ANN.ipynb

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      "name": "ANN.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP5ekWPM0buxjnc/jqJfi0h",
      "include_colab_link": true
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      "name": "python3",
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/prasantpoudel/100-days-of-machine-learning/blob/main/ANN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MixL-NNMXXUt",
        "outputId": "7e695ad4-54cd-4aed-9ec2-2dc1acdda785"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow-gpu\n",
            "  Downloading tensorflow_gpu-2.9.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (511.7 MB)\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow-gpu) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow-gpu) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow-gpu) (2022.6.15)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<2.10,>=2.9->tensorflow-gpu) (3.0.4)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow-gpu) (3.2.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->tensorflow-gpu) (3.0.9)\n",
            "Installing collected packages: tensorflow-estimator, tensorboard, keras, gast, flatbuffers, tensorflow-gpu\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.8.0\n",
            "    Uninstalling tensorflow-estimator-2.8.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.8.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.8.0\n",
            "    Uninstalling tensorboard-2.8.0:\n",
            "      Successfully uninstalled tensorboard-2.8.0\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.8.0\n",
            "    Uninstalling keras-2.8.0:\n",
            "      Successfully uninstalled keras-2.8.0\n",
            "  Attempting uninstall: gast\n",
            "    Found existing installation: gast 0.5.3\n",
            "    Uninstalling gast-0.5.3:\n",
            "      Successfully uninstalled gast-0.5.3\n",
            "  Attempting uninstall: flatbuffers\n",
            "    Found existing installation: flatbuffers 2.0\n",
            "    Uninstalling flatbuffers-2.0:\n",
            "      Successfully uninstalled flatbuffers-2.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.8.2+zzzcolab20220527125636 requires keras<2.9,>=2.8.0rc0, but you have keras 2.9.0 which is incompatible.\n",
            "tensorflow 2.8.2+zzzcolab20220527125636 requires tensorboard<2.9,>=2.8, but you have tensorboard 2.9.1 which is incompatible.\n",
            "tensorflow 2.8.2+zzzcolab20220527125636 requires tensorflow-estimator<2.9,>=2.8, but you have tensorflow-estimator 2.9.0 which is incompatible.\u001b[0m\n",
            "Successfully installed flatbuffers-1.12 gast-0.4.0 keras-2.9.0 tensorboard-2.9.1 tensorflow-estimator-2.9.0 tensorflow-gpu-2.9.1\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow-gpu"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J-tRIY8uX6x1",
        "outputId": "9cdbc6eb-741d-4675-d081-463890abf65b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.9.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## basic library\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "4jVZouDcYU7n"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv('Churn_Modelling.csv')\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "BPFxZjyiYqwQ",
        "outputId": "5593bc5b-99ea-4f79-dc31-d9909d67d595"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
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              "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
              "\n",
              "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
              "0       2       0.00              1          1               1   \n",
              "1       1   83807.86              1          0               1   \n",
              "2       8  159660.80              3          1               0   \n",
              "3       1       0.00              2          0               0   \n",
              "4       2  125510.82              1          1               1   \n",
              "\n",
              "   EstimatedSalary  Exited  \n",
              "0        101348.88       1  \n",
              "1        112542.58       0  \n",
              "2        113931.57       1  \n",
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              "      <td>Mitchell</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fce1c101-ae68-416d-b2be-802930e49849');\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## divide the dataset into dependent and independent\n",
        "x=dataset.iloc[:,3:13]\n",
        "y=dataset.iloc[:,13]"
      ],
      "metadata": {
        "id": "ivqBIbWQY2XW"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x.head)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pi8Csj-mcDjs",
        "outputId": "54b115c5-c546-4d33-c777-342a19437632"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<bound method NDFrame.head of       CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
            "0             619    France  Female   42       2       0.00              1   \n",
            "1             608     Spain  Female   41       1   83807.86              1   \n",
            "2             502    France  Female   42       8  159660.80              3   \n",
            "3             699    France  Female   39       1       0.00              2   \n",
            "4             850     Spain  Female   43       2  125510.82              1   \n",
            "...           ...       ...     ...  ...     ...        ...            ...   \n",
            "9995          771    France    Male   39       5       0.00              2   \n",
            "9996          516    France    Male   35      10   57369.61              1   \n",
            "9997          709    France  Female   36       7       0.00              1   \n",
            "9998          772   Germany    Male   42       3   75075.31              2   \n",
            "9999          792    France  Female   28       4  130142.79              1   \n",
            "\n",
            "      HasCrCard  IsActiveMember  EstimatedSalary  \n",
            "0             1               1        101348.88  \n",
            "1             0               1        112542.58  \n",
            "2             1               0        113931.57  \n",
            "3             0               0         93826.63  \n",
            "4             1               1         79084.10  \n",
            "...         ...             ...              ...  \n",
            "9995          1               0         96270.64  \n",
            "9996          1               1        101699.77  \n",
            "9997          0               1         42085.58  \n",
            "9998          1               0         92888.52  \n",
            "9999          1               0         38190.78  \n",
            "\n",
            "[10000 rows x 10 columns]>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L8YaELuMcFhk",
        "outputId": "317c0379-1e14-498a-8264-2b2be17b68f8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of 0       1\n",
              "1       0\n",
              "2       1\n",
              "3       0\n",
              "4       0\n",
              "       ..\n",
              "9995    0\n",
              "9996    0\n",
              "9997    1\n",
              "9998    1\n",
              "9999    0\n",
              "Name: Exited, Length: 10000, dtype: int64>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## feature eng\n",
        "geography=pd.get_dummies(x['Geography'],drop_first=True)\n",
        "gender=pd.get_dummies(x['Gender'],drop_first=True)"
      ],
      "metadata": {
        "id": "gstEmIOwcN2Z"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(geography)\n",
        "print(gender)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QlVJ3cO5cl-1",
        "outputId": "a30f420f-3ca6-454c-f39b-a043208842b9"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Germany  Spain\n",
            "0           0      0\n",
            "1           0      1\n",
            "2           0      0\n",
            "3           0      0\n",
            "4           0      1\n",
            "...       ...    ...\n",
            "9995        0      0\n",
            "9996        0      0\n",
            "9997        0      0\n",
            "9998        1      0\n",
            "9999        0      0\n",
            "\n",
            "[10000 rows x 2 columns]\n",
            "      Male\n",
            "0        0\n",
            "1        0\n",
            "2        0\n",
            "3        0\n",
            "4        0\n",
            "...    ...\n",
            "9995     1\n",
            "9996     1\n",
            "9997     0\n",
            "9998     1\n",
            "9999     0\n",
            "\n",
            "[10000 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=x.drop(['Geography','Gender'],axis=1)"
      ],
      "metadata": {
        "id": "s7vqE92-cvSM"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rCuizA1SeBRc",
        "outputId": "9c1bea4c-10a2-46ec-c420-848b27e4a5b7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
              "0          619   42       2       0.00              1          1   \n",
              "1          608   41       1   83807.86              1          0   \n",
              "2          502   42       8  159660.80              3          1   \n",
              "3          699   39       1       0.00              2          0   \n",
              "4          850   43       2  125510.82              1          1   \n",
              "\n",
              "   IsActiveMember  EstimatedSalary  \n",
              "0               1        101348.88  \n",
              "1               1        112542.58  \n",
              "2               0        113931.57  \n",
              "3               0         93826.63  \n",
              "4               1         79084.10  "
            ],
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              "      <th></th>\n",
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              "                                                     [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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      "cell_type": "code",
      "source": [
        "x=pd.concat([x,geography,gender],axis=1)"
      ],
      "metadata": {
        "id": "RoCGrYhkeFqa"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "4b948md8emy8",
        "outputId": "3f87354a-0e06-4d95-cff1-69bed4fb228c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
              "0          619   42       2       0.00              1          1   \n",
              "1          608   41       1   83807.86              1          0   \n",
              "2          502   42       8  159660.80              3          1   \n",
              "3          699   39       1       0.00              2          0   \n",
              "4          850   43       2  125510.82              1          1   \n",
              "\n",
              "   IsActiveMember  EstimatedSalary  Germany  Spain  Male  \n",
              "0               1        101348.88        0      0     0  \n",
              "1               1        112542.58        0      1     0  \n",
              "2               0        113931.57        0      0     0  \n",
              "3               0         93826.63        0      0     0  \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-20930a00-88a9-4b92-9287-6cbd1cfd5c5f 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-20930a00-88a9-4b92-9287-6cbd1cfd5c5f');\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": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#split into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)"
      ],
      "metadata": {
        "id": "QfiTVhKJeomM"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " #feature scalling \n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc=StandardScaler()\n",
        "x_train=sc.fit_transform(x_train)\n",
        "x_test=sc.fit_transform(x_test)"
      ],
      "metadata": {
        "id": "wNol8mZeflww"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i7ovRCvhhqRI",
        "outputId": "ca9ed30c-3718-4101-b684-317954da66ee"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.16958176, -0.46460796,  0.00666099, ..., -0.5698444 ,\n",
              "         1.74309049, -1.09168714],\n",
              "       [-2.30455945,  0.30102557, -1.37744033, ...,  1.75486502,\n",
              "        -0.57369368,  0.91601335],\n",
              "       [-1.19119591, -0.94312892, -1.031415  , ..., -0.5698444 ,\n",
              "        -0.57369368, -1.09168714],\n",
              "       ...,\n",
              "       [ 0.9015152 , -0.36890377,  0.00666099, ..., -0.5698444 ,\n",
              "        -0.57369368,  0.91601335],\n",
              "       [-0.62420521, -0.08179119,  1.39076231, ..., -0.5698444 ,\n",
              "         1.74309049, -1.09168714],\n",
              "       [-0.28401079,  0.87525072, -1.37744033, ...,  1.75486502,\n",
              "        -0.57369368, -1.09168714]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9uyN1GLmiPvJ",
        "outputId": "c272642e-e131-4717-d07e-4395b80bc8c0"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.56129438, -0.39401698,  0.9869706 , ...,  1.62776996,\n",
              "        -0.57427105, -1.11339196],\n",
              "       [-1.33847768,  0.07611425, -1.08432132, ..., -0.61433742,\n",
              "        -0.57427105, -1.11339196],\n",
              "       [ 0.58347561,  0.26416674,  0.9869706 , ..., -0.61433742,\n",
              "         1.74133801, -1.11339196],\n",
              "       ...,\n",
              "       [-0.76084144, -0.29999074, -1.42953664, ..., -0.61433742,\n",
              "         1.74133801,  0.8981563 ],\n",
              "       [-0.0046631 , -0.48804323, -0.39389068, ...,  1.62776996,\n",
              "        -0.57427105,  0.8981563 ],\n",
              "       [-0.81335383, -0.86414821,  0.9869706 , ...,  1.62776996,\n",
              "        -0.57427105,  0.8981563 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aH_EIY35ikY5",
        "outputId": "7bcbcbe9-64ec-4835-d670-467d3586075d"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8000, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## part two lets create an ANN\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LeakyReLU,PReLU,ReLU\n",
        "from tensorflow.keras.layers import Dropout"
      ],
      "metadata": {
        "id": "y4bGbkHwisaE"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#lets initialize the ANN\n",
        "classifier=Sequential()"
      ],
      "metadata": {
        "id": "2rmyxI7ZkGaC"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## add input layer\n",
        "classifier.add(Dense(units=11,activation='relu'))"
      ],
      "metadata": {
        "id": "hmO-jmPOmzhT"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## adding the first hideen layer\n",
        "classifier.add(Dense(units=7,activation='relu'))\n",
        "classifier.add(Dropout(0.2))"
      ],
      "metadata": {
        "id": "DPehA3WinEVE"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#adding 2nd hidden layer\n",
        "classifier.add(Dense(units=6,activation='relu'))\n",
        "classifier.add(Dropout(0.3))"
      ],
      "metadata": {
        "id": "EoP8e5N8nW7M"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#adding the output layer\n",
        "classifier.add(Dense(1,activation='sigmoid'))"
      ],
      "metadata": {
        "id": "x27eh7K2njNa"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow"
      ],
      "metadata": {
        "id": "jsERFwbLoDxw"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "opt=tensorflow.keras.optimizers.Adam(learning_rate=0.01)"
      ],
      "metadata": {
        "id": "qzmuUOfvoGOb"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.compile(optimizer=opt,loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "9ZOPOdVUnwvK"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## early stopping\n",
        "\n",
        "early_stopping=tf.keras.callbacks.EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    min_delta=0.0001,\n",
        "    patience=20,\n",
        "    verbose=1,\n",
        "    mode=\"auto\",\n",
        "    baseline=None,\n",
        "    restore_best_weights=False,\n",
        ")\n"
      ],
      "metadata": {
        "id": "GnELky5Rp3AY"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_history=classifier.fit(x_train,y_train,validation_split=0.33,batch_size=10,epochs=1000,callbacks=early_stopping)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SPWiQGxoU5q",
        "outputId": "b9a9ab21-771d-483c-e16f-0110689d7167"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "536/536 [==============================] - 3s 4ms/step - loss: 0.4730 - accuracy: 0.7962 - val_loss: 0.4252 - val_accuracy: 0.7955\n",
            "Epoch 2/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4306 - accuracy: 0.7962 - val_loss: 0.4077 - val_accuracy: 0.7955\n",
            "Epoch 3/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4168 - accuracy: 0.7962 - val_loss: 0.4014 - val_accuracy: 0.7955\n",
            "Epoch 4/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4109 - accuracy: 0.7966 - val_loss: 0.4017 - val_accuracy: 0.7955\n",
            "Epoch 5/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4086 - accuracy: 0.8162 - val_loss: 0.3952 - val_accuracy: 0.8421\n",
            "Epoch 6/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4026 - accuracy: 0.8429 - val_loss: 0.3894 - val_accuracy: 0.8466\n",
            "Epoch 7/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4040 - accuracy: 0.8197 - val_loss: 0.3926 - val_accuracy: 0.7955\n",
            "Epoch 8/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4033 - accuracy: 0.8291 - val_loss: 0.4068 - val_accuracy: 0.8311\n",
            "Epoch 9/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4038 - accuracy: 0.8336 - val_loss: 0.3846 - val_accuracy: 0.8429\n",
            "Epoch 10/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.4019 - accuracy: 0.8373 - val_loss: 0.3866 - val_accuracy: 0.8413\n",
            "Epoch 11/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3965 - accuracy: 0.8408 - val_loss: 0.4116 - val_accuracy: 0.8042\n",
            "Epoch 12/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3967 - accuracy: 0.8403 - val_loss: 0.3910 - val_accuracy: 0.8357\n",
            "Epoch 13/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3957 - accuracy: 0.8440 - val_loss: 0.3994 - val_accuracy: 0.8410\n",
            "Epoch 14/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3935 - accuracy: 0.8395 - val_loss: 0.3932 - val_accuracy: 0.8258\n",
            "Epoch 15/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3971 - accuracy: 0.8446 - val_loss: 0.4087 - val_accuracy: 0.8262\n",
            "Epoch 16/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3940 - accuracy: 0.8453 - val_loss: 0.3926 - val_accuracy: 0.8357\n",
            "Epoch 17/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3916 - accuracy: 0.8472 - val_loss: 0.3871 - val_accuracy: 0.8395\n",
            "Epoch 18/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3918 - accuracy: 0.8464 - val_loss: 0.3920 - val_accuracy: 0.8466\n",
            "Epoch 19/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3922 - accuracy: 0.8490 - val_loss: 0.3867 - val_accuracy: 0.8417\n",
            "Epoch 20/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3908 - accuracy: 0.8489 - val_loss: 0.3867 - val_accuracy: 0.8451\n",
            "Epoch 21/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3864 - accuracy: 0.8453 - val_loss: 0.4111 - val_accuracy: 0.8383\n",
            "Epoch 22/1000\n",
            "536/536 [==============================] - 4s 7ms/step - loss: 0.3819 - accuracy: 0.8507 - val_loss: 0.4022 - val_accuracy: 0.8395\n",
            "Epoch 23/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3817 - accuracy: 0.8528 - val_loss: 0.3836 - val_accuracy: 0.8360\n",
            "Epoch 24/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3857 - accuracy: 0.8479 - val_loss: 0.3892 - val_accuracy: 0.8391\n",
            "Epoch 25/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3797 - accuracy: 0.8509 - val_loss: 0.3910 - val_accuracy: 0.8330\n",
            "Epoch 26/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3835 - accuracy: 0.8503 - val_loss: 0.3785 - val_accuracy: 0.8432\n",
            "Epoch 27/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3902 - accuracy: 0.8530 - val_loss: 0.3886 - val_accuracy: 0.8406\n",
            "Epoch 28/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3836 - accuracy: 0.8541 - val_loss: 0.3779 - val_accuracy: 0.8410\n",
            "Epoch 29/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3826 - accuracy: 0.8533 - val_loss: 0.3883 - val_accuracy: 0.8395\n",
            "Epoch 30/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3813 - accuracy: 0.8511 - val_loss: 0.3885 - val_accuracy: 0.8349\n",
            "Epoch 31/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3784 - accuracy: 0.8584 - val_loss: 0.3974 - val_accuracy: 0.8383\n",
            "Epoch 32/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3813 - accuracy: 0.8509 - val_loss: 0.3846 - val_accuracy: 0.8444\n",
            "Epoch 33/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3779 - accuracy: 0.8545 - val_loss: 0.3869 - val_accuracy: 0.8466\n",
            "Epoch 34/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3763 - accuracy: 0.8522 - val_loss: 0.3880 - val_accuracy: 0.8315\n",
            "Epoch 35/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3796 - accuracy: 0.8558 - val_loss: 0.3870 - val_accuracy: 0.8436\n",
            "Epoch 36/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3745 - accuracy: 0.8535 - val_loss: 0.3941 - val_accuracy: 0.8395\n",
            "Epoch 37/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3681 - accuracy: 0.8576 - val_loss: 0.3880 - val_accuracy: 0.8376\n",
            "Epoch 38/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3779 - accuracy: 0.8554 - val_loss: 0.3873 - val_accuracy: 0.8516\n",
            "Epoch 39/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3826 - accuracy: 0.8563 - val_loss: 0.3939 - val_accuracy: 0.8489\n",
            "Epoch 40/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3749 - accuracy: 0.8574 - val_loss: 0.3856 - val_accuracy: 0.8451\n",
            "Epoch 41/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3697 - accuracy: 0.8541 - val_loss: 0.3943 - val_accuracy: 0.8466\n",
            "Epoch 42/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3754 - accuracy: 0.8574 - val_loss: 0.3870 - val_accuracy: 0.8451\n",
            "Epoch 43/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3807 - accuracy: 0.8546 - val_loss: 0.3816 - val_accuracy: 0.8402\n",
            "Epoch 44/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3793 - accuracy: 0.8511 - val_loss: 0.3959 - val_accuracy: 0.8485\n",
            "Epoch 45/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3781 - accuracy: 0.8569 - val_loss: 0.3875 - val_accuracy: 0.8281\n",
            "Epoch 46/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3781 - accuracy: 0.8554 - val_loss: 0.4080 - val_accuracy: 0.8448\n",
            "Epoch 47/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3690 - accuracy: 0.8502 - val_loss: 0.4029 - val_accuracy: 0.8417\n",
            "Epoch 48/1000\n",
            "536/536 [==============================] - 2s 4ms/step - loss: 0.3799 - accuracy: 0.8496 - val_loss: 0.4047 - val_accuracy: 0.8466\n",
            "Epoch 48: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_history.history.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmiHDkpro0S4",
        "outputId": "054e454f-6719-4eb0-a1b1-1428ace502b5"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#summarize history for accuracy\n",
        "plt.plot(model_history.history['accuracy'])\n",
        "plt.plot(model_history.history['val_accuracy'])\n",
        "plt.title(\"model accuracy\")\n",
        "plt.ylabel(\"accuracy\")\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.legend(['train','test'],loc='upper_left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "FT_4XASPpsCI",
        "outputId": "a65b3993-0633-4b67-cbb4-ff2f58f2de27"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: MatplotlibDeprecationWarning: Unrecognized location 'upper_left'. Falling back on 'best'; valid locations are\n",
            "\tbest\n",
            "\tupper right\n",
            "\tupper left\n",
            "\tlower left\n",
            "\tlower right\n",
            "\tright\n",
            "\tcenter left\n",
            "\tcenter right\n",
            "\tlower center\n",
            "\tupper center\n",
            "\tcenter\n",
            "This will raise an exception in 3.3.\n",
            "  import sys\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#summarize history for loss\n",
        "plt.plot(model_history.history['loss'])\n",
        "plt.plot(model_history.history['val_loss'])\n",
        "plt.title(\"model loss\")\n",
        "plt.ylabel(\"loss\")\n",
        "plt.xlabel(\"epoch\")\n",
        "plt.legend(['train','test'],loc='upper_left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "wvbTFs8ZsBBM",
        "outputId": "2d3abae1-9fb0-40bf-b197-130f621afb82"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: MatplotlibDeprecationWarning: Unrecognized location 'upper_left'. Falling back on 'best'; valid locations are\n",
            "\tbest\n",
            "\tupper right\n",
            "\tupper left\n",
            "\tlower left\n",
            "\tlower right\n",
            "\tright\n",
            "\tcenter left\n",
            "\tcenter right\n",
            "\tlower center\n",
            "\tupper center\n",
            "\tcenter\n",
            "This will raise an exception in 3.3.\n",
            "  import sys\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#prediction \n",
        "y_pred=classifier.predict(x_test)\n",
        "y_pred=(y_pred>=0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L3g1Q8TfsxOQ",
        "outputId": "d42fbeba-8d77-4f9b-aaea-e1ec5271bba8"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 1ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## make the confusion matrics\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm=confusion_matrix(y_test,y_pred)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCZw3uZYtK4A",
        "outputId": "7293712f-518c-4fd4-c224-b72611b79769"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1494,  101],\n",
              "       [ 196,  209]])"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##accuracy\n",
        "from sklearn.metrics import accuracy_score\n",
        "score=accuracy_score(y_pred,y_test)\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "efzLReQctZc5",
        "outputId": "609e2d1b-5e1a-4c2f-f158-bfdef7667598"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8515"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to get the weights\n",
        "classifier.get_weights()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6kNWDNZlt3bT",
        "outputId": "24cf59b9-f3ba-4361-ff75-17b3233e75da"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[-2.1538500e-01,  2.3315471e-01, -4.0069083e-03,  2.4433912e-01,\n",
              "         -5.5157280e-01,  7.7608520e-01,  2.0334990e+00, -4.4643307e-01,\n",
              "          8.2275432e-01, -8.2499966e-02,  4.7821274e-01],\n",
              "        [ 4.1459260e+00, -4.0483561e+00,  1.9377550e+00, -6.2064915e+00,\n",
              "         -4.3234682e+00, -3.1840515e+00, -4.4105964e+00,  3.0826573e+00,\n",
              "         -1.4852288e+00, -7.0847452e-02,  1.2763630e+00],\n",
              "        [-7.0358294e-01,  5.2093881e-01, -1.9926377e-01, -7.6987588e-01,\n",
              "         -1.0061058e+00,  1.2838877e+00,  4.1751280e-01, -1.6838955e+00,\n",
              "          1.3802212e+00,  3.6436209e-01,  1.6956114e+00],\n",
              "        [ 7.3057017e-04, -8.0599803e-01,  1.2054406e+00, -7.0010495e-01,\n",
              "         -1.0459361e+00,  2.1100993e+00, -2.3235579e+00,  4.7971508e-01,\n",
              "         -4.3193855e+00, -5.1678143e+00, -3.7249980e+00],\n",
              "        [ 8.8264990e-01, -2.9737775e+00,  4.5682249e+00, -1.9122864e+00,\n",
              "          8.3958846e-01, -1.3402228e+00, -8.8807130e-01, -2.0607386e+00,\n",
              "         -3.0621085e-01, -4.7921433e+00,  2.9152257e+00],\n",
              "        [-2.1024613e-01,  1.0839720e+00, -6.1061507e-01, -4.7810730e-01,\n",
              "         -1.3956121e+00, -8.9013532e-02,  2.2746265e+00, -1.2687002e+00,\n",
              "         -1.7252074e+00,  4.3879944e-01,  1.8094768e-01],\n",
              "        [ 3.0611012e+00,  2.7361863e+00, -6.0153919e-01,  1.8395511e+00,\n",
              "         -9.1206872e-01, -3.7796752e+00,  8.0417275e-01, -2.3573501e+00,\n",
              "          3.1722632e-01,  6.0974795e-01,  2.0161555e+00],\n",
              "        [ 1.4218438e-01,  1.2864507e+00, -2.7346128e-01, -5.0119305e-01,\n",
              "         -6.6778308e-01,  2.9541559e+00,  2.7183931e+00,  8.8433522e-01,\n",
              "         -8.1332898e-01,  3.5696316e-01,  1.1790500e+00],\n",
              "        [ 2.5023809e-01,  1.2258660e+00, -2.5425625e-01,  7.7727455e-01,\n",
              "         -1.0666051e+00,  2.1271393e-01,  6.1147106e-01, -1.2746050e+00,\n",
              "          2.0526316e+00,  3.6881599e+00,  1.6996269e+00],\n",
              "        [ 3.4549439e-01,  1.4265093e+00,  5.8689493e-01,  7.7367073e-01,\n",
              "         -9.3694633e-01, -5.7629615e-01, -9.2465597e-01, -7.6339275e-01,\n",
              "         -1.7931734e-01,  1.5603767e-01, -5.3678882e-01],\n",
              "        [ 8.4094948e-01,  4.5892444e-01,  8.9978024e-02, -1.0170242e+00,\n",
              "         -3.5350904e-01, -6.4203238e-01,  1.0045768e+00, -4.7896698e-01,\n",
              "          2.6154664e+00,  1.5154048e-02,  2.2207286e+00]], dtype=float32),\n",
              " array([-6.697588  , -0.09542423, -6.038302  , -4.691702  ,  3.8912117 ,\n",
              "        -2.5026941 , -0.52012503, -1.6089154 , -4.335178  , -2.2421663 ,\n",
              "        -3.4805372 ], dtype=float32),\n",
              " array([[-5.39802885e+00,  3.26818132e+00, -6.20614910e+00,\n",
              "         -2.26844835e+00,  1.36385095e+00, -5.51321983e-01,\n",
              "         -6.72762096e-01],\n",
              "        [-1.12903380e+00,  1.02591145e+00,  3.46325971e-02,\n",
              "         -2.43571591e+00, -5.98735273e-01,  5.28749704e-01,\n",
              "          4.98802401e-03],\n",
              "        [ 2.27110529e+00, -3.42677450e+00, -1.02805412e+00,\n",
              "          3.58535028e+00,  6.67260408e-01, -3.19658470e+00,\n",
              "         -5.21808565e-01],\n",
              "        [-7.52691936e+00,  3.60981554e-01, -6.64571524e+00,\n",
              "          1.31948805e+00, -1.12503862e+00,  9.76446509e-01,\n",
              "         -8.22078586e-01],\n",
              "        [-2.00518274e+00,  1.42123878e+00,  5.51936686e-01,\n",
              "          4.87762541e-01,  4.92051363e-01, -2.26158157e-01,\n",
              "         -3.87388825e-01],\n",
              "        [ 1.83095951e-02, -7.55223393e-01,  4.62632835e-01,\n",
              "         -3.50320190e-01, -1.58041716e+00, -7.64806747e+00,\n",
              "         -2.47595936e-01],\n",
              "        [-2.55368519e+00,  1.55323601e+00,  5.86167157e-01,\n",
              "          1.33810088e-01, -2.99223280e+00,  2.36395985e-01,\n",
              "         -1.10127494e-01],\n",
              "        [ 1.14407766e+00,  3.94312114e-01, -5.14253080e-01,\n",
              "         -2.91804314e+00, -5.49358559e+00, -1.53904366e+00,\n",
              "         -4.38356251e-01],\n",
              "        [ 6.27345681e-01,  2.67288280e+00,  1.79812789e+00,\n",
              "         -4.10675526e+00,  1.38817513e+00,  2.75287628e-01,\n",
              "         -1.03602201e-01],\n",
              "        [ 6.77797854e-01, -2.42449212e+00, -5.04493237e+00,\n",
              "         -1.14973392e+01, -9.17264968e-02, -1.78576422e+00,\n",
              "         -4.40625668e-01],\n",
              "        [ 7.22590148e-01,  1.32902992e+00, -2.01217985e+00,\n",
              "          1.39162207e+00, -5.69302416e+00,  2.04321194e+00,\n",
              "         -3.00018281e-01]], dtype=float32),\n",
              " array([ 2.9064376 , -1.3277566 ,  3.8115838 , -2.5499914 ,  0.33218396,\n",
              "         3.366599  , -0.96838975], dtype=float32),\n",
              " array([[ 0.06152385, -0.19465801, -0.5617812 , -1.0117629 , -0.4355857 ,\n",
              "          0.61556673],\n",
              "        [-1.4900048 , -0.23275596, -1.0768123 ,  0.28445938, -1.1279927 ,\n",
              "         -1.6955569 ],\n",
              "        [ 0.64879245, -0.36989215, -0.20954497,  0.68813616,  0.03850906,\n",
              "         -2.5778718 ],\n",
              "        [ 0.67006326, -0.08860922, -0.5065097 , -1.7473644 , -0.14249021,\n",
              "          0.53089434],\n",
              "        [ 0.12730739, -0.3273004 , -0.22174929,  0.72214264, -0.13622817,\n",
              "         -1.3546281 ],\n",
              "        [ 1.0250334 , -0.22257221, -0.58996046,  1.0588158 ,  0.19638771,\n",
              "          0.17671427],\n",
              "        [-0.08106774, -0.04315788,  0.32544705,  0.12713632, -0.14160094,\n",
              "         -0.6784324 ]], dtype=float32),\n",
              " array([-4.6736536,  0.       , -0.9254664, -0.8766883, -2.4981937,\n",
              "         0.9117954], dtype=float32),\n",
              " array([[ 0.20555119],\n",
              "        [-0.16638547],\n",
              "        [-0.1711096 ],\n",
              "        [-0.25961074],\n",
              "        [-0.1700715 ],\n",
              "        [ 0.28419384]], dtype=float32),\n",
              " array([-0.7469555], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "zrCqD8NcuNBd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}