ANN.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "VYdOO7ZbYQdb"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlbFKAL0ZqZL",
        "outputId": "636c46a4-9031-457a-faa5-320cf618e507"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        0        1       2   3   4          5  6  7  8          9\n",
            "0     619   France  Female  42   2        0.0  1  1  1  101348.88\n",
            "1     608    Spain  Female  41   1   83807.86  1  0  1  112542.58\n",
            "2     502   France  Female  42   8   159660.8  3  1  0  113931.57\n",
            "3     699   France  Female  39   1        0.0  2  0  0   93826.63\n",
            "4     850    Spain  Female  43   2  125510.82  1  1  1    79084.1\n",
            "...   ...      ...     ...  ..  ..        ... .. .. ..        ...\n",
            "9995  771   France    Male  39   5        0.0  2  1  0   96270.64\n",
            "9996  516   France    Male  35  10   57369.61  1  1  1  101699.77\n",
            "9997  709   France  Female  36   7        0.0  1  0  1   42085.58\n",
            "9998  772  Germany    Male  42   3   75075.31  2  1  0   92888.52\n",
            "9999  792   France  Female  28   4  130142.79  1  1  0   38190.78\n",
            "\n",
            "[10000 rows x 10 columns]\n",
            "      0\n",
            "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",
            "\n",
            "[10000 rows x 1 columns]\n"
          ]
        }
      ],
      "source": [
        "dataset = pd.read_csv('/content/Churn_Modelling.csv')\n",
        "x=dataset.iloc[:, 3:13].values\n",
        "y=dataset.iloc[:, 13].values\n",
        "\n",
        "#visualisation du resultat pour comprendre\n",
        "q=pd.DataFrame(x)\n",
        "print(q)\n",
        "q1=pd.DataFrame(y)\n",
        "print(q1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "msz4u65Tbwog"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "MAwPyhCccp6R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b29d088-ba92-4894-d244-db8ff286d73a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        0  1  2   3   4          5  6  7  8          9\n",
            "0     619  0  0  42   2        0.0  1  1  1  101348.88\n",
            "1     608  2  0  41   1   83807.86  1  0  1  112542.58\n",
            "2     502  0  0  42   8   159660.8  3  1  0  113931.57\n",
            "3     699  0  0  39   1        0.0  2  0  0   93826.63\n",
            "4     850  2  0  43   2  125510.82  1  1  1    79084.1\n",
            "...   ... .. ..  ..  ..        ... .. .. ..        ...\n",
            "9995  771  0  1  39   5        0.0  2  1  0   96270.64\n",
            "9996  516  0  1  35  10   57369.61  1  1  1  101699.77\n",
            "9997  709  0  0  36   7        0.0  1  0  1   42085.58\n",
            "9998  772  1  1  42   3   75075.31  2  1  0   92888.52\n",
            "9999  792  0  0  28   4  130142.79  1  1  0   38190.78\n",
            "\n",
            "[10000 rows x 10 columns]\n"
          ]
        }
      ],
      "source": [
        "#coudage des valeurs catigoriel\n",
        "labeloncoder_X_1 = LabelEncoder()\n",
        "x[:, 1]=labeloncoder_X_1.fit_transform(x[:, 1])\n",
        "labeloncoder_X_2 = LabelEncoder()\n",
        "x[:, 2]=labeloncoder_X_2.fit_transform(x[:, 2])\n",
        "\n",
        "#visualisation du resultat pour comprendre\n",
        "q=pd.DataFrame(x)\n",
        "print(q)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "qGSijfiOhcr8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb4008d8-7ca2-4a47-a744-f7171a3a8e1b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       0    1    2  3   4   5          6  7  8  9          10\n",
            "0     0.0  0.0  619  0  42   2        0.0  1  1  1  101348.88\n",
            "1     0.0  1.0  608  0  41   1   83807.86  1  0  1  112542.58\n",
            "2     0.0  0.0  502  0  42   8   159660.8  3  1  0  113931.57\n",
            "3     0.0  0.0  699  0  39   1        0.0  2  0  0   93826.63\n",
            "4     0.0  1.0  850  0  43   2  125510.82  1  1  1    79084.1\n",
            "...   ...  ...  ... ..  ..  ..        ... .. .. ..        ...\n",
            "9995  0.0  0.0  771  1  39   5        0.0  2  1  0   96270.64\n",
            "9996  0.0  0.0  516  1  35  10   57369.61  1  1  1  101699.77\n",
            "9997  0.0  0.0  709  0  36   7        0.0  1  0  1   42085.58\n",
            "9998  1.0  0.0  772  1  42   3   75075.31  2  1  0   92888.52\n",
            "9999  0.0  0.0  792  0  28   4  130142.79  1  1  0   38190.78\n",
            "\n",
            "[10000 rows x 11 columns]\n"
          ]
        }
      ],
      "source": [
        "#pour eviter la clasification entre les pays\n",
        "ct = ColumnTransformer([(\"Geography\", OneHotEncoder(), [1])],remainder='passthrough')\n",
        "x=ct.fit_transform(x)\n",
        "x=x[: , 1:]\n",
        "#visualisation du resultat pour comprendre\n",
        "q=pd.DataFrame(x)\n",
        "print(q)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Hz01S5VvkYey"
      },
      "outputs": [],
      "source": [
        "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "ijDrrsiDlXah",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "288cff7d-20a7-4a31-fb3e-890d646101a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            0         1         2         3         4         5         6   \\\n",
            "0    -0.569844  1.743090  0.169582 -1.091687 -0.464608  0.006661 -1.215717   \n",
            "1     1.754865 -0.573694 -2.304559  0.916013  0.301026 -1.377440 -0.006312   \n",
            "2    -0.569844 -0.573694 -1.191196 -1.091687 -0.943129 -1.031415  0.579935   \n",
            "3    -0.569844  1.743090  0.035566  0.916013  0.109617  0.006661  0.473128   \n",
            "4    -0.569844  1.743090  2.056114 -1.091687  1.736588  1.044737  0.810193   \n",
            "...        ...       ...       ...       ...       ...       ...       ...   \n",
            "7995  1.754865 -0.573694 -0.582970 -1.091687 -0.656016 -0.339364  0.703104   \n",
            "7996 -0.569844  1.743090  1.478815 -1.091687 -1.613058 -0.339364  0.613060   \n",
            "7997 -0.569844 -0.573694  0.901515  0.916013 -0.368904  0.006661  1.361474   \n",
            "7998 -0.569844  1.743090 -0.624205 -1.091687 -0.081791  1.390762 -1.215717   \n",
            "7999  1.754865 -0.573694 -0.284011 -1.091687  0.875251 -1.377440  0.511364   \n",
            "\n",
            "            7         8         9         10  \n",
            "0     0.809503  0.642595 -1.032270  1.106432  \n",
            "1    -0.921591  0.642595  0.968738 -0.748664  \n",
            "2    -0.921591  0.642595 -1.032270  1.485335  \n",
            "3    -0.921591  0.642595 -1.032270  1.276528  \n",
            "4     0.809503  0.642595  0.968738  0.558378  \n",
            "...        ...       ...       ...       ...  \n",
            "7995  0.809503  0.642595  0.968738  1.091330  \n",
            "7996 -0.921591  0.642595  0.968738  0.131760  \n",
            "7997  0.809503  0.642595 -1.032270  1.412320  \n",
            "7998  0.809503  0.642595  0.968738  0.844321  \n",
            "7999 -0.921591  0.642595 -1.032270  0.324725  \n",
            "\n",
            "[8000 rows x 11 columns]\n",
            "            0         1         2         3         4         5         6   \\\n",
            "0     1.754865 -0.573694 -0.552043 -1.091687 -0.368904  1.044737  0.879303   \n",
            "1    -0.569844 -0.573694 -1.314903 -1.091687  0.109617 -1.031415  0.429722   \n",
            "2    -0.569844  1.743090  0.571630 -1.091687  0.301026  1.044737  0.308583   \n",
            "3    -0.569844 -0.573694  1.416961  0.916013 -0.656016 -0.339364  0.575336   \n",
            "4     1.754865 -0.573694  0.571630  0.916013 -0.081791  0.006661  1.389611   \n",
            "...        ...       ...       ...       ...       ...       ...       ...   \n",
            "1995 -0.569844 -0.573694 -0.263393 -1.091687 -1.421650 -1.377440 -1.215717   \n",
            "1996 -0.569844 -0.573694 -0.665441 -1.091687 -0.368904  0.698712 -1.215717   \n",
            "1997 -0.569844  1.743090 -0.747912  0.916013 -0.273200 -1.377440  1.297455   \n",
            "1998  1.754865 -0.573694 -0.005670  0.916013 -0.464608 -0.339364  1.059752   \n",
            "1999  1.754865 -0.573694 -0.799457  0.916013 -0.847425  1.044737  0.820263   \n",
            "\n",
            "            7         8         9         10  \n",
            "0    -0.921591  0.642595  0.968738  1.610857  \n",
            "1    -0.921591  0.642595 -1.032270  0.495870  \n",
            "2    -0.921591  0.642595  0.968738 -0.424787  \n",
            "3    -0.921591 -1.556190 -1.032270 -0.187777  \n",
            "4     0.809503  0.642595  0.968738  0.616842  \n",
            "...        ...       ...       ...       ...  \n",
            "1995  0.809503  0.642595  0.968738  1.404319  \n",
            "1996  0.809503  0.642595 -1.032270 -0.511196  \n",
            "1997  0.809503  0.642595 -1.032270  0.718885  \n",
            "1998 -0.921591  0.642595  0.968738 -1.545078  \n",
            "1999 -0.921591  0.642595 -1.032270  1.612559  \n",
            "\n",
            "[2000 rows x 11 columns]\n"
          ]
        }
      ],
      "source": [
        "#Feature Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc=StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test=sc.transform(x_test)\n",
        "x=sc.fit_transform(x)\n",
        "#visualisation du resultat pour comprendre\n",
        "q=pd.DataFrame(x_train)\n",
        "print(q)\n",
        "q1=pd.DataFrame(x_test)\n",
        "print(q1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FeScatRsn568"
      },
      "source": [
        "#Création de notre réseau ANN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "VMQtbb-bn_IY"
      },
      "outputs": [],
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "wCDZZOf6o9-K"
      },
      "outputs": [],
      "source": [
        "#Etape 1 : Initialistion du ANN\n",
        "classifier = Sequential()\n",
        "#Ajouter la couche d'entrée et une couche cachée\n",
        "classifier.add(Dense(units=6 , activation=\"relu\" , kernel_initializer=\"uniform\",input_dim=11))\n",
        "#classifier.add(Dropout(rat=0.1))\n",
        "#ajouter une deuxiem couche cachée\n",
        "classifier.add(Dense(units=6 , activation=\"relu\" , kernel_initializer=\"uniform\"))\n",
        "#classifier.add(Dropout(rat=0.1))\n",
        "#ajouter une deuxiem couche de sortie\n",
        "classifier.add(Dense(units=1 , activation=\"sigmoid\" , kernel_initializer=\"uniform\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56z8M0Hfq6CS"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "OvT-y4LNrbwI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "703f8c23-6758-4ab3-ca08-3982e16b2ae3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.4893 - accuracy: 0.7959\n",
            "Epoch 2/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.4251 - accuracy: 0.7960\n",
            "Epoch 3/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.4165 - accuracy: 0.8246\n",
            "Epoch 4/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.4119 - accuracy: 0.8324\n",
            "Epoch 5/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.4080 - accuracy: 0.8322\n",
            "Epoch 6/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.4053 - accuracy: 0.8322\n",
            "Epoch 7/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.4044 - accuracy: 0.8324\n",
            "Epoch 8/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.4027 - accuracy: 0.8338\n",
            "Epoch 9/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.4011 - accuracy: 0.8338\n",
            "Epoch 10/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.4006 - accuracy: 0.8336\n",
            "Epoch 11/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3997 - accuracy: 0.8355\n",
            "Epoch 12/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3989 - accuracy: 0.8354\n",
            "Epoch 13/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3985 - accuracy: 0.8336\n",
            "Epoch 14/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3982 - accuracy: 0.8344\n",
            "Epoch 15/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3978 - accuracy: 0.8364\n",
            "Epoch 16/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3979 - accuracy: 0.8351\n",
            "Epoch 17/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3975 - accuracy: 0.8346\n",
            "Epoch 18/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3973 - accuracy: 0.8332\n",
            "Epoch 19/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3968 - accuracy: 0.8355\n",
            "Epoch 20/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3966 - accuracy: 0.8345\n",
            "Epoch 21/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3966 - accuracy: 0.8344\n",
            "Epoch 22/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3960 - accuracy: 0.8359\n",
            "Epoch 23/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3958 - accuracy: 0.8355\n",
            "Epoch 24/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3957 - accuracy: 0.8339\n",
            "Epoch 25/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3963 - accuracy: 0.8339\n",
            "Epoch 26/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3963 - accuracy: 0.8353\n",
            "Epoch 27/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3956 - accuracy: 0.8351\n",
            "Epoch 28/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3955 - accuracy: 0.8359\n",
            "Epoch 29/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3958 - accuracy: 0.8364\n",
            "Epoch 30/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3953 - accuracy: 0.8355\n",
            "Epoch 31/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3954 - accuracy: 0.8356\n",
            "Epoch 32/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3957 - accuracy: 0.8350\n",
            "Epoch 33/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3954 - accuracy: 0.8349\n",
            "Epoch 34/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3953 - accuracy: 0.8346\n",
            "Epoch 35/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3952 - accuracy: 0.8353\n",
            "Epoch 36/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3950 - accuracy: 0.8356\n",
            "Epoch 37/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3954 - accuracy: 0.8346\n",
            "Epoch 38/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3946 - accuracy: 0.8344\n",
            "Epoch 39/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3951 - accuracy: 0.8341\n",
            "Epoch 40/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3949 - accuracy: 0.8356\n",
            "Epoch 41/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3948 - accuracy: 0.8354\n",
            "Epoch 42/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3944 - accuracy: 0.8361\n",
            "Epoch 43/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3942 - accuracy: 0.8353\n",
            "Epoch 44/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3945 - accuracy: 0.8330\n",
            "Epoch 45/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3944 - accuracy: 0.8360\n",
            "Epoch 46/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3941 - accuracy: 0.8367\n",
            "Epoch 47/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3942 - accuracy: 0.8359\n",
            "Epoch 48/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3940 - accuracy: 0.8339\n",
            "Epoch 49/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3937 - accuracy: 0.8374\n",
            "Epoch 50/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3936 - accuracy: 0.8367\n",
            "Epoch 51/100\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3935 - accuracy: 0.8367\n",
            "Epoch 52/100\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3934 - accuracy: 0.8367\n",
            "Epoch 53/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3935 - accuracy: 0.8364\n",
            "Epoch 54/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3934 - accuracy: 0.8350\n",
            "Epoch 55/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3934 - accuracy: 0.8363\n",
            "Epoch 56/100\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3935 - accuracy: 0.8353\n",
            "Epoch 57/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3934 - accuracy: 0.8365\n",
            "Epoch 58/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3932 - accuracy: 0.8364\n",
            "Epoch 59/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3928 - accuracy: 0.8369\n",
            "Epoch 60/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3931 - accuracy: 0.8388\n",
            "Epoch 61/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3927 - accuracy: 0.8369\n",
            "Epoch 62/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3924 - accuracy: 0.8364\n",
            "Epoch 63/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3926 - accuracy: 0.8366\n",
            "Epoch 64/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3927 - accuracy: 0.8365\n",
            "Epoch 65/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3920 - accuracy: 0.8372\n",
            "Epoch 66/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3926 - accuracy: 0.8361\n",
            "Epoch 67/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3923 - accuracy: 0.8356\n",
            "Epoch 68/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3921 - accuracy: 0.8378\n",
            "Epoch 69/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3917 - accuracy: 0.8391\n",
            "Epoch 70/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3917 - accuracy: 0.8365\n",
            "Epoch 71/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3911 - accuracy: 0.8369\n",
            "Epoch 72/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3910 - accuracy: 0.8381\n",
            "Epoch 73/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3905 - accuracy: 0.8376\n",
            "Epoch 74/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3886 - accuracy: 0.8379\n",
            "Epoch 75/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3869 - accuracy: 0.8391\n",
            "Epoch 76/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3844 - accuracy: 0.8407\n",
            "Epoch 77/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3812 - accuracy: 0.8407\n",
            "Epoch 78/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3778 - accuracy: 0.8399\n",
            "Epoch 79/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3736 - accuracy: 0.8410\n",
            "Epoch 80/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3691 - accuracy: 0.8428\n",
            "Epoch 81/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3652 - accuracy: 0.8425\n",
            "Epoch 82/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3620 - accuracy: 0.8491\n",
            "Epoch 83/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3586 - accuracy: 0.8521\n",
            "Epoch 84/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3564 - accuracy: 0.8522\n",
            "Epoch 85/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3543 - accuracy: 0.8561\n",
            "Epoch 86/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3526 - accuracy: 0.8583\n",
            "Epoch 87/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3518 - accuracy: 0.8579\n",
            "Epoch 88/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3490 - accuracy: 0.8604\n",
            "Epoch 89/100\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3494 - accuracy: 0.8594\n",
            "Epoch 90/100\n",
            "800/800 [==============================] - 2s 2ms/step - loss: 0.3473 - accuracy: 0.8600\n",
            "Epoch 91/100\n",
            "800/800 [==============================] - 1s 1ms/step - loss: 0.3477 - accuracy: 0.8614\n",
            "Epoch 92/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3466 - accuracy: 0.8606\n",
            "Epoch 93/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3457 - accuracy: 0.8616\n",
            "Epoch 94/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3456 - accuracy: 0.8610\n",
            "Epoch 95/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3437 - accuracy: 0.8616\n",
            "Epoch 96/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3438 - accuracy: 0.8622\n",
            "Epoch 97/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3433 - accuracy: 0.8612\n",
            "Epoch 98/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3429 - accuracy: 0.8622\n",
            "Epoch 99/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3431 - accuracy: 0.8627\n",
            "Epoch 100/100\n",
            "800/800 [==============================] - 1s 2ms/step - loss: 0.3423 - accuracy: 0.8636\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa74dbe2510>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "#Compilation\n",
        "classifier.compile(optimizer=\"adam\", loss=\"binary_crossentropy\",metrics=[\"accuracy\"])                                           \n",
        "#Entrainement du reseau\n",
        "classifier.fit(x_train,y_train, batch_size=10, epochs=100)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "r_mqhU8hCC76",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11e45d55-5427-4eb6-d005-59a8d3f1defd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1507   88]\n",
            " [ 191  214]]\n",
            "[[False]\n",
            " [False]\n",
            " [False]\n",
            " ...\n",
            " [False]\n",
            " [False]\n",
            " [False]]\n"
          ]
        }
      ],
      "source": [
        "y_pred= classifier.predict(x_test)\n",
        "y_pred= (y_pred > 0.5)\n",
        "#confusion matrix\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cn = confusion_matrix(y_test , y_pred)\n",
        "print(cn)\n",
        "print(y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "uevTB8SsHbiV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25a8821e-b627-4581-8459-e743f85bcaad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "[[0.12393361]]\n",
            "[[False]]\n"
          ]
        }
      ],
      "source": [
        "from numpy import zeros,array\n",
        "client_test = np.array([[0.0,1,450,0,30,8,5000,1,1,0,25000]])\n",
        "client_test= sc.fit_transform(client_test)\n",
        "print(client_test)\n",
        "pr= classifier.predict(client_test)\n",
        "print(pr)\n",
        "pr=(pr>0.5)\n",
        "print(pr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "zTF9QRDLRhKV"
      },
      "outputs": [],
      "source": [
        "\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "def build_classifier():\n",
        "\n",
        "  classifier=Sequential()\n",
        "  #Ajouter la couche d'entrée et une couche cachée\n",
        "  classifier.add(Dense(units=6 , activation=\"relu\" , kernel_initializer=\"uniform\",input_dim=11))\n",
        "  #classifier.add(Dropout(rat=0.1))\n",
        "  #ajouter une deuxiem couche cachée\n",
        "  classifier.add(Dense(units=6 , activation=\"relu\" , kernel_initializer=\"uniform\"))\n",
        "  #classifier.add(Dropout(rat=0.1))\n",
        "  #ajouter une deuxiem couche de sortie\n",
        "  classifier.add(Dense(units=1 , activation=\"sigmoid\" , kernel_initializer=\"uniform\"))\n",
        "  #Compilation\n",
        "  classifier.compile(optimizer=\"adam\", loss=\"binary_crossentropy\",metrics=[\"accuracy\"])\n",
        "  return classifier\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "VH8nRXw1TPwq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4babbe5-3939-497b-fe47-3c5dd22f1705"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: KerasClassifier is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "909/909 [==============================] - 2s 1ms/step - loss: 0.4769 - accuracy: 0.7963\n",
            "Epoch 2/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4289 - accuracy: 0.7966\n",
            "Epoch 3/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.4240 - accuracy: 0.8014\n",
            "Epoch 4/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.4193 - accuracy: 0.8240\n",
            "Epoch 5/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.4163 - accuracy: 0.8279\n",
            "Epoch 6/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4142 - accuracy: 0.8312\n",
            "Epoch 7/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4122 - accuracy: 0.8304\n",
            "Epoch 8/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4107 - accuracy: 0.8323\n",
            "Epoch 9/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4099 - accuracy: 0.8345\n",
            "Epoch 10/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4085 - accuracy: 0.8351\n",
            "Epoch 11/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.4072 - accuracy: 0.8345\n",
            "Epoch 12/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.4071 - accuracy: 0.8356\n",
            "Epoch 13/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.4061 - accuracy: 0.8360\n",
            "Epoch 14/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.4054 - accuracy: 0.8342\n",
            "Epoch 15/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4052 - accuracy: 0.8350\n",
            "Epoch 16/100\n",
            "909/909 [==============================] - 2s 2ms/step - loss: 0.4048 - accuracy: 0.8356\n",
            "Epoch 17/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4039 - accuracy: 0.8359\n",
            "Epoch 18/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4043 - accuracy: 0.8358\n",
            "Epoch 19/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4036 - accuracy: 0.8362\n",
            "Epoch 20/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4035 - accuracy: 0.8355\n",
            "Epoch 21/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4035 - accuracy: 0.8361\n",
            "Epoch 22/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4030 - accuracy: 0.8350\n",
            "Epoch 23/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4029 - accuracy: 0.8367\n",
            "Epoch 24/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.4021 - accuracy: 0.8370\n",
            "Epoch 25/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4026 - accuracy: 0.8364\n",
            "Epoch 26/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4021 - accuracy: 0.8377\n",
            "Epoch 27/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4026 - accuracy: 0.8359\n",
            "Epoch 28/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.4016 - accuracy: 0.8363\n",
            "Epoch 29/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4017 - accuracy: 0.8367\n",
            "Epoch 30/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4015 - accuracy: 0.8373\n",
            "Epoch 31/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4011 - accuracy: 0.8355\n",
            "Epoch 32/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4014 - accuracy: 0.8347\n",
            "Epoch 33/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4013 - accuracy: 0.8364\n",
            "Epoch 34/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4011 - accuracy: 0.8375\n",
            "Epoch 35/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4009 - accuracy: 0.8363\n",
            "Epoch 36/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4006 - accuracy: 0.8374\n",
            "Epoch 37/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4001 - accuracy: 0.8363\n",
            "Epoch 38/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4005 - accuracy: 0.8372\n",
            "Epoch 39/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.4000 - accuracy: 0.8376\n",
            "Epoch 40/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3995 - accuracy: 0.8394\n",
            "Epoch 41/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3993 - accuracy: 0.8374\n",
            "Epoch 42/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3995 - accuracy: 0.8386\n",
            "Epoch 43/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3992 - accuracy: 0.8380\n",
            "Epoch 44/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3993 - accuracy: 0.8385\n",
            "Epoch 45/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3991 - accuracy: 0.8383\n",
            "Epoch 46/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3987 - accuracy: 0.8382\n",
            "Epoch 47/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3988 - accuracy: 0.8378\n",
            "Epoch 48/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3989 - accuracy: 0.8385\n",
            "Epoch 49/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3985 - accuracy: 0.8385\n",
            "Epoch 50/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3985 - accuracy: 0.8383\n",
            "Epoch 51/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3982 - accuracy: 0.8400\n",
            "Epoch 52/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3985 - accuracy: 0.8395\n",
            "Epoch 53/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3986 - accuracy: 0.8381\n",
            "Epoch 54/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3981 - accuracy: 0.8394\n",
            "Epoch 55/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3976 - accuracy: 0.8392\n",
            "Epoch 56/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3980 - accuracy: 0.8380\n",
            "Epoch 57/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3981 - accuracy: 0.8383\n",
            "Epoch 58/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3978 - accuracy: 0.8399\n",
            "Epoch 59/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3978 - accuracy: 0.8377\n",
            "Epoch 60/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3976 - accuracy: 0.8394\n",
            "Epoch 61/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3979 - accuracy: 0.8384\n",
            "Epoch 62/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3977 - accuracy: 0.8403\n",
            "Epoch 63/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3975 - accuracy: 0.8410\n",
            "Epoch 64/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3970 - accuracy: 0.8394\n",
            "Epoch 65/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3973 - accuracy: 0.8395\n",
            "Epoch 66/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3976 - accuracy: 0.8403\n",
            "Epoch 67/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3975 - accuracy: 0.8409\n",
            "Epoch 68/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3970 - accuracy: 0.8406\n",
            "Epoch 69/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3976 - accuracy: 0.8378\n",
            "Epoch 70/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3974 - accuracy: 0.8399\n",
            "Epoch 71/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3972 - accuracy: 0.8393\n",
            "Epoch 72/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3972 - accuracy: 0.8399\n",
            "Epoch 73/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3971 - accuracy: 0.8402\n",
            "Epoch 74/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3971 - accuracy: 0.8380\n",
            "Epoch 75/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3973 - accuracy: 0.8381\n",
            "Epoch 76/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3970 - accuracy: 0.8375\n",
            "Epoch 77/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3972 - accuracy: 0.8411\n",
            "Epoch 78/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3969 - accuracy: 0.8409\n",
            "Epoch 79/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3972 - accuracy: 0.8414\n",
            "Epoch 80/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3968 - accuracy: 0.8392\n",
            "Epoch 81/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3971 - accuracy: 0.8392\n",
            "Epoch 82/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3973 - accuracy: 0.8384\n",
            "Epoch 83/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3970 - accuracy: 0.8416\n",
            "Epoch 84/100\n",
            "909/909 [==============================] - 1s 1ms/step - loss: 0.3967 - accuracy: 0.8407\n",
            "Epoch 85/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3968 - accuracy: 0.8391\n",
            "Epoch 86/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3970 - accuracy: 0.8408\n",
            "Epoch 87/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3973 - accuracy: 0.8391\n",
            "Epoch 88/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3970 - accuracy: 0.8397\n",
            "Epoch 89/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3967 - accuracy: 0.8400\n",
            "Epoch 90/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3972 - accuracy: 0.8392\n",
            "Epoch 91/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3965 - accuracy: 0.8395\n",
            "Epoch 92/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3964 - accuracy: 0.8402\n",
            "Epoch 93/100\n",
            "909/909 [==============================] - 1s 2ms/step - loss: 0.3969 - accuracy: 0.8405\n",
            "Epoch 94/100\n",
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            "Epoch 95/100\n",
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            "91/91 [==============================] - 0s 2ms/step - loss: 0.3875 - accuracy: 0.8372\n",
            "Epoch 1/100\n",
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            "Epoch 1/100\n",
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            "910/910 [==============================] - 2s 2ms/step - loss: 0.3979 - accuracy: 0.8364\n",
            "Epoch 71/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3973 - accuracy: 0.8370\n",
            "Epoch 72/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3976 - accuracy: 0.8354\n",
            "Epoch 73/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3980 - accuracy: 0.8350\n",
            "Epoch 74/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3978 - accuracy: 0.8352\n",
            "Epoch 75/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3976 - accuracy: 0.8352\n",
            "Epoch 76/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3974 - accuracy: 0.8365\n",
            "Epoch 77/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3974 - accuracy: 0.8368\n",
            "Epoch 78/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3975 - accuracy: 0.8365\n",
            "Epoch 79/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3970 - accuracy: 0.8361\n",
            "Epoch 80/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3979 - accuracy: 0.8372\n",
            "Epoch 81/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3973 - accuracy: 0.8350\n",
            "Epoch 82/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3972 - accuracy: 0.8361\n",
            "Epoch 83/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3973 - accuracy: 0.8357\n",
            "Epoch 84/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3971 - accuracy: 0.8365\n",
            "Epoch 85/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3975 - accuracy: 0.8346\n",
            "Epoch 86/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3970 - accuracy: 0.8361\n",
            "Epoch 87/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3972 - accuracy: 0.8374\n",
            "Epoch 88/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3970 - accuracy: 0.8351\n",
            "Epoch 89/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3978 - accuracy: 0.8373\n",
            "Epoch 90/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3972 - accuracy: 0.8371\n",
            "Epoch 91/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3974 - accuracy: 0.8360\n",
            "Epoch 92/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3974 - accuracy: 0.8354\n",
            "Epoch 93/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3974 - accuracy: 0.8357\n",
            "Epoch 94/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3972 - accuracy: 0.8368\n",
            "Epoch 95/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3969 - accuracy: 0.8362\n",
            "Epoch 96/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3972 - accuracy: 0.8361\n",
            "Epoch 97/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3971 - accuracy: 0.8354\n",
            "Epoch 98/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3970 - accuracy: 0.8363\n",
            "Epoch 99/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3977 - accuracy: 0.8353\n",
            "Epoch 100/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3971 - accuracy: 0.8345\n",
            "91/91 [==============================] - 0s 2ms/step - loss: 0.4188 - accuracy: 0.8339\n",
            "Epoch 1/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4874 - accuracy: 0.7930\n",
            "Epoch 2/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4279 - accuracy: 0.7934\n",
            "Epoch 3/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4227 - accuracy: 0.7980\n",
            "Epoch 4/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4188 - accuracy: 0.8225\n",
            "Epoch 5/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4164 - accuracy: 0.8280\n",
            "Epoch 6/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4144 - accuracy: 0.8303\n",
            "Epoch 7/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4126 - accuracy: 0.8309\n",
            "Epoch 8/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4104 - accuracy: 0.8331\n",
            "Epoch 9/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4073 - accuracy: 0.8353\n",
            "Epoch 10/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4050 - accuracy: 0.8340\n",
            "Epoch 11/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4031 - accuracy: 0.8341\n",
            "Epoch 12/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4020 - accuracy: 0.8354\n",
            "Epoch 13/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4003 - accuracy: 0.8358\n",
            "Epoch 14/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.4001 - accuracy: 0.8350\n",
            "Epoch 15/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3991 - accuracy: 0.8369\n",
            "Epoch 16/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3992 - accuracy: 0.8363\n",
            "Epoch 17/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3986 - accuracy: 0.8353\n",
            "Epoch 18/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3979 - accuracy: 0.8371\n",
            "Epoch 19/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3975 - accuracy: 0.8354\n",
            "Epoch 20/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3977 - accuracy: 0.8361\n",
            "Epoch 21/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3973 - accuracy: 0.8352\n",
            "Epoch 22/100\n",
            "910/910 [==============================] - 2s 3ms/step - loss: 0.3972 - accuracy: 0.8367\n",
            "Epoch 23/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3968 - accuracy: 0.8370\n",
            "Epoch 24/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3968 - accuracy: 0.8356\n",
            "Epoch 25/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3966 - accuracy: 0.8363\n",
            "Epoch 26/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3966 - accuracy: 0.8361\n",
            "Epoch 27/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3960 - accuracy: 0.8364\n",
            "Epoch 28/100\n",
            "910/910 [==============================] - 3s 3ms/step - loss: 0.3965 - accuracy: 0.8369\n",
            "Epoch 29/100\n",
            "910/910 [==============================] - 2s 3ms/step - loss: 0.3958 - accuracy: 0.8368\n",
            "Epoch 30/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3957 - accuracy: 0.8363\n",
            "Epoch 31/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3960 - accuracy: 0.8368\n",
            "Epoch 32/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3953 - accuracy: 0.8375\n",
            "Epoch 33/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3957 - accuracy: 0.8372\n",
            "Epoch 34/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3955 - accuracy: 0.8357\n",
            "Epoch 35/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3952 - accuracy: 0.8357\n",
            "Epoch 36/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3953 - accuracy: 0.8362\n",
            "Epoch 37/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3954 - accuracy: 0.8363\n",
            "Epoch 38/100\n",
            "910/910 [==============================] - 3s 3ms/step - loss: 0.3951 - accuracy: 0.8373\n",
            "Epoch 39/100\n",
            "910/910 [==============================] - 3s 3ms/step - loss: 0.3952 - accuracy: 0.8371\n",
            "Epoch 40/100\n",
            "910/910 [==============================] - 2s 3ms/step - loss: 0.3947 - accuracy: 0.8364\n",
            "Epoch 41/100\n",
            "910/910 [==============================] - 2s 3ms/step - loss: 0.3947 - accuracy: 0.8367\n",
            "Epoch 42/100\n",
            "910/910 [==============================] - 3s 3ms/step - loss: 0.3951 - accuracy: 0.8382\n",
            "Epoch 43/100\n",
            "910/910 [==============================] - 3s 3ms/step - loss: 0.3946 - accuracy: 0.8364\n",
            "Epoch 44/100\n",
            "910/910 [==============================] - 3s 3ms/step - loss: 0.3946 - accuracy: 0.8368\n",
            "Epoch 45/100\n",
            "910/910 [==============================] - 3s 3ms/step - loss: 0.3946 - accuracy: 0.8368\n",
            "Epoch 46/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3943 - accuracy: 0.8360\n",
            "Epoch 47/100\n",
            "910/910 [==============================] - 3s 3ms/step - loss: 0.3946 - accuracy: 0.8358\n",
            "Epoch 48/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3942 - accuracy: 0.8384\n",
            "Epoch 49/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3943 - accuracy: 0.8371\n",
            "Epoch 50/100\n",
            "910/910 [==============================] - 2s 3ms/step - loss: 0.3944 - accuracy: 0.8395\n",
            "Epoch 51/100\n",
            "910/910 [==============================] - 2s 3ms/step - loss: 0.3942 - accuracy: 0.8364\n",
            "Epoch 52/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3935 - accuracy: 0.8376\n",
            "Epoch 53/100\n",
            "910/910 [==============================] - 3s 3ms/step - loss: 0.3945 - accuracy: 0.8362\n",
            "Epoch 54/100\n",
            "910/910 [==============================] - 3s 3ms/step - loss: 0.3942 - accuracy: 0.8369\n",
            "Epoch 55/100\n",
            "910/910 [==============================] - 2s 3ms/step - loss: 0.3947 - accuracy: 0.8378\n",
            "Epoch 56/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3943 - accuracy: 0.8374\n",
            "Epoch 57/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3937 - accuracy: 0.8385\n",
            "Epoch 58/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3936 - accuracy: 0.8390\n",
            "Epoch 59/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3938 - accuracy: 0.8383\n",
            "Epoch 60/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3941 - accuracy: 0.8380\n",
            "Epoch 61/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3938 - accuracy: 0.8371\n",
            "Epoch 62/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3940 - accuracy: 0.8386\n",
            "Epoch 63/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3941 - accuracy: 0.8382\n",
            "Epoch 64/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3939 - accuracy: 0.8358\n",
            "Epoch 65/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3941 - accuracy: 0.8381\n",
            "Epoch 66/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3939 - accuracy: 0.8373\n",
            "Epoch 67/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3933 - accuracy: 0.8362\n",
            "Epoch 68/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3930 - accuracy: 0.8378\n",
            "Epoch 69/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3935 - accuracy: 0.8392\n",
            "Epoch 70/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3938 - accuracy: 0.8391\n",
            "Epoch 71/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3932 - accuracy: 0.8385\n",
            "Epoch 72/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3932 - accuracy: 0.8379\n",
            "Epoch 73/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3926 - accuracy: 0.8385\n",
            "Epoch 74/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3924 - accuracy: 0.8380\n",
            "Epoch 75/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3923 - accuracy: 0.8384\n",
            "Epoch 76/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3909 - accuracy: 0.8398\n",
            "Epoch 77/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3899 - accuracy: 0.8385\n",
            "Epoch 78/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3887 - accuracy: 0.8405\n",
            "Epoch 79/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3858 - accuracy: 0.8413\n",
            "Epoch 80/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3820 - accuracy: 0.8411\n",
            "Epoch 81/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3784 - accuracy: 0.8427\n",
            "Epoch 82/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3744 - accuracy: 0.8439\n",
            "Epoch 83/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3706 - accuracy: 0.8425\n",
            "Epoch 84/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3651 - accuracy: 0.8457\n",
            "Epoch 85/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3605 - accuracy: 0.8486\n",
            "Epoch 86/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3581 - accuracy: 0.8510\n",
            "Epoch 87/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3551 - accuracy: 0.8536\n",
            "Epoch 88/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3527 - accuracy: 0.8524\n",
            "Epoch 89/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3520 - accuracy: 0.8530\n",
            "Epoch 90/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3504 - accuracy: 0.8538\n",
            "Epoch 91/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3495 - accuracy: 0.8540\n",
            "Epoch 92/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3480 - accuracy: 0.8582\n",
            "Epoch 93/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3475 - accuracy: 0.8554\n",
            "Epoch 94/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3470 - accuracy: 0.8579\n",
            "Epoch 95/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3457 - accuracy: 0.8577\n",
            "Epoch 96/100\n",
            "910/910 [==============================] - 4s 4ms/step - loss: 0.3452 - accuracy: 0.8571\n",
            "Epoch 97/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3450 - accuracy: 0.8573\n",
            "Epoch 98/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3437 - accuracy: 0.8568\n",
            "Epoch 99/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3447 - accuracy: 0.8563\n",
            "Epoch 100/100\n",
            "910/910 [==============================] - 2s 2ms/step - loss: 0.3440 - accuracy: 0.8590\n",
            "91/91 [==============================] - 0s 2ms/step - loss: 0.3399 - accuracy: 0.8647\n",
            "[0.83076924 0.82508248 0.82508248 0.83718371 0.8338834  0.82728273\n",
            " 0.8547855  0.84488451 0.86358637 0.8338834  0.86468649]\n",
            "0.8401009386236017\n",
            "0.014086920237236797\n"
          ]
        }
      ],
      "source": [
        "\n",
        "classifier= KerasClassifier(build_fn=build_classifier, batch_size=10, epochs=100)\n",
        "Precision = cross_val_score(estimator = classifier,X=x,y=y, cv=11)\n",
        "moyenne = Precision.mean()\n",
        "ecart_type= Precision.std()\n",
        "\n",
        "print(Precision)\n",
        "print(moyenne)\n",
        "print(ecart_type)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "ANN_TP2022 .ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}