 176/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.1201e-11\n",
            "Epoch 177/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.8580e-11\n",
            "Epoch 178/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.1115e-11\n",
            "Epoch 179/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.1536e-11\n",
            "Epoch 180/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 8.3823e-11\n",
            "Epoch 181/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.7540e-11\n",
            "Epoch 182/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 7.9315e-11\n",
            "Epoch 183/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.2185e-11\n",
            "Epoch 184/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.3593e-11\n",
            "Epoch 185/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.7514e-11\n",
            "Epoch 186/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.1881e-11\n",
            "Epoch 187/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 7.8452e-11\n",
            "Epoch 188/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.2663e-11\n",
            "Epoch 189/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.0299e-10\n",
            "Epoch 190/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.6219e-11\n",
            "Epoch 191/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.9730e-11\n",
            "Epoch 192/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.0577e-10\n",
            "Epoch 193/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.4452e-11\n",
            "Epoch 194/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.4397e-11\n",
            "Epoch 195/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.1469e-10\n",
            "Epoch 196/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 9.5362e-11\n",
            "Epoch 197/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.0176e-11\n",
            "Epoch 198/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.2491e-10\n",
            "Epoch 199/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.7848e-11\n",
            "Epoch 200/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 7.9405e-11\n",
            "Epoch 201/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 1.0038e-10\n",
            "Epoch 202/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.1515e-11\n",
            "Epoch 203/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 7.3813e-11\n",
            "Epoch 204/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.7482e-11\n",
            "Epoch 205/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 9.0264e-11\n",
            "Epoch 206/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 6.6741e-11\n",
            "Epoch 207/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 1.0025e-10\n",
            "Epoch 208/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 9.5180e-11\n",
            "Epoch 209/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 7.7068e-11\n",
            "Epoch 210/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 9.5044e-11\n",
            "Epoch 211/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.3103e-10\n",
            "Epoch 212/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.0056e-10\n",
            "Epoch 213/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.7310e-11\n",
            "Epoch 214/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 1.2617e-10\n",
            "Epoch 215/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.1529e-11\n",
            "Epoch 216/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 9.2272e-11\n",
            "Epoch 217/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 8.5372e-11\n",
            "Epoch 218/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.4832e-11\n",
            "Epoch 219/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 7.3343e-11\n",
            "Epoch 220/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 8.7573e-11\n",
            "Epoch 221/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 8.1196e-11\n",
            "Epoch 222/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 6.3301e-11\n",
            "Epoch 223/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 6.7175e-11\n",
            "Epoch 224/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.9819e-11\n",
            "Epoch 225/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 6.0175e-11\n",
            "Epoch 226/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 6.3578e-11\n",
            "Epoch 227/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 6.4177e-11\n",
            "Epoch 228/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 6.1002e-11\n",
            "Epoch 229/300\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 6.5427e-11\n",
            "Epoch 230/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 6.6351e-11\n",
            "Epoch 231/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.9467e-11\n",
            "Epoch 232/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.9111e-11\n",
            "Epoch 233/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 6.0739e-11\n",
            "Epoch 234/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.6106e-11\n",
            "Epoch 235/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.6548e-11\n",
            "Epoch 236/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 5.9242e-11\n",
            "Epoch 237/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 5.7688e-11\n",
            "Epoch 238/300\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 5.6349e-11\n",
            "Epoch 239/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.4119e-11\n",
            "Epoch 240/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.9337e-11\n",
            "Epoch 241/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.4894e-11\n",
            "Epoch 242/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.4705e-11\n",
            "Epoch 243/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.3840e-11\n",
            "Epoch 244/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.2098e-11\n",
            "Epoch 245/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.5371e-11\n",
            "Epoch 246/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.2181e-11\n",
            "Epoch 247/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.4889e-11\n",
            "Epoch 248/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.6919e-11\n",
            "Epoch 249/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.0568e-11\n",
            "Epoch 250/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.1917e-11\n",
            "Epoch 251/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.1982e-11\n",
            "Epoch 252/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.6602e-11\n",
            "Epoch 253/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.0131e-11\n",
            "Epoch 254/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.4344e-11\n",
            "Epoch 255/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 4.8398e-11\n",
            "Epoch 256/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.8836e-11\n",
            "Epoch 257/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.0072e-11\n",
            "Epoch 258/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.7009e-11\n",
            "Epoch 259/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.7992e-11\n",
            "Epoch 260/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.3725e-11\n",
            "Epoch 261/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.0189e-11\n",
            "Epoch 262/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.6093e-11\n",
            "Epoch 263/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 4.9260e-11\n",
            "Epoch 264/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 4.8143e-11\n",
            "Epoch 265/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.2687e-11\n",
            "Epoch 266/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.2246e-11\n",
            "Epoch 267/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.1994e-11\n",
            "Epoch 268/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 5.0232e-11\n",
            "Epoch 269/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.8999e-11\n",
            "Epoch 270/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.0849e-11\n",
            "Epoch 271/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.9621e-11\n",
            "Epoch 272/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.7511e-11\n",
            "Epoch 273/300\n",
            "2/2 [==============================] - 0s 14ms/step - loss: 4.6682e-11\n",
            "Epoch 274/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.5193e-11\n",
            "Epoch 275/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.5351e-11\n",
            "Epoch 276/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 4.2442e-11\n",
            "Epoch 277/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.5494e-11\n",
            "Epoch 278/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.7859e-11\n",
            "Epoch 279/300\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 4.2896e-11\n",
            "Epoch 280/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 4.5311e-11\n",
            "Epoch 281/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.5292e-11\n",
            "Epoch 282/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.0664e-11\n",
            "Epoch 283/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.6522e-11\n",
            "Epoch 284/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 3.9611e-11\n",
            "Epoch 285/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.1575e-11\n",
            "Epoch 286/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.5460e-11\n",
            "Epoch 287/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.3401e-11\n",
            "Epoch 288/300\n",
            "2/2 [==============================] - 0s 10ms/step - loss: 3.9640e-11\n",
            "Epoch 289/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 6.0018e-11\n",
            "Epoch 290/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.8931e-11\n",
            "Epoch 291/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 5.0033e-11\n",
            "Epoch 292/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 5.2191e-11\n",
            "Epoch 293/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 5.4822e-11\n",
            "Epoch 294/300\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 4.2828e-11\n",
            "Epoch 295/300\n",
            "2/2 [==============================] - 0s 12ms/step - loss: 4.5276e-11\n",
            "Epoch 296/300\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 4.4269e-11\n",
            "Epoch 297/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.4756e-11\n",
            "Epoch 298/300\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 3.8088e-11\n",
            "Epoch 299/300\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 4.1550e-11\n",
            "Epoch 300/300\n",
            "2/2 [==============================] - 0s 5ms/step - loss: 3.8731e-11\n",
            "13/13 [==============================] - 0s 3ms/step - loss: 0.0112\n",
            "Test loss 0.011193943209946156\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4-Loss vs epoch**\n",
        "Let's plot the model loss per epoch as well as the mean-average error per epoch for both training and validation data."
      ],
      "metadata": {
        "id": "CAdehO644Br5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving training history\n",
        "model2 = Sequential()\n",
        "model2.add(Dense(64,activation = 'relu', input_shape=(X_train.shape[1],)))\n",
        "model2.add(Dense(32, activation = 'relu'))\n",
        "model2.add(Dense(32, activation = 'relu'))\n",
        "model2.add(Dense(1))\n",
        "model2.add(Dense(1))\n",
        "model2.compile(optimizer = Adam(learning_rate = 0.001), loss='mean_squared_error')\n",
        "history = model2.fit(X_train,y_train,epochs = 250, batch_size=32,validation_data=(X_test, y_test),verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M62LEkp_33fz",
        "outputId": "d34e6349-b9f5-428e-ab9d-a2c3fbbaf6f6"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/250\n",
            "4/4 [==============================] - 2s 115ms/step - loss: 0.5235 - val_loss: 0.4441\n",
            "Epoch 2/250\n",
            "4/4 [==============================] - 0s 47ms/step - loss: 0.4456 - val_loss: 0.3713\n",
            "Epoch 3/250\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.3670 - val_loss: 0.2993\n",
            "Epoch 4/250\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.2905 - val_loss: 0.2284\n",
            "Epoch 5/250\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.2149 - val_loss: 0.1620\n",
            "Epoch 6/250\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.1482 - val_loss: 0.1055\n",
            "Epoch 7/250\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.0929 - val_loss: 0.0671\n",
            "Epoch 8/250\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.0614 - val_loss: 0.0508\n",
            "Epoch 9/250\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 0.0521 - val_loss: 0.0500\n",
            "Epoch 10/250\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0533 - val_loss: 0.0472\n",
            "Epoch 11/250\n",
            "4/4 [==============================] - 0s 48ms/step - loss: 0.0484 - val_loss: 0.0421\n",
            "Epoch 12/250\n",
            "4/4 [==============================] - 0s 44ms/step - loss: 0.0420 - val_loss: 0.0393\n",
            "Epoch 13/250\n",
            "4/4 [==============================] - 0s 51ms/step - loss: 0.0378 - val_loss: 0.0379\n",
            "Epoch 14/250\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0354 - val_loss: 0.0366\n",
            "Epoch 15/250\n",
            "4/4 [==============================] - 0s 44ms/step - loss: 0.0333 - val_loss: 0.0349\n",
            "Epoch 16/250\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.0311 - val_loss: 0.0321\n",
            "Epoch 17/250\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.0285 - val_loss: 0.0291\n",
            "Epoch 18/250\n",
            "4/4 [==============================] - 0s 48ms/step - loss: 0.0268 - val_loss: 0.0271\n",
            "Epoch 19/250\n",
            "4/4 [==============================] - 0s 46ms/step - loss: 0.0248 - val_loss: 0.0258\n",
            "Epoch 20/250\n",
            "4/4 [==============================] - 0s 48ms/step - loss: 0.0233 - val_loss: 0.0248\n",
            "Epoch 21/250\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 0.0223 - val_loss: 0.0241\n",
            "Epoch 22/250\n",
            "4/4 [==============================] - 0s 44ms/step - loss: 0.0210 - val_loss: 0.0229\n",
            "Epoch 23/250\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0198 - val_loss: 0.0216\n",
            "Epoch 24/250\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0189 - val_loss: 0.0206\n",
            "Epoch 25/250\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0180 - val_loss: 0.0197\n",
            "Epoch 26/250\n",
            "4/4 [==============================] - 0s 48ms/step - loss: 0.0174 - val_loss: 0.0188\n",
            "Epoch 27/250\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.0168 - val_loss: 0.0180\n",
            "Epoch 28/250\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0163 - val_loss: 0.0174\n",
            "Epoch 29/250\n",
            "4/4 [==============================] - 0s 24ms/step - loss: 0.0154 - val_loss: 0.0169\n",
            "Epoch 30/250\n",
            "4/4 [==============================] - 0s 25ms/step - loss: 0.0147 - val_loss: 0.0175\n",
            "Epoch 31/250\n",
            "4/4 [==============================] - 0s 25ms/step - loss: 0.0147 - val_loss: 0.0181\n",
            "Epoch 32/250\n",
            "4/4 [==============================] - 0s 29ms/step - loss: 0.0144 - val_loss: 0.0173\n",
            "Epoch 33/250\n",
            "4/4 [==============================] - 0s 25ms/step - loss: 0.0135 - val_loss: 0.0164\n",
            "Epoch 34/250\n",
            "4/4 [==============================] - 0s 26ms/step - loss: 0.0126 - val_loss: 0.0153\n",
            "Epoch 35/250\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 0.0120 - val_loss: 0.0148\n",
            "Epoch 36/250\n",
            "4/4 [==============================] - 0s 54ms/step - loss: 0.0117 - val_loss: 0.0144\n",
            "Epoch 37/250\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 0.0111 - val_loss: 0.0141\n",
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            "Epoch 65/250\n",
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            "Epoch 66/250\n",
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            "Epoch 68/250\n",
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            "Epoch 74/250\n",
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            "Epoch 79/250\n",
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            "Epoch 89/250\n",
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            "Epoch 90/250\n",
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            "Epoch 99/250\n",
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            "Epoch 100/250\n",
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            "Epoch 101/250\n",
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            "Epoch 104/250\n",
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            "Epoch 106/250\n",
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            "Epoch 108/250\n",
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            "Epoch 109/250\n",
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            "Epoch 110/250\n",
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            "Epoch 111/250\n",
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            "Epoch 112/250\n",
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            "Epoch 113/250\n",
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            "Epoch 114/250\n",
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            "Epoch 115/250\n",
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            "Epoch 116/250\n",
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            "Epoch 117/250\n",
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            "Epoch 118/250\n",
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            "Epoch 119/250\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.0012 - val_loss: 0.0062\n",
            "Epoch 120/250\n",
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            "Epoch 121/250\n",
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            "Epoch 122/250\n",
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            "Epoch 123/250\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 0.0011 - val_loss: 0.0064\n",
            "Epoch 124/250\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.0012 - val_loss: 0.0063\n",
            "Epoch 125/250\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 0.0011 - val_loss: 0.0062\n",
            "Epoch 126/250\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.0011 - val_loss: 0.0061\n",
            "Epoch 127/250\n",
            "4/4 [==============================] - 0s 46ms/step - loss: 0.0011 - val_loss: 0.0059\n",
            "Epoch 128/250\n",
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            "Epoch 129/250\n",
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            "Epoch 130/250\n",
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            "Epoch 131/250\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 0.0011 - val_loss: 0.0059\n",
            "Epoch 132/250\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 9.9320e-04 - val_loss: 0.0059\n",
            "Epoch 133/250\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 0.0012 - val_loss: 0.0060\n",
            "Epoch 134/250\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 0.0012 - val_loss: 0.0059\n",
            "Epoch 135/250\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 9.9049e-04 - val_loss: 0.0059\n",
            "Epoch 136/250\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 9.7036e-04 - val_loss: 0.0062\n",
            "Epoch 137/250\n",
            "4/4 [==============================] - 0s 51ms/step - loss: 0.0011 - val_loss: 0.0064\n",
            "Epoch 138/250\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 0.0010 - val_loss: 0.0061\n",
            "Epoch 139/250\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 9.3475e-04 - val_loss: 0.0059\n",
            "Epoch 140/250\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 0.0010 - val_loss: 0.0059\n",
            "Epoch 141/250\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 9.2657e-04 - val_loss: 0.0061\n",
            "Epoch 142/250\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 9.0790e-04 - val_loss: 0.0061\n",
            "Epoch 143/250\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 8.6010e-04 - val_loss: 0.0059\n",
            "Epoch 144/250\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 8.3339e-04 - val_loss: 0.0058\n",
            "Epoch 145/250\n",
            "4/4 [==============================] - 0s 51ms/step - loss: 8.5243e-04 - val_loss: 0.0059\n",
            "Epoch 146/250\n",
            "4/4 [==============================] - 0s 45ms/step - loss: 8.0817e-04 - val_loss: 0.0060\n",
            "Epoch 147/250\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 8.2090e-04 - val_loss: 0.0061\n",
            "Epoch 148/250\n",
            "4/4 [==============================] - 0s 29ms/step - loss: 8.1969e-04 - val_loss: 0.0060\n",
            "Epoch 149/250\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 7.7743e-04 - val_loss: 0.0059\n",
            "Epoch 150/250\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 8.1400e-04 - val_loss: 0.0059\n",
            "Epoch 151/250\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 7.6901e-04 - val_loss: 0.0059\n",
            "Epoch 152/250\n",
            "4/4 [==============================] - 0s 47ms/step - loss: 7.6558e-04 - val_loss: 0.0061\n",
            "Epoch 153/250\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 7.8662e-04 - val_loss: 0.0059\n",
            "Epoch 154/250\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 7.5017e-04 - val_loss: 0.0059\n",
            "Epoch 155/250\n",
            "4/4 [==============================] - 0s 48ms/step - loss: 7.6910e-04 - val_loss: 0.0060\n",
            "Epoch 156/250\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 7.9202e-04 - val_loss: 0.0059\n",
            "Epoch 157/250\n",
            "4/4 [==============================] - 0s 26ms/step - loss: 7.6379e-04 - val_loss: 0.0058\n",
            "Epoch 158/250\n",
            "4/4 [==============================] - 0s 50ms/step - loss: 7.3591e-04 - val_loss: 0.0057\n",
            "Epoch 159/250\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 7.4759e-04 - val_loss: 0.0057\n",
            "Epoch 160/250\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 7.2033e-04 - val_loss: 0.0058\n",
            "Epoch 161/250\n",
            "4/4 [==============================] - 0s 45ms/step - loss: 7.0377e-04 - val_loss: 0.0063\n",
            "Epoch 162/250\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 8.2988e-04 - val_loss: 0.0062\n",
            "Epoch 163/250\n",
            "4/4 [==============================] - 0s 45ms/step - loss: 7.2422e-04 - val_loss: 0.0061\n",
            "Epoch 164/250\n",
            "4/4 [==============================] - 0s 45ms/step - loss: 6.8299e-04 - val_loss: 0.0060\n",
            "Epoch 165/250\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 7.4208e-04 - val_loss: 0.0060\n",
            "Epoch 166/250\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 7.7757e-04 - val_loss: 0.0059\n",
            "Epoch 167/250\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 6.6972e-04 - val_loss: 0.0059\n",
            "Epoch 168/250\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 6.8734e-04 - val_loss: 0.0061\n",
            "Epoch 169/250\n",
            "4/4 [==============================] - 0s 45ms/step - loss: 7.3396e-04 - val_loss: 0.0060\n",
            "Epoch 170/250\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 6.8024e-04 - val_loss: 0.0059\n",
            "Epoch 171/250\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 6.2527e-04 - val_loss: 0.0058\n",
            "Epoch 172/250\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 6.1578e-04 - val_loss: 0.0058\n",
            "Epoch 173/250\n",
            "4/4 [==============================] - 0s 25ms/step - loss: 5.9300e-04 - val_loss: 0.0058\n",
            "Epoch 174/250\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 5.8540e-04 - val_loss: 0.0059\n",
            "Epoch 175/250\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 6.0462e-04 - val_loss: 0.0060\n",
            "Epoch 176/250\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 6.0215e-04 - val_loss: 0.0059\n",
            "Epoch 177/250\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 5.7570e-04 - val_loss: 0.0058\n",
            "Epoch 178/250\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 5.6360e-04 - val_loss: 0.0058\n",
            "Epoch 179/250\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 5.4677e-04 - val_loss: 0.0058\n",
            "Epoch 180/250\n",
            "4/4 [==============================] - 0s 27ms/step - loss: 5.7812e-04 - val_loss: 0.0058\n",
            "Epoch 181/250\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 5.5380e-04 - val_loss: 0.0058\n",
            "Epoch 182/250\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 5.3261e-04 - val_loss: 0.0058\n",
            "Epoch 183/250\n",
            "4/4 [==============================] - 0s 25ms/step - loss: 5.2439e-04 - val_loss: 0.0058\n",
            "Epoch 184/250\n",
            "4/4 [==============================] - 0s 26ms/step - loss: 5.2220e-04 - val_loss: 0.0058\n",
            "Epoch 185/250\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 5.4355e-04 - val_loss: 0.0059\n",
            "Epoch 186/250\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 5.5029e-04 - val_loss: 0.0060\n",
            "Epoch 187/250\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 5.3970e-04 - val_loss: 0.0060\n",
            "Epoch 188/250\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 5.2157e-04 - val_loss: 0.0059\n",
            "Epoch 189/250\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 5.1791e-04 - val_loss: 0.0058\n",
            "Epoch 190/250\n",
            "4/4 [==============================] - 0s 24ms/step - loss: 5.0068e-04 - val_loss: 0.0058\n",
            "Epoch 191/250\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 4.8528e-04 - val_loss: 0.0057\n",
            "Epoch 192/250\n",
            "4/4 [==============================] - 0s 24ms/step - loss: 4.9264e-04 - val_loss: 0.0058\n",
            "Epoch 193/250\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 4.6338e-04 - val_loss: 0.0058\n",
            "Epoch 194/250\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 4.9338e-04 - val_loss: 0.0058\n",
            "Epoch 195/250\n",
            "4/4 [==============================] - 0s 26ms/step - loss: 4.8003e-04 - val_loss: 0.0060\n",
            "Epoch 196/250\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 4.9513e-04 - val_loss: 0.0059\n",
            "Epoch 197/250\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 4.5660e-04 - val_loss: 0.0059\n",
            "Epoch 198/250\n",
            "4/4 [==============================] - 0s 27ms/step - loss: 5.5898e-04 - val_loss: 0.0059\n",
            "Epoch 199/250\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 5.3614e-04 - val_loss: 0.0060\n",
            "Epoch 200/250\n",
            "4/4 [==============================] - 0s 27ms/step - loss: 5.0290e-04 - val_loss: 0.0059\n",
            "Epoch 201/250\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 4.6204e-04 - val_loss: 0.0058\n",
            "Epoch 202/250\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 4.7459e-04 - val_loss: 0.0057\n",
            "Epoch 203/250\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 4.5843e-04 - val_loss: 0.0056\n",
            "Epoch 204/250\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 4.5147e-04 - val_loss: 0.0057\n",
            "Epoch 205/250\n",
            "4/4 [==============================] - 0s 25ms/step - loss: 4.5970e-04 - val_loss: 0.0057\n",
            "Epoch 206/250\n",
            "4/4 [==============================] - 0s 24ms/step - loss: 4.3004e-04 - val_loss: 0.0057\n",
            "Epoch 207/250\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 4.1601e-04 - val_loss: 0.0058\n",
            "Epoch 208/250\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 4.1498e-04 - val_loss: 0.0058\n",
            "Epoch 209/250\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 4.0364e-04 - val_loss: 0.0059\n",
            "Epoch 210/250\n",
            "4/4 [==============================] - 0s 27ms/step - loss: 4.0228e-04 - val_loss: 0.0059\n",
            "Epoch 211/250\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 4.3782e-04 - val_loss: 0.0058\n",
            "Epoch 212/250\n",
            "4/4 [==============================] - 0s 44ms/step - loss: 4.0824e-04 - val_loss: 0.0057\n",
            "Epoch 213/250\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 4.4566e-04 - val_loss: 0.0057\n",
            "Epoch 214/250\n",
            "4/4 [==============================] - 0s 46ms/step - loss: 4.2757e-04 - val_loss: 0.0057\n",
            "Epoch 215/250\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 3.7635e-04 - val_loss: 0.0057\n",
            "Epoch 216/250\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 3.9917e-04 - val_loss: 0.0058\n",
            "Epoch 217/250\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 3.8124e-04 - val_loss: 0.0057\n",
            "Epoch 218/250\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 3.9147e-04 - val_loss: 0.0058\n",
            "Epoch 219/250\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 4.1134e-04 - val_loss: 0.0059\n",
            "Epoch 220/250\n",
            "4/4 [==============================] - 0s 27ms/step - loss: 3.7436e-04 - val_loss: 0.0059\n",
            "Epoch 221/250\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 3.9987e-04 - val_loss: 0.0060\n",
            "Epoch 222/250\n",
            "4/4 [==============================] - 0s 27ms/step - loss: 4.3522e-04 - val_loss: 0.0058\n",
            "Epoch 223/250\n",
            "4/4 [==============================] - 0s 43ms/step - loss: 3.8598e-04 - val_loss: 0.0059\n",
            "Epoch 224/250\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 3.7670e-04 - val_loss: 0.0058\n",
            "Epoch 225/250\n",
            "4/4 [==============================] - 0s 40ms/step - loss: 3.5992e-04 - val_loss: 0.0058\n",
            "Epoch 226/250\n",
            "4/4 [==============================] - 0s 25ms/step - loss: 3.5354e-04 - val_loss: 0.0058\n",
            "Epoch 227/250\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 3.4780e-04 - val_loss: 0.0058\n",
            "Epoch 228/250\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 3.5002e-04 - val_loss: 0.0058\n",
            "Epoch 229/250\n",
            "4/4 [==============================] - 0s 28ms/step - loss: 3.5610e-04 - val_loss: 0.0057\n",
            "Epoch 230/250\n",
            "4/4 [==============================] - 0s 25ms/step - loss: 3.3612e-04 - val_loss: 0.0058\n",
            "Epoch 231/250\n",
            "4/4 [==============================] - 0s 41ms/step - loss: 3.3230e-04 - val_loss: 0.0057\n",
            "Epoch 232/250\n",
            "4/4 [==============================] - 0s 29ms/step - loss: 3.2247e-04 - val_loss: 0.0057\n",
            "Epoch 233/250\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 3.1516e-04 - val_loss: 0.0057\n",
            "Epoch 234/250\n",
            "4/4 [==============================] - 0s 42ms/step - loss: 3.1343e-04 - val_loss: 0.0057\n",
            "Epoch 235/250\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 3.2298e-04 - val_loss: 0.0057\n",
            "Epoch 236/250\n",
            "4/4 [==============================] - 0s 46ms/step - loss: 3.1415e-04 - val_loss: 0.0058\n",
            "Epoch 237/250\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 3.6727e-04 - val_loss: 0.0059\n",
            "Epoch 238/250\n",
            "4/4 [==============================] - 0s 29ms/step - loss: 3.4228e-04 - val_loss: 0.0058\n",
            "Epoch 239/250\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 3.4066e-04 - val_loss: 0.0058\n",
            "Epoch 240/250\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 3.1996e-04 - val_loss: 0.0058\n",
            "Epoch 241/250\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 3.2510e-04 - val_loss: 0.0058\n",
            "Epoch 242/250\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 3.0909e-04 - val_loss: 0.0056\n",
            "Epoch 243/250\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 3.1495e-04 - val_loss: 0.0057\n",
            "Epoch 244/250\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 2.9775e-04 - val_loss: 0.0058\n",
            "Epoch 245/250\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 3.5845e-04 - val_loss: 0.0058\n",
            "Epoch 246/250\n",
            "4/4 [==============================] - 0s 20ms/step - loss: 3.3997e-04 - val_loss: 0.0057\n",
            "Epoch 247/250\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 2.8434e-04 - val_loss: 0.0057\n",
            "Epoch 248/250\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 3.1571e-04 - val_loss: 0.0057\n",
            "Epoch 249/250\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 2.9125e-04 - val_loss: 0.0057\n",
            "Epoch 250/250\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 2.9183e-04 - val_loss: 0.0057\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the training and validation loss\n",
        "train_loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']"
      ],
      "metadata": {
        "id": "DulhwkPq7UXS"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the model loss per epoch\n",
        "plt.plot(train_loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Model Loss per Epoch')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "-DHMODqV7Kn-",
        "outputId": "4b4dc7d9-97b7-44f6-b352-6773f87f1e03"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **5 - R² Score**"
      ],
      "metadata": {
        "id": "nAy0PcZSA35w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted = model2.predict(X_test)\n",
        "print(r2_score(y_test, y_predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9dyRr7AmA3m0",
        "outputId": "fc5ffe26-a9ff-4481-cfb7-1a3a15de9719"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/13 [==============================] - 0s 2ms/step\n",
            "0.7083769165586394\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Conclusion:***\n",
        "Our R² score indicates that our regression model explains approximately 70.8% of the variance in the target variable.\n",
        "\n",
        "However, there is still room for improvement, as there is a significant portion of the variance that remains unexplained."
      ],
      "metadata": {
        "id": "LzLKezMPGlKQ"
      }
    }
  ]
}